diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index b6bc062a..f82b6e9f 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -68,6 +68,8 @@ jobs:
         run: uv run python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B')"
       - name: Run tests with coverage
         run: uv run pytest -m "not integration" --cov=haiku --cov-report=xml
+        env:
+          HF_HUB_OFFLINE: "1"
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
         with:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 54dfc4aa..f104e9d1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,27 @@
 # Changelog
 ## [Unreleased]
 
+### Added
+
+- **RAG skill** (`haiku.rag.skills.rag`): haiku.skills integration with search, list_documents, get_document, ask, and research tools plus managed `RAGState`
+- **RLM skill** (`haiku.rag.skills.rlm`): haiku.skills integration with analyze tool for computational analysis via code execution
+- **`HaikuRAG.research()`**: Client method for multi-agent research
+- **haiku.skills entry points**: `rag = "haiku.rag.skills.rag:create_skill"`, `rag-rlm = "haiku.rag.skills.rlm:create_skill"`
+
+### Changed
+
+- **Chat TUI**: Rebuilt on RAG skill + haiku.skills `SkillToolset`
+- **Web app backend**: Rebuilt on RAG skill + `AGUIAdapter`
+- **Toolsets simplified**: Removed `ToolContext`, `SessionState`, `AgentDeps`, `Toolkit`; kept core `FunctionToolset` factories
+- **Research graph**: Removed `session_context` and conversational output mode
+
+### Removed
+
+- **`agents/chat/`**: Entire chat agent module (replaced by RAG skill)
+- **`--deep` flag**: Removed from `ask` CLI (use `research` command instead)
+- **`--context`/`--context-file`**: Removed from `ask` CLI
+- **`tools/` state machinery**: `ToolContext`, `ToolContextCache`, `SessionState`, `AgentDeps`, `Toolkit`, etc.
+
 ## [0.30.2] - 2026-02-19
 
 ### Fixed
diff --git a/README.md b/README.md
index 8a63aeac..1f09a20d 100644
--- a/README.md
+++ b/README.md
@@ -59,9 +59,6 @@ haiku-rag search "attention mechanism"
 # Ask questions with citations
 haiku-rag ask "What datasets were used for evaluation?" --cite
 
-# Deep QA — decomposes complex questions into sub-queries
-haiku-rag ask "How does the proposed method compare to the baseline on MMLU?" --deep
-
 # Research mode — iterative planning and search
 haiku-rag research "What are the limitations of the approach?"
 
@@ -140,7 +137,7 @@ Full documentation at: https://ggozad.github.io/haiku.rag/
 - [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - YAML configuration
 - [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
 - [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
-- [Agents](https://ggozad.github.io/haiku.rag/agents/) - QA, chat, and research agents
+- [Agents](https://ggozad.github.io/haiku.rag/agents/) - QA and research agents
 - [RLM Agent](https://ggozad.github.io/haiku.rag/rlm/) - Complex analytical tasks via code execution
 - [Applications](https://ggozad.github.io/haiku.rag/apps/) - Chat TUI, web app, and inspector
 - [Server](https://ggozad.github.io/haiku.rag/server/) - File monitoring and MCP
diff --git a/app/README.md b/app/README.md
index c3c1648b..efbf30fe 100644
--- a/app/README.md
+++ b/app/README.md
@@ -46,7 +46,7 @@ A conversational RAG interface built with [CopilotKit](https://copilotkit.ai/) a
 
 ### haiku.rag.yaml
 
-Configure the chat agent's LLM, embeddings, and search settings:
+Configure the LLM, embeddings, and search settings:
 
 ```yaml
 qa:
@@ -100,7 +100,7 @@ docker compose -f docker-compose.dev.yml up -d --build
 
 ## Chat Capabilities
 
-The chat agent can:
+The chat can:
 
 - **Search** your documents with hybrid vector + full-text search
 - **Answer questions** with citations from your knowledge base
diff --git a/app/backend/main.py b/app/backend/main.py
index 7bbcf4cf..50876a8c 100644
--- a/app/backend/main.py
+++ b/app/backend/main.py
@@ -3,8 +3,9 @@ import os
 from pathlib import Path
 
 from dotenv import find_dotenv, load_dotenv
+from pydantic_ai import Agent
+from pydantic_ai.ag_ui import AGUIAdapter
 from pydantic_ai.ui import SSE_CONTENT_TYPE
-from pydantic_ai.ui.ag_ui import AGUIAdapter
 from starlette.applications import Starlette
 from starlette.middleware import Middleware
 from starlette.middleware.cors import CORSMiddleware
@@ -12,22 +13,14 @@ from starlette.requests import Request
 from starlette.responses import JSONResponse, Response, StreamingResponse
 from starlette.routing import Route
 
-from haiku.rag.agents.chat import (
-    AGUI_STATE_KEY,
-    ChatDeps,
-    build_chat_toolkit,
-    create_chat_agent,
-)
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import load_yaml_config
 from haiku.rag.config.models import AppConfig
-from haiku.rag.tools.context import ToolContextCache
+from haiku.rag.skills.rag import AGENT_PREAMBLE, create_skill
+from haiku.skills import SkillDeps, SkillToolset
 
 load_dotenv(find_dotenv(usecwd=True))
 
-# Cache ToolContext instances by thread_id across requests
-context_cache = ToolContextCache()
-
 # Configure logfire (only sends data if LOGFIRE_TOKEN is present)
 try:
     import logfire
@@ -69,34 +62,26 @@ def get_client() -> HaikuRAG:
     return _client
 
 
-# Toolkit and agent are created once at module level
-chat_toolkit = build_chat_toolkit(Config)
-agent = create_chat_agent(Config, toolkit=chat_toolkit)
+# Create skill, toolset, and agent
+skill = create_skill(db_path=db_path, config=Config)
+toolset = SkillToolset(skills=[skill])
+
+agent = Agent(
+    os.getenv("HAIKU_CHAT_MODEL", "openai:gpt-4o"),
+    instructions=AGENT_PREAMBLE + toolset.system_prompt,
+    toolsets=[toolset],
+    deps_type=SkillDeps,
+)
 
 
 async def stream_chat(request: Request) -> Response:
-    """Chat streaming endpoint with AG-UI protocol.
-
-    Uses ToolContextCache to maintain state across requests for the same thread.
-    AGUIAdapter restores client-sent state via ChatDeps.state setter.
-    """
+    """Chat streaming endpoint with AG-UI protocol."""
     body = await request.body()
     accept = request.headers.get("accept", SSE_CONTENT_TYPE)
     run_input = AGUIAdapter.build_run_input(body)
 
-    thread_id = getattr(run_input, "thread_id", None) or "default"
-    context, is_new = context_cache.get_or_create(thread_id)
-    if is_new:
-        chat_toolkit.prepare(context, state_key=AGUI_STATE_KEY)
-
-    deps = ChatDeps(
-        config=Config,
-        client=get_client(),
-        tool_context=context,
-    )
-
     adapter = AGUIAdapter(agent=agent, run_input=run_input, accept=accept)
-    event_stream = adapter.run_stream(deps=deps)
+    event_stream = adapter.run_stream(deps=SkillDeps())
     sse_event_stream = adapter.encode_stream(event_stream)
 
     return StreamingResponse(
diff --git a/app/frontend/components/Chat.tsx b/app/frontend/components/Chat.tsx
index 3401fc52..7f069a05 100644
--- a/app/frontend/components/Chat.tsx
+++ b/app/frontend/components/Chat.tsx
@@ -17,27 +17,27 @@ import {
 	useMemo,
 	useState,
 } from "react";
-import { BrainIcon, FilterIcon } from "../lib/icons";
-import type { ChatSessionState } from "../lib/sessionStorage";
+import { FilterIcon } from "../lib/icons";
+import type { RAGState } from "../lib/sessionStorage";
 import {
 	createSession,
+	deriveCitationsHistory,
 	getActiveSessionId,
 	getSession,
-	normalizeChatState,
+	normalizeRAGState,
 	updateSessionMessages,
 } from "../lib/sessionStorage";
 import CitationBlock from "./CitationBlock";
-import ContextPanel from "./ContextPanel";
 import DbInfo from "./DbInfo";
 import DocumentFilter from "./DocumentFilter";
 import SessionManager from "./SessionManager";
 
-// Must match AGUI_STATE_KEY from haiku.rag.agents.chat
-const AGUI_STATE_KEY = "haiku.rag.chat";
+// Must match state_namespace from haiku.rag.skills.rag
+const AGUI_STATE_KEY = "rag";
 
 // AG-UI state is namespaced under AGUI_STATE_KEY
 interface AgentState {
-	[AGUI_STATE_KEY]?: ChatSessionState;
+	[AGUI_STATE_KEY]?: RAGState;
 }
 
 // biome-ignore lint/suspicious/noExplicitAny: CopilotKit message objects vary at runtime
@@ -152,6 +152,8 @@ function ToolCallIndicator({
 				return <MessageIcon />;
 			case "get_document":
 				return <FileIcon />;
+			case "execute_skill":
+				return <MessageIcon />;
 			default:
 				return <SearchIcon />;
 		}
@@ -165,6 +167,12 @@ function ToolCallIndicator({
 				return "Ask";
 			case "get_document":
 				return "Document";
+			case "execute_skill":
+				return "Skill";
+			case "analyze":
+				return "Analyze";
+			case "research":
+				return "Research";
 			default:
 				return toolName;
 		}
@@ -172,38 +180,30 @@ function ToolCallIndicator({
 
 	const getDescription = () => {
 		switch (toolName) {
+			case "execute_skill": {
+				const skill = args.skill_name as string | undefined;
+				const request = args.request as string | undefined;
+				return (
+					<span className="tool-query">
+						{skill ? `${skill}: ` : ""}
+						{request ?? "Processing..."}
+					</span>
+				);
+			}
 			case "search": {
 				const query = args.query as string;
-				const docName = args.document_name as string | undefined;
-				return (
-					<>
-						<span className="tool-query">{query}</span>
-						{docName && (
-							<span className="tool-context">
-								{" "}
-								in <em>{docName}</em>
-							</span>
-						)}
-					</>
-				);
+				return <span className="tool-query">{query}</span>;
 			}
 			case "ask": {
 				const question = args.question as string;
-				const docName = args.document_name as string | undefined;
-				return (
-					<>
-						<span className="tool-query">{question}</span>
-						{docName && (
-							<span className="tool-context">
-								{" "}
-								from <em>{docName}</em>
-							</span>
-						)}
-					</>
-				);
+				return <span className="tool-query">{question}</span>;
 			}
 			case "get_document":
 				return <span className="tool-query">{args.query as string}</span>;
+			case "analyze":
+				return <span className="tool-query">{args.question as string}</span>;
+			case "research":
+				return <span className="tool-query">{args.question as string}</span>;
 			default:
 				return <span>Processing...</span>;
 		}
@@ -231,7 +231,7 @@ function ToolCallIndicator({
 }
 
 // Context for sharing chat state with the message view
-const ChatStateContext = createContext<ChatSessionState | null>(null);
+const ChatStateContext = createContext<RAGState | null>(null);
 
 // Wildcard tool call renderer for all server-side tools
 const toolCallRenderers = [
@@ -251,14 +251,15 @@ const toolCallRenderers = [
 // Uses CopilotChatMessageView's children render prop to post-process the
 // rendered message elements and inject citations at the right positions.
 function MessageViewWithCitations({
-	messages,
-	isRunning,
+	messages = [],
+	isRunning = false,
 }: {
 	// biome-ignore lint/suspicious/noExplicitAny: AG-UI Message type is a broad union
-	messages: any[];
-	isRunning: boolean;
+	messages?: any[];
+	isRunning?: boolean;
 }) {
-	const chatState = useContext(ChatStateContext);
+	const ragState = useContext(ChatStateContext);
+	const citationsHistory = ragState ? deriveCitationsHistory(ragState) : [];
 
 	const cursor = isRunning ? (
 		<div key="cursor" className="streaming-cursor">
@@ -271,7 +272,7 @@ function MessageViewWithCitations({
 	return (
 		<CopilotChatMessageView messages={messages} isRunning={isRunning}>
 			{({ messageElements }) => {
-				if (!chatState?.citations_history?.length) {
+				if (!citationsHistory.length) {
 					return (
 						<>
 							{messageElements}
@@ -284,9 +285,9 @@ function MessageViewWithCitations({
 				// message (tool messages produce nothing). We correlate elements with
 				// messages to inject CitationBlocks after the right assistant responses.
 				//
-				// Both search and ask tools append to citations_history in order,
+				// Both search and ask tools append to citations via qa_history,
 				// so after each assistant text response that followed tool calls,
-				// we inject the next citations_history entry.
+				// we inject the next citations entry.
 				const result: React.ReactNode[] = [];
 				let citIdx = 0;
 				let seenToolCalls = false;
@@ -317,10 +318,10 @@ function MessageViewWithCitations({
 					}
 
 					// After an assistant text response that followed tool calls,
-					// inject the next citations_history entry (one per turn)
+					// inject the next citations entry (one per turn)
 					if (msg.role === "assistant" && msg.content && seenToolCalls) {
-						if (citIdx < chatState.citations_history.length) {
-							const citations = chatState.citations_history[citIdx];
+						if (citIdx < citationsHistory.length) {
+							const citations = citationsHistory[citIdx];
 							if (citations?.length) {
 								result.push(
 									<CitationBlock
@@ -350,6 +351,7 @@ function MessageViewWithCitations({
 		</CopilotChatMessageView>
 	);
 }
+MessageViewWithCitations.Cursor = CopilotChatMessageView.Cursor;
 
 function ChatContentInner({
 	sessionId,
@@ -358,8 +360,9 @@ function ChatContentInner({
 	sessionId: string;
 	onSessionChange: (id: string) => void;
 }) {
-	const [contextOpen, setContextOpen] = useState(false);
 	const [filterOpen, setFilterOpen] = useState(false);
+	// Track selected document names locally (frontend-only)
+	const [selectedDocuments, setSelectedDocuments] = useState<string[]>([]);
 
 	const { agent } = useAgent({
 		agentId: "chat_agent",
@@ -376,23 +379,10 @@ function ChatContentInner({
 		agent.threadId = sessionId;
 	}, [agent, sessionId]);
 
-	const chatState = normalizeChatState(
+	const ragState = normalizeRAGState(
 		(agent.state as AgentState)?.[AGUI_STATE_KEY],
 	);
 
-	const mergeChatState = (partial: Partial<ChatSessionState>) => {
-		const current = normalizeChatState(
-			(agent.state as AgentState)?.[AGUI_STATE_KEY],
-		);
-		agent.setState({
-			...agent.state,
-			[AGUI_STATE_KEY]: {
-				...current,
-				...partial,
-			},
-		});
-	};
-
 	// Restore session from localStorage when agent reference changes.
 	// useAgent returns a provisional agent initially, then the real agent
 	// after runtime connects — re-run restore each time so messages stick.
@@ -400,9 +390,9 @@ function ChatContentInner({
 		if (agent.messages.length > 0) return;
 		const session = getSession(sessionId);
 		if (!session) return;
-		if (session.chatState) {
+		if (session.ragState) {
 			agent.setState({
-				[AGUI_STATE_KEY]: normalizeChatState(session.chatState),
+				[AGUI_STATE_KEY]: normalizeRAGState(session.ragState),
 			});
 		}
 		if (session.messages.length > 0) {
@@ -412,21 +402,21 @@ function ChatContentInner({
 	}, [agent, sessionId]);
 
 	// Persist messages and state to localStorage.
-	// Read chatState from agent.state at effect time (not render time) so that
+	// Read ragState from agent.state at effect time (not render time) so that
 	// restore and persist effects in the same commit see consistent state.
 	// biome-ignore lint/correctness/useExhaustiveDependencies: JSON.stringify tracks content changes
 	useEffect(() => {
 		if (sessionId && agent.messages.length > 0) {
-			const currentChatState = normalizeChatState(
+			const currentRagState = normalizeRAGState(
 				(agent.state as AgentState)?.[AGUI_STATE_KEY],
 			);
 			updateSessionMessages(
 				sessionId,
 				serializeMessages(agent.messages),
-				currentChatState,
+				currentRagState,
 			);
 		}
-	}, [JSON.stringify(agent.messages), chatState, sessionId]);
+	}, [JSON.stringify(agent.messages), ragState, sessionId]);
 
 	// biome-ignore lint/correctness/useExhaustiveDependencies: stable identity via agent ref
 	const messages = useMemo(
@@ -458,24 +448,29 @@ function ChatContentInner({
 		}
 	}, [agent, ck]);
 
-	const sessionContext = chatState.session_context;
-	const documentFilter = chatState.document_filter;
-	const initialContext = chatState.initial_context ?? "";
-
-	// Context is locked after first message (qa_history has entries)
-	const isContextLocked = (chatState.qa_history?.length ?? 0) > 0;
-
 	const handleFilterApply = (selected: string[]) => {
-		mergeChatState({ document_filter: selected });
-	};
-
-	const handleInitialContextChange = (value: string) => {
-		if (isContextLocked) return;
-		mergeChatState({ initial_context: value || null });
+		setSelectedDocuments(selected);
+		// Convert selected document names to SQL filter for the backend
+		const filter =
+			selected.length > 0
+				? selected
+						.map(
+							(name) =>
+								`(title LIKE '%${name.replace(/'/g, "''")}%' OR uri LIKE '%${name.replace(/'/g, "''")}%')`,
+						)
+						.join(" OR ")
+				: null;
+		agent.setState({
+			...agent.state,
+			[AGUI_STATE_KEY]: {
+				...ragState,
+				document_filter: filter,
+			},
+		});
 	};
 
 	return (
-		<ChatStateContext.Provider value={chatState}>
+		<ChatStateContext.Provider value={ragState}>
 			<div className="chat-wrapper">
 				<div className="chat-container">
 					<div className="chat-header">
@@ -485,36 +480,19 @@ function ChatContentInner({
 						/>
 						<button
 							type="button"
-							className={`header-btn ${documentFilter.length > 0 ? "has-content" : ""}`}
+							className={`header-btn ${selectedDocuments.length > 0 ? "has-content" : ""}`}
 							onClick={() => setFilterOpen(true)}
 							title={
-								documentFilter.length > 0
-									? `Filtering: ${documentFilter.length} document(s)`
+								selectedDocuments.length > 0
+									? `Filtering: ${selectedDocuments.length} document(s)`
 									: "Filter documents"
 							}
 						>
 							<FilterIcon />
-							{documentFilter.length > 0
-								? `Filter (${documentFilter.length})`
+							{selectedDocuments.length > 0
+								? `Filter (${selectedDocuments.length})`
 								: "Filter"}
 						</button>
-						<button
-							type="button"
-							className={`header-btn ${initialContext || sessionContext?.summary ? "has-content" : ""}`}
-							onClick={() => setContextOpen(true)}
-							title={
-								isContextLocked
-									? sessionContext?.summary
-										? "View session context"
-										: "No session context yet"
-									: initialContext
-										? "Edit initial context"
-										: "Set initial context"
-							}
-						>
-							<BrainIcon />
-							Memory
-						</button>
 					</div>
 					<div className="chat-content">
 						<CopilotChatView
@@ -535,18 +513,10 @@ function ChatContentInner({
 					<DbInfo />
 				</div>
 			</div>
-			<ContextPanel
-				isOpen={contextOpen}
-				onClose={() => setContextOpen(false)}
-				sessionContext={sessionContext}
-				initialContext={initialContext}
-				onInitialContextChange={handleInitialContextChange}
-				isLocked={isContextLocked}
-			/>
 			<DocumentFilter
 				isOpen={filterOpen}
 				onClose={() => setFilterOpen(false)}
-				selected={documentFilter}
+				selected={selectedDocuments}
 				onApply={handleFilterApply}
 			/>
 		</ChatStateContext.Provider>
diff --git a/app/frontend/components/ContextPanel.tsx b/app/frontend/components/ContextPanel.tsx
deleted file mode 100644
index 69a8f7c7..00000000
--- a/app/frontend/components/ContextPanel.tsx
+++ /dev/null
@@ -1,131 +0,0 @@
-"use client";
-
-import { useCallback, useEffect, useId, useState } from "react";
-import { formatRelativeTime } from "../lib/format";
-import { BrainIcon } from "../lib/icons";
-import type { SessionContext } from "../lib/sessionStorage";
-
-interface ContextPanelProps {
-	isOpen: boolean;
-	onClose: () => void;
-	sessionContext: SessionContext | null;
-	initialContext?: string;
-	onInitialContextChange?: (value: string) => void;
-	isLocked?: boolean;
-}
-
-export default function ContextPanel({
-	isOpen,
-	onClose,
-	sessionContext,
-	initialContext = "",
-	onInitialContextChange,
-	isLocked = false,
-}: ContextPanelProps) {
-	const titleId = useId();
-	const [localValue, setLocalValue] = useState(initialContext);
-
-	useEffect(() => {
-		if (isOpen) {
-			setLocalValue(initialContext);
-		}
-	}, [isOpen, initialContext]);
-
-	const handleKeyDown = useCallback(
-		(e: React.KeyboardEvent) => {
-			if (e.key === "Escape") {
-				onClose();
-			}
-		},
-		[onClose],
-	);
-
-	const handleSave = useCallback(() => {
-		onInitialContextChange?.(localValue);
-		onClose();
-	}, [localValue, onInitialContextChange, onClose]);
-
-	if (!isOpen) {
-		return null;
-	}
-
-	const hasSessionContext = sessionContext?.summary?.trim();
-	// Show edit mode when: not locked AND no session context yet
-	const isEditMode = !isLocked && !hasSessionContext;
-
-	return (
-		<div
-			className="context-modal-overlay"
-			onClick={onClose}
-			onKeyDown={handleKeyDown}
-			role="dialog"
-			aria-modal="true"
-			aria-labelledby={titleId}
-		>
-			{/* biome-ignore lint/a11y/noStaticElementInteractions: modal content wrapper */}
-			<div
-				className="context-modal"
-				onClick={(e) => e.stopPropagation()}
-				onKeyDown={(e) => e.stopPropagation()}
-			>
-				<div className="context-modal-header">
-					<div className="context-modal-icon">
-						<BrainIcon size={24} strokeWidth={1.5} />
-					</div>
-					<h2 id={titleId} className="context-modal-title">
-						{isEditMode ? "Initial Context" : "Session Context"}
-					</h2>
-				</div>
-				<p className="context-modal-description">
-					{isEditMode
-						? "Set background context to guide the conversation. This will be locked after you send your first message."
-						: "This is what the assistant has learned from your conversation so far. It uses this context to provide more relevant answers."}
-				</p>
-				{isEditMode ? (
-					<textarea
-						className="context-textarea"
-						placeholder="Enter any background context or instructions for the assistant..."
-						value={localValue}
-						onChange={(e) => setLocalValue(e.target.value)}
-					/>
-				) : hasSessionContext ? (
-					<div className="context-content">{sessionContext.summary}</div>
-				) : (
-					<div className="context-empty">
-						<div className="context-empty-icon">
-							<BrainIcon size={24} strokeWidth={1.5} />
-						</div>
-						<div className="context-empty-text">
-							No context yet. Ask some questions to build context.
-						</div>
-					</div>
-				)}
-				<div className="context-footer">
-					<span className="context-timestamp">
-						{sessionContext?.last_updated
-							? `Last updated: ${formatRelativeTime(sessionContext.last_updated)}`
-							: ""}
-					</span>
-					<div className="context-footer-buttons">
-						<button
-							type="button"
-							className="context-btn context-btn-close"
-							onClick={onClose}
-						>
-							{isEditMode ? "Cancel" : "Close"}
-						</button>
-						{isEditMode && (
-							<button
-								type="button"
-								className="context-btn context-btn-save"
-								onClick={handleSave}
-							>
-								Save
-							</button>
-						)}
-					</div>
-				</div>
-			</div>
-		</div>
-	);
-}
diff --git a/app/frontend/lib/sessionStorage.ts b/app/frontend/lib/sessionStorage.ts
index 76a4e9b5..bf1f39c2 100644
--- a/app/frontend/lib/sessionStorage.ts
+++ b/app/frontend/lib/sessionStorage.ts
@@ -9,26 +9,33 @@ export interface Citation {
 	content: string;
 }
 
-export interface QAResponse {
+export interface QAHistoryEntry {
 	question: string;
 	answer: string;
-	confidence: number;
 	citations: Citation[];
 }
 
-export interface SessionContext {
-	summary: string;
-	last_updated: string | null;
+export interface DocumentInfo {
+	id: string;
+	title: string;
+	uri: string;
+	created: string;
 }
 
-export interface ChatSessionState {
-	initial_context: string | null;
+export interface ResearchEntry {
+	question: string;
+	title: string;
+	executive_summary: string;
+}
+
+// Matches RAGState from the backend skill
+export interface RAGState {
 	citations: Citation[];
-	citations_history: Citation[][];
-	qa_history: QAResponse[];
-	session_context: SessionContext | null;
-	document_filter: string[];
-	citation_registry: Record<string, number>;
+	qa_history: QAHistoryEntry[];
+	document_filter: string | null;
+	searches: Record<string, unknown[]>;
+	documents: DocumentInfo[];
+	reports: ResearchEntry[];
 }
 
 export interface StoredMessage {
@@ -42,7 +49,7 @@ export interface StoredSession {
 	id: string;
 	title: string;
 	messages: StoredMessage[];
-	chatState: ChatSessionState;
+	ragState: RAGState;
 	createdAt: string;
 	updatedAt: string;
 }
@@ -50,18 +57,24 @@ export interface StoredSession {
 const SESSIONS_KEY = "haiku.rag.sessions";
 const ACTIVE_SESSION_KEY = "haiku.rag.activeSession";
 
-export function normalizeChatState(state?: ChatSessionState): ChatSessionState {
+export function normalizeRAGState(state?: Partial<RAGState>): RAGState {
 	return {
-		initial_context: state?.initial_context ?? null,
 		citations: state?.citations ?? [],
-		citations_history: state?.citations_history ?? [],
 		qa_history: state?.qa_history ?? [],
-		session_context: state?.session_context ?? null,
-		document_filter: state?.document_filter ?? [],
-		citation_registry: state?.citation_registry ?? {},
+		document_filter: state?.document_filter ?? null,
+		searches: state?.searches ?? {},
+		documents: state?.documents ?? [],
+		reports: state?.reports ?? [],
 	};
 }
 
+// Derive per-turn citation arrays from qa_history
+export function deriveCitationsHistory(state: RAGState): Citation[][] {
+	return state.qa_history
+		.filter((entry) => entry.citations?.length > 0)
+		.map((entry) => entry.citations);
+}
+
 export function getAllSessions(): StoredSession[] {
 	const raw = localStorage.getItem(SESSIONS_KEY);
 	if (!raw) return [];
@@ -90,7 +103,7 @@ export function createSession(): StoredSession {
 		id: crypto.randomUUID(),
 		title: "New Session",
 		messages: [],
-		chatState: normalizeChatState(),
+		ragState: normalizeRAGState(),
 		createdAt: now,
 		updatedAt: now,
 	};
@@ -115,7 +128,7 @@ export function saveSession(session: StoredSession): void {
 export function updateSessionMessages(
 	id: string,
 	messages: StoredMessage[],
-	chatState: ChatSessionState,
+	ragState: RAGState,
 ): void {
 	const sessions = getAllSessions();
 	const idx = sessions.findIndex((s) => s.id === id);
@@ -123,7 +136,7 @@ export function updateSessionMessages(
 
 	const session = sessions[idx];
 	session.messages = messages;
-	session.chatState = chatState;
+	session.ragState = ragState;
 	session.updatedAt = new Date().toISOString();
 
 	// Derive title from first user message
diff --git a/docs/agents/index.md b/docs/agents/index.md
index 90a9a369..611a7ca7 100644
--- a/docs/agents/index.md
+++ b/docs/agents/index.md
@@ -1,12 +1,13 @@
 # Agents
 
-Four agentic flows are provided by haiku.rag:
+Three agentic flows are provided by haiku.rag:
 
 - **Simple QA Agent** — a focused question answering agent
-- **Chat Agent** — multi-turn conversational RAG with session memory
 - **Research Graph** — a multi-step research workflow with question decomposition
 - **RLM Agent** — complex analytical tasks via sandboxed Python code execution (see [RLM Agent](rlm.md))
 
+For multi-turn conversational RAG, haiku.rag provides [skills](../skills/index.md) built on [haiku.skills](https://github.com/ggozad/haiku.skills). The skills bundle search, Q&A, analysis, and research tools with session state management.
+
 See [QA and Research Configuration](../configuration/qa-research.md) for configuring model, iterations, concurrency, and other settings.
 
 ## Simple QA Agent
@@ -26,9 +27,6 @@ haiku-rag ask "What is climate change?"
 
 # With citations
 haiku-rag ask "What is climate change?" --cite
-
-# Deep mode (uses research graph with optimized settings)
-haiku-rag ask "What are the main features of haiku.rag?" --deep
 ```
 
 **Python usage:**
@@ -48,121 +46,6 @@ async with HaikuRAG(path_to_db) as client:
     print(answer)
 ```
 
-## Chat Agent
-
-The chat agent enables multi-turn conversational RAG. It is built from composable [toolsets](../tools.md) and maintains session state to improve follow-up answers.
-
-Key features:
-
-- **Composable toolsets**: Built from reusable `FunctionToolset` factories — see [Toolsets](../tools.md)
-- **Semantic prior answer recall**: Similar prior Q/A pairs are retrieved and passed to the research planner, which can skip searching when they suffice
-- **Background summarization**: After each `ask` call, the QA history is summarized into a compact session context for the next request
-- **Document filtering**: Session-level or per-query document filtering
-
-### CLI Usage
-
-```bash
-haiku-rag chat
-haiku-rag chat --db /path/to/database.lancedb
-```
-
-See [Applications](../apps.md#chat-tui) for the full TUI interface guide.
-
-### Python Usage
-
-```python
-from haiku.rag.client import HaikuRAG
-from haiku.rag.agents.chat import create_chat_agent, prepare_chat_context, ChatDeps
-from haiku.rag.tools import ToolContext
-
-agent = create_chat_agent(config)
-
-async with HaikuRAG(path_to_db) as client:
-    context = ToolContext()
-    prepare_chat_context(context)
-    deps = ChatDeps(config=config, client=client, tool_context=context)
-
-    # First question
-    result = await agent.run("What is haiku.rag?", deps=deps)
-    print(result.output)
-
-    # Follow-up (uses session context)
-    result = await agent.run("How does it handle PDFs?", deps=deps)
-    print(result.output)
-```
-
-### Feature Selection
-
-By default, `create_chat_agent` enables search, documents, and QA toolsets. You can customize which capabilities the agent has via the `features` parameter:
-
-```python
-from haiku.rag.agents.chat import (
-    create_chat_agent,
-    FEATURE_SEARCH,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_ANALYSIS,
-)
-
-# Search-only agent
-agent = create_chat_agent(config, features=[FEATURE_SEARCH])
-
-# All features including code analysis
-agent = create_chat_agent(
-    config,
-    features=[FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA, FEATURE_ANALYSIS],
-)
-```
-
-Available features:
-
-| Feature | Constant | Tools added |
-|---------|----------|-------------|
-| Search | `FEATURE_SEARCH` | `search` |
-| Documents | `FEATURE_DOCUMENTS` | `list_documents`, `get_document`, `summarize_document` |
-| QA | `FEATURE_QA` | `ask` |
-| Analysis | `FEATURE_ANALYSIS` | `analyze` |
-
-The system prompt is automatically composed to match the selected features. See [Toolsets](../tools.md) for details on each toolset's parameters and behavior.
-
-### Session State
-
-Session state is managed through `ToolContext` — a namespace-based state container shared across all toolsets. The chat agent uses two namespaces:
-
-**`SessionState`** (session management):
-
-- `document_filter` — List of document titles/URIs to restrict searches
-- `citation_registry` — Stable mapping of chunk IDs to citation indices
-- `citations` — Citations from the current query
-
-**`QASessionState`** (QA history and context):
-
-- `qa_history` — List of previous Q/A pairs with embeddings
-- `session_context` — Automatically maintained session context summary
-
-For multi-session applications (e.g., web backends), use `ToolContextCache` to cache `ToolContext` instances by external session/thread ID:
-
-```python
-from haiku.rag.tools import ToolContext, ToolContextCache
-
-cache = ToolContextCache()  # TTL-based, defaults to 1 hour
-context, _is_new = cache.get_or_create(thread_id)
-```
-
-**Citation Registry**: Citation indices persist across tool calls within a session. The same `chunk_id` always returns the same citation index (first-occurrence-wins). This ensures consistent citation numbering in multi-turn conversations — `[1]` always refers to the same source.
-
-### Conversational Memory
-
-The chat agent maintains two layers of conversational memory:
-
-**1. Semantic prior answer recall**
-
-When the `ask` tool receives a question, it embeds the question and compares it against prior Q/A embeddings. Sufficiently similar prior answers are passed to the research planner, which can skip searching entirely if they already cover the question.
-
-**2. Background session summarization**
-
-After each `ask` call, a background task summarizes the full QA history into a compact session context. This summary is injected into the research planner on the next request, allowing it to resolve ambiguous references ("Tell me more about the authentication part") without having seen the full conversation.
-
 ## Research Graph
 
 The research workflow is implemented as a typed pydantic-graph. It uses an iterative feedback loop where the planner proposes one question at a time, sees the answer, then decides whether to continue or synthesize.
@@ -181,8 +64,7 @@ stateDiagram-v2
   synthesize --> [*]
 
   note right of plan_next
-    Receives session_context as background
-    and prior_answers from conversation history.
+    Uses prior_answers from previous iterations.
     Uses a different prompt when prior answers exist.
   end note
 ```
@@ -190,8 +72,7 @@ stateDiagram-v2
 The graph receives a `ResearchContext` containing:
 
 - `original_question` — the user's question
-- `session_context` — summary of conversation history (injected as `<background>` XML)
-- `qa_responses` — prior answers from semantic matching or previous iterations (injected as `<prior_answers>` XML)
+- `qa_responses` — prior answers from previous iterations (injected as `<prior_answers>` XML)
 
 When prior answers are provided, the planner uses a context-aware prompt that evaluates whether existing evidence is sufficient. If it is, the planner marks `is_complete=True` and the graph skips directly to synthesis without any searches.
 
@@ -201,20 +82,10 @@ When prior answers are provided, the planner uses a context-aware prompt that ev
 - **search_one**: Answers a single question using the knowledge base (up to 3 search calls per question). Each answer is added to `ResearchContext.qa_responses` for the next planning iteration.
 - **synthesize**: Generates the final output from all gathered evidence.
 
-**Output modes:**
-
-The graph supports two output modes via `build_research_graph(output_mode=...)`:
-
-| Mode | Output type | Used by |
-|------|-------------|---------|
-| `"report"` | `ResearchReport` (title, executive summary, findings, conclusions, recommendations) | CLI `haiku-rag research`, Python API |
-| `"conversational"` | `ConversationalAnswer` (answer, citations, confidence) | Chat agent's `ask` tool |
-
 **Iterative flow:**
 
 - Each iteration: planner evaluates context → proposes one question → search answers it → loop back
 - Planner can decompose complex questions (e.g., "benefits and drawbacks" → start with "benefits")
-- Session context resolves ambiguous references and informs planning
 - Prior answers let the planner skip redundant searches
 - Loop terminates when planner marks `is_complete=True` or `max_iterations` is reached
 
@@ -277,40 +148,6 @@ async with HaikuRAG(path_to_db) as client:
     report = await graph.run(state=state, deps=deps)
 ```
 
-**Conversational mode with prior answers:**
-
-```python
-from haiku.rag.config import Config
-from haiku.rag.agents.research.dependencies import ResearchContext
-from haiku.rag.agents.research.graph import build_research_graph
-from haiku.rag.agents.research.models import SearchAnswer
-from haiku.rag.agents.research.state import ResearchDeps, ResearchState
-
-# Conversational mode returns ConversationalAnswer instead of ResearchReport
-graph = build_research_graph(config=Config, output_mode="conversational")
-
-# Pass session context and prior answers from conversation history
-context = ResearchContext(
-    original_question="How does it handle authentication?",
-    session_context="User is building a Python web app with FastAPI.",
-    qa_responses=[
-        SearchAnswer(
-            query="What authentication methods are supported?",
-            answer="JWT and OAuth2 are supported.",
-            confidence=0.95,
-            cited_chunks=["chunk-1"],
-        )
-    ],
-)
-state = ResearchState.from_config(context=context, config=Config)
-deps = ResearchDeps(client=client)
-
-result = await graph.run(state=state, deps=deps)
-print(result.answer)       # Direct conversational answer
-print(result.confidence)   # 0.0-1.0
-print(result.citations)    # Deduplicated citations from all searches
-```
-
 ### Filtering Documents
 
 Restrict searches to specific documents via the `search_filter` parameter:
diff --git a/docs/apps.md b/docs/apps.md
index a1bfdc54..2f0ac690 100644
--- a/docs/apps.md
+++ b/docs/apps.md
@@ -37,7 +37,7 @@ Press `Ctrl+P` to open the command palette:
 
 | Command | Description |
 |---------|-------------|
-| Memory | Edit initial context (before first message) or view session context (after) |
+| View state | View the current session state |
 | Filter documents | Select documents to restrict searches |
 | Show database info | View document/chunk counts and storage info |
 | Visual grounding | View chunk source location in document |
@@ -46,12 +46,10 @@ Press `Ctrl+P` to open the command palette:
 ### Session Management
 
 - Conversation history is maintained in memory for the session
-- Previous Q/A pairs are used as context for follow-up questions
+- Previous Q/A pairs are automatically used as context for follow-up questions via the `ask` tool
 - Citations are tracked per response and can be inspected
 - Document filter restricts all searches to selected documents
-- Initial context can be set via CLI (`--initial-context`) or command palette
-- Initial context is editable until the first message is sent, then becomes read-only
-- Clearing chat resets session state, restores CLI-provided context, and unlocks editing
+- Clearing chat resets session state
 
 ## Web Application
 
@@ -63,7 +61,7 @@ Browser-based conversational RAG with a CopilotKit frontend.
 - Expandable citations with source documents, pages, and headings
 - Visual grounding to view chunk source locations in documents
 - Document filter to restrict searches to selected documents
-- Memory panel: set initial context before first message, view session context after
+- Session state view for inspecting accumulated Q&A history, citations, and documents
 
 ### Quick Start
 
diff --git a/docs/architecture.md b/docs/architecture.md
index 00417156..4c9ed1ff 100644
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -24,7 +24,7 @@ flowchart TB
 
     subgraph Agents["Agent Layer"]
         QA[QA Agent]
-        Chat[Chat Agent]
+        Skill[RAG Skill]
         Research[Research Graph]
         RLM[RLM Agent]
     end
@@ -98,7 +98,7 @@ flowchart LR
 
 ### Agent Layer
 
-Four agent types for different use cases:
+Three agent types and a RAG skill for different use cases:
 
 ```mermaid
 flowchart TB
@@ -107,12 +107,12 @@ flowchart TB
         S1 --> A1[Answer]
     end
 
-    subgraph Chat["Chat Agent"]
+    subgraph Skill["RAG Skill"]
         Q2[Question] --> Tools[Tool Selection]
-        Tools --> S2[Search / Ask / Get]
+        Tools --> S2[Search / Ask / Analyze]
         S2 --> A2[Answer]
-        A2 --> History[Session History]
-        History -.-> Q2
+        A2 --> State[RAG State]
+        State -.-> Q2
     end
 
     subgraph Research["Research Graph"]
@@ -138,19 +138,18 @@ flowchart TB
 - Expands context around results
 - Generates answer with optional citations
 
-**Chat Agent** - Multi-turn conversational RAG:
+**RAG Skill** - Multi-turn conversational RAG via [haiku.skills](https://github.com/ggozad/haiku.skills):
 
-- Composed from reusable [toolsets](tools.md) (search, documents, QA, analysis)
-- Maintains session history with prior answer recall
-- Background summarization for context continuity
-- Session-level document filtering
+- Bundles search, list_documents, get_document, ask, analyze, and research tools
+- Managed `RAGState` for session state (citations, QA history, document filters)
+- Integrates with any pydantic-ai agent via `SkillToolset`
+- Powers both the Chat TUI and web application
 
 **Research Graph** - Iterative research workflow:
 
 - Proposes one question at a time, evaluates the answer, then decides whether to continue
-- Session context resolves ambiguous references
 - Prior answers let the planner skip redundant searches
-- Synthesizes structured report or conversational answer
+- Synthesizes structured report
 
 **RLM Agent** - Complex analytical tasks via code execution:
 
diff --git a/docs/benchmarks.md b/docs/benchmarks.md
index b1175d03..59bc248e 100644
--- a/docs/benchmarks.md
+++ b/docs/benchmarks.md
@@ -60,27 +60,9 @@ evaluations run repliqa --config /path/to/haiku.rag.yaml --db /path/to/custom.la
 - `--skip-qa` - Skip QA benchmark
 - `--limit N` - Limit number of test cases
 - `--name NAME` - Override the evaluation name
-- `--deep` - Use deep QA mode (multi-step reasoning with research graph)
 
 If no config file is specified, the script searches standard locations: `./haiku.rag.yaml`, user config directory, then falls back to defaults.
 
-### Deep QA Mode
-
-The `--deep` flag enables multi-step reasoning using the research graph instead of the simple QA agent:
-
-```bash
-evaluations run repliqa --skip-db --deep
-```
-
-In deep mode:
-
-- Questions are decomposed into sub-questions by a planning agent
-- Each sub-question is answered by searching the knowledge base
-- A synthesis agent combines findings into a comprehensive answer
-- The graph runs for up to 2 iterations with no early exit (confidence threshold disabled)
-
-This matches the behavior of `haiku-rag ask --deep` in the CLI. Deep mode typically produces more thorough answers but requires more LLM calls per question.
-
 ## Methodology
 
 ### Retrieval Metrics
diff --git a/docs/cli.md b/docs/cli.md
index 853ec828..4622be47 100644
--- a/docs/cli.md
+++ b/docs/cli.md
@@ -143,31 +143,17 @@ Ask questions with citations showing source documents:
 haiku-rag ask "Who is the author of haiku.rag?" --cite
 ```
 
-Use deep QA for complex questions (multi-agent decomposition):
-```bash
-haiku-rag ask "What are the main features and architecture of haiku.rag?" --deep --cite
-```
-
 Filter to specific documents:
 ```bash
 haiku-rag ask "What are the main findings?" --filter "uri LIKE '%paper%'"
 ```
 
-Provide background context for the question:
-```bash
-haiku-rag ask "What are the protocols?" --context "Focus on security best practices"
-haiku-rag ask "Summarize the findings" --context-file background.txt
-```
-
 The QA agent searches your documents for relevant information and provides a comprehensive answer. When available, citations use the document title; otherwise they fall back to the URI.
 
 Flags:
 
 - `--cite`: Include citations showing which documents were used
-- `--deep`: Decompose the question into sub-questions answered in parallel before synthesizing a final answer
 - `--filter` / `-f`: Restrict searches to documents matching the filter (see [Filtering Search Results](python.md#filtering-search-results))
-- `--context`: Background context for the question (passed to the agent as system context)
-- `--context-file`: Path to a file containing background context
 
 ## Chat
 
@@ -178,11 +164,6 @@ haiku-rag chat
 haiku-rag chat --db /path/to/database.lancedb
 ```
 
-Provide initial background context for the conversation:
-```bash
-haiku-rag chat --initial-context "Focus on Python programming concepts"
-```
-
 !!! note
     Requires the `tui` extra: `pip install haiku.rag-slim[tui]` (included in full `haiku.rag` package)
 
@@ -192,18 +173,6 @@ The chat interface provides:
 - Expandable citations with source metadata
 - Session memory for context-aware follow-up questions
 - Visual grounding to inspect chunk source locations
-- Initial context that can be edited before the first message
-
-**Initial Context Behavior:**
-
-- Edit initial context via command palette before sending your first message
-- Once you send a message, initial context becomes read-only
-- The agent uses initial context as a starting point for session summarization
-- Clearing chat resets to the CLI-provided context and unlocks editing
-
-Flags:
-
-- `--initial-context`: Initial background context for the conversation (editable until first message)
 
 See [Applications](apps.md#chat-tui) for keyboard shortcuts and features.
 
@@ -242,18 +211,9 @@ Filter to specific documents:
 haiku-rag research "What are the key findings?" --filter "uri LIKE '%paper%'"
 ```
 
-Provide background context for the research:
-
-```bash
-haiku-rag research "What are the safety protocols?" --context "Industrial manufacturing context"
-haiku-rag research "Analyze the methodology" --context-file research-background.txt
-```
-
 Flags:
 
 - `--filter` / `-f`: SQL WHERE clause to filter documents (see [Filtering Search Results](python.md#filtering-search-results))
-- `--context`: Background context for the research
-- `--context-file`: Path to a file containing background context
 
 Research parameters like `max_iterations` and `max_concurrency` are configured in your [configuration file](configuration/index.md) under the `research` section.
 
diff --git a/docs/configuration/qa-research.md b/docs/configuration/qa-research.md
index 0fee4bb3..0ebe931e 100644
--- a/docs/configuration/qa-research.md
+++ b/docs/configuration/qa-research.md
@@ -40,8 +40,6 @@ qa:
 - **max_iterations**: Maximum search iterations (default: 2)
 - **max_concurrency**: Number of concurrent search operations (default: 1)
 
-Deep QA mode (`haiku-rag ask --deep`) uses the research graph with a single iteration for quick, focused answers.
-
 ## Research Configuration
 
 Configure the multi-agent research workflow:
diff --git a/docs/python.md b/docs/python.md
index 91f80f09..9a21508e 100644
--- a/docs/python.md
+++ b/docs/python.md
@@ -429,25 +429,23 @@ See [RLM Agent](agents/rlm.md) for details on capabilities and configuration.
 
 ## Building Custom Agents
 
-haiku.rag provides composable toolset factories that can be mixed into any pydantic-ai agent. This lets you build custom agents with exactly the capabilities you need — search, document management, Q&A, or code analysis — sharing state across tools via `ToolContext`.
+haiku.rag provides a RAG skill built on [haiku.skills](https://github.com/ggozad/haiku.skills) that bundles all capabilities into a composable agent:
 
 ```python
 from pydantic_ai import Agent
-from haiku.rag.tools import AgentDeps, build_toolkit
+from haiku.rag.skills.rag import create_skill
+from haiku.skills.agent import SkillToolset
 
-toolkit = build_toolkit(config, features=["search", "qa"])
+skill = create_skill(db_path=db_path, config=config)
+toolset = SkillToolset(skills=[skill])
 
 agent = Agent(
     "openai:gpt-4o",
-    deps_type=AgentDeps,
-    instructions=f"You are a helpful assistant.\n{toolkit.prompt}",
-    toolsets=toolkit.toolsets,
+    instructions=toolset.system_prompt,
+    toolsets=[toolset],
 )
 
-async with HaikuRAG("path/to/db.lancedb") as client:
-    context = toolkit.create_context()
-    deps = AgentDeps(client=client, tool_context=context)
-    result = await agent.run("What are the main findings?", deps=deps)
+result = await agent.run("What are the main findings?")
 ```
 
-See [Toolsets](tools.md) for the full API reference and composition guide, and the [`examples/`](https://github.com/ggozad/haiku.rag/tree/main/examples) directory for runnable scripts.
+See [Toolsets](tools.md) for the full API reference.
diff --git a/docs/skills/index.md b/docs/skills/index.md
new file mode 100644
index 00000000..fdf689f1
--- /dev/null
+++ b/docs/skills/index.md
@@ -0,0 +1,72 @@
+# Skills
+
+haiku.rag exposes its RAG capabilities as [haiku.skills](https://github.com/ggozad/haiku.skills) skills. Skills are self-contained units that bundle tools, instructions, and state — they can be composed into any pydantic-ai agent via `SkillToolset`.
+
+## Available Skills
+
+| Skill | Description |
+|-------|-------------|
+| [`rag`](rag.md) | Search, retrieve, and answer questions from the knowledge base |
+| [`rag-rlm`](rlm.md) | Computational analysis via code execution (requires Docker) |
+
+## Discovery
+
+Skills are registered as Python entrypoints under `haiku.skills`. They are discovered automatically by `haiku.skills`:
+
+```bash
+haiku-skills list --use-entrypoints
+# rag — Search, retrieve and analyze documents using RAG.
+# rag-rlm — Analyze documents using code execution in a Docker sandbox.
+```
+
+## Usage
+
+```python
+from haiku.rag.skills.rag import create_skill
+from haiku.skills.agent import SkillToolset
+from pydantic_ai import Agent
+
+skill = create_skill(db_path=db_path, config=config)
+toolset = SkillToolset(skills=[skill])
+
+agent = Agent(
+    "openai:gpt-4o",
+    instructions=toolset.system_prompt,
+    toolsets=[toolset],
+)
+
+result = await agent.run("What documents do we have?")
+```
+
+## Database Path Resolution
+
+Both skills resolve the database path in the same order:
+
+1. `db_path` argument passed to `create_skill()`
+2. `HAIKU_RAG_DB` environment variable
+3. Config default (`config.storage.data_dir / "haiku.rag.lancedb"`)
+
+## State Management
+
+Each skill manages its own state under a dedicated namespace. State is automatically synced via the AG-UI protocol when using `AGUIAdapter`.
+
+```python
+rag_state = toolset.get_namespace("rag")
+rlm_state = toolset.get_namespace("rlm")
+```
+
+See the individual skill pages for state model details.
+
+## AG-UI Streaming
+
+For web applications, use pydantic-ai's `AGUIAdapter` to stream tool calls, text, and state deltas:
+
+```python
+from pydantic_ai.ag_ui import AGUIAdapter
+
+adapter = AGUIAdapter(agent=agent, run_input=run_input)
+event_stream = adapter.run_stream()
+sse_event_stream = adapter.encode_stream(event_stream)
+```
+
+See the [Web Application](../apps.md#web-application) for a complete implementation.
diff --git a/docs/skills/rag.md b/docs/skills/rag.md
new file mode 100644
index 00000000..e9d38a1a
--- /dev/null
+++ b/docs/skills/rag.md
@@ -0,0 +1,47 @@
+# RAG Skill
+
+The RAG skill is the primary way to use haiku.rag tools. It bundles search, Q&A, document browsing, and research into a single skill with managed state.
+
+## `create_skill(db_path?, config?)`
+
+```python
+from haiku.rag.skills.rag import create_skill
+
+skill = create_skill(db_path=db_path, config=config)
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `db_path` | `None` | Path to LanceDB database. Falls back to `HAIKU_RAG_DB` env var, then config default. |
+| `config` | `None` | `AppConfig` instance. If None, uses `get_config()`. |
+
+## Tools
+
+| Tool | Purpose |
+|------|---------|
+| `search(query, limit?)` | Hybrid search (vector + full-text) with context expansion |
+| `list_documents(limit?, offset?, filter?)` | Paginated document listing |
+| `get_document(query)` | Retrieve a document by ID, title, or URI |
+| `ask(question)` | Q&A with citations via the QA agent |
+| `research(question)` | Deep multi-agent research producing comprehensive reports |
+
+## State
+
+The skill manages a `RAGState` under the `"rag"` namespace:
+
+```python
+class RAGState(BaseModel):
+    citations: list[Citation] = []
+    qa_history: list[QAHistoryEntry] = []
+    document_filter: str | None = None
+    searches: dict[str, list[SearchResult]] = {}
+    documents: list[DocumentInfo] = []
+    reports: list[ResearchEntry] = []
+```
+
+- **citations** — Accumulated citations from `ask` calls, with sequential indexing across calls.
+- **qa_history** — Questions and answers from `ask` calls. Prior Q&A is used as context for follow-up questions when embeddings are similar.
+- **document_filter** — SQL WHERE clause applied to `search`, `ask`, and `research` calls. Set this to scope queries to specific documents.
+- **searches** — Search results keyed by query string.
+- **documents** — Documents seen via `list_documents` or `get_document` (deduplicated by ID).
+- **reports** — Research reports from `research` calls.
diff --git a/docs/skills/rlm.md b/docs/skills/rlm.md
new file mode 100644
index 00000000..45f6dc14
--- /dev/null
+++ b/docs/skills/rlm.md
@@ -0,0 +1,70 @@
+# RLM Skill
+
+The RLM (Reflexion Language Model) skill provides computational analysis via code execution. It writes and runs Python code in an isolated Docker sandbox to answer questions that require computation, aggregation, or data traversal.
+
+!!! warning "Requires Docker"
+    The `analyze` tool executes code in a Docker sandbox. Docker must be running on the host machine. This skill is not suitable for Docker-deployed applications — use the [`rag`](rag.md) skill alone in those environments.
+
+## `create_skill(db_path?, config?)`
+
+```python
+from haiku.rag.skills.rlm import create_skill
+
+skill = create_skill(db_path=db_path, config=config)
+```
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `db_path` | `None` | Path to LanceDB database. Falls back to `HAIKU_RAG_DB` env var, then config default. |
+| `config` | `None` | `AppConfig` instance. If None, uses `get_config()`. |
+
+## Tools
+
+| Tool | Purpose |
+|------|---------|
+| `analyze(question, document?, filter?)` | Answer analytical questions using code execution |
+
+**Parameters:**
+
+- `question` — The analytical question to answer.
+- `document` — Optional document ID or title to pre-load for analysis.
+- `filter` — Optional SQL WHERE clause to filter documents.
+
+## State
+
+The skill manages an `RLMState` under the `"rlm"` namespace:
+
+```python
+class RLMState(BaseModel):
+    analyses: list[AnalysisEntry] = []
+
+class AnalysisEntry(BaseModel):
+    question: str
+    answer: str
+    program: str | None = None
+```
+
+Each `analyze` call appends an `AnalysisEntry` with the question, answer, and executed program.
+
+## Usage with RAG Skill
+
+Combine both skills to give the agent full RAG + analysis capabilities:
+
+```python
+from haiku.rag.skills.rag import create_skill as create_rag_skill
+from haiku.rag.skills.rlm import create_skill as create_rlm_skill
+from haiku.skills.agent import SkillToolset
+from pydantic_ai import Agent
+
+rag = create_rag_skill(db_path=db_path)
+rlm = create_rlm_skill(db_path=db_path)
+toolset = SkillToolset(skills=[rag, rlm])
+
+agent = Agent(
+    "openai:gpt-4o",
+    instructions=toolset.system_prompt,
+    toolsets=[toolset],
+)
+```
+
+See the [RLM Agent](../agents/rlm.md) documentation for details on how the underlying agent works.
diff --git a/docs/tools.md b/docs/tools.md
index 96f91f74..f129c801 100644
--- a/docs/tools.md
+++ b/docs/tools.md
@@ -1,77 +1,28 @@
 # Toolsets
 
-haiku.rag provides composable `FunctionToolset` factories in `haiku.rag.tools`. Each factory creates a pydantic-ai `FunctionToolset` that can be mixed into any agent. A shared `ToolContext` lets toolsets accumulate state (search results, citations, QA history) across invocations.
+haiku.rag exposes its RAG capabilities as [haiku.skills](https://github.com/ggozad/haiku.skills) skills. See the [Skills](skills/index.md) section for the primary way to use haiku.rag tools.
 
-## ToolContext
+For lower-level access, `haiku.rag.tools` provides individual `FunctionToolset` factories used internally by agents.
 
-`ToolContext` is a namespace-based state container. Toolsets register Pydantic models under string namespaces, and any toolset sharing the same context can read or write the same state.
+## Low-Level Toolsets
+
+For advanced use cases, individual toolset factories are available in `haiku.rag.tools`. These are used internally by the QA agent and can be composed into custom agents.
+
+### RAGDeps Protocol
+
+All toolsets use the `RAGDeps` protocol for dependency injection:
 
 ```python
-from haiku.rag.tools import ToolContext
+from haiku.rag.tools import RAGDeps
 
-context = ToolContext()
+class MyDeps:
+    def __init__(self, client: HaikuRAG):
+        self.client = client
 ```
 
-### Registering and accessing state
+### Search Toolset
 
-```python
-from pydantic import BaseModel
-
-class MyState(BaseModel):
-    count: int = 0
-
-context.register("my_namespace", MyState())
-
-# Get state (returns None if not registered)
-state = context.get("my_namespace")
-
-# Get with type checking (returns None if wrong type)
-state = context.get("my_namespace", MyState)
-
-# Get or create (creates default if not registered)
-state = context.get_or_create("my_namespace", MyState)
-```
-
-### Serialization
-
-The entire context can be serialized and restored:
-
-```python
-# Serialize all namespaces (keyed by namespace)
-data = context.dump_namespaces()
-# {"my_namespace": {"count": 0}}
-
-# Restore a namespace from serialized data
-context.load_namespace("my_namespace", MyState, data["my_namespace"])
-```
-
-For AG-UI state management, use flat snapshots:
-
-```python
-# Flat snapshot of all namespaces (for AG-UI state)
-snapshot = context.build_state_snapshot()
-# {"document_filter": [], "citations": [], "citation_registry": {}, "qa_history": []}
-
-# Restore from flat snapshot (updates registered namespaces in place)
-context.restore_state_snapshot(snapshot)
-```
-
-### Preparing context for toolsets
-
-`prepare_context()` registers the required namespaces for a given set of features:
-
-```python
-from haiku.rag.tools import ToolContext, prepare_context
-
-context = ToolContext()
-prepare_context(context, features=["search", "qa"], state_key="my_app")
-```
-
-This is idempotent and registers `SessionState` (for search, QA, and analysis features) and `QASessionState` (for QA). The chat agent's `prepare_chat_context()` is a thin wrapper that defaults to chat features and sets the AG-UI state key.
-
-## Search Toolset
-
-`create_search_toolset()` provides hybrid search (vector + full-text) with context expansion and citation tracking.
+`create_search_toolset()` provides hybrid search with context expansion.
 
 ```python
 from haiku.rag.tools import create_search_toolset
@@ -79,24 +30,17 @@ from haiku.rag.tools import create_search_toolset
 search = create_search_toolset(config)
 ```
 
-**Parameters:**
-
 | Parameter | Default | Description |
 |-----------|---------|-------------|
-| `config` | required | AppConfig |
+| `config` | required | `AppConfig` |
 | `expand_context` | `True` | Expand results with surrounding chunks |
 | `base_filter` | `None` | SQL WHERE clause applied to all searches |
 | `tool_name` | `"search"` | Name of the tool exposed to the agent |
+| `on_results` | `None` | Callback `(list[SearchResult]) -> None` invoked with results |
 
-**Tool: `search(query, limit?, filter?)`**
+### Document Toolset
 
-Searches the knowledge base and returns formatted results. When a `ToolContext` with `SessionState` is registered, citations get stable indices via `citation_registry`.
-
-**State:** Search results accumulate in `SearchState.results` under the `haiku.rag.search` namespace.
-
-## Document Toolset
-
-`create_document_toolset()` provides document browsing, retrieval, and summarization.
+`create_document_toolset()` provides document browsing and retrieval.
 
 ```python
 from haiku.rag.tools import create_document_toolset
@@ -104,75 +48,20 @@ from haiku.rag.tools import create_document_toolset
 docs = create_document_toolset(config)
 ```
 
-**Parameters:**
-
 | Parameter | Default | Description |
 |-----------|---------|-------------|
-| `config` | required | AppConfig (used for summarization LLM) |
+| `config` | required | `AppConfig` |
 | `base_filter` | `None` | SQL WHERE clause for list operations |
 
 **Tools:**
 
-- `list_documents(page?)` — Paginated document listing (50 per page). Returns `DocumentListResponse` with document titles, URIs, and pagination info.
-- `get_document(query)` — Retrieve a document by title or URI. Uses `find_document()` which tries exact URI match, then partial URI match, then partial title match.
+- `list_documents(page?)` — Paginated document listing (50 per page).
+- `get_document(query)` — Retrieve a document by title or URI.
 - `summarize_document(query)` — Generate an LLM summary of a document's content.
 
-## QA Toolset
+### Analysis Toolset
 
-`create_qa_toolset()` provides question answering via the research graph, with prior answer recall and background summarization.
-
-```python
-from haiku.rag.tools import create_qa_toolset
-
-qa = create_qa_toolset(config)
-```
-
-**Parameters:**
-
-| Parameter | Default | Description |
-|-----------|---------|-------------|
-| `config` | required | AppConfig |
-| `base_filter` | `None` | SQL WHERE clause applied to searches |
-| `tool_name` | `"ask"` | Name of the tool exposed to the agent |
-| `on_ask_complete` | `None` | Callback `(QASessionState, AppConfig) -> None` invoked after each QA cycle |
-
-**Tool: `ask(question, document_name?)`**
-
-Runs the research graph in conversational mode and returns a `QAResult`. When a `ToolContext` is provided:
-
-- Prior answers from `QASessionState.qa_history` are matched via embedding similarity
-- The answer is appended to `qa_history`
-- `on_ask_complete` callback is invoked (if provided)
-- Citations get stable indices via `SessionState.citation_registry`
-
-**State:** QA history accumulates in `QASessionState` under the `haiku.rag.qa_session` namespace.
-
-### Using `run_qa_core()` directly
-
-For programmatic use without an agent, `run_qa_core()` provides the same QA flow:
-
-```python
-from haiku.rag.tools.qa import run_qa_core
-
-result = await run_qa_core(
-    client=client,
-    config=config,
-    question="What are the main features?",
-    document_name="User Guide",       # optional document filter
-    context=context,                   # optional ToolContext
-    session_context="User is building a web app",  # optional
-    on_qa_complete=my_callback,        # optional post-QA callback
-)
-
-print(result.answer)
-print(result.confidence)
-for citation in result.citations:
-    print(f"  [{citation.index}] {citation.document_title}")
-```
-
-## Analysis Toolset
-
-`create_analysis_toolset()` provides computational analysis via the RLM agent, which writes and executes Python code in a Docker sandbox.
+`create_analysis_toolset()` provides computational analysis via the RLM agent (Docker sandbox).
 
 ```python
 from haiku.rag.tools import create_analysis_toolset
@@ -180,218 +69,16 @@ from haiku.rag.tools import create_analysis_toolset
 analysis = create_analysis_toolset(config)
 ```
 
-**Parameters:**
-
 | Parameter | Default | Description |
 |-----------|---------|-------------|
-| `config` | required | AppConfig |
+| `config` | required | `AppConfig` |
 | `base_filter` | `None` | SQL WHERE clause applied to searches |
 | `tool_name` | `"analyze"` | Name of the tool exposed to the agent |
 
-**Tool: `analyze(task, document_name?)`**
-
-Executes a computational task via code execution and returns an `AnalysisResult`. Requires Docker — see [RLM Agent](agents/rlm.md) for setup.
-
-## Tool Prompts
-
-`build_tools_prompt()` generates system prompt guidance for your toolsets — when to use each tool, the `document_name` parameter pattern, and usage examples. It's designed to be spliced into any agent's instructions alongside your own domain-specific guidance.
-
-```python
-from haiku.rag.tools import build_tools_prompt
-
-# Generate guidance for the toolsets you're using
-tools_prompt = build_tools_prompt(["search", "qa", "documents"])
-```
-
-Combine it with your own instructions:
-
-```python
-from pydantic_ai import Agent
-from haiku.rag.tools import AgentDeps, build_tools_prompt
-
-tools_prompt = build_tools_prompt(["search", "qa"])
-
-agent = Agent(
-    "anthropic:claude-sonnet-4-5-20250929",
-    deps_type=AgentDeps,
-    instructions=f"""You are a medical research assistant.
-{tools_prompt}
-
-You also have access to:
-- "check_interactions" - Use when the user asks about drug interactions.""",
-    toolsets=[search_toolset, qa_toolset, my_custom_toolset],
-)
-```
-
-Available features: `"search"`, `"qa"`, `"documents"`, `"analysis"`.
-
-## Composing Custom Agents
-
-### Using `build_toolkit` (recommended)
-
-`build_toolkit()` bundles toolsets, prompt, and context creation for a given feature set:
-
-```python
-from pydantic_ai import Agent
-from haiku.rag.client import HaikuRAG
-from haiku.rag.tools import AgentDeps, build_toolkit
-
-toolkit = build_toolkit(config, features=["search", "documents", "qa"])
-
-agent = Agent(
-    "openai:gpt-4o",
-    deps_type=AgentDeps,
-    instructions=f"You are a helpful research assistant.\n{toolkit.prompt}",
-    toolsets=toolkit.toolsets,
-)
-
-async with HaikuRAG("path/to/db.lancedb") as client:
-    context = toolkit.create_context()
-    deps = AgentDeps(client=client, tool_context=context)
-
-    result = await agent.run("What documents do we have about climate?", deps=deps)
-    print(result.output)
-
-    # Access accumulated state
-    from haiku.rag.tools.search import SearchState, SEARCH_NAMESPACE
-    search_state = context.get(SEARCH_NAMESPACE, SearchState)
-    if search_state:
-        print(f"Total search results: {len(search_state.results)}")
-```
-
-**Parameters:**
-
-| Parameter | Default | Description |
-|-----------|---------|-------------|
-| `config` | required | AppConfig |
-| `features` | `["search", "documents"]` | Features to enable |
-| `base_filter` | `None` | SQL WHERE clause applied to all toolsets |
-| `expand_context` | `True` | Expand search results with surrounding chunks |
-| `on_qa_complete` | `None` | Callback invoked after each QA cycle |
-
-**`Toolkit` properties:**
-
-- `toolsets` — list of `FunctionToolset` instances to pass to the Agent
-- `prompt` — tool guidance text for the system prompt
-- `features` — the feature list this toolkit was built from
-- `create_context(state_key=None)` — create a prepared `ToolContext` matching these features
-- `prepare(context, state_key=None)` — register namespaces on an existing `ToolContext`
-
-### Using individual factories
-
-For full control, create toolsets individually with `create_*_toolset()`, `build_tools_prompt()`, and `prepare_context()`:
-
-```python
-from pydantic_ai import Agent
-from haiku.rag.client import HaikuRAG
-from haiku.rag.tools import (
-    AgentDeps,
-    ToolContext,
-    build_tools_prompt,
-    prepare_context,
-    create_search_toolset,
-    create_qa_toolset,
-    create_document_toolset,
-)
-
-search = create_search_toolset(config)
-qa = create_qa_toolset(config)
-docs = create_document_toolset(config)
-
-features = ["search", "documents", "qa"]
-tools_prompt = build_tools_prompt(features)
-
-agent = Agent(
-    "openai:gpt-4o",
-    deps_type=AgentDeps,
-    instructions=f"You are a helpful research assistant.\n{tools_prompt}",
-    toolsets=[search, qa, docs],
-)
-
-async with HaikuRAG("path/to/db.lancedb") as client:
-    context = ToolContext()
-    prepare_context(context, features=features)
-    deps = AgentDeps(client=client, tool_context=context)
-
-    result = await agent.run("What documents do we have about climate?", deps=deps)
-    print(result.output)
-```
-
-`AgentDeps` satisfies the `RAGDeps` protocol and implements the AG-UI state protocol (`state` getter/setter). For AG-UI streaming, set `state_key` on the `ToolContext` (via `prepare_context` or `toolkit.create_context`):
-
-```python
-context = toolkit.create_context(state_key="my_app")
-deps = AgentDeps(client=client, tool_context=context)
-```
-
-Tool functions access `client` and `tool_context` via pydantic-ai's `RunContext.deps`, so toolsets can be created once and reused across requests.
-
-For complete runnable examples, see [`examples/custom_agent.py`](https://github.com/ggozad/haiku.rag/tree/main/examples/custom_agent.py) (standalone) and [`examples/custom_agent_agui.py`](https://github.com/ggozad/haiku.rag/tree/main/examples/custom_agent_agui.py) (AG-UI streaming server).
-
-All toolsets respect session-level document filters when a `SessionState` is registered in the context. This means setting `SessionState.document_filter` restricts all tools simultaneously.
-
-## AG-UI State Management
-
-Both `AgentDeps` and `ChatDeps` implement the AG-UI `StateHandler` protocol. `ChatDeps` extends `AgentDeps` with chat-specific config and state handling. State is emitted under a namespaced key via `state_key` on the `ToolContext` — set it once via `prepare_context()`.
-
-**Custom agents** use `AgentDeps` + `prepare_context`:
-
-```python
-from haiku.rag.tools import AgentDeps, ToolContext, ToolContextCache, prepare_context
-
-context = ToolContext()
-prepare_context(context, features=["search", "qa"], state_key="my_app")
-deps = AgentDeps(client=client, tool_context=context)
-```
-
-**Chat agent** uses `ChatDeps` + `build_chat_toolkit` (adds chat-specific defaults like background summarization):
-
-```python
-from haiku.rag.agents.chat import (
-    AGUI_STATE_KEY, ChatDeps, build_chat_toolkit, create_chat_agent,
-)
-from haiku.rag.tools import ToolContextCache
-
-chat_toolkit = build_chat_toolkit(config)
-agent = create_chat_agent(config, toolkit=chat_toolkit)
-
-# For multi-session apps, cache ToolContext per thread
-cache = ToolContextCache()
-context, is_new = cache.get_or_create(thread_id)
-if is_new:
-    chat_toolkit.prepare(context, state_key=AGUI_STATE_KEY)
-
-deps = ChatDeps(
-    config=config,
-    client=client,
-    tool_context=context,
-)
-```
-
-The emitted state structure:
-
-```json
-{
-  "haiku.rag.chat": {
-    "citations": [],
-    "qa_history": [],
-    "session_context": null,
-    "document_filter": [],
-    "citation_registry": {}
-  }
-}
-```
-
-State flows bidirectionally — the frontend sends its current state on each request, and the agent emits deltas (JSON Patch) reflecting server-side updates (new citations, QA history entries, session context). The server always prefers its own `session_context` over the client's value, since background summarization may have updated it between requests. See the [Web Application](apps.md#web-application) for a complete implementation.
-
 ## Filter Helpers
 
 `haiku.rag.tools.filters` provides utilities for building SQL filters:
 
-**`build_document_filter(document_name)`** — Builds a LIKE filter matching against both `uri` and `title`, case-insensitive. Also matches without spaces (e.g., "TB MED 593" matches "tbmed593").
-
-**`build_multi_document_filter(document_names)`** — Combines multiple document name filters with OR logic.
-
-**`combine_filters(filter1, filter2)`** — Combines two filters with AND logic. Returns `None` if both are `None`.
-
-**`get_session_filter(context, base_filter?)`** — Extracts `document_filter` from `SessionState` in the `ToolContext`, builds a SQL filter from it, and combines with an optional `base_filter`.
+- **`build_document_filter(document_name)`** — Builds a LIKE filter matching against both `uri` and `title`, case-insensitive. Also matches without spaces (e.g., "TB MED 593" matches "tbmed593").
+- **`build_multi_document_filter(document_names)`** — Combines multiple document name filters with OR logic.
+- **`combine_filters(filter1, filter2)`** — Combines two filters with AND logic. Returns `None` if both are `None`.
diff --git a/evaluations/evaluations/benchmark.py b/evaluations/evaluations/benchmark.py
index 247f2df5..2dcae976 100644
--- a/evaluations/evaluations/benchmark.py
+++ b/evaluations/evaluations/benchmark.py
@@ -17,9 +17,6 @@ from rich.progress import Progress
 from evaluations.config import DatasetSpec
 from evaluations.datasets import DATASETS
 from evaluations.evaluators import ANSWER_EQUIVALENCE_RUBRIC
-from haiku.rag.agents.research.dependencies import ResearchContext
-from haiku.rag.agents.research.graph import build_research_graph
-from haiku.rag.agents.research.state import ResearchDeps, ResearchState
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import AppConfig, find_config_file, load_yaml_config
 from haiku.rag.config.models import ModelConfig
@@ -42,13 +39,11 @@ def build_experiment_metadata(
     test_cases: int,
     config: AppConfig,
     judge_config: ModelConfig,
-    deep: bool = False,
 ) -> dict[str, Any]:
     """Build experiment metadata for Logfire tracking."""
     return {
         "dataset": dataset_key,
         "test_cases": test_cases,
-        "deep_ask": deep,
         "embedder_provider": config.embeddings.model.provider,
         "embedder_model": config.embeddings.model.name,
         "embedder_dim": config.embeddings.model.vector_dim,
@@ -270,7 +265,6 @@ async def run_qa_benchmark(
     limit: int | None = None,
     name: str | None = None,
     db_path: Path | None = None,
-    deep: bool = False,
 ) -> ReportCaseFailure[str, str, dict[str, str]] | None:
     corpus = spec.qa_loader()
     if limit is not None:
@@ -303,32 +297,19 @@ async def run_qa_benchmark(
 
     db = spec.db_path(db_path)
     async with HaikuRAG(db, config=config) as rag:
-        if deep:
-            graph = build_research_graph(config=config)
+        qa = get_qa_agent(rag, system_prompt=spec.system_prompt)
 
-            async def answer_question(question: str) -> str:
-                context = ResearchContext(original_question=question)
-                state = ResearchState.from_config(context=context, config=config)
-                deps = ResearchDeps(client=rag)
-                report = await graph.run(state=state, deps=deps)
-                return report.executive_summary if report else ""
-        else:
-            qa = get_qa_agent(rag, system_prompt=spec.system_prompt)
-
-            async def answer_question(question: str) -> str:
-                answer, _ = await qa.answer(question)
-                return answer
+        async def answer_question(question: str) -> str:
+            answer, _ = await qa.answer(question)
+            return answer
 
         eval_name = name if name is not None else f"{spec.key}_qa_evaluation"
-        if deep:
-            eval_name = f"{eval_name}_deep"
 
         experiment_metadata = build_experiment_metadata(
             dataset_key=spec.key,
             test_cases=len(cases),
             config=config,
             judge_config=judge_config,
-            deep=deep,
         )
 
         report = await evaluation_dataset.evaluate(
@@ -378,7 +359,6 @@ async def evaluate_dataset(
     db_path: Path | None,
     vacuum_interval: int = 100,
     multimodal_only: bool = False,
-    deep: bool = False,
 ) -> None:
     if not skip_db:
         console.print(f"Using dataset: {spec.key}", style="bold magenta")
@@ -398,11 +378,8 @@ async def evaluate_dataset(
         )
 
     if not skip_qa:
-        mode_label = "deep QA" if deep else "QA"
-        console.print(f"\nRunning {mode_label} benchmarks...", style="bold yellow")
-        await run_qa_benchmark(
-            spec, config, limit=limit, name=name, db_path=db_path, deep=deep
-        )
+        console.print("\nRunning QA benchmarks...", style="bold yellow")
+        await run_qa_benchmark(spec, config, limit=limit, name=name, db_path=db_path)
 
 
 app = typer.Typer(help="Run retrieval and QA benchmarks for configured datasets.")
@@ -434,11 +411,6 @@ def run(
         "--multimodal-only",
         help="Only evaluate queries requiring image understanding.",
     ),
-    deep: bool = typer.Option(
-        False,
-        "--deep",
-        help="Use deep QA mode (multi-step reasoning with research graph).",
-    ),
 ) -> None:
     spec = DATASETS.get(dataset.lower())
     if spec is None:
@@ -477,7 +449,6 @@ def run(
             db_path=db,
             vacuum_interval=vacuum_interval,
             multimodal_only=multimodal_only,
-            deep=deep,
         )
     )
 
diff --git a/examples/README.md b/examples/README.md
index 74613d23..1cae68b0 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -16,7 +16,7 @@ See `docker/README.md` for setup instructions.
 
 **Script:** `custom_agent.py`
 
-Composes `search`, `qa`, and `document` toolsets into a pydantic-ai `Agent` using `AgentDeps` and `prepare_context`. Shows how to run queries and inspect accumulated state (citations, QA history).
+Uses the RAG skill with `SkillToolset` to build a conversational agent.
 
 ```bash
 uv run python examples/custom_agent.py /path/to/db.lancedb
@@ -26,7 +26,7 @@ uv run python examples/custom_agent.py /path/to/db.lancedb
 
 **Script:** `custom_agent_agui.py`
 
-A Starlette app that serves an AG-UI streaming endpoint using composed toolsets, `AgentDeps`, and `ToolContextCache` for multi-session support.
+A Starlette app that serves an AG-UI streaming endpoint using the RAG skill with `SkillToolset`.
 
 ```bash
 DB_PATH=/path/to/db.lancedb uv run uvicorn examples.custom_agent_agui:app --reload --port 8000
diff --git a/examples/custom_agent.py b/examples/custom_agent.py
index 6b5a838c..52238b9a 100644
--- a/examples/custom_agent.py
+++ b/examples/custom_agent.py
@@ -1,7 +1,7 @@
-"""Custom agent using haiku.rag composable toolsets.
+"""Custom agent using the haiku.rag RAG skill.
 
-Demonstrates how to compose search, QA, and document toolsets into a
-pydantic-ai Agent using AgentDeps and prepare_context.
+Demonstrates how to use the RAG skill with haiku.skills SkillToolset
+to build a conversational agent.
 
 Requirements:
     - An Ollama instance running locally (default embedder)
@@ -14,61 +14,36 @@ Usage:
 
 import asyncio
 import sys
+from pathlib import Path
 
 from pydantic_ai import Agent
 
-from haiku.rag.client import HaikuRAG
-from haiku.rag.tools import (
-    AgentDeps,
-    ToolContext,
-    build_tools_prompt,
-    create_document_toolset,
-    create_qa_toolset,
-    create_search_toolset,
-    prepare_context,
-)
+from haiku.rag.skills.rag import create_skill
+from haiku.skills.agent import SkillToolset
 
 
 async def main(db_path: str) -> None:
-    async with HaikuRAG(db_path) as client:
-        # Compose toolsets into an agent
-        config = client.config
-        search_toolset = create_search_toolset(config)
-        qa_toolset = create_qa_toolset(config)
-        document_toolset = create_document_toolset(config)
+    skill = create_skill(db_path=Path(db_path))
+    toolset = SkillToolset(skills=[skill])
 
-        features = ["search", "documents", "qa"]
-        tools_prompt = build_tools_prompt(features)
+    agent = Agent(
+        "anthropic:claude-haiku-4-5-20251001",
+        instructions=toolset.system_prompt,
+        toolsets=[toolset],
+    )
 
-        agent = Agent(
-            "anthropic:claude-haiku-4-5-20251001",
-            deps_type=AgentDeps,
-            output_type=str,
-            instructions=(
-                "You are a helpful assistant with access to a knowledge base.\n"
-                f"{tools_prompt}"
-            ),
-            toolsets=[search_toolset, qa_toolset, document_toolset],
-        )
+    print("Custom agent ready. Ctrl+C to exit.\n")
+    while True:
+        try:
+            user_input = input("You: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            break
 
-        # Prepare a shared ToolContext
-        context = ToolContext()
-        prepare_context(context, features=["search", "documents", "qa"])
+        if not user_input:
+            continue
 
-        deps = AgentDeps(client=client, tool_context=context)
-
-        print("Custom agent ready. Ctrl+C to exit.\n")
-        while True:
-            try:
-                user_input = input("You: ").strip()
-            except (EOFError, KeyboardInterrupt):
-                break
-
-            if not user_input:
-                continue
-
-            result = await agent.run(user_input, deps=deps)
-            print(f"\nAgent: {result.output}\n")
+        result = await agent.run(user_input)
+        print(f"\nAgent: {result.output}\n")
 
 
 if __name__ == "__main__":
diff --git a/examples/custom_agent_agui.py b/examples/custom_agent_agui.py
index ce812d59..5ca7069e 100644
--- a/examples/custom_agent_agui.py
+++ b/examples/custom_agent_agui.py
@@ -1,7 +1,7 @@
 """Custom agent with AG-UI streaming.
 
-A Starlette app that composes haiku.rag toolsets into an AG-UI compatible
-agent. Multi-session support via ToolContextCache.
+A Starlette app that serves an AG-UI streaming endpoint using the
+haiku.rag RAG skill with haiku.skills SkillToolset.
 
 Requirements:
     - An Ollama instance running locally (default embedder)
@@ -13,25 +13,18 @@ Usage:
 
 import os
 import sys
+from pathlib import Path
 
 from pydantic_ai import Agent
+from pydantic_ai.ag_ui import AGUIAdapter
 from pydantic_ai.ui import SSE_CONTENT_TYPE
-from pydantic_ai.ui.ag_ui import AGUIAdapter
 from starlette.applications import Starlette
 from starlette.requests import Request
 from starlette.responses import JSONResponse, Response, StreamingResponse
 from starlette.routing import Route
 
-from haiku.rag.client import HaikuRAG
-from haiku.rag.config.models import AppConfig
-from haiku.rag.tools import (
-    AgentDeps,
-    ToolContextCache,
-    build_tools_prompt,
-    create_qa_toolset,
-    create_search_toolset,
-    prepare_context,
-)
+from haiku.rag.skills.rag import create_skill
+from haiku.skills.agent import SkillToolset
 
 db_path = os.environ.get("DB_PATH")
 if not db_path:
@@ -40,39 +33,13 @@ if not db_path:
     )
     sys.exit(1)
 
-AGUI_STATE_KEY = "my_app"
+skill = create_skill(db_path=Path(db_path))
+toolset = SkillToolset(skills=[skill])
 
-config = AppConfig()
-
-# ToolContextCache maintains per-thread state across requests
-context_cache = ToolContextCache()
-
-# Singleton client
-_client: HaikuRAG | None = None
-
-
-def get_client() -> HaikuRAG:
-    global _client
-    if _client is None:
-        _client = HaikuRAG(db_path=db_path)
-    return _client
-
-
-features = ["search", "qa"]
-tools_prompt = build_tools_prompt(features)
-
-# Create the agent once at module level
 agent = Agent(
     "anthropic:claude-haiku-4-5-20251001",
-    deps_type=AgentDeps,
-    output_type=str,
-    instructions=(
-        f"You are a helpful assistant with access to a knowledge base.\n{tools_prompt}"
-    ),
-    toolsets=[
-        create_search_toolset(config),
-        create_qa_toolset(config),
-    ],
+    instructions=toolset.system_prompt,
+    toolsets=[toolset],
 )
 
 
@@ -81,19 +48,8 @@ async def stream_chat(request: Request) -> Response:
     accept = request.headers.get("accept", SSE_CONTENT_TYPE)
     run_input = AGUIAdapter.build_run_input(body)
 
-    thread_id = getattr(run_input, "thread_id", None) or "default"
-    context, is_new = context_cache.get_or_create(thread_id)
-    if is_new:
-        prepare_context(
-            context,
-            features=["search", "qa"],
-            state_key=AGUI_STATE_KEY,
-        )
-
-    deps = AgentDeps(client=get_client(), tool_context=context)
-
     adapter = AGUIAdapter(agent=agent, run_input=run_input, accept=accept)
-    event_stream = adapter.run_stream(deps=deps)
+    event_stream = adapter.run_stream()
     sse_event_stream = adapter.encode_stream(event_stream)
 
     return StreamingResponse(
diff --git a/haiku_rag_slim/haiku/rag/agents/chat/__init__.py b/haiku_rag_slim/haiku/rag/agents/chat/__init__.py
deleted file mode 100644
index cf95c9cc..00000000
--- a/haiku_rag_slim/haiku/rag/agents/chat/__init__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from haiku.rag.agents.chat.agent import (
-    DEFAULT_FEATURES,
-    FEATURE_ANALYSIS,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_SEARCH,
-    ChatDeps,
-    build_chat_toolkit,
-    create_chat_agent,
-    prepare_chat_context,
-    run_chat_agent,
-    trigger_background_summarization,
-)
-from haiku.rag.agents.chat.prompts import build_chat_prompt
-from haiku.rag.agents.chat.state import (
-    AGUI_STATE_KEY,
-    ChatSessionState,
-    _rebuild_models,
-)
-from haiku.rag.tools.qa import QAHistoryEntry
-
-_rebuild_models(QAHistoryEntry)
-
-__all__ = [
-    "AGUI_STATE_KEY",
-    "DEFAULT_FEATURES",
-    "FEATURE_ANALYSIS",
-    "FEATURE_DOCUMENTS",
-    "FEATURE_QA",
-    "FEATURE_SEARCH",
-    "build_chat_prompt",
-    "build_chat_toolkit",
-    "create_chat_agent",
-    "prepare_chat_context",
-    "run_chat_agent",
-    "trigger_background_summarization",
-    "ChatDeps",
-    "ChatSessionState",
-]
diff --git a/haiku_rag_slim/haiku/rag/agents/chat/agent.py b/haiku_rag_slim/haiku/rag/agents/chat/agent.py
deleted file mode 100644
index 4ec79f75..00000000
--- a/haiku_rag_slim/haiku/rag/agents/chat/agent.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Any
-
-from pydantic_ai import Agent
-
-from haiku.rag.agents.chat.context import (
-    trigger_background_summarization as _trigger_summarization,
-)
-from haiku.rag.agents.chat.prompts import build_chat_prompt
-from haiku.rag.agents.chat.state import AGUI_STATE_KEY
-from haiku.rag.config.models import AppConfig
-from haiku.rag.tools.context import ToolContext
-from haiku.rag.tools.deps import AgentDeps
-from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-from haiku.rag.tools.session import SessionContext
-from haiku.rag.tools.toolkit import (
-    FEATURE_ANALYSIS,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_SEARCH,
-    Toolkit,
-    build_toolkit,
-)
-from haiku.rag.utils import get_model
-
-DEFAULT_FEATURES = [FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA]
-
-
-def _on_qa_complete(qa_session_state: QASessionState, config: AppConfig) -> None:
-    _trigger_summarization(qa_session_state=qa_session_state, config=config)
-
-
-@dataclass
-class ChatDeps(AgentDeps):
-    """Dependencies for chat agent.
-
-    Extends AgentDeps with chat-specific config and state handling.
-    """
-
-    config: AppConfig = field(default_factory=AppConfig)
-
-    @AgentDeps.state.setter
-    def state(self, value: dict[str, Any] | None) -> None:
-        """Set state from AG-UI protocol with chat-specific overrides."""
-        if value is None:
-            return
-
-        state_data = self._extract_state_data(value)
-
-        # Preserve server's session_context before restore overwrites it
-        qa_session_state = self.tool_context.get(QA_SESSION_NAMESPACE, QASessionState)
-        server_session_context = (
-            qa_session_state.session_context if qa_session_state is not None else None
-        )
-
-        self.tool_context.restore_state_snapshot(state_data)
-
-        # Chat-specific overrides after generic restore
-        if qa_session_state is not None:
-            # Prefer server's session_context (background summarizer may
-            # have updated it since the client's last snapshot).
-            if server_session_context is not None:
-                qa_session_state.session_context = server_session_context
-
-            # Handle initial_context -> session_context for first message
-            if qa_session_state.session_context is None:
-                if "initial_context" in state_data:
-                    initial = state_data.get("initial_context")
-                    if initial:
-                        qa_session_state.session_context = SessionContext(
-                            summary=initial
-                        )
-
-
-def build_chat_toolkit(
-    config: AppConfig,
-    features: list[str] | None = None,
-) -> Toolkit:
-    """Build a Toolkit configured for the chat agent.
-
-    Includes the on_qa_complete callback that triggers background
-    session summarization.
-
-    Args:
-        config: Application configuration.
-        features: List of features to enable. Defaults to DEFAULT_FEATURES.
-
-    Returns:
-        A Toolkit ready for chat agent composition and context creation.
-    """
-    if features is None:
-        features = DEFAULT_FEATURES
-
-    return build_toolkit(config, features=features, on_qa_complete=_on_qa_complete)
-
-
-def prepare_chat_context(
-    context: ToolContext,
-    features: list[str] | None = None,
-) -> None:
-    """Register required namespaces in a ToolContext for chat agent use.
-
-    Idempotent — safe to call multiple times on the same context.
-
-    Args:
-        context: ToolContext to prepare.
-        features: List of enabled features. Defaults to DEFAULT_FEATURES.
-    """
-    from haiku.rag.tools.context import prepare_context
-
-    if features is None:
-        features = DEFAULT_FEATURES
-
-    prepare_context(context, features=features, state_key=AGUI_STATE_KEY)
-
-
-def create_chat_agent(
-    config: AppConfig,
-    features: list[str] | None = None,
-    preamble: str | None = None,
-    toolkit: Toolkit | None = None,
-) -> Agent[ChatDeps, str]:
-    """Create the chat agent with composed toolsets.
-
-    Args:
-        config: Application configuration.
-        features: List of features to enable. Defaults to DEFAULT_FEATURES
-            (search, documents, qa). Available features: "search",
-            "documents", "qa", "analysis".
-        preamble: Optional custom identity/rules section for the system prompt.
-            When provided, replaces the default identity prompt. Tool guidance,
-            feature rules, and closing are still appended by the builder.
-        toolkit: Optional pre-built Toolkit. When provided, its toolsets are
-            used directly. When omitted, a toolkit is built from config and
-            features.
-
-    Returns:
-        The configured chat agent.
-
-    Example:
-        async with HaikuRAG(db_path, create=True) as client:
-            toolkit = build_chat_toolkit(config)
-            context = toolkit.create_context(state_key=AGUI_STATE_KEY)
-            agent = create_chat_agent(config, toolkit=toolkit)
-            deps = ChatDeps(config=config, client=client, tool_context=context)
-            result = await agent.run("Search for X", deps=deps)
-    """
-    if features is None:
-        features = DEFAULT_FEATURES
-
-    if toolkit is None:
-        toolkit = build_chat_toolkit(config, features=features)
-
-    model = get_model(config.qa.model, config)
-
-    return Agent(
-        model,
-        deps_type=ChatDeps,
-        output_type=str,
-        instructions=build_chat_prompt(features, preamble=preamble),
-        toolsets=toolkit.toolsets,
-        retries=3,
-    )
-
-
-def trigger_background_summarization(deps: ChatDeps) -> None:
-    """Trigger background session summarization if qa_history has entries.
-
-    Call this after agent.run() or agent.run_stream() completes to update
-    the session context summary in the background.
-
-    Args:
-        deps: Chat dependencies with tool_context containing QASessionState.
-    """
-    qa_session_state = deps.tool_context.get(QA_SESSION_NAMESPACE, QASessionState)
-    if qa_session_state is None or not qa_session_state.qa_history:
-        return
-
-    _trigger_summarization(
-        qa_session_state=qa_session_state,
-        config=deps.config,
-    )
-
-
-async def run_chat_agent(
-    agent: Agent[ChatDeps, str],
-    deps: ChatDeps,
-    message: str,
-) -> str:
-    """Run the chat agent.
-
-    Args:
-        agent: The chat agent.
-        deps: Chat dependencies.
-        message: User message.
-
-    Returns:
-        Agent response.
-    """
-    result = await agent.run(message, deps=deps)
-    return result.output
-
-
-__all__ = [
-    "build_chat_toolkit",
-    "create_chat_agent",
-    "prepare_chat_context",
-    "run_chat_agent",
-    "trigger_background_summarization",
-    "ChatDeps",
-    "AGUI_STATE_KEY",
-    "FEATURE_SEARCH",
-    "FEATURE_DOCUMENTS",
-    "FEATURE_QA",
-    "FEATURE_ANALYSIS",
-    "DEFAULT_FEATURES",
-]
diff --git a/haiku_rag_slim/haiku/rag/agents/chat/context.py b/haiku_rag_slim/haiku/rag/agents/chat/context.py
deleted file mode 100644
index d463211e..00000000
--- a/haiku_rag_slim/haiku/rag/agents/chat/context.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import asyncio
-from datetime import datetime
-from typing import TYPE_CHECKING
-
-from pydantic_ai import Agent
-
-from haiku.rag.agents.chat.prompts import SESSION_SUMMARY_PROMPT
-from haiku.rag.config.models import AppConfig
-from haiku.rag.tools.session import SessionContext
-from haiku.rag.utils import get_model
-
-if TYPE_CHECKING:
-    from haiku.rag.tools.qa import QAHistoryEntry, QASessionState
-
-
-# Track summarization tasks to allow cancellation
-_summarization_tasks: dict[int, asyncio.Task[None]] = {}
-
-
-async def summarize_session(
-    qa_history: list["QAHistoryEntry"],
-    config: AppConfig,
-    current_context: str | None = None,
-) -> str:
-    """Summarize qa_history into compact context.
-
-    Args:
-        qa_history: List of Q&A pairs from the conversation.
-        config: AppConfig for model selection.
-        current_context: Previous session_context.summary to incorporate.
-            The summarizer will build upon this.
-
-    Returns:
-        Markdown summary of the conversation history.
-    """
-    if not qa_history:
-        return ""
-
-    model = get_model(config.qa.model, config)
-    agent: Agent[None, str] = Agent(
-        model,
-        output_type=str,
-        instructions=SESSION_SUMMARY_PROMPT,
-        retries=2,
-    )
-
-    history_text = _format_qa_history(qa_history)
-    if current_context:
-        history_text = f"## Current Context\n{current_context}\n\n{history_text}"
-    result = await agent.run(history_text)
-    return result.output
-
-
-async def update_session_context(
-    qa_history: list["QAHistoryEntry"],
-    config: AppConfig,
-    current_context: str | None = None,
-) -> SessionContext:
-    """Summarize qa_history and return the resulting session context.
-
-    Args:
-        qa_history: List of Q&A pairs from the conversation.
-        config: AppConfig for model selection.
-        current_context: Previous summary to incorporate.
-
-    Returns:
-        The new SessionContext with summary and timestamp.
-    """
-    summary = await summarize_session(
-        qa_history, config, current_context=current_context
-    )
-    return SessionContext(
-        summary=summary,
-        last_updated=datetime.now(),
-    )
-
-
-def _format_qa_history(qa_history: list["QAHistoryEntry"]) -> str:
-    """Format qa_history for input to summarization."""
-    lines: list[str] = []
-    for i, qa in enumerate(qa_history, 1):
-        lines.append(f"## Q{i}: {qa.question}")
-        lines.append(f"**Answer** (confidence: {qa.confidence:.0%}):")
-        lines.append(qa.answer)
-
-        if qa.sources:
-            lines.append(f"**Sources:** {', '.join(qa.sources)}")
-        lines.append("")
-
-    return "\n".join(lines)
-
-
-async def _update_context_background(
-    qa_session_state: "QASessionState",
-    config: AppConfig,
-) -> None:
-    """Background task to update session context after an ask."""
-    try:
-        current_summary = (
-            qa_session_state.session_context.summary
-            if qa_session_state.session_context is not None
-            else None
-        )
-        result = await update_session_context(
-            qa_history=list(qa_session_state.qa_history),
-            config=config,
-            current_context=current_summary,
-        )
-
-        if result.summary:
-            qa_session_state.session_context = result
-
-    except asyncio.CancelledError:
-        pass
-    except Exception as e:  # pragma: no cover
-        import logging
-
-        logging.getLogger(__name__).exception(f"Background summarization failed: {e}")
-
-
-def trigger_background_summarization(
-    qa_session_state: "QASessionState",
-    config: AppConfig,
-) -> None:
-    """Trigger background session summarization if qa_history has entries.
-
-    Args:
-        qa_session_state: QASessionState with qa_history to summarize.
-        config: AppConfig for model selection.
-    """
-    if not qa_session_state.qa_history:
-        return
-
-    key = id(qa_session_state)
-
-    # Cancel any existing summarization task for this state
-    if key in _summarization_tasks:
-        _summarization_tasks[key].cancel()
-
-    # Spawn background task
-    task = asyncio.create_task(
-        _update_context_background(
-            qa_session_state=qa_session_state,
-            config=config,
-        )
-    )
-    _summarization_tasks[key] = task
-    task.add_done_callback(lambda _t, k=key: _summarization_tasks.pop(k, None))
diff --git a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py b/haiku_rag_slim/haiku/rag/agents/chat/prompts.py
deleted file mode 100644
index f86d9f3e..00000000
--- a/haiku_rag_slim/haiku/rag/agents/chat/prompts.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from haiku.rag.tools.prompts import build_tools_prompt
-
-_PROMPT_BASE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-CRITICAL RULES:
-1. For greetings or casual chat: respond directly WITHOUT using any tools
-2. NEVER call the same tool multiple times for a single user message
-3. NEVER make up information - always use tools to get facts from the knowledge base"""
-
-_PROMPT_QA_RULES = """
-4. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context"""
-
-_PROMPT_SEARCH_RULES = """
-5. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally"""
-
-_PROMPT_SEARCH_OUTPUT = """
-After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results."""
-
-_PROMPT_CLOSING = """
-Be friendly and conversational."""
-
-_PROMPT_QA_CLOSING = (
-    """ When you use the "ask" tool, summarize the key findings for the user."""
-)
-
-
-def build_chat_prompt(
-    features: list[str],
-    preamble: str | None = None,
-) -> str:
-    """Build a chat system prompt from the given feature list.
-
-    Each feature adds its relevant tool guidance to the prompt.
-    The base identity, critical rules, and closing are always included.
-
-    Args:
-        features: List of feature names (e.g., ["search", "documents", "qa"]).
-        preamble: Optional custom identity/rules section. When provided,
-            replaces the default identity prompt. Tool guidance, feature
-            rules, and closing are still appended.
-
-    Returns:
-        The composed system prompt string.
-    """
-    parts = [preamble if preamble is not None else _PROMPT_BASE]
-
-    # Add feature-specific critical rules
-    if "qa" in features:
-        parts.append(_PROMPT_QA_RULES)
-    if "search" in features:
-        parts.append(_PROMPT_SEARCH_RULES)
-
-    # Tool guidance (reusable across agents)
-    tools_prompt = build_tools_prompt(features)
-    if tools_prompt:
-        parts.append(tools_prompt)
-
-    # Chat-specific search output rule
-    if "search" in features:
-        parts.append(_PROMPT_SEARCH_OUTPUT)
-
-    parts.append(_PROMPT_CLOSING)
-    if "qa" in features:
-        parts.append(_PROMPT_QA_CLOSING)
-
-    return "".join(parts)
-
-
-CHAT_SYSTEM_PROMPT = build_chat_prompt(["search", "documents", "qa"])
-
-SESSION_SUMMARY_PROMPT = """You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-Your summary should be concise (aim for 500-1500 tokens) and include:
-
-1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-3. **Current Focus** - What topic or question thread the user is currently exploring
-
-Rules:
-- Extract only high-signal information that would help answer follow-up questions
-- When building on existing context, merge new information with prior context
-- Omit small talk, greetings, or low-confidence answers
-- Use bullet points for clarity
-- Keep technical details but compress verbose explanations
-- Preserve document names/titles when mentioned in sources
-
-Output the summary directly in markdown format. Do not include meta-commentary about the summary itself."""
diff --git a/haiku_rag_slim/haiku/rag/agents/chat/state.py b/haiku_rag_slim/haiku/rag/agents/chat/state.py
deleted file mode 100644
index ef985cb0..00000000
--- a/haiku_rag_slim/haiku/rag/agents/chat/state.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from typing import TYPE_CHECKING
-
-from pydantic import BaseModel
-
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.tools.session import SessionContext
-
-if TYPE_CHECKING:
-    from haiku.rag.tools.qa import QAHistoryEntry
-
-AGUI_STATE_KEY = "haiku.rag.chat"
-
-
-class ChatSessionState(BaseModel):
-    """State shared between frontend and agent via AG-UI."""
-
-    initial_context: str | None = None
-    citations: list[Citation] = []
-    citations_history: list[list[Citation]] = []
-    qa_history: list["QAHistoryEntry"] = []
-    session_context: SessionContext | None = None
-    document_filter: list[str] = []
-    citation_registry: dict[str, int] = {}
-
-
-def _rebuild_models(qa_history_entry_cls: type) -> None:
-    """Resolve ChatSessionState forward reference to QAHistoryEntry.
-
-    Must be called after QAHistoryEntry is defined, passing the class.
-    """
-    ChatSessionState.model_rebuild(
-        _types_namespace={"QAHistoryEntry": qa_history_entry_cls}
-    )
diff --git a/haiku_rag_slim/haiku/rag/agents/qa/agent.py b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
index 5ba044fa..9e5ea2c0 100644
--- a/haiku_rag_slim/haiku/rag/agents/qa/agent.py
+++ b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
@@ -11,15 +11,14 @@ from haiku.rag.agents.research.models import (
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import Config
 from haiku.rag.config.models import AppConfig, ModelConfig
-from haiku.rag.tools.context import ToolContext
-from haiku.rag.tools.search import SEARCH_NAMESPACE, SearchState, create_search_toolset
+from haiku.rag.store.models import SearchResult
+from haiku.rag.tools.search import create_search_toolset
 from haiku.rag.utils import get_model
 
 
 @dataclass
 class _QARunDeps:
     client: HaikuRAG
-    tool_context: ToolContext | None = None
 
 
 class QuestionAnswerAgent:
@@ -47,11 +46,12 @@ class QuestionAnswerAgent:
         Returns:
             Tuple of (answer text, list of resolved citations)
         """
-        context = ToolContext()
+        accumulated_results: list[SearchResult] = []
         search_toolset = create_search_toolset(
             self._config,
             base_filter=filter,
             tool_name="search_documents",
+            on_results=accumulated_results.extend,
         )
 
         # Agent created per-call: toolset varies with filter, and Agent
@@ -66,15 +66,9 @@ class QuestionAnswerAgent:
             retries=3,
         )
 
-        deps = _QARunDeps(client=self._client, tool_context=context)
+        deps = _QARunDeps(client=self._client)
         result = await agent.run(question, deps=deps)
         output = result.output
 
-        # Get search results from context for citation resolution
-        search_state = context.get(SEARCH_NAMESPACE)
-        search_results = (
-            search_state.results if isinstance(search_state, SearchState) else []
-        )
-
-        citations = resolve_citations(output.cited_chunks, search_results)
+        citations = resolve_citations(output.cited_chunks, accumulated_results)
         return output.answer, citations
diff --git a/haiku_rag_slim/haiku/rag/agents/research/dependencies.py b/haiku_rag_slim/haiku/rag/agents/research/dependencies.py
index 8a112d2a..dd47ac21 100644
--- a/haiku_rag_slim/haiku/rag/agents/research/dependencies.py
+++ b/haiku_rag_slim/haiku/rag/agents/research/dependencies.py
@@ -16,10 +16,6 @@ class ResearchContext(BaseModel):
     qa_responses: list[Any] = Field(
         default_factory=list, description="Structured QA pairs used during research"
     )
-    session_context: str | None = Field(
-        default=None,
-        description="Session context from previous Q&A summarization",
-    )
 
     def add_qa_response(self, qa: "SearchAnswer") -> None:
         """Add a structured QA response."""
diff --git a/haiku_rag_slim/haiku/rag/agents/research/graph.py b/haiku_rag_slim/haiku/rag/agents/research/graph.py
index 18b95a9f..e445622e 100644
--- a/haiku_rag_slim/haiku/rag/agents/research/graph.py
+++ b/haiku_rag_slim/haiku/rag/agents/research/graph.py
@@ -1,5 +1,4 @@
 import asyncio
-from typing import Literal, overload
 
 from pydantic_ai import Agent, RunContext, format_as_xml
 from pydantic_ai.output import ToolOutput
@@ -7,15 +6,12 @@ from pydantic_graph.beta import Graph, GraphBuilder, StepContext
 
 from haiku.rag.agents.research.dependencies import ResearchContext, ResearchDependencies
 from haiku.rag.agents.research.models import (
-    Citation,
-    ConversationalAnswer,
     IterativePlanResult,
     RawSearchAnswer,
     ResearchReport,
     SearchAnswer,
 )
 from haiku.rag.agents.research.prompts import (
-    CONVERSATIONAL_SYNTHESIS_PROMPT,
     ITERATIVE_PLAN_PROMPT,
     ITERATIVE_PLAN_PROMPT_WITH_CONTEXT,
     SEARCH_PROMPT,
@@ -31,9 +27,6 @@ def format_context_for_prompt(context: ResearchContext) -> str:
     """Format the research context as XML for prompts."""
     context_data: dict[str, object] = {}
 
-    if context.session_context:
-        context_data["background"] = context.session_context
-
     context_data["question"] = context.original_question
 
     if context.qa_responses:
@@ -169,29 +162,13 @@ async def _search_one_step_logic(
         return SearchAnswer(query=sub_q, answer="", confidence=0.0)
 
 
-@overload
-def build_research_graph(
-    config: AppConfig = ...,
-    output_mode: Literal["report"] = ...,
-) -> Graph[ResearchState, ResearchDeps, None, ResearchReport]: ...
-
-
-@overload
-def build_research_graph(
-    config: AppConfig = ...,
-    output_mode: Literal["conversational"] = ...,
-) -> Graph[ResearchState, ResearchDeps, None, ConversationalAnswer]: ...
-
-
 def build_research_graph(
     config: AppConfig = Config,
-    output_mode: Literal["report", "conversational"] = "report",
-) -> Graph[ResearchState, ResearchDeps, None, ResearchReport | ConversationalAnswer]:
+) -> Graph[ResearchState, ResearchDeps, None, ResearchReport]:
     """Build the iterative research graph.
 
     Args:
         config: AppConfig object (uses config.research for provider, model, and graph parameters)
-        output_mode: Output format - "report" for ResearchReport, "conversational" for ConversationalAnswer
 
     Returns:
         Configured research graph with iterative planning
@@ -199,18 +176,14 @@ def build_research_graph(
     model_config = config.research.model
 
     search_prompt = build_prompt(SEARCH_PROMPT, config)
-
-    if output_mode == "report":
-        synthesis_prompt = build_prompt(
-            config.prompts.synthesis or SYNTHESIS_PROMPT, config
-        )
-    else:
-        synthesis_prompt = build_prompt(CONVERSATIONAL_SYNTHESIS_PROMPT, config)
+    synthesis_prompt = build_prompt(
+        config.prompts.synthesis or SYNTHESIS_PROMPT, config
+    )
 
     g = GraphBuilder(
         state_type=ResearchState,
         deps_type=ResearchDeps,
-        output_type=ResearchReport if output_mode == "report" else ConversationalAnswer,
+        output_type=ResearchReport,
     )
 
     @g.step
@@ -236,81 +209,35 @@ def build_research_graph(
                 confidence=0.0,
             )
 
-    if output_mode == "report":
+    @g.step
+    async def synthesize(
+        ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
+    ) -> ResearchReport:
+        """Generate final research report."""
+        state = ctx.state
+        deps = ctx.deps
 
-        @g.step
-        async def synthesize(
-            ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
-        ) -> ResearchReport:
-            """Generate final research report."""
-            state = ctx.state
-            deps = ctx.deps
+        agent: Agent[ResearchDependencies, ResearchReport] = Agent(  # type: ignore[assignment]
+            model=get_model(model_config, config),
+            output_type=ResearchReport,
+            instructions=synthesis_prompt,
+            retries=3,
+            output_retries=3,
+            deps_type=ResearchDependencies,
+        )
 
-            agent: Agent[ResearchDependencies, ResearchReport] = Agent(  # type: ignore[assignment]
-                model=get_model(model_config, config),
-                output_type=ResearchReport,
-                instructions=synthesis_prompt,
-                retries=3,
-                output_retries=3,
-                deps_type=ResearchDependencies,
-            )
-
-            context_xml = format_context_for_prompt(state.context)
-            prompt = (
-                "Generate a comprehensive research report based on all gathered information.\n\n"
-                f"{context_xml}\n\n"
-                "Create a detailed report that synthesizes all findings into a coherent response."
-            )
-            agent_deps = ResearchDependencies(
-                client=deps.client,
-                context=state.context,
-            )
-            result = await agent.run(prompt, deps=agent_deps)
-            return result.output
-
-    else:
-
-        @g.step
-        async def synthesize(
-            ctx: StepContext[ResearchState, ResearchDeps, IterativePlanResult],
-        ) -> ConversationalAnswer:
-            """Generate conversational answer from gathered evidence."""
-            state = ctx.state
-            deps = ctx.deps
-
-            agent: Agent[ResearchDependencies, ConversationalAnswer] = Agent(  # type: ignore[assignment]
-                model=get_model(model_config, config),
-                output_type=ConversationalAnswer,
-                instructions=synthesis_prompt,
-                retries=3,
-                output_retries=3,
-                deps_type=ResearchDependencies,
-            )
-
-            context_xml = format_context_for_prompt(state.context)
-            prompt = (
-                f"Answer the question based on the gathered evidence.\n\n{context_xml}"
-            )
-            agent_deps = ResearchDependencies(
-                client=deps.client,
-                context=state.context,
-            )
-            result = await agent.run(prompt, deps=agent_deps)
-
-            # Collect unique citations from qa_responses (dedupe by chunk_id)
-            seen_chunks: set[str] = set()
-            unique_citations: list[Citation] = []
-            for qa in state.context.qa_responses:
-                for c in qa.citations:
-                    if c.chunk_id not in seen_chunks:
-                        seen_chunks.add(c.chunk_id)
-                        unique_citations.append(c)
-
-            return ConversationalAnswer(
-                answer=result.output.answer,
-                citations=unique_citations,
-                confidence=result.output.confidence,
-            )
+        context_xml = format_context_for_prompt(state.context)
+        prompt = (
+            "Generate a comprehensive research report based on all gathered information.\n\n"
+            f"{context_xml}\n\n"
+            "Create a detailed report that synthesizes all findings into a coherent response."
+        )
+        agent_deps = ResearchDependencies(
+            client=deps.client,
+            context=state.context,
+        )
+        result = await agent.run(prompt, deps=agent_deps)
+        return result.output
 
     # Build graph edges: iterative loop
     #
diff --git a/haiku_rag_slim/haiku/rag/agents/research/models.py b/haiku_rag_slim/haiku/rag/agents/research/models.py
index bb9e0d93..a986769f 100644
--- a/haiku_rag_slim/haiku/rag/agents/research/models.py
+++ b/haiku_rag_slim/haiku/rag/agents/research/models.py
@@ -21,7 +21,7 @@ class IterativePlanResult(BaseModel):
 class Citation(BaseModel):
     """Resolved citation with full metadata for display/visual grounding.
 
-    Used by both research graph and chat agent. The optional index field
+    Used by research graph and chat applications. The optional index field
     supports UI display ordering in chat contexts.
     """
 
@@ -111,18 +111,6 @@ def resolve_citations(
     return citations
 
 
-class ConversationalAnswer(BaseModel):
-    """Conversational answer for chat context."""
-
-    answer: str = Field(description="Direct answer to the question")
-    citations: list[Citation] = Field(
-        default_factory=list, description="Citations supporting the answer"
-    )
-    confidence: float = Field(
-        default=1.0, description="Confidence score (0-1)", ge=0.0, le=1.0
-    )
-
-
 class ResearchReport(BaseModel):
     """Final research report structure."""
 
diff --git a/haiku_rag_slim/haiku/rag/agents/research/prompts.py b/haiku_rag_slim/haiku/rag/agents/research/prompts.py
index bb21bc55..8cfd0dac 100644
--- a/haiku_rag_slim/haiku/rag/agents/research/prompts.py
+++ b/haiku_rag_slim/haiku/rag/agents/research/prompts.py
@@ -1,7 +1,5 @@
 ITERATIVE_PLAN_PROMPT = """You are the research orchestrator planning the investigation.
 
-If a <background> section is provided, use it to understand the conversation context.
-
 Your task:
 1. Analyze the original question
 2. Propose the first question to investigate
@@ -23,7 +21,6 @@ The question must be standalone and self-contained:
 ITERATIVE_PLAN_PROMPT_WITH_CONTEXT = """You are the research orchestrator evaluating gathered evidence.
 
 You have access to context that may include:
-- <background>: Domain context for the conversation
 - <prior_answers>: Previous Q&A pairs with confidence scores
 
 Your task:
@@ -115,21 +112,3 @@ Style:
 - Be professional, objective, and specific.
 - NEVER use meta-commentary like "This report covers..." or "The findings show...".
   Instead, state the actual information directly."""
-
-CONVERSATIONAL_SYNTHESIS_PROMPT = """Generate a direct, conversational answer
-to the question based on the gathered evidence.
-
-Output:
-- answer: Direct, comprehensive answer with a natural, helpful tone.
-  Write the actual answer, not a description of what you found.
-  Use as many sentences as needed to fully address the question.
-- confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-Guidelines:
-- Base your answer solely on the evidence provided in the context.
-- If a <background> section is provided, use it to frame your answer appropriately.
-- Be thorough - include all relevant information from the evidence.
-- Use formatting (bullet points, numbered lists) when it improves clarity.
-- Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-  Instead, directly state the information.
-- If the evidence is incomplete, acknowledge limitations briefly."""
diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py
index 7fa5618a..33c82d64 100644
--- a/haiku_rag_slim/haiku/rag/app.py
+++ b/haiku_rag_slim/haiku/rag/app.py
@@ -18,9 +18,6 @@ from rich.progress import (
 )
 from rich.syntax import Syntax
 
-from haiku.rag.agents.research.dependencies import ResearchContext
-from haiku.rag.agents.research.graph import build_research_graph
-from haiku.rag.agents.research.state import ResearchDeps, ResearchState
 from haiku.rag.client import HaikuRAG, RebuildMode
 from haiku.rag.config import AppConfig, Config
 from haiku.rag.mcp import create_mcp_server
@@ -375,7 +372,6 @@ class HaikuRAGApp:  # pragma: no cover
         self,
         question: str,
         cite: bool = False,
-        deep: bool = False,
         filter: str | None = None,
     ):
         """Ask a question using the RAG system.
@@ -383,7 +379,6 @@ class HaikuRAGApp:  # pragma: no cover
         Args:
             question: The question to ask
             cite: Include citations in the answer
-            deep: Use deep QA mode (multi-step reasoning)
             filter: SQL WHERE clause to filter documents
         """
         async with HaikuRAG(
@@ -392,46 +387,15 @@ class HaikuRAGApp:  # pragma: no cover
             read_only=self.read_only,
             before=self.before,
         ) as self.client:
-            citations = []
-            if deep:
-                graph = build_research_graph(config=self.config)
-                context = ResearchContext(original_question=question)
-                state = ResearchState.from_config(
-                    context=context,
-                    config=self.config,
-                    max_iterations=1,
-                )
-                state.search_filter = filter
-                deps = ResearchDeps(client=self.client)
+            answer, citations = await self.client.ask(question, filter=filter)
 
-                report = await graph.run(state=state, deps=deps)
-
-                self.console.print(f"[bold blue]Question:[/bold blue] {question}")
-                self.console.print()
-                if report:
-                    self.console.print("[bold green]Answer:[/bold green]")
-                    self.console.print(Markdown(report.executive_summary))
-                    if report.main_findings:
-                        self.console.print()
-                        self.console.print("[bold cyan]Key Findings:[/bold cyan]")
-                        for finding in report.main_findings:
-                            self.console.print(f"• {finding}")
-                    if report.sources_summary:
-                        self.console.print()
-                        self.console.print("[bold cyan]Sources:[/bold cyan]")
-                        self.console.print(report.sources_summary)
-                else:
-                    self.console.print("[yellow]No answer generated.[/yellow]")
-            else:
-                answer, citations = await self.client.ask(question, filter=filter)
-
-                self.console.print(f"[bold blue]Question:[/bold blue] {question}")
-                self.console.print()
-                self.console.print("[bold green]Answer:[/bold green]")
-                self.console.print(Markdown(answer))
-                if cite and citations:
-                    for renderable in format_citations_rich(citations):
-                        self.console.print(renderable)
+            self.console.print(f"[bold blue]Question:[/bold blue] {question}")
+            self.console.print()
+            self.console.print("[bold green]Answer:[/bold green]")
+            self.console.print(Markdown(answer))
+            if cite and citations:
+                for renderable in format_citations_rich(citations):
+                    self.console.print(renderable)
 
     async def rlm(
         self,
@@ -488,13 +452,7 @@ class HaikuRAGApp:  # pragma: no cover
             self.console.print(f"[bold blue]Question:[/bold blue] {question}")
             self.console.print()
 
-            graph = build_research_graph(config=self.config)
-            context = ResearchContext(original_question=question)
-            state = ResearchState.from_config(context=context, config=self.config)
-            state.search_filter = filter
-            deps = ResearchDeps(client=client)
-
-            report = await graph.run(state=state, deps=deps)
+            report = await client.research(question=question, filter=filter)
 
             if report is None:
                 self.console.print("[red]Research did not produce a report.[/red]")
@@ -518,8 +476,6 @@ class HaikuRAGApp:  # pragma: no cover
                     self.console.print(f"• {finding}")
                 self.console.print()
 
-            # (Themes section removed)
-
             # Conclusions
             if report.conclusions:
                 self.console.print("[bold cyan]Conclusions:[/bold cyan]")
diff --git a/haiku_rag_slim/haiku/rag/chat/__init__.py b/haiku_rag_slim/haiku/rag/chat/__init__.py
index 31ee094c..07d8da3a 100644
--- a/haiku_rag_slim/haiku/rag/chat/__init__.py
+++ b/haiku_rag_slim/haiku/rag/chat/__init__.py
@@ -6,7 +6,7 @@ def run_chat(
     db_path: Path | None = None,
     read_only: bool = False,
     before: datetime | None = None,
-    initial_context: str | None = None,
+    model: str | None = None,
 ) -> None:
     """Run the chat TUI.
 
@@ -14,25 +14,29 @@ def run_chat(
         db_path: Path to the LanceDB database. If None, uses default from config.
         read_only: Whether to open the database in read-only mode.
         before: Query database as it existed before this datetime.
-        initial_context: Initial background context to provide to the conversation.
+        model: Model to use for the chat.
     """
     try:
         from haiku.rag.chat.app import ChatApp
-    except ImportError as e:  # pragma: no cover
+    except ImportError as e:
         raise ImportError(
             "textual is not installed. Please install it with `pip install 'haiku.rag-slim[tui]'` or use the full haiku.rag package."
         ) from e
 
     from haiku.rag.config import get_config
+    from haiku.rag.skills.rag import create_skill
 
     config = get_config()
     if db_path is None:
         db_path = config.storage.data_dir / "haiku.rag.lancedb"
 
+    skill = create_skill(db_path=db_path, config=config)
+
     app = ChatApp(
         db_path,
+        skill=skill,
         read_only=read_only,
         before=before,
-        initial_context=initial_context,
+        model=model,
     )
     app.run()
diff --git a/haiku_rag_slim/haiku/rag/chat/app.py b/haiku_rag_slim/haiku/rag/chat/app.py
index 47552bc8..e0cd6142 100644
--- a/haiku_rag_slim/haiku/rag/chat/app.py
+++ b/haiku_rag_slim/haiku/rag/chat/app.py
@@ -1,30 +1,17 @@
 # pyright: reportPossiblyUnboundVariable=false
 import asyncio
+import json
 import uuid
-from collections.abc import AsyncIterable, Iterable
+from collections.abc import Iterable
 from datetime import datetime
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
-from pydantic_ai import (
-    Agent,
-    AgentStreamEvent,
-    FunctionToolCallEvent,
-    FunctionToolResultEvent,
-    RunContext,
-)
-from pydantic_ai.messages import ModelMessage
-
-from haiku.rag.agents.chat.agent import (
-    ChatDeps,
-    build_chat_toolkit,
-    create_chat_agent,
-    trigger_background_summarization,
-)
-from haiku.rag.agents.chat.state import AGUI_STATE_KEY
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import get_config
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
+from haiku.rag.skills.rag import AGENT_PREAMBLE, RAGState
+from haiku.skills.agent import SkillToolset
+from haiku.skills.models import Skill
 
 if TYPE_CHECKING:
     from textual.app import ComposeResult
@@ -34,11 +21,26 @@ try:
 
     logfire.configure(send_to_logfire="if-token-present", console=False)
     logfire.instrument_pydantic_ai()
-except ImportError:  # pragma: no cover
+except ImportError:
     pass
 
 try:
     import textual_image.widget  # noqa: F401 - import early for renderer detection
+    from ag_ui.core import (
+        AssistantMessage,
+        BaseEvent,
+        EventType,
+        RunAgentInput,
+        StateDeltaEvent,
+        TextMessageContentEvent,
+        ToolCallArgsEvent,
+        ToolCallEndEvent,
+        ToolCallStartEvent,
+        UserMessage,
+    )
+    from jsonpatch import JsonPatch
+    from pydantic_ai import Agent
+    from pydantic_ai.ag_ui import AGUIAdapter
     from textual.app import App, SystemCommand
     from textual.binding import Binding
     from textual.widgets import Footer, Header, Input
@@ -47,12 +49,15 @@ try:
     from haiku.rag.chat.widgets.chat_history import ChatHistory, CitationWidget
 
     TEXTUAL_AVAILABLE = True
-except ImportError:  # pragma: no cover
+except ImportError:
     TEXTUAL_AVAILABLE = False
     App = object  # type: ignore
     SystemCommand = object  # type: ignore
 
 
+RAG_STATE_NAMESPACE = "rag"
+
+
 class ChatApp(App):
     """Textual TUI for conversational RAG."""
 
@@ -86,23 +91,25 @@ class ChatApp(App):
     def __init__(
         self,
         db_path: Path,
+        skill: Skill,
         read_only: bool = False,
         before: datetime | None = None,
-        initial_context: str | None = None,
+        model: str | None = None,
     ) -> None:
         super().__init__()
         self.db_path = db_path
+        self._skill = skill
         self.read_only = read_only
         self.before = before
-        self._initial_context = initial_context
-        self._context_locked = False
+        self._model = model or "openai:gpt-4o"
         self.client: HaikuRAG | None = None
         self.config = get_config()
-        self.agent: Agent[ChatDeps, str] | None = None
+        self._toolset: SkillToolset | None = None
+        self._agent: Agent[None, str] | None = None
+        self._messages: list[Any] = []
+        self._state: dict[str, Any] = {}
         self._is_processing = False
-        self._tool_call_widgets: dict[str, Any] = {}
         self._current_worker: Worker[None] | None = None
-        self._message_history: list[ModelMessage] = []
         self._document_filter: list[str] = []
 
     def compose(self) -> "ComposeResult":
@@ -136,9 +143,9 @@ class ChatApp(App):
             self.action_show_info,
         )
         yield SystemCommand(
-            "Memory",
-            "View/edit context (editable before first message)",
-            self.action_show_context,
+            "View state",
+            "Show the current session state",
+            self.action_view_state,
         )
 
     async def on_mount(self) -> None:
@@ -151,17 +158,14 @@ class ChatApp(App):
         )
         await self.client.__aenter__()
 
-        # Create toolkit, context, and agent
-        self.toolkit = build_chat_toolkit(self.config)
-        self.tool_context = self.toolkit.create_context(state_key=AGUI_STATE_KEY)
-        self.agent = create_chat_agent(self.config, toolkit=self.toolkit)
+        self._toolset = SkillToolset(skills=[self._skill])
+        self._agent = Agent(
+            self._model,
+            instructions=AGENT_PREAMBLE + self._toolset.system_prompt,
+            toolsets=[self._toolset],
+        )
+        self._state = self._toolset.build_state_snapshot()
 
-        # Sync document filter to tool context
-        session_state = self.tool_context.get(SESSION_NAMESPACE, SessionState)
-        if session_state is not None:
-            session_state.document_filter = self._document_filter
-
-        # Focus the input field
         self.query_one(Input).focus()
 
     async def on_unmount(self) -> None:
@@ -169,60 +173,25 @@ class ChatApp(App):
         if self.client:
             await self.client.__aexit__(None, None, None)
 
-    async def _handle_stream_event(self, event: AgentStreamEvent) -> None:
-        """Handle streaming events from the agent."""
-        chat_history = self.query_one(ChatHistory)
-
-        if isinstance(event, FunctionToolCallEvent):
-            tool_name = event.part.tool_name
-            tool_call_id = event.part.tool_call_id or str(uuid.uuid4())
-            args = event.part.args_as_dict()
-            widget = await chat_history.add_tool_call(tool_name, args)
-            self._tool_call_widgets[tool_call_id] = widget
-
-        elif isinstance(event, FunctionToolResultEvent):
-            tool_call_id = event.tool_call_id
-            if tool_call_id and tool_call_id in self._tool_call_widgets:
-                widget = self._tool_call_widgets[tool_call_id]
-                chat_history.mark_tool_complete(widget)
-
-    async def _event_stream_handler(
-        self,
-        _ctx: RunContext[ChatDeps],
-        event_stream: AsyncIterable[AgentStreamEvent],
-    ) -> None:
-        """Handle streaming events from the agent."""
-        async for event in event_stream:
-            await self._handle_stream_event(event)
-            # Yield to event loop to keep UI responsive
-            await asyncio.sleep(0)
-
     async def on_input_submitted(self, event: Input.Submitted) -> None:
         """Handle user input submission."""
         user_message = event.value.strip()
         if not user_message or self._is_processing:
             return
 
-        if not self.client or not self.agent:
-            return
-
-        # Lock context after first message
-        self._context_locked = True
-
-        # Clear the input
         event.input.clear()
 
-        # Add user message to history
         chat_history = self.query_one(ChatHistory)
         await chat_history.add_message("user", user_message)
 
-        # Clear for new query
-        self._tool_call_widgets.clear()
-        session_state = self.tool_context.get(SESSION_NAMESPACE, SessionState)
-        if session_state:
-            session_state.citations.clear()
+        self._messages.append(
+            UserMessage(
+                id=str(uuid.uuid4()),
+                role="user",
+                content=user_message,
+            )
+        )
 
-        # Run agent in a worker to keep UI responsive
         self._is_processing = True
         self.query_one(Input).disabled = True
         self._current_worker = self.run_worker(
@@ -231,64 +200,87 @@ class ChatApp(App):
 
     async def _run_agent(self, user_message: str) -> None:
         """Run the agent in a background worker."""
-        if not self.client or not self.agent:
+        if not self._agent or not self._toolset:
             return
 
         chat_history = self.query_one(ChatHistory)
-
-        # Show thinking indicator
         await chat_history.show_thinking()
 
+        run_input = RunAgentInput(
+            thread_id="tui",
+            run_id=str(uuid.uuid4()),
+            messages=self._messages,
+            state=self._state,
+            tools=[],
+            context=[],
+            forwarded_props={},
+        )
+
+        adapter = AGUIAdapter(agent=self._agent, run_input=run_input)
+
+        message = None
+        accumulated_text = ""
+        tool_args_deltas: dict[str, str] = {}
+
         try:
-            # Promote initial_context to QA session context on first run
-            from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-            qa_session_state = self.tool_context.get(
-                QA_SESSION_NAMESPACE, QASessionState
-            )
-            if qa_session_state is not None:
-                if qa_session_state.session_context is None and self._initial_context:
-                    from haiku.rag.tools.session import SessionContext
-
-                    qa_session_state.session_context = SessionContext(
-                        summary=self._initial_context
+            async for event in adapter.run_stream():
+                if not isinstance(event, BaseEvent):
+                    continue
+                if event.type == EventType.TEXT_MESSAGE_START:
+                    chat_history.hide_thinking()
+                    message = await chat_history.add_message("assistant")
+                    accumulated_text = ""
+                elif event.type == EventType.TEXT_MESSAGE_CONTENT:
+                    assert isinstance(event, TextMessageContentEvent)
+                    accumulated_text += event.delta
+                    if message:
+                        message.update_content(accumulated_text)
+                        chat_history.scroll_end(animate=False)
+                elif event.type == EventType.TEXT_MESSAGE_END:
+                    self._messages.append(
+                        AssistantMessage(
+                            id=str(uuid.uuid4()),
+                            role="assistant",
+                            content=accumulated_text,
+                        )
                     )
-
-            deps = ChatDeps(
-                config=self.config,
-                client=self.client,
-                tool_context=self.tool_context,
-            )
-
-            async with self.agent.run_stream(
-                user_message,
-                deps=deps,
-                message_history=self._message_history,
-                event_stream_handler=self._event_stream_handler,
-            ) as stream:
-                # Hide thinking when we start getting content
-                chat_history.hide_thinking()
-
-                # Create assistant message for streaming
-                assistant_msg = await chat_history.add_message("assistant", "")
-
-                # Stream text updates
-                async for text in stream.stream_text():
-                    assistant_msg.update_content(text)
-                    chat_history.scroll_end(animate=False)
-                    # Yield to event loop to keep UI responsive
-                    await asyncio.sleep(0)
-
-                # Update message history with this conversation
-                self._message_history = stream.all_messages()
-
-                # Add citations from ToolContext
-                session_state = self.tool_context.get(SESSION_NAMESPACE, SessionState)
-                if session_state and session_state.citations:
-                    await chat_history.add_citations(session_state.citations)
-
-                # Trigger background summarization
-                trigger_background_summarization(deps)
+                    # Show citations from RAG state
+                    await self._show_citations(chat_history)
+                elif event.type == EventType.TOOL_CALL_START:
+                    assert isinstance(event, ToolCallStartEvent)
+                    chat_history.hide_thinking()
+                    await chat_history.add_tool_call(
+                        event.tool_call_id, event.tool_call_name
+                    )
+                    tool_args_deltas[event.tool_call_id] = ""
+                    await chat_history.show_thinking("Executing tasks...")
+                elif event.type == EventType.TOOL_CALL_ARGS:
+                    assert isinstance(event, ToolCallArgsEvent)
+                    tool_args_deltas[event.tool_call_id] = (
+                        tool_args_deltas.get(event.tool_call_id, "") + event.delta
+                    )
+                    try:
+                        args = json.loads(tool_args_deltas[event.tool_call_id])
+                        chat_history.update_tool_args(event.tool_call_id, args)
+                    except json.JSONDecodeError:
+                        pass
+                elif event.type == EventType.TOOL_CALL_END:
+                    assert isinstance(event, ToolCallEndEvent)
+                    chat_history.mark_tool_complete(event.tool_call_id)
+                elif event.type == EventType.STATE_DELTA:
+                    assert isinstance(event, StateDeltaEvent)
+                    patch = JsonPatch(event.delta)
+                    self._state = patch.apply(self._state)
+                    self._toolset.restore_state_snapshot(self._state)
+                elif event.type == EventType.STATE_SNAPSHOT:
+                    self._state = getattr(event, "snapshot", self._state)
+                    self._toolset.restore_state_snapshot(self._state)
+                elif event.type == EventType.RUN_FINISHED:
+                    chat_history.hide_thinking()
+                elif event.type == EventType.RUN_ERROR:
+                    chat_history.hide_thinking()
+                    error_msg = getattr(event, "message", "Unknown error")
+                    await chat_history.add_message("assistant", f"Error: {error_msg}")
 
         except asyncio.CancelledError:
             chat_history.hide_thinking()
@@ -303,20 +295,26 @@ class ChatApp(App):
             chat_input.disabled = False
             chat_input.focus()
 
+    async def _show_citations(self, chat_history: "ChatHistory") -> None:
+        """Show citations from the RAG state after an agent response."""
+        if not self._toolset:
+            return
+        rag_state = self._toolset.get_namespace(RAG_STATE_NAMESPACE)
+        if rag_state is None:
+            return
+        citations = getattr(rag_state, "citations", [])
+        if citations:
+            # Show only new citations (since last response)
+            await chat_history.add_citations(citations)
+
     async def action_clear_chat(self) -> None:
         """Clear the chat history and reset session."""
-        from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
         chat_history = self.query_one(ChatHistory)
         await chat_history.clear_messages()
-        self._message_history.clear()
-        self._context_locked = False
-        # Re-register fresh states in ToolContext
-        self.tool_context.register(
-            SESSION_NAMESPACE,
-            SessionState(document_filter=self._document_filter),
-        )
-        self.tool_context.register(QA_SESSION_NAMESPACE, QASessionState())
+        self._messages.clear()
+        # Reset state
+        if self._toolset:
+            self._state = self._toolset.build_state_snapshot()
 
     def action_focus_input(self) -> None:
         """Focus the input field, or cancel if processing."""
@@ -340,7 +338,6 @@ class ChatApp(App):
         if not self.client:
             return
 
-        # Get citation from selected widget directly
         chat_history = self.query_one(ChatHistory)
         selected_widgets = list(chat_history.query(CitationWidget).filter(".selected"))
         if not selected_widgets:
@@ -364,38 +361,19 @@ class ChatApp(App):
 
         await self.push_screen(InfoModal(self.client, self.db_path))
 
-    async def action_show_context(self) -> None:
-        """Show context modal (edit initial context or view session context)."""
-        from haiku.rag.chat.widgets.context_modal import ContextModal
-        from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
+    def action_view_state(self) -> None:
+        """Show the current session state."""
+        from haiku.skills.chat.app import StateScreen
 
-        session_context = None
-        qa_session_state = self.tool_context.get(QA_SESSION_NAMESPACE, QASessionState)
-        if qa_session_state and qa_session_state.session_context is not None:
-            session_context = qa_session_state.session_context
-
-        await self.push_screen(
-            ContextModal(
-                initial_context=self._initial_context,
-                session_context=session_context,
-                is_locked=self._context_locked,
-            )
-        )
-
-    def on_context_modal_context_updated(self, event: Any) -> None:
-        """Handle context updates from modal."""
-        if not self._context_locked:
-            self._initial_context = event.context or None
+        self.push_screen(StateScreen(self._state))
 
     def on_citation_widget_selected(self, event: CitationWidget.Selected) -> None:
         """Handle citation selection."""
         chat_history = self.query_one(ChatHistory)
 
-        # Remove selected class from all citations
         for widget in chat_history.query(CitationWidget):
             widget.remove_class("selected")
 
-        # Add selected class to the widget that was focused
         event.widget.add_class("selected")
 
     async def action_show_filter(self) -> None:
@@ -414,7 +392,14 @@ class ChatApp(App):
 
     def on_document_filter_modal_filter_changed(self, event: Any) -> None:
         """Handle document filter changes from modal."""
+        from haiku.rag.tools.filters import build_multi_document_filter
+
         self._document_filter = event.selected
-        session_state = self.tool_context.get(SESSION_NAMESPACE, SessionState)
-        if session_state:
-            session_state.document_filter = self._document_filter
+
+        if self._toolset:
+            rag_state = self._toolset.get_namespace(RAG_STATE_NAMESPACE)
+            if isinstance(rag_state, RAGState):
+                rag_state.document_filter = build_multi_document_filter(
+                    self._document_filter
+                )
+                self._state = self._toolset.build_state_snapshot()
diff --git a/haiku_rag_slim/haiku/rag/chat/widgets/chat_history.py b/haiku_rag_slim/haiku/rag/chat/widgets/chat_history.py
index 328e1087..fdd4f940 100644
--- a/haiku_rag_slim/haiku/rag/chat/widgets/chat_history.py
+++ b/haiku_rag_slim/haiku/rag/chat/widgets/chat_history.py
@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from textual.containers import Horizontal, VerticalScroll
 from textual.message import Message
@@ -32,52 +32,57 @@ class ChatMessage(Static):
 
 
 class ToolCallWidget(Static):
-    """Styled inline display of a tool call."""
+    """Displays a single tool call with status indicator."""
 
-    TOOL_LABELS = {
-        "search": "Searching",
-        "ask": "Asking",
-        "get_document": "Fetching",
-    }
-
-    def __init__(self, tool_name: str, args: dict | None = None, **kwargs) -> None:
+    def __init__(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        args: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
         super().__init__(**kwargs)
+        self.tool_call_id = tool_call_id
         self.tool_name = tool_name
         self.args = args or {}
-        self._complete = False
+        self._completed = False
 
     def compose(self) -> "ComposeResult":
-        label = self.TOOL_LABELS.get(self.tool_name, self.tool_name)
-
-        # Build description based on tool type
-        if self.tool_name == "search":
-            query = self.args.get("query", "...")
-            doc = self.args.get("document_name")
-            desc = f'"{query}"'
-            if doc:
-                desc += f" in {doc}"
-        elif self.tool_name == "ask":
-            question = self.args.get("question", "...")
-            doc = self.args.get("document_name")
-            desc = f'"{question}"'
-            if doc:
-                desc += f" from {doc}"
-        elif self.tool_name == "get_document":
-            query = self.args.get("query", "...")
-            desc = f'"{query}"'
-        else:
-            desc = str(self.args) if self.args else ""
-
         with Horizontal(classes="tool-row"):
-            if self._complete:
+            if self._completed:
                 yield Static("✓", classes="tool-status")
             else:
                 yield LoadingIndicator(classes="tool-spinner")
-            yield Static(label, classes="tool-badge")
-            yield Static(desc, classes="tool-desc")
+            yield Static(self.tool_name, classes="tool-badge")
+            desc = self._build_description()
+            if desc:
+                yield Static(desc, classes="tool-desc")
 
-    def mark_complete(self) -> None:
-        self._complete = True
+    def _build_description(self) -> str:
+        if self.tool_name == "execute_skill":
+            skill = self.args.get("skill_name", "")
+            request = self.args.get("request", "...")
+            prefix = f"{skill}: " if skill else ""
+            return f'{prefix}"{request}"'
+        elif self.tool_name == "search":
+            query = self.args.get("query", "...")
+            return f'"{query}"'
+        elif self.tool_name == "ask":
+            question = self.args.get("question", "...")
+            return f'"{question}"'
+        elif self.tool_name == "get_document":
+            query = self.args.get("query", "...")
+            return f'"{query}"'
+        elif self.args:
+            return str(self.args)
+        return ""
+
+    def update_args(self, args: dict[str, Any]) -> None:
+        self.args = args
+        self.refresh(recompose=True)
+
+    def mark_completed(self) -> None:
+        self._completed = True
         self.refresh(recompose=True)
 
 
@@ -102,7 +107,6 @@ class CitationWidget(Collapsible):
                 pages += "..."
             title += f" (p.{pages})"
 
-        # Build content widgets
         content = citation.content
         if len(content) > 500:
             content = content[:500] + "..."
@@ -132,10 +136,22 @@ class CitationWidget(Collapsible):
 class ThinkingWidget(Static):
     """Thinking indicator shown while agent is processing."""
 
+    def __init__(self, text: str = "Thinking...", **kwargs) -> None:
+        super().__init__(**kwargs)
+        self._text = text
+
     def compose(self) -> "ComposeResult":
         with Horizontal(classes="thinking-row"):
             yield LoadingIndicator(classes="thinking-spinner")
-            yield Static("Thinking...", classes="thinking-text")
+            yield Static(self._text, classes="thinking-text", id="thinking-label")
+
+    def update_text(self, text: str) -> None:
+        self._text = text
+        try:
+            label = self.query_one("#thinking-label", Static)
+            label.update(text)
+        except Exception:
+            pass
 
 
 class SourcesHeader(Static):
@@ -303,6 +319,7 @@ class ChatHistory(VerticalScroll):
     def __init__(self, **kwargs) -> None:
         super().__init__(**kwargs)
         self.messages: list[tuple[str, str]] = []
+        self._tool_widgets: dict[str, ToolCallWidget] = {}
 
     async def add_message(self, role: str, content: str = "") -> ChatMessage:
         """Add a message to the chat history."""
@@ -313,18 +330,30 @@ class ChatHistory(VerticalScroll):
         return message_widget
 
     async def add_tool_call(
-        self, tool_name: str, args: dict | None = None
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        args: dict[str, Any] | None = None,
     ) -> ToolCallWidget:
         """Add an inline tool call indicator."""
-        widget = ToolCallWidget(tool_name, args)
+        widget = ToolCallWidget(tool_call_id, tool_name, args)
+        self._tool_widgets[tool_call_id] = widget
         await self.mount(widget)
         self.scroll_end(animate=False)
         return widget
 
-    def mark_tool_complete(self, widget: ToolCallWidget) -> None:
-        """Mark a tool call as complete."""
-        widget.mark_complete()
-        widget.add_class("complete")
+    def update_tool_args(self, tool_call_id: str, args: dict[str, Any]) -> None:
+        """Update the args of a tool call widget."""
+        widget = self._tool_widgets.get(tool_call_id)
+        if widget:
+            widget.update_args(args)
+
+    def mark_tool_complete(self, tool_call_id: str) -> None:
+        """Mark a tool call as complete by its ID."""
+        widget = self._tool_widgets.get(tool_call_id)
+        if widget:
+            widget.mark_completed()
+            widget.add_class("complete")
 
     async def add_citations(self, citations: list[Citation]) -> None:
         """Add citations inline after a response."""
@@ -336,9 +365,12 @@ class ChatHistory(VerticalScroll):
             await self.mount(widget)
         self.scroll_end(animate=False)
 
-    async def show_thinking(self) -> None:
+    async def show_thinking(self, text: str = "Thinking...") -> None:
         """Show the thinking indicator."""
-        await self.mount(ThinkingWidget(id="thinking"))
+        try:
+            self.query_one("#thinking", ThinkingWidget).update_text(text)
+        except Exception:
+            await self.mount(ThinkingWidget(text, id="thinking"))
         self.scroll_end(animate=False)
 
     def hide_thinking(self) -> None:
@@ -351,4 +383,5 @@ class ChatHistory(VerticalScroll):
     async def clear_messages(self) -> None:
         """Clear all messages from the chat history."""
         self.messages.clear()
+        self._tool_widgets.clear()
         await self.remove_children()
diff --git a/haiku_rag_slim/haiku/rag/chat/widgets/context_modal.py b/haiku_rag_slim/haiku/rag/chat/widgets/context_modal.py
index 92a17316..3dc74659 100644
--- a/haiku_rag_slim/haiku/rag/chat/widgets/context_modal.py
+++ b/haiku_rag_slim/haiku/rag/chat/widgets/context_modal.py
@@ -1,22 +1,12 @@
-from typing import TYPE_CHECKING
-
 from textual.app import ComposeResult
 from textual.binding import Binding
 from textual.containers import Horizontal, Vertical, VerticalScroll
-from textual.message import Message
 from textual.screen import ModalScreen
-from textual.widgets import Button, Markdown, Static, TextArea
-
-if TYPE_CHECKING:
-    from haiku.rag.tools.session import SessionContext
+from textual.widgets import Button, Markdown, Static
 
 
-class ContextModal(ModalScreen):  # pragma: no cover
-    """Modal screen for viewing/editing context.
-
-    Before first message (not locked, no session context): Edit initial context
-    After first message (locked or has session context): View session context
-    """
+class ContextModal(ModalScreen):
+    """Modal screen for viewing session Q&A history."""
 
     BINDINGS = [
         Binding("escape", "cancel", "Close", show=False),
@@ -49,12 +39,6 @@ class ContextModal(ModalScreen):  # pragma: no cover
         color: $text-muted;
     }
 
-    #context-editor {
-        height: 12;
-        min-height: 8;
-        max-height: 16;
-    }
-
     #context-content {
         height: 1fr;
         max-height: 16;
@@ -73,81 +57,39 @@ class ContextModal(ModalScreen):  # pragma: no cover
     }
     """
 
-    class ContextUpdated(Message):
-        """Emitted when the context is saved."""
-
-        def __init__(self, context: str) -> None:
-            super().__init__()
-            self.context = context
-
-    def __init__(
-        self,
-        initial_context: str | None = None,
-        session_context: "SessionContext | None" = None,
-        is_locked: bool = False,
-    ) -> None:
+    def __init__(self, qa_history: list | None = None) -> None:
         super().__init__()
-        self._initial_context = initial_context
-        self._session_context = session_context
-        self._is_locked = is_locked
-
-    @property
-    def _is_edit_mode(self) -> bool:
-        """Edit mode when not locked and no session context yet."""
-        has_session_context = self._session_context and self._session_context.summary
-        return not self._is_locked and not has_session_context
+        self._qa_history = qa_history or []
 
     def compose(self) -> ComposeResult:
         with Vertical(id="context-container"):
-            if self._is_edit_mode:
-                yield Static("[bold]Initial Context[/bold]", id="context-header")
-                yield Static(
-                    "Set background context to guide the conversation. "
-                    "This will be locked after you send your first message.",
-                    id="context-description",
-                )
-                initial_value = self._initial_context or ""
-                yield TextArea(initial_value, id="context-editor")
-                with Horizontal(id="button-row"):
-                    yield Button("Cancel", id="cancel-btn", variant="default")
-                    yield Button("Save", id="save-btn", variant="primary")
-            else:
-                yield Static("[bold]Session Context[/bold]", id="context-header")
-                yield Static(
-                    "What the assistant has learned from your conversation.",
-                    id="context-description",
-                )
-                with VerticalScroll(id="context-content"):
-                    yield Markdown(self._get_session_content())
-                with Horizontal(id="button-row"):
-                    yield Button("Close", id="cancel-btn", variant="primary")
+            yield Static("[bold]Session Context[/bold]", id="context-header")
+            yield Static(
+                "Questions and answers from this session.",
+                id="context-description",
+            )
+            with VerticalScroll(id="context-content"):
+                yield Markdown(self._get_content())
+            with Horizontal(id="button-row"):
+                yield Button("Close", id="cancel-btn", variant="primary")
 
-    def _get_session_content(self) -> str:
-        if not self._session_context:
-            return "*No session context yet. Ask a question first.*"
+    def _get_content(self) -> str:
+        if not self._qa_history:
+            return "*No questions asked yet.*"
 
-        ctx = self._session_context
-        updated = (
-            ctx.last_updated.strftime("%Y-%m-%d %H:%M:%S")
-            if ctx.last_updated
-            else "unknown"
-        )
+        parts = []
+        for entry in self._qa_history:
+            q = getattr(entry, "question", str(entry))
+            a = getattr(entry, "answer", "")
+            parts.append(f"**Q:** {q}\n\n**A:** {a}")
 
-        return f"**Last updated:** {updated}\n\n---\n\n{ctx.summary}"
+        return "\n\n---\n\n".join(parts)
 
     def on_button_pressed(self, event: Button.Pressed) -> None:
         """Handle button presses."""
         if event.button.id == "cancel-btn":
             self.action_cancel()
-        elif event.button.id == "save-btn":
-            self.action_save()
 
     def action_cancel(self) -> None:
-        """Cancel and close without saving."""
-        self.app.pop_screen()
-
-    def action_save(self) -> None:
-        """Save context and close."""
-        editor = self.query_one("#context-editor", TextArea)
-        self.post_message(self.ContextUpdated(editor.text))
+        """Cancel and close."""
         self.app.pop_screen()
diff --git a/haiku_rag_slim/haiku/rag/chat/widgets/document_filter_modal.py b/haiku_rag_slim/haiku/rag/chat/widgets/document_filter_modal.py
index 6da412bf..45a05ebb 100644
--- a/haiku_rag_slim/haiku/rag/chat/widgets/document_filter_modal.py
+++ b/haiku_rag_slim/haiku/rag/chat/widgets/document_filter_modal.py
@@ -8,7 +8,7 @@ from textual.widgets import Button, Checkbox, Input, Static
 from haiku.rag.client import HaikuRAG
 
 
-class DocumentFilterModal(ModalScreen):  # pragma: no cover
+class DocumentFilterModal(ModalScreen):
     """Modal screen for selecting documents to filter searches."""
 
     BINDINGS = [
diff --git a/haiku_rag_slim/haiku/rag/cli.py b/haiku_rag_slim/haiku/rag/cli.py
index 79e2144e..f9afa8aa 100644
--- a/haiku_rag_slim/haiku/rag/cli.py
+++ b/haiku_rag_slim/haiku/rag/cli.py
@@ -341,11 +341,6 @@ def ask(  # pragma: no cover
         "--cite",
         help="Include citations in the response",
     ),
-    deep: bool = typer.Option(
-        False,
-        "--deep",
-        help="Use deep multi-agent QA for complex questions",
-    ),
     filter: str | None = typer.Option(
         None,
         "--filter",
@@ -358,7 +353,6 @@ def ask(  # pragma: no cover
         app.ask(
             question=question,
             cite=cite,
-            deep=deep,
             filter=filter,
         )
     )
@@ -626,10 +620,10 @@ def chat(  # pragma: no cover
         "--db",
         help="Path to the LanceDB database file",
     ),
-    initial_context: str | None = typer.Option(
+    model: str | None = typer.Option(
         None,
-        "--initial-context",
-        help="Initial background context to provide to the conversation",
+        "--model",
+        help="Model to use for the chat (e.g. openai:gpt-4o)",
     ),
 ):
     """Launch the chat TUI for conversational RAG."""
@@ -641,7 +635,7 @@ def chat(  # pragma: no cover
         db_path,
         read_only=_read_only,
         before=_before,
-        initial_context=initial_context,
+        model=model,
     )
 
 
diff --git a/haiku_rag_slim/haiku/rag/client.py b/haiku_rag_slim/haiku/rag/client.py
index b1159876..b18f30d0 100644
--- a/haiku_rag_slim/haiku/rag/client.py
+++ b/haiku_rag_slim/haiku/rag/client.py
@@ -31,7 +31,10 @@ from haiku.rag.store.repositories.settings import SettingsRepository
 if TYPE_CHECKING:
     from docling_core.types.doc.document import DoclingDocument
 
-    from haiku.rag.agents.research.models import Citation
+    from haiku.rag.agents.research.models import (
+        Citation,
+        ResearchReport,
+    )
     from haiku.rag.agents.rlm.models import RLMResult
 
 logger = logging.getLogger(__name__)
@@ -1324,6 +1327,37 @@ class HaikuRAG:
         qa_agent = get_qa_agent(self, config=self._config, system_prompt=system_prompt)
         return await qa_agent.answer(question, filter=filter)
 
+    async def research(
+        self,
+        question: str,
+        *,
+        filter: str | None = None,
+        max_iterations: int | None = None,
+    ) -> "ResearchReport":
+        """Run multi-agent research to investigate a question.
+
+        Args:
+            question: The research question to investigate.
+            filter: SQL WHERE clause to filter documents.
+            max_iterations: Override max iterations (None uses config default).
+
+        Returns:
+            ResearchReport with structured findings.
+        """
+        from haiku.rag.agents.research.dependencies import ResearchContext
+        from haiku.rag.agents.research.graph import build_research_graph
+        from haiku.rag.agents.research.state import ResearchDeps, ResearchState
+
+        graph = build_research_graph(config=self._config)
+        context = ResearchContext(original_question=question)
+        state = ResearchState.from_config(
+            context=context, config=self._config, max_iterations=max_iterations
+        )
+        state.search_filter = filter
+        deps = ResearchDeps(client=self)
+
+        return await graph.run(state=state, deps=deps)
+
     async def rlm(
         self,
         question: str,
diff --git a/haiku_rag_slim/haiku/rag/inspector/__init__.py b/haiku_rag_slim/haiku/rag/inspector/__init__.py
index 6eebb52d..bac3b5e8 100644
--- a/haiku_rag_slim/haiku/rag/inspector/__init__.py
+++ b/haiku_rag_slim/haiku/rag/inspector/__init__.py
@@ -1,6 +1,6 @@
 try:
     from haiku.rag.inspector.app import run_inspector
-except ImportError as e:  # pragma: no cover
+except ImportError as e:
     raise ImportError(
         "textual is not installed. Please install it with `pip install 'haiku.rag-slim[tui]'` or use the full haiku.rag package."
     ) from e
diff --git a/haiku_rag_slim/haiku/rag/inspector/app.py b/haiku_rag_slim/haiku/rag/inspector/app.py
index 6fc8d774..6a62ed12 100644
--- a/haiku_rag_slim/haiku/rag/inspector/app.py
+++ b/haiku_rag_slim/haiku/rag/inspector/app.py
@@ -21,12 +21,12 @@ try:
     from haiku.rag.inspector.widgets.search_modal import SearchModal
 
     TEXTUAL_AVAILABLE = True
-except ImportError:  # pragma: no cover
+except ImportError:
     TEXTUAL_AVAILABLE = False
     App = object  # type: ignore
 
 
-class InspectorApp(App):  # pragma: no cover
+class InspectorApp(App):
     """Textual TUI for inspecting LanceDB data."""
 
     TITLE = "haiku.rag DB Inspector"
@@ -243,7 +243,7 @@ def run_inspector(
     db_path: Path | None = None,
     read_only: bool = False,
     before: datetime | None = None,
-) -> None:  # pragma: no cover
+) -> None:
     """Run the inspector TUI.
 
     Args:
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/chunk_list.py b/haiku_rag_slim/haiku/rag/inspector/widgets/chunk_list.py
index d690ce5f..c926e520 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/chunk_list.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/chunk_list.py
@@ -10,7 +10,7 @@ from haiku.rag.store.models import Chunk
 BATCH_SIZE = 50
 
 
-class ChunkList(VerticalScroll):  # pragma: no cover
+class ChunkList(VerticalScroll):
     """Widget for displaying and browsing chunks."""
 
     can_focus = False
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/context_modal.py b/haiku_rag_slim/haiku/rag/inspector/widgets/context_modal.py
index b3340d82..ad42edfa 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/context_modal.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/context_modal.py
@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from haiku.rag.store.models import Chunk
 
 
-class ContextModal(Screen):  # pragma: no cover
+class ContextModal(Screen):
     """Modal screen for displaying how a chunk appears to agents."""
 
     BINDINGS = [
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/detail_view.py b/haiku_rag_slim/haiku/rag/inspector/widgets/detail_view.py
index a3e16556..5b9c4e14 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/detail_view.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/detail_view.py
@@ -16,7 +16,7 @@ class ProvenanceData(Protocol):
     doc_item_refs: list[str]
 
 
-class DetailView(VerticalScroll):  # pragma: no cover
+class DetailView(VerticalScroll):
     """Widget for displaying detailed content of documents or chunks."""
 
     can_focus = True
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/document_list.py b/haiku_rag_slim/haiku/rag/inspector/widgets/document_list.py
index e83d8286..2efd4992 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/document_list.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/document_list.py
@@ -10,7 +10,7 @@ from haiku.rag.store.models import Document
 BATCH_SIZE = 50
 
 
-class DocumentList(VerticalScroll):  # pragma: no cover
+class DocumentList(VerticalScroll):
     """Widget for displaying and browsing documents."""
 
     can_focus = False
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/info_modal.py b/haiku_rag_slim/haiku/rag/inspector/widgets/info_modal.py
index ed823c1d..96013434 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/info_modal.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/info_modal.py
@@ -14,7 +14,7 @@ if TYPE_CHECKING:
     from haiku.rag.client import HaikuRAG
 
 
-class InfoModal(ModalScreen):  # pragma: no cover
+class InfoModal(ModalScreen):
     """Modal screen for displaying database information."""
 
     BINDINGS = [
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/search_modal.py b/haiku_rag_slim/haiku/rag/inspector/widgets/search_modal.py
index a431c113..b6609309 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/search_modal.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/search_modal.py
@@ -11,7 +11,7 @@ from haiku.rag.inspector.widgets.detail_view import DetailView
 from haiku.rag.store.models import Chunk, SearchResult
 
 
-class SearchModal(Screen):  # pragma: no cover
+class SearchModal(Screen):
     """Screen for searching chunks."""
 
     BINDINGS = [
diff --git a/haiku_rag_slim/haiku/rag/inspector/widgets/visual_modal.py b/haiku_rag_slim/haiku/rag/inspector/widgets/visual_modal.py
index eb09ba76..66fddbc6 100644
--- a/haiku_rag_slim/haiku/rag/inspector/widgets/visual_modal.py
+++ b/haiku_rag_slim/haiku/rag/inspector/widgets/visual_modal.py
@@ -15,7 +15,7 @@ if TYPE_CHECKING:
     from haiku.rag.store.models import Chunk
 
 
-class VisualGroundingModal(Screen):  # pragma: no cover
+class VisualGroundingModal(Screen):
     """Modal screen for displaying visual grounding with bounding boxes."""
 
     BINDINGS = [
diff --git a/haiku_rag_slim/haiku/rag/mcp.py b/haiku_rag_slim/haiku/rag/mcp.py
index 974d9439..90056476 100644
--- a/haiku_rag_slim/haiku/rag/mcp.py
+++ b/haiku_rag_slim/haiku/rag/mcp.py
@@ -2,26 +2,16 @@ from pathlib import Path
 from typing import Any
 
 from fastmcp import FastMCP
-from pydantic import BaseModel
 
 from haiku.rag.agents.research.models import ResearchReport
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import AppConfig, Config
-from haiku.rag.store.models import SearchResult
+from haiku.rag.store.models import Document, SearchResult
+from haiku.rag.tools.document import DocumentInfo
 from haiku.rag.utils import format_citations
 
 
-class DocumentResult(BaseModel):
-    id: str | None
-    content: str
-    uri: str | None = None
-    title: str | None = None
-    metadata: dict[str, Any] = {}
-    created_at: str
-    updated_at: str
-
-
-def create_mcp_server(  # pragma: no cover
+def create_mcp_server(
     db_path: Path, config: AppConfig = Config, read_only: bool = False
 ) -> FastMCP:
     """Create an MCP server with the specified database path.
@@ -111,24 +101,11 @@ def create_mcp_server(  # pragma: no cover
             return []
 
     @mcp.tool()
-    async def get_document(document_id: str) -> DocumentResult | None:
+    async def get_document(document_id: str) -> Document | None:
         """Get a document by its ID."""
         try:
             async with HaikuRAG(db_path, config=config, read_only=read_only) as rag:
-                document = await rag.get_document_by_id(document_id)
-
-                if document is None:
-                    return None
-
-                return DocumentResult(
-                    id=document.id,
-                    content=document.content,
-                    uri=document.uri,
-                    title=document.title,
-                    metadata=document.metadata,
-                    created_at=str(document.created_at),
-                    updated_at=str(document.updated_at),
-                )
+                return await rag.get_document_by_id(document_id)
         except Exception:
             return None
 
@@ -137,30 +114,24 @@ def create_mcp_server(  # pragma: no cover
         limit: int | None = None,
         offset: int | None = None,
         filter: str | None = None,
-    ) -> list[DocumentResult]:
+    ) -> list[DocumentInfo]:
         """List all documents with optional pagination and filtering.
 
         Args:
             limit: Maximum number of documents to return.
             offset: Number of documents to skip.
             filter: Optional SQL WHERE clause to filter documents.
-
-        Returns:
-            List of DocumentResult instances matching the criteria.
         """
         try:
             async with HaikuRAG(db_path, config=config, read_only=read_only) as rag:
                 documents = await rag.list_documents(limit, offset, filter)
 
                 return [
-                    DocumentResult(
+                    DocumentInfo(
                         id=doc.id,
-                        content=doc.content,
-                        uri=doc.uri,
-                        title=doc.title,
-                        metadata=doc.metadata,
-                        created_at=str(doc.created_at),
-                        updated_at=str(doc.updated_at),
+                        title=doc.title or "Untitled",
+                        uri=doc.uri or "",
+                        created=doc.created_at.strftime("%Y-%m-%d"),
                     )
                     for doc in documents
                 ]
@@ -171,42 +142,19 @@ def create_mcp_server(  # pragma: no cover
     async def ask_question(
         question: str,
         cite: bool = False,
-        deep: bool = False,
     ) -> str:
         """Ask a question using the QA agent.
 
         Args:
             question: The question to ask.
             cite: Whether to include citations in the response.
-            deep: Use deep multi-agent QA for complex questions that require decomposition.
 
         Returns:
             The answer as a string.
         """
         try:
             async with HaikuRAG(db_path, config=config, read_only=read_only) as rag:
-                if deep:
-                    from haiku.rag.agents.research.dependencies import ResearchContext
-                    from haiku.rag.agents.research.graph import build_research_graph
-                    from haiku.rag.agents.research.state import (
-                        ResearchDeps,
-                        ResearchState,
-                    )
-
-                    graph = build_research_graph(config=config)
-                    context = ResearchContext(original_question=question)
-                    state = ResearchState.from_config(
-                        context=context,
-                        config=config,
-                        max_iterations=2,
-                    )
-                    deps = ResearchDeps(client=rag)
-
-                    result = await graph.run(state=state, deps=deps)
-                    answer = result.executive_summary
-                    citations = []
-                else:
-                    answer, citations = await rag.ask(question)
+                answer, citations = await rag.ask(question)
                 if cite and citations:
                     answer += "\n\n" + format_citations(citations)
                 return answer
@@ -229,19 +177,8 @@ def create_mcp_server(  # pragma: no cover
             A research report with findings, or None if an error occurred.
         """
         try:
-            from haiku.rag.agents.research.dependencies import ResearchContext
-            from haiku.rag.agents.research.graph import build_research_graph
-            from haiku.rag.agents.research.state import ResearchDeps, ResearchState
-
             async with HaikuRAG(db_path, config=config, read_only=read_only) as rag:
-                graph = build_research_graph(config=config)
-                context = ResearchContext(original_question=question)
-                state = ResearchState.from_config(context=context, config=config)
-                deps = ResearchDeps(client=rag)
-
-                result = await graph.run(state=state, deps=deps)
-
-                return result
+                return await rag.research(question=question)
         except Exception:
             return None
 
diff --git a/haiku_rag_slim/haiku/rag/skills/__init__.py b/haiku_rag_slim/haiku/rag/skills/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/haiku_rag_slim/haiku/rag/skills/rag-rlm/SKILL.md b/haiku_rag_slim/haiku/rag/skills/rag-rlm/SKILL.md
new file mode 100644
index 00000000..93253102
--- /dev/null
+++ b/haiku_rag_slim/haiku/rag/skills/rag-rlm/SKILL.md
@@ -0,0 +1,13 @@
+---
+name: rag-rlm
+description: >
+  Computational analysis of the knowledge base via code execution in a Docker sandbox.
+  Use for questions requiring counting, aggregation, statistics, data traversal,
+  comparison across documents, or any task best answered by writing Python code.
+  Examples: "how many pages?", "compare table 3 across documents",
+  "calculate average word count", "extract all email addresses".
+---
+
+# RLM Analysis
+
+Use the `analyze` tool for complex analytical questions. It writes and executes Python code against the knowledge base in an isolated Docker sandbox.
diff --git a/haiku_rag_slim/haiku/rag/skills/rag.py b/haiku_rag_slim/haiku/rag/skills/rag.py
new file mode 100644
index 00000000..776db28a
--- /dev/null
+++ b/haiku_rag_slim/haiku/rag/skills/rag.py
@@ -0,0 +1,343 @@
+import os
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel, Field
+from pydantic_ai import RunContext
+
+from haiku.rag.agents.research.models import Citation
+from haiku.rag.store.models.chunk import SearchResult
+from haiku.rag.tools.document import DocumentInfo
+from haiku.rag.tools.qa import QAHistoryEntry
+from haiku.skills.models import Skill, SkillSource
+from haiku.skills.parser import parse_skill_md
+from haiku.skills.state import SkillRunDeps
+
+AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
+
+CRITICAL RULES:
+1. For greetings or casual chat: respond directly WITHOUT using any tools
+2. NEVER make up information - always use tools to get facts from the knowledge base
+3. For questions: Use the "ask" tool - it handles search and citation automatically
+4. For searches: Use the "search" tool - copy the ENTIRE tool response to your output INCLUDING content snippets
+5. When you use the "ask" tool, summarize the key findings and always include citations in your response
+"""
+
+
+class ResearchEntry(BaseModel):
+    question: str
+    title: str
+    executive_summary: str
+
+
+class RAGState(BaseModel):
+    citations: list[Citation] = Field(default_factory=list)
+    qa_history: list[QAHistoryEntry] = Field(default_factory=list)
+    document_filter: str | None = None
+    searches: dict[str, list[SearchResult]] = Field(default_factory=dict)
+    documents: list[DocumentInfo] = Field(default_factory=list)
+    reports: list[ResearchEntry] = Field(default_factory=list)
+
+
+def create_skill(
+    db_path: Path | None = None,
+    config: Any = None,
+) -> Skill:
+    """Create a RAG skill for searching and analyzing documents.
+
+    Args:
+        db_path: Path to the LanceDB database. Resolved from:
+            1. This argument
+            2. HAIKU_RAG_DB environment variable
+            3. haiku.rag default (config.storage.data_dir / "haiku.rag.lancedb")
+        config: haiku.rag AppConfig instance. If None, uses get_config().
+    """
+    from haiku.rag.config import get_config
+
+    if config is None:
+        config = get_config()
+
+    if db_path is None:
+        env_db = os.environ.get("HAIKU_RAG_DB")
+        if env_db:
+            db_path = Path(env_db).expanduser()
+        else:
+            db_path = config.storage.data_dir / "haiku.rag.lancedb"
+
+    path = Path(__file__).parent / "rag"
+    metadata, instructions = parse_skill_md(path / "SKILL.md")
+
+    async def _find_relevant_prior_qa(
+        state: RAGState, query: str
+    ) -> list[QAHistoryEntry]:
+        from haiku.rag.embeddings import get_embedder
+        from haiku.rag.tools.qa import PRIOR_ANSWER_RELEVANCE_THRESHOLD
+        from haiku.rag.utils import cosine_similarity
+
+        if not state.qa_history:
+            return []
+
+        embedder = get_embedder(config)
+        query_embedding = await embedder.embed_query(query)
+
+        to_embed = []
+        to_embed_indices = []
+        for i, qa in enumerate(state.qa_history):
+            if qa.question_embedding is None:
+                to_embed.append(qa.question)
+                to_embed_indices.append(i)
+
+        if to_embed:
+            new_embeddings = await embedder.embed_documents(to_embed)
+            for i, idx in enumerate(to_embed_indices):
+                state.qa_history[idx].question_embedding = new_embeddings[i]
+
+        matches = []
+        for qa in state.qa_history:
+            if qa.question_embedding is not None:
+                similarity = cosine_similarity(query_embedding, qa.question_embedding)
+                if similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD:
+                    matches.append(qa)
+
+        return matches
+
+    async def search(
+        ctx: RunContext[SkillRunDeps], query: str, limit: int | None = None
+    ) -> str:
+        """Search the knowledge base using hybrid search (vector + full-text).
+
+        Returns ranked results with content and metadata.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results.
+        """
+        from haiku.rag.client import HaikuRAG
+
+        state = (
+            ctx.deps.state
+            if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState)
+            else None
+        )
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            results = await rag.search(
+                query,
+                limit=limit,
+                filter=state.document_filter if state else None,
+            )
+            results = await rag.expand_context(results)
+
+        if state:
+            state.searches[query] = list(results)
+
+        return "\n\n---\n\n".join(
+            r.format_for_agent(rank=i + 1, total=len(results))
+            for i, r in enumerate(results)
+        )
+
+    async def list_documents(
+        ctx: RunContext[SkillRunDeps],
+        limit: int | None = None,
+        offset: int | None = None,
+        filter: str | None = None,
+    ) -> list[dict[str, Any]]:
+        """List documents in the knowledge base with optional pagination and filtering.
+
+        Args:
+            limit: Maximum number of documents to return.
+            offset: Number of documents to skip.
+            filter: Optional SQL WHERE clause to filter documents.
+        """
+        from haiku.rag.client import HaikuRAG
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            documents = await rag.list_documents(limit, offset, filter)
+            result = [
+                {
+                    "id": doc.id,
+                    "title": doc.title,
+                    "uri": doc.uri,
+                    "metadata": doc.metadata,
+                    "created_at": str(doc.created_at),
+                    "updated_at": str(doc.updated_at),
+                }
+                for doc in documents
+            ]
+
+        if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState):
+            for doc_dict in result:
+                doc_info = DocumentInfo(
+                    id=str(doc_dict["id"]),
+                    title=doc_dict["title"] or "Untitled",
+                    uri=doc_dict.get("uri") or "",
+                    created=doc_dict.get("created_at", ""),
+                )
+                if not any(d.id == doc_info.id for d in ctx.deps.state.documents):
+                    ctx.deps.state.documents.append(doc_info)
+
+        return result
+
+    async def get_document(
+        ctx: RunContext[SkillRunDeps], query: str
+    ) -> dict[str, Any] | None:
+        """Retrieve a document by ID, title, or URI.
+
+        Args:
+            query: Document ID, title, or URI to look up.
+        """
+        from haiku.rag.client import HaikuRAG
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            document = await rag.resolve_document(query)
+            if document is None:
+                return None
+            result = {
+                "id": document.id,
+                "content": document.content,
+                "title": document.title,
+                "uri": document.uri,
+                "metadata": document.metadata,
+                "created_at": str(document.created_at),
+                "updated_at": str(document.updated_at),
+            }
+
+        if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState):
+            doc_info = DocumentInfo(
+                id=str(result["id"]),
+                title=result["title"] or "Untitled",
+                uri=result.get("uri") or "",
+                created=result.get("created_at", ""),
+            )
+            if not any(d.id == doc_info.id for d in ctx.deps.state.documents):
+                ctx.deps.state.documents.append(doc_info)
+
+        return result
+
+    async def ask(ctx: RunContext[SkillRunDeps], question: str) -> str:
+        """Ask a question and get an answer with citations from the knowledge base.
+
+        Args:
+            question: The question to ask.
+        """
+        from haiku.rag.client import HaikuRAG
+        from haiku.rag.utils import format_citations
+
+        state = (
+            ctx.deps.state
+            if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState)
+            else None
+        )
+
+        ask_question = question
+        if state:
+            matches = await _find_relevant_prior_qa(state, question)
+            if matches:
+                prior_parts = []
+                for qa in matches:
+                    part = f"Q: {qa.question}\nA: {qa.answer}"
+                    if qa.citations:
+                        part += "\n" + format_citations(qa.citations)
+                    prior_parts.append(part)
+                ask_question = (
+                    "Context from prior questions in this session:\n\n"
+                    + "\n\n---\n\n".join(prior_parts)
+                    + "\n\n---\n\nCurrent question: "
+                    + question
+                )
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            answer, citations = await rag.ask(
+                ask_question,
+                filter=state.document_filter if state else None,
+            )
+
+        if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState):
+            next_index = len(ctx.deps.state.citations) + 1
+            for citation in citations:
+                citation.index = next_index
+                next_index += 1
+            ctx.deps.state.citations.extend(citations)
+            ctx.deps.state.qa_history.append(
+                QAHistoryEntry(question=question, answer=answer, citations=citations)
+            )
+
+        if citations:
+            answer += "\n\n" + format_citations(citations)
+
+        return answer
+
+    async def research(ctx: RunContext[SkillRunDeps], question: str) -> str:
+        """Conduct deep multi-agent research on a question.
+
+        Iteratively searches, analyzes, and synthesizes information from the
+        knowledge base to produce a comprehensive research report.
+        Only use when the user explicitly requests deep research.
+
+        Args:
+            question: The research question to investigate.
+        """
+        from haiku.rag.client import HaikuRAG
+
+        state = (
+            ctx.deps.state
+            if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RAGState)
+            else None
+        )
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            report = await rag.research(
+                question, filter=state.document_filter if state else None
+            )
+
+        if state:
+            state.reports.append(
+                ResearchEntry(
+                    question=question,
+                    title=report.title,
+                    executive_summary=report.executive_summary,
+                )
+            )
+            state.qa_history.append(
+                QAHistoryEntry(question=question, answer=report.executive_summary)
+            )
+
+        parts = [
+            f"# {report.title}",
+            f"\n## Executive Summary\n{report.executive_summary}",
+        ]
+        if report.main_findings:
+            parts.append("\n## Main Findings")
+            for finding in report.main_findings:
+                parts.append(f"- {finding}")
+        if report.conclusions:
+            parts.append("\n## Conclusions")
+            for conclusion in report.conclusions:
+                parts.append(f"- {conclusion}")
+        if report.limitations:
+            parts.append("\n## Limitations")
+            for limitation in report.limitations:
+                parts.append(f"- {limitation}")
+        if report.recommendations:
+            parts.append("\n## Recommendations")
+            for rec in report.recommendations:
+                parts.append(f"- {rec}")
+        parts.append(f"\n## Sources\n{report.sources_summary}")
+
+        return "\n".join(parts)
+
+    return Skill(
+        metadata=metadata,
+        source=SkillSource.ENTRYPOINT,
+        path=path,
+        instructions=instructions,
+        tools=[
+            search,
+            list_documents,
+            get_document,
+            ask,
+            research,
+        ],
+        state_type=RAGState,
+        state_namespace="rag",
+    )
diff --git a/haiku_rag_slim/haiku/rag/skills/rag/SKILL.md b/haiku_rag_slim/haiku/rag/skills/rag/SKILL.md
new file mode 100644
index 00000000..6509e89d
--- /dev/null
+++ b/haiku_rag_slim/haiku/rag/skills/rag/SKILL.md
@@ -0,0 +1,33 @@
+---
+name: rag
+description: Search, retrieve and analyze documents using RAG (Retrieval Augmented Generation).
+---
+
+# RAG
+
+You are a RAG (Retrieval Augmented Generation) assistant with access to a document knowledge base.
+Use your tools to search and answer questions. Never make up information — always use tools to get facts from the knowledge base.
+
+## How to decide which tool to use
+
+- **list_documents** — Use when the user wants to browse or see what documents are available (e.g., "what documents do you have?", "show me the documents", "list available docs").
+- **get_document** — Use when the user wants the full content of a specific document (e.g., "get the paper about X", "show me document Y"). Accepts a document ID, title, or URI — partial matches work.
+- **search** — Use when the user wants to find relevant passages across documents (e.g., "search for embeddings", "find mentions of transformers"). Returns matching chunks with metadata.
+- **ask** — Use for questions about topics in the knowledge base (e.g., "what is DocLayNet?", "explain the methodology"). Returns an answer with citations. Always include the citations in your response.
+- **research** — Deep multi-agent research that produces comprehensive reports. **Only use when the user explicitly requests deep research** (e.g., "do a deep research on X", "research this topic thoroughly"). Never call this tool on your own — it is slow and expensive.
+
+## When search returns irrelevant results
+
+If your first search returns results that clearly don't match the question, **do not keep searching with variations**. Instead:
+- Use **ask** if the question is factual
+- Report that the knowledge base doesn't contain relevant information
+
+## When the user mentions a specific document
+
+If the user says "search in [doc]", "find in [doc]", or "answer from [doc]":
+- Extract the **topic** as the `query`/`question` parameter
+- Use **get_document** or **list_documents** first to identify the document, then search/ask with a filter
+
+Examples:
+- "search for embeddings in the ML paper" -> first identify "ML paper", then search for "embeddings"
+- "what does the DocLayNet paper say about annotations?" -> ask with question="what are the annotation methods?"
diff --git a/haiku_rag_slim/haiku/rag/skills/rlm.py b/haiku_rag_slim/haiku/rag/skills/rlm.py
new file mode 100644
index 00000000..60f75e00
--- /dev/null
+++ b/haiku_rag_slim/haiku/rag/skills/rlm.py
@@ -0,0 +1,95 @@
+import os
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel
+from pydantic_ai import RunContext
+
+from haiku.skills.models import Skill, SkillSource
+from haiku.skills.parser import parse_skill_md
+from haiku.skills.state import SkillRunDeps
+
+
+class AnalysisEntry(BaseModel):
+    question: str
+    answer: str
+    program: str | None = None
+
+
+class RLMState(BaseModel):
+    analyses: list[AnalysisEntry] = []
+
+
+def create_skill(
+    db_path: Path | None = None,
+    config: Any = None,
+) -> Skill:
+    """Create an RLM analysis skill for computational document analysis.
+
+    Args:
+        db_path: Path to the LanceDB database. Resolved from:
+            1. This argument
+            2. HAIKU_RAG_DB environment variable
+            3. haiku.rag default (config.storage.data_dir / "haiku.rag.lancedb")
+        config: haiku.rag AppConfig instance. If None, uses get_config().
+    """
+    from haiku.rag.config import get_config
+
+    if config is None:
+        config = get_config()
+
+    if db_path is None:
+        env_db = os.environ.get("HAIKU_RAG_DB")
+        if env_db:
+            db_path = Path(env_db).expanduser()
+        else:
+            db_path = config.storage.data_dir / "haiku.rag.lancedb"
+
+    path = Path(__file__).parent / "rag-rlm"
+    metadata, instructions = parse_skill_md(path / "SKILL.md")
+
+    async def analyze(
+        ctx: RunContext[SkillRunDeps],
+        question: str,
+        document: str | None = None,
+        filter: str | None = None,
+    ) -> str:
+        """Answer complex analytical questions using code execution.
+
+        Use this for questions requiring computation, aggregation, or
+        data traversal across documents.
+
+        Args:
+            question: The question to answer.
+            document: Optional document ID or title to pre-load for analysis.
+            filter: Optional SQL WHERE clause to filter documents.
+        """
+        from haiku.rag.client import HaikuRAG
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            documents = [document] if document else None
+            result = await rag.rlm(question, documents=documents, filter=filter)
+            output = result.answer
+            if result.program:
+                output += f"\n\nProgram:\n{result.program}"
+
+        if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, RLMState):
+            ctx.deps.state.analyses.append(
+                AnalysisEntry(
+                    question=question,
+                    answer=result.answer,
+                    program=result.program,
+                )
+            )
+
+        return output
+
+    return Skill(
+        metadata=metadata,
+        source=SkillSource.ENTRYPOINT,
+        path=path,
+        instructions=instructions,
+        tools=[analyze],
+        state_type=RLMState,
+        state_namespace="rlm",
+    )
diff --git a/haiku_rag_slim/haiku/rag/store/models/document.py b/haiku_rag_slim/haiku/rag/store/models/document.py
index 0d98ce9d..02406238 100644
--- a/haiku_rag_slim/haiku/rag/store/models/document.py
+++ b/haiku_rag_slim/haiku/rag/store/models/document.py
@@ -43,8 +43,8 @@ class Document(BaseModel):
     uri: str | None = None
     title: str | None = None
     metadata: dict = {}
-    docling_document: bytes | None = None
-    docling_version: str | None = None
+    docling_document: bytes | None = Field(default=None, exclude=True)
+    docling_version: str | None = Field(default=None, exclude=True)
     created_at: datetime = Field(default_factory=datetime.now)
     updated_at: datetime = Field(default_factory=datetime.now)
 
diff --git a/haiku_rag_slim/haiku/rag/tools/__init__.py b/haiku_rag_slim/haiku/rag/tools/__init__.py
index 3e961c25..a9237d80 100644
--- a/haiku_rag_slim/haiku/rag/tools/__init__.py
+++ b/haiku_rag_slim/haiku/rag/tools/__init__.py
@@ -1,52 +1,23 @@
-from haiku.rag.tools.analysis import create_analysis_toolset
-from haiku.rag.tools.context import (
-    RAGDeps,
-    ToolContext,
-    ToolContextCache,
-    prepare_context,
-)
-from haiku.rag.tools.deps import AgentDeps
+from haiku.rag.tools.analysis import AnalysisResult, create_analysis_toolset
+from haiku.rag.tools.context import RAGDeps
 from haiku.rag.tools.document import create_document_toolset
 from haiku.rag.tools.filters import (
     build_document_filter,
     build_multi_document_filter,
     combine_filters,
-    get_session_filter,
 )
-from haiku.rag.tools.models import AnalysisResult, QAResult
-from haiku.rag.tools.prompts import build_tools_prompt
-from haiku.rag.tools.qa import create_qa_toolset
+from haiku.rag.tools.qa import PRIOR_ANSWER_RELEVANCE_THRESHOLD, QAHistoryEntry
 from haiku.rag.tools.search import create_search_toolset
-from haiku.rag.tools.toolkit import (
-    FEATURE_ANALYSIS,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_SEARCH,
-    Toolkit,
-    build_toolkit,
-)
 
 __all__ = [
-    "AgentDeps",
     "AnalysisResult",
-    "FEATURE_ANALYSIS",
-    "FEATURE_DOCUMENTS",
-    "FEATURE_QA",
-    "FEATURE_SEARCH",
-    "QAResult",
+    "PRIOR_ANSWER_RELEVANCE_THRESHOLD",
+    "QAHistoryEntry",
     "RAGDeps",
-    "ToolContext",
-    "ToolContextCache",
-    "Toolkit",
     "build_document_filter",
     "build_multi_document_filter",
-    "build_toolkit",
-    "build_tools_prompt",
     "combine_filters",
     "create_analysis_toolset",
     "create_document_toolset",
-    "create_qa_toolset",
     "create_search_toolset",
-    "get_session_filter",
-    "prepare_context",
 ]
diff --git a/haiku_rag_slim/haiku/rag/tools/analysis.py b/haiku_rag_slim/haiku/rag/tools/analysis.py
index 2a546ef4..b0c4a804 100644
--- a/haiku_rag_slim/haiku/rag/tools/analysis.py
+++ b/haiku_rag_slim/haiku/rag/tools/analysis.py
@@ -1,3 +1,4 @@
+from pydantic import BaseModel, Field
 from pydantic_ai import FunctionToolset, RunContext
 
 from haiku.rag.agents.rlm.agent import create_rlm_agent
@@ -8,9 +9,17 @@ from haiku.rag.tools.context import RAGDeps
 from haiku.rag.tools.filters import (
     build_document_filter,
     combine_filters,
-    get_session_filter,
 )
-from haiku.rag.tools.models import AnalysisResult
+
+
+class AnalysisResult(BaseModel):
+    """Result from the analysis toolset (RLM execution)."""
+
+    answer: str = Field(description="The answer produced by analysis")
+    code_executed: bool = Field(
+        default=True,
+        description="Whether code was executed to produce this answer",
+    )
 
 
 def create_analysis_toolset(
@@ -47,12 +56,9 @@ def create_analysis_toolset(
             AnalysisResult with answer and execution metadata.
         """
         client = ctx.deps.client
-        tool_context = ctx.deps.tool_context
 
         doc_filter = build_document_filter(document_name) if document_name else None
-        effective_filter = combine_filters(
-            get_session_filter(tool_context, base_filter), doc_filter
-        )
+        effective_filter = combine_filters(base_filter, doc_filter)
 
         rlm_context = RLMContext(filter=effective_filter)
 
diff --git a/haiku_rag_slim/haiku/rag/tools/context.py b/haiku_rag_slim/haiku/rag/tools/context.py
index 4296e75d..dc4aa859 100644
--- a/haiku_rag_slim/haiku/rag/tools/context.py
+++ b/haiku_rag_slim/haiku/rag/tools/context.py
@@ -1,13 +1,8 @@
-from datetime import datetime, timedelta
-from typing import TYPE_CHECKING, Any, Protocol, TypeVar, overload, runtime_checkable
-
-from pydantic import BaseModel, PrivateAttr
+from typing import TYPE_CHECKING, Protocol, runtime_checkable
 
 if TYPE_CHECKING:
     from haiku.rag.client import HaikuRAG
 
-T = TypeVar("T", bound=BaseModel)
-
 
 @runtime_checkable
 class RAGDeps(Protocol):
@@ -18,253 +13,3 @@ class RAGDeps(Protocol):
     """
 
     client: "HaikuRAG"
-    tool_context: "ToolContext | None"
-
-
-class ToolContext(BaseModel):
-    """Generic state container for haiku.rag toolsets.
-
-    Toolsets register their own Pydantic model state under namespaces.
-    Multiple toolsets can share state by registering under the same namespace.
-
-    All registered states must be Pydantic BaseModel subclasses, making
-    the entire context serializable via model_dump()/model_validate().
-
-    Example:
-        # Define toolset-specific state
-        class SearchState(BaseModel):
-            results: list[SearchResult] = []
-            filter: str | None = None
-
-        SEARCH_NAMESPACE = "haiku.rag.search"
-
-        # In toolset factory
-        def create_search_toolset(config):
-            async def search(ctx: RunContext[RAGDeps], query: str):
-                tool_context = ctx.deps.tool_context
-                if tool_context:
-                    state = tool_context.get_or_create(SEARCH_NAMESPACE, SearchState)
-                ...
-
-        # Usage
-        search_tools = create_search_toolset(config)
-        agent = Agent(..., toolsets=[search_tools])
-        await agent.run("...", deps=my_deps)
-
-        # Access accumulated state
-        search_state = context.get(SEARCH_NAMESPACE)
-        for result in search_state.results:
-            print(f"{result.document_title}")
-
-        # Serialize entire context
-        ns_data = context.dump_namespaces()
-    """
-
-    state_key: str | None = None
-    _namespaces: dict[str, BaseModel] = PrivateAttr(default_factory=dict)
-    _client_snapshot: dict[str, Any] | None = PrivateAttr(default=None)
-
-    def register(self, namespace: str, state: BaseModel) -> None:
-        """Register state for a namespace.
-
-        Args:
-            namespace: Unique identifier for the toolset (e.g., "haiku.rag.search")
-            state: A Pydantic BaseModel instance to store
-
-        Overwrites any existing state for the namespace.
-        """
-        self._namespaces[namespace] = state
-
-    @overload
-    def get(self, namespace: str) -> BaseModel | None: ...
-
-    @overload
-    def get(self, namespace: str, state_type: type[T]) -> T | None: ...
-
-    def get(
-        self, namespace: str, state_type: type[T] | None = None
-    ) -> BaseModel | T | None:
-        """Get state for a namespace, or None if not registered.
-
-        When state_type is provided, returns the state only if it matches
-        the expected type, otherwise returns None.
-        """
-        state = self._namespaces.get(namespace)
-        if state_type is not None:
-            return state if isinstance(state, state_type) else None
-        return state
-
-    def get_or_create(self, namespace: str, state_type: type[T]) -> T:
-        """Get state for a namespace, creating it if not registered.
-
-        Args:
-            namespace: The namespace to get or create state for.
-            state_type: A Pydantic BaseModel subclass to instantiate if needed.
-
-        Returns:
-            The state for the namespace.
-        """
-        if namespace not in self._namespaces:
-            self._namespaces[namespace] = state_type()
-        return self._namespaces[namespace]  # type: ignore[return-value]
-
-    def clear_namespace(self, namespace: str) -> None:
-        """Clear state for a specific namespace."""
-        if namespace in self._namespaces:
-            del self._namespaces[namespace]
-
-    def clear_all(self) -> None:
-        """Clear all namespaces."""
-        self._namespaces.clear()
-
-    @property
-    def namespaces(self) -> list[str]:
-        """List all registered namespaces."""
-        return list(self._namespaces.keys())
-
-    @property
-    def client_snapshot(self) -> dict[str, Any] | None:
-        """Snapshot captured after the last restore_state_snapshot call.
-
-        Represents what the client has, before any server-side overrides.
-        Tools use this as the baseline for delta computation so that
-        server-side changes (e.g. background summarization) are included.
-        """
-        return self._client_snapshot
-
-    def dump_namespaces(self) -> dict[str, dict[str, Any]]:
-        """Serialize all namespace states to a dictionary.
-
-        Returns:
-            Dict mapping namespace -> serialized state dict.
-        """
-        return {ns: state.model_dump() for ns, state in self._namespaces.items()}
-
-    def build_state_snapshot(self) -> dict[str, Any]:
-        """Build a flat snapshot of all namespace states for AG-UI.
-
-        Merges model_dump(mode="json") from every registered namespace
-        into a single flat dict.
-
-        Returns:
-            Combined dict of all namespace fields.
-        """
-        snapshot: dict[str, Any] = {}
-        for state in self._namespaces.values():
-            snapshot.update(state.model_dump(mode="json"))
-        return snapshot
-
-    def restore_state_snapshot(self, data: dict[str, Any]) -> None:
-        """Restore namespace states from a flat snapshot dict.
-
-        For each registered namespace, finds matching fields in *data*,
-        validates them via the namespace model, and updates the state
-        in place.  Fields not present in *data* are left unchanged.
-
-        After restoring, captures a snapshot as ``client_snapshot`` so
-        tools can compute deltas against what the client actually has.
-
-        Args:
-            data: Flat dict as produced by build_state_snapshot().
-        """
-        for state in self._namespaces.values():
-            model_fields = state.model_fields
-            matching = {k: v for k, v in data.items() if k in model_fields}
-            if matching:
-                # Fill in current values for fields not in data
-                current = state.model_dump()
-                current.update(matching)
-                updated = state.model_validate(current)
-                for field_name in matching:
-                    setattr(state, field_name, getattr(updated, field_name))
-        self._client_snapshot = self.build_state_snapshot()
-
-    def load_namespace(self, namespace: str, state_type: type[T], data: dict) -> T:
-        """Deserialize and register state for a namespace.
-
-        Args:
-            namespace: The namespace to register the state under.
-            state_type: The Pydantic model class to deserialize into.
-            data: The serialized state data.
-
-        Returns:
-            The deserialized and registered state.
-        """
-        state = state_type.model_validate(data)
-        self._namespaces[namespace] = state
-        return state
-
-
-def prepare_context(
-    context: ToolContext,
-    features: list[str] | None = None,
-    state_key: str | None = None,
-) -> None:
-    """Register required namespaces in a ToolContext based on feature flags.
-
-    Idempotent — safe to call multiple times on the same context.
-
-    Args:
-        context: ToolContext to prepare.
-        features: List of enabled features. Defaults to ["search", "documents"].
-        state_key: Optional AG-UI state key to set on the context.
-    """
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-    from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-    if features is None:
-        features = ["search", "documents"]
-
-    if any(f in features for f in ("search", "qa", "analysis")):
-        context.get_or_create(SESSION_NAMESPACE, SessionState)
-
-    if "qa" in features:
-        context.get_or_create(QA_SESSION_NAMESPACE, QASessionState)
-
-    if state_key is not None:
-        context.state_key = state_key
-
-
-class ToolContextCache:
-    """In-memory cache for ToolContext instances, keyed by external session/thread ID."""
-
-    def __init__(self, ttl: timedelta = timedelta(hours=1)) -> None:
-        self._cache: dict[str, ToolContext] = {}
-        self._timestamps: dict[str, datetime] = {}
-        self._ttl = ttl
-
-    def get_or_create(self, key: str) -> tuple[ToolContext, bool]:
-        """Get an existing context or create a new one.
-
-        Returns:
-            Tuple of (context, is_new) where is_new is True if a new context was created.
-        """
-        self._cleanup()
-        if key in self._cache:
-            self._timestamps[key] = datetime.now()
-            return self._cache[key], False
-
-        context = ToolContext()
-        self._cache[key] = context
-        self._timestamps[key] = datetime.now()
-        return context, True
-
-    def remove(self, key: str) -> None:
-        """Remove a specific key from the cache."""
-        self._cache.pop(key, None)
-        self._timestamps.pop(key, None)
-
-    def clear(self) -> None:
-        """Clear all entries."""
-        self._cache.clear()
-        self._timestamps.clear()
-
-    def _cleanup(self) -> None:
-        """Remove entries older than TTL."""
-        now = datetime.now()
-        expired = [
-            key for key, ts in self._timestamps.items() if (now - ts) >= self._ttl
-        ]
-        for key in expired:
-            self._cache.pop(key, None)
-            self._timestamps.pop(key, None)
diff --git a/haiku_rag_slim/haiku/rag/tools/deps.py b/haiku_rag_slim/haiku/rag/tools/deps.py
deleted file mode 100644
index d9ed5123..00000000
--- a/haiku_rag_slim/haiku/rag/tools/deps.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from dataclasses import dataclass
-from typing import Any
-
-from haiku.rag.client import HaikuRAG
-from haiku.rag.tools.context import ToolContext
-
-
-@dataclass
-class AgentDeps:
-    """Generic dependencies for agents using haiku.rag toolsets.
-
-    Implements RAGDeps protocol and AG-UI state protocol.
-    """
-
-    client: HaikuRAG
-    tool_context: ToolContext
-
-    @property
-    def state(self) -> dict[str, Any]:
-        """Get current state for AG-UI protocol."""
-        snapshot = self.tool_context.build_state_snapshot()
-        state_key = self.tool_context.state_key
-        if state_key:
-            return {state_key: snapshot}
-        return snapshot
-
-    @state.setter
-    def state(self, value: dict[str, Any] | None) -> None:
-        """Set state from AG-UI protocol."""
-        if value is None:
-            return
-        data = self._extract_state_data(value)
-        self.tool_context.restore_state_snapshot(data)
-
-    def _extract_state_data(self, value: dict[str, Any]) -> dict[str, Any]:
-        """Extract flat state dict, unwrapping state_key if present."""
-        state_key = self.tool_context.state_key
-        if state_key and state_key in value:
-            nested = value[state_key]
-            if isinstance(nested, dict):
-                return nested
-        return value
diff --git a/haiku_rag_slim/haiku/rag/tools/document.py b/haiku_rag_slim/haiku/rag/tools/document.py
index f053f137..0f0f0385 100644
--- a/haiku_rag_slim/haiku/rag/tools/document.py
+++ b/haiku_rag_slim/haiku/rag/tools/document.py
@@ -4,7 +4,6 @@ from pydantic_ai import Agent, FunctionToolset, RunContext
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config.models import AppConfig
 from haiku.rag.tools.context import RAGDeps
-from haiku.rag.tools.filters import get_session_filter
 from haiku.rag.utils import get_model
 
 DOCUMENT_SUMMARY_PROMPT = """Generate a summary of the document content provided below.
@@ -24,6 +23,7 @@ Document content:
 class DocumentInfo(BaseModel):
     """Document info for list_documents response."""
 
+    id: str | None = None
     title: str
     uri: str
     created: str
@@ -91,22 +91,20 @@ def create_document_toolset(
             Paginated list of documents with metadata.
         """
         client = ctx.deps.client
-        tool_context = ctx.deps.tool_context
 
         page_size = 50
         offset = (page - 1) * page_size
 
-        effective_filter = get_session_filter(tool_context, base_filter)
-
         docs = await client.list_documents(
-            limit=page_size, offset=offset, filter=effective_filter
+            limit=page_size, offset=offset, filter=base_filter
         )
-        total = await client.count_documents(filter=effective_filter)
+        total = await client.count_documents(filter=base_filter)
         total_pages = (total + page_size - 1) // page_size if total > 0 else 1
 
         return DocumentListResponse(
             documents=[
                 DocumentInfo(
+                    id=doc.id,
                     title=doc.title or "Untitled",
                     uri=doc.uri or "",
                     created=doc.created_at.strftime("%Y-%m-%d"),
diff --git a/haiku_rag_slim/haiku/rag/tools/filters.py b/haiku_rag_slim/haiku/rag/tools/filters.py
index 3a74429b..b09d14db 100644
--- a/haiku_rag_slim/haiku/rag/tools/filters.py
+++ b/haiku_rag_slim/haiku/rag/tools/filters.py
@@ -1,9 +1,3 @@
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from haiku.rag.tools.context import ToolContext
-
-
 def build_document_filter(document_name: str) -> str:
     """Build SQL filter for document name matching.
 
@@ -31,35 +25,6 @@ def build_multi_document_filter(document_names: list[str]) -> str | None:
     return " OR ".join(f"({f})" for f in filters)
 
 
-def get_session_filter(
-    context: "ToolContext | None",
-    base_filter: str | None = None,
-) -> str | None:
-    """Build effective filter from session state document filter and base filter.
-
-    Checks the ToolContext for a registered SessionState. If it has a
-    document_filter, builds a SQL filter from it and combines with base_filter.
-
-    Args:
-        context: Optional ToolContext that may contain a SessionState.
-        base_filter: Optional base SQL WHERE clause to combine with.
-
-    Returns:
-        Combined filter string, or None if no filters apply.
-    """
-    if context is None:
-        return base_filter
-
-    from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-    session_state = context.get(SESSION_NAMESPACE, SessionState)
-    if session_state is None or not session_state.document_filter:
-        return base_filter
-
-    session_filter = build_multi_document_filter(session_state.document_filter)
-    return combine_filters(base_filter, session_filter)
-
-
 def combine_filters(filter1: str | None, filter2: str | None) -> str | None:
     """Combine two SQL filters with AND logic.
 
diff --git a/haiku_rag_slim/haiku/rag/tools/models.py b/haiku_rag_slim/haiku/rag/tools/models.py
deleted file mode 100644
index 6d69b5df..00000000
--- a/haiku_rag_slim/haiku/rag/tools/models.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from pydantic import BaseModel, Field
-
-from haiku.rag.agents.research.models import Citation
-
-
-class QAResult(BaseModel):
-    """Result from the QA toolset."""
-
-    question: str = Field(description="The question that was answered")
-    answer: str = Field(description="The answer to the question")
-    confidence: float = Field(
-        default=1.0,
-        description="Confidence score for this answer (0-1)",
-        ge=0.0,
-        le=1.0,
-    )
-    citations: list[Citation] = Field(
-        default_factory=list,
-        description="Citations supporting the answer",
-    )
-
-    @property
-    def sources(self) -> list[str]:
-        """Source names for display."""
-        return list(
-            dict.fromkeys(c.document_title or c.document_uri for c in self.citations)
-        )
-
-
-class AnalysisResult(BaseModel):
-    """Result from the analysis toolset (RLM execution)."""
-
-    answer: str = Field(description="The answer produced by analysis")
-    code_executed: bool = Field(
-        default=True,
-        description="Whether code was executed to produce this answer",
-    )
diff --git a/haiku_rag_slim/haiku/rag/tools/prompts.py b/haiku_rag_slim/haiku/rag/tools/prompts.py
deleted file mode 100644
index 74b8fd56..00000000
--- a/haiku_rag_slim/haiku/rag/tools/prompts.py
+++ /dev/null
@@ -1,71 +0,0 @@
-_TOOL_HEADER = """
-
-How to decide which tool to use:"""
-
-_TOOL_DOCUMENTS = """
-- "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-- "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-- "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document")."""
-
-_TOOL_QA = """
-- "ask" - Use for questions about topics in the knowledge base. Searches across documents and returns answers with citations. Prior answers are recalled to avoid redundant work."""
-
-_TOOL_SEARCH = """
-- "search" - Use when the user explicitly asks to search, find, or explore documents. Handles multi-query expansion internally and returns matching passages with surrounding context."""
-
-_TOOL_ANALYSIS = """
-- "analyze" - Use when the user asks for computation, data analysis, or quantitative tasks that require code execution (e.g., "calculate the average", "compare the numbers", "plot the data"). Runs Python code in a sandbox to produce results."""
-
-_DOCUMENT_NAME_HEADER = """
-
-IMPORTANT - When user mentions a document in search/ask:
-- If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-  - Extract the TOPIC as `query`/`question`
-  - Extract the DOCUMENT NAME as `document_name`"""
-
-_DOCUMENT_NAME_SEARCH_EXAMPLES = """
-- Examples for search:
-  - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-  - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566" """
-
-_DOCUMENT_NAME_QA_EXAMPLES = """
-- Examples for ask:
-  - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-  - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566" """
-
-_FEATURE_TOOLS: dict[str, str] = {
-    "documents": _TOOL_DOCUMENTS,
-    "qa": _TOOL_QA,
-    "search": _TOOL_SEARCH,
-    "analysis": _TOOL_ANALYSIS,
-}
-
-
-def build_tools_prompt(features: list[str]) -> str:
-    """Build tool guidance for the given features.
-
-    Returns prompt text describing when and how to use each tool.
-    Designed to be spliced into a custom agent's system prompt.
-
-    Args:
-        features: List of feature names (e.g., ["search", "documents", "qa"]).
-
-    Returns:
-        Tool guidance prompt text.
-    """
-    parts: list[str] = []
-
-    tool_sections = [_FEATURE_TOOLS[f] for f in features if f in _FEATURE_TOOLS]
-
-    if tool_sections:
-        parts.append(_TOOL_HEADER)
-        parts.extend(tool_sections)
-
-    if "search" in features or "qa" in features:
-        parts.append(_DOCUMENT_NAME_HEADER)
-        if "search" in features:
-            parts.append(_DOCUMENT_NAME_SEARCH_EXAMPLES)
-        if "qa" in features:
-            parts.append(_DOCUMENT_NAME_QA_EXAMPLES)
-
-    return "".join(parts)
diff --git a/haiku_rag_slim/haiku/rag/tools/qa.py b/haiku_rag_slim/haiku/rag/tools/qa.py
index 76d301ac..116377ea 100644
--- a/haiku_rag_slim/haiku/rag/tools/qa.py
+++ b/haiku_rag_slim/haiku/rag/tools/qa.py
@@ -1,50 +1,17 @@
-import math
-from collections.abc import Callable
-
 from pydantic import BaseModel, Field
-from pydantic_ai import FunctionToolset, RunContext, ToolReturn
 
-from haiku.rag.agents.research.dependencies import ResearchContext
-from haiku.rag.agents.research.graph import build_research_graph
 from haiku.rag.agents.research.models import Citation, SearchAnswer
-from haiku.rag.agents.research.state import ResearchDeps, ResearchState
-from haiku.rag.client import HaikuRAG
-from haiku.rag.config.models import AppConfig
-from haiku.rag.embeddings import get_embedder
-from haiku.rag.tools.context import RAGDeps, ToolContext
-from haiku.rag.tools.filters import (
-    build_document_filter,
-    combine_filters,
-    get_session_filter,
-)
-from haiku.rag.tools.models import QAResult
-from haiku.rag.tools.session import (
-    SESSION_NAMESPACE,
-    SessionContext,
-    SessionState,
-    compute_combined_state_delta,
-)
 
 PRIOR_ANSWER_RELEVANCE_THRESHOLD = 0.7
 
 
-def _cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
-    """Compute cosine similarity between two vectors."""
-    dot_product = sum(a * b for a, b in zip(vec1, vec2))
-    norm1 = math.sqrt(sum(a * a for a in vec1))
-    norm2 = math.sqrt(sum(b * b for b in vec2))
-    if norm1 == 0 or norm2 == 0:
-        return 0.0
-    return dot_product / (norm1 * norm2)
-
-
 class QAHistoryEntry(BaseModel):
     """A Q&A pair with optional cached embedding for similarity matching."""
 
     question: str
     answer: str
     confidence: float = 0.9
-    citations: list[Citation] = []
+    citations: list[Citation] = Field(default_factory=list)
     question_embedding: list[float] | None = Field(default=None, exclude=True)
 
     @property
@@ -63,224 +30,3 @@ class QAHistoryEntry(BaseModel):
             cited_chunks=[c.chunk_id for c in self.citations],
             citations=self.citations,
         )
-
-
-class QASessionState(BaseModel):
-    """Extended session state for QA with embedding cache."""
-
-    qa_history: list[QAHistoryEntry] = []
-    session_context: SessionContext | None = None
-
-
-QA_SESSION_NAMESPACE = "haiku.rag.qa_session"
-MAX_QA_HISTORY = 50
-
-
-async def run_qa_core(
-    client: HaikuRAG,
-    config: AppConfig,
-    question: str,
-    document_name: str | None = None,
-    *,
-    context: ToolContext | None = None,
-    base_filter: str | None = None,
-    session_context: str | None = None,
-    prior_answers: list[SearchAnswer] | None = None,
-    on_qa_complete: Callable[[QASessionState, AppConfig], None] | None = None,
-) -> QAResult:
-    """Run the QA flow and return a QAResult.
-
-    This is the core QA implementation shared by toolsets and client APIs.
-    It updates session state and QA history when context is provided.
-    """
-    session_state: SessionState | None = None
-    qa_session_state: QASessionState | None = None
-
-    if context is not None:
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
-
-    doc_filter = build_document_filter(document_name) if document_name else None
-    effective_filter = combine_filters(
-        get_session_filter(context, base_filter), doc_filter
-    )
-
-    effective_session_context = session_context
-    if qa_session_state is not None and qa_session_state.session_context is not None:
-        effective_session_context = qa_session_state.session_context.summary
-
-    effective_prior_answers = prior_answers or []
-    if qa_session_state is not None and qa_session_state.qa_history:
-        embedder = get_embedder(config)
-        question_embedding = await embedder.embed_query(question)
-
-        to_embed = []
-        to_embed_indices = []
-        for i, qa in enumerate(qa_session_state.qa_history):
-            if qa.question_embedding is None:
-                to_embed.append(qa.question)
-                to_embed_indices.append(i)
-
-        if to_embed:
-            new_embeddings = await embedder.embed_documents(to_embed)
-            for i, idx in enumerate(to_embed_indices):
-                qa_session_state.qa_history[idx].question_embedding = new_embeddings[i]
-
-        matched_answers = []
-        for qa in qa_session_state.qa_history:
-            if qa.question_embedding is not None:
-                similarity = _cosine_similarity(
-                    question_embedding, qa.question_embedding
-                )
-                if similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD:
-                    matched_answers.append(qa.to_search_answer())
-
-        if matched_answers:
-            effective_prior_answers = matched_answers
-
-    graph = build_research_graph(config=config, output_mode="conversational")
-
-    research_context = ResearchContext(
-        original_question=question,
-        session_context=effective_session_context,
-        qa_responses=effective_prior_answers,
-    )
-    research_state = ResearchState(
-        context=research_context,
-        max_iterations=1,
-        search_filter=effective_filter,
-        max_concurrency=config.research.max_concurrency,
-    )
-    deps = ResearchDeps(client=client)
-
-    result = await graph.run(state=research_state, deps=deps)
-
-    # Build citations with stable indices from session state
-    citations = []
-    for i, c in enumerate(result.citations):
-        if session_state is not None:
-            index = session_state.get_or_assign_index(c.chunk_id)
-        else:
-            index = i + 1
-
-        citations.append(
-            Citation(
-                index=index,
-                document_id=c.document_id,
-                chunk_id=c.chunk_id,
-                document_uri=c.document_uri,
-                document_title=c.document_title,
-                page_numbers=c.page_numbers,
-                headings=c.headings,
-                content=c.content,
-            )
-        )
-
-    qa_result = QAResult(
-        question=question,
-        answer=result.answer,
-        confidence=result.confidence,
-        citations=citations,
-    )
-
-    if session_state is not None:
-        session_state.citations = citations
-        session_state.citations_history.append(citations)
-
-    if qa_session_state is not None:
-        qa_session_state.qa_history.append(
-            QAHistoryEntry(
-                question=question,
-                answer=result.answer,
-                confidence=result.confidence,
-                citations=citations,
-            )
-        )
-        # Enforce FIFO limit
-        if len(qa_session_state.qa_history) > MAX_QA_HISTORY:
-            qa_session_state.qa_history = qa_session_state.qa_history[-MAX_QA_HISTORY:]
-        if on_qa_complete is not None:
-            on_qa_complete(qa_session_state, config)
-
-    return qa_result
-
-
-def create_qa_toolset(
-    config: AppConfig,
-    base_filter: str | None = None,
-    tool_name: str = "ask",
-    on_ask_complete: Callable[[QASessionState, AppConfig], None] | None = None,
-) -> FunctionToolset[RAGDeps]:
-    """Create a toolset with Q&A capabilities using research graph.
-
-    Args:
-        config: Application configuration.
-        base_filter: Optional base SQL WHERE clause applied to searches.
-        tool_name: Name for the ask tool. Defaults to "ask".
-        on_ask_complete: Optional callback invoked after each QA cycle with
-            the updated QASessionState and config. Use this to trigger
-            background summarization or other post-processing.
-
-    Returns:
-        FunctionToolset with an ask tool.
-    """
-
-    async def ask(
-        ctx: RunContext[RAGDeps],
-        question: str,
-        document_name: str | None = None,
-    ) -> ToolReturn | QAResult:
-        """Answer a question using the knowledge base.
-
-        Uses a research graph for searching and synthesizing answers.
-
-        Args:
-            question: The question to answer.
-            document_name: Optional document name/title to search within.
-
-        Returns:
-            QAResult with answer, confidence, and citations.
-        """
-        client = ctx.deps.client
-        tool_context = ctx.deps.tool_context
-
-        state_key: str | None = None
-        client_snapshot: dict | None = None
-
-        if tool_context is not None:
-            state_key = tool_context.state_key
-            if tool_context.namespaces:
-                client_snapshot = (
-                    tool_context.client_snapshot or tool_context.build_state_snapshot()
-                )
-
-        qa_result = await run_qa_core(
-            client=client,
-            config=config,
-            question=question,
-            document_name=document_name,
-            context=tool_context,
-            base_filter=base_filter,
-            on_qa_complete=on_ask_complete,
-        )
-
-        if client_snapshot is not None and tool_context is not None:
-            new_snapshot = tool_context.build_state_snapshot()
-            state_event = compute_combined_state_delta(
-                client_snapshot, new_snapshot, state_key=state_key
-            )
-
-            if state_event is not None:
-                answer_text = qa_result.answer
-                if qa_result.citations:
-                    citation_refs = " ".join(
-                        f"[{c.index}]" for c in qa_result.citations
-                    )
-                    answer_text = f"{answer_text}\n\nSources: {citation_refs}"
-                return ToolReturn(return_value=answer_text, metadata=[state_event])
-
-        return qa_result
-
-    toolset: FunctionToolset[RAGDeps] = FunctionToolset()
-    toolset.add_function(ask, name=tool_name)
-    return toolset
diff --git a/haiku_rag_slim/haiku/rag/tools/search.py b/haiku_rag_slim/haiku/rag/tools/search.py
index a0bd5dbb..fb0b6a20 100644
--- a/haiku_rag_slim/haiku/rag/tools/search.py
+++ b/haiku_rag_slim/haiku/rag/tools/search.py
@@ -1,23 +1,11 @@
-from pydantic import BaseModel
-from pydantic_ai import FunctionToolset, RunContext, ToolReturn
+from collections.abc import Callable
+
+from pydantic_ai import FunctionToolset, RunContext
 
-from haiku.rag.agents.research.models import Citation
 from haiku.rag.config.models import AppConfig
 from haiku.rag.store.models import SearchResult
 from haiku.rag.tools.context import RAGDeps
-from haiku.rag.tools.filters import combine_filters, get_session_filter
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState, compute_state_delta
-
-SEARCH_NAMESPACE = "haiku.rag.search"
-
-
-class SearchState(BaseModel):
-    """State for search toolset.
-
-    Accumulates search results across tool invocations.
-    """
-
-    results: list[SearchResult] = []
+from haiku.rag.tools.filters import combine_filters
 
 
 def create_search_toolset(
@@ -25,6 +13,7 @@ def create_search_toolset(
     expand_context: bool = True,
     base_filter: str | None = None,
     tool_name: str = "search",
+    on_results: Callable[[list[SearchResult]], None] | None = None,
 ) -> FunctionToolset[RAGDeps]:
     """Create a toolset with search capabilities.
 
@@ -35,6 +24,8 @@ def create_search_toolset(
         base_filter: Optional base SQL WHERE clause applied to all searches.
             Combined with any filter passed to the search tool.
         tool_name: Name for the search tool. Defaults to "search".
+        on_results: Optional callback invoked with search results after each search.
+            Useful for accumulating results externally (e.g., for citation resolution).
 
     Returns:
         FunctionToolset with a search tool.
@@ -45,7 +36,7 @@ def create_search_toolset(
         query: str,
         limit: int | None = None,
         filter: str | None = None,
-    ) -> ToolReturn | str:
+    ) -> str:
         """Search the knowledge base for relevant documents.
 
         Args:
@@ -57,26 +48,8 @@ def create_search_toolset(
             Formatted search results with content and metadata.
         """
         client = ctx.deps.client
-        tool_context = ctx.deps.tool_context
-
-        search_state: SearchState | None = None
-        if tool_context is not None:
-            search_state = tool_context.get_or_create(SEARCH_NAMESPACE, SearchState)
-
-        session_state: SessionState | None = None
-        old_session_state: SessionState | None = None
-        state_key: str | None = None
-        if tool_context is not None:
-            session_state = tool_context.get(SESSION_NAMESPACE, SessionState)
-            state_key = tool_context.state_key
-            if session_state is not None:
-                old_session_state = session_state.model_copy(deep=True)
-
-        # Combine all filters: base_filter AND session_filter AND tool filter
-        effective_filter = combine_filters(
-            get_session_filter(tool_context, base_filter), filter
-        )
 
+        effective_filter = combine_filters(base_filter, filter)
         effective_limit = limit or config.search.limit
         results = await client.search(
             query, limit=effective_limit, filter=effective_filter
@@ -85,68 +58,18 @@ def create_search_toolset(
         if expand_context:
             results = await client.expand_context(results)
 
-        if search_state is not None:
-            search_state.results.extend(results)
+        results_list = list(results)
 
-        if not results:
+        if on_results:
+            on_results(results_list)
+
+        if not results_list:
             return "No results found."
 
-        if session_state is not None:
-            citations = []
-            for r in results:
-                chunk_id = r.chunk_id or ""
-                if chunk_id:
-                    index = session_state.get_or_assign_index(chunk_id)
-                else:  # pragma: no cover
-                    index = len(session_state.citation_registry) + 1
-                citations.append(
-                    Citation(
-                        index=index,
-                        document_id=r.document_id or "",
-                        chunk_id=chunk_id,
-                        document_uri=r.document_uri or "",
-                        document_title=r.document_title,
-                        page_numbers=r.page_numbers or [],
-                        headings=r.headings,
-                        content=r.content,
-                    )
-                )
-            session_state.citations = citations
-            session_state.citations_history.append(citations)
-
-            result_lines = []
-            for c in citations:
-                title = c.document_title or c.document_uri or "Unknown"
-                snippet = c.content[:300].replace("\n", " ").strip()
-                if len(c.content) > 300:
-                    snippet += "..."
-
-                line = f"[{c.index}] **{title}**"
-                if c.page_numbers:  # pragma: no cover
-                    line += f" (pages {', '.join(map(str, c.page_numbers))})"
-                line += f"\n    {snippet}"
-                result_lines.append(line)
-
-            formatted = f"Found {len(results)} results:\n\n" + "\n\n".join(result_lines)
-
-            if old_session_state is not None:
-                state_event = compute_state_delta(
-                    old_session_state,
-                    session_state,
-                    state_key=state_key,
-                )
-                if state_event is not None:
-                    return ToolReturn(
-                        return_value=formatted,
-                        metadata=[state_event],
-                    )
-
-            return formatted  # pragma: no cover
-
-        # Format results without citation indexing (standalone use)
-        total = len(results)
+        total = len(results_list)
         formatted = [
-            r.format_for_agent(rank=i + 1, total=total) for i, r in enumerate(results)
+            r.format_for_agent(rank=i + 1, total=total)
+            for i, r in enumerate(results_list)
         ]
         return "\n\n".join(formatted)
 
diff --git a/haiku_rag_slim/haiku/rag/tools/session.py b/haiku_rag_slim/haiku/rag/tools/session.py
deleted file mode 100644
index 3c097871..00000000
--- a/haiku_rag_slim/haiku/rag/tools/session.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from datetime import datetime
-from typing import Any
-
-import jsonpatch
-from ag_ui.core import EventType, StateDeltaEvent
-from pydantic import BaseModel
-
-from haiku.rag.agents.research.models import Citation
-
-SESSION_NAMESPACE = "haiku.rag.session"
-
-
-class SessionContext(BaseModel):
-    """Compressed summary of conversation history for research graph."""
-
-    summary: str = ""
-    last_updated: datetime | None = None
-
-
-class SessionState(BaseModel):
-    """Session-level state for AG-UI integration.
-
-    This state is shared across toolsets and enables:
-    - Dynamic document filtering
-    - Stable citation indices across tool calls
-    - AG-UI state synchronization
-    """
-
-    document_filter: list[str] = []
-    citation_registry: dict[str, int] = {}
-    citations: list[Citation] = []
-    citations_history: list[list[Citation]] = []
-
-    def get_or_assign_index(self, chunk_id: str) -> int:
-        """Get or assign a stable citation index for a chunk_id.
-
-        Citation indices persist across tool calls within a session.
-        The first chunk gets index 1, subsequent new chunks get incrementing indices.
-        Same chunk_id always returns the same index.
-        """
-        if chunk_id in self.citation_registry:
-            return self.citation_registry[chunk_id]
-
-        new_index = len(self.citation_registry) + 1
-        self.citation_registry[chunk_id] = new_index
-        return new_index
-
-
-def compute_state_delta(
-    old_state: SessionState,
-    new_state: SessionState,
-    state_key: str | None = None,
-) -> StateDeltaEvent | None:
-    """Compute state delta between old and new session state.
-
-    Returns a StateDeltaEvent if there are changes, None otherwise.
-    """
-    return compute_combined_state_delta(
-        old_state.model_dump(mode="json"),
-        new_state.model_dump(mode="json"),
-        state_key=state_key,
-    )
-
-
-def compute_combined_state_delta(
-    old_snapshot: dict[str, Any],
-    new_snapshot: dict[str, Any],
-    state_key: str | None = None,
-) -> StateDeltaEvent | None:
-    """Compute state delta between old and new combined state snapshots.
-
-    This function computes delta for the combined chat state that includes
-    both SessionState and QASessionState fields.
-
-    Args:
-        old_snapshot: Previous state dict (e.g., from ChatDeps.state format).
-        new_snapshot: New state dict.
-        state_key: Optional namespace key for the state (e.g., "haiku.rag.chat").
-
-    Returns:
-        StateDeltaEvent if there are changes, None otherwise.
-    """
-    wrapped_old = {state_key: old_snapshot} if state_key else old_snapshot
-    wrapped_new = {state_key: new_snapshot} if state_key else new_snapshot
-
-    patch = jsonpatch.make_patch(wrapped_old, wrapped_new)
-
-    if not patch.patch:
-        return None
-
-    return StateDeltaEvent(
-        type=EventType.STATE_DELTA,
-        delta=patch.patch,
-    )
diff --git a/haiku_rag_slim/haiku/rag/tools/toolkit.py b/haiku_rag_slim/haiku/rag/tools/toolkit.py
deleted file mode 100644
index 091e1a68..00000000
--- a/haiku_rag_slim/haiku/rag/tools/toolkit.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from collections.abc import Callable
-from dataclasses import dataclass, field
-from typing import Any
-
-from pydantic_ai import FunctionToolset
-
-from haiku.rag.config.models import AppConfig
-from haiku.rag.tools.context import ToolContext, prepare_context
-from haiku.rag.tools.prompts import build_tools_prompt
-
-FEATURE_SEARCH = "search"
-FEATURE_DOCUMENTS = "documents"
-FEATURE_QA = "qa"
-FEATURE_ANALYSIS = "analysis"
-
-
-@dataclass(frozen=True)
-class Toolkit:
-    """Bundled toolsets, prompt, and context factory for haiku.rag agents.
-
-    Created via build_toolkit(). Provides everything needed to compose
-    an agent with haiku.rag toolsets and create matching ToolContexts.
-    """
-
-    toolsets: list[FunctionToolset[Any]] = field(default_factory=list)
-    prompt: str = ""
-    features: list[str] = field(default_factory=list)
-
-    def create_context(self, state_key: str | None = None) -> ToolContext:
-        """Create a ToolContext with namespaces matching this toolkit's features.
-
-        Args:
-            state_key: Optional AG-UI state key to set on the context.
-
-        Returns:
-            A prepared ToolContext.
-        """
-        context = ToolContext()
-        prepare_context(context, features=self.features, state_key=state_key)
-        return context
-
-    def prepare(self, context: ToolContext, state_key: str | None = None) -> None:
-        """Register namespaces on an existing ToolContext for this toolkit's features.
-
-        Idempotent — safe to call multiple times on the same context.
-
-        Args:
-            context: ToolContext to prepare.
-            state_key: Optional AG-UI state key to set on the context.
-        """
-        prepare_context(context, features=self.features, state_key=state_key)
-
-
-def build_toolkit(
-    config: AppConfig,
-    features: list[str] | None = None,
-    base_filter: str | None = None,
-    expand_context: bool = True,
-    on_qa_complete: Callable | None = None,
-) -> Toolkit:
-    """Build a Toolkit with toolsets, prompt, and context factory for the given features.
-
-    Args:
-        config: Application configuration.
-        features: List of features to enable. Defaults to ["search", "documents"].
-        base_filter: Optional base SQL WHERE clause applied to all toolset factories.
-        expand_context: Whether to expand search results with surrounding context.
-        on_qa_complete: Optional callback invoked after each QA cycle.
-
-    Returns:
-        A Toolkit ready for agent composition.
-    """
-    if features is None:
-        features = [FEATURE_SEARCH, FEATURE_DOCUMENTS]
-
-    toolsets: list[FunctionToolset[Any]] = []
-
-    if FEATURE_SEARCH in features:
-        from haiku.rag.tools.search import create_search_toolset
-
-        toolsets.append(
-            create_search_toolset(
-                config, expand_context=expand_context, base_filter=base_filter
-            )
-        )
-
-    if FEATURE_DOCUMENTS in features:
-        from haiku.rag.tools.document import create_document_toolset
-
-        toolsets.append(create_document_toolset(config, base_filter=base_filter))
-
-    if FEATURE_QA in features:
-        from haiku.rag.tools.qa import create_qa_toolset
-
-        toolsets.append(
-            create_qa_toolset(
-                config, base_filter=base_filter, on_ask_complete=on_qa_complete
-            )
-        )
-
-    if FEATURE_ANALYSIS in features:
-        from haiku.rag.tools.analysis import create_analysis_toolset
-
-        toolsets.append(create_analysis_toolset(config, base_filter=base_filter))
-
-    prompt = build_tools_prompt(features)
-
-    return Toolkit(toolsets=toolsets, prompt=prompt, features=features)
diff --git a/haiku_rag_slim/haiku/rag/utils.py b/haiku_rag_slim/haiku/rag/utils.py
index 79e4f9c6..0c89f49c 100644
--- a/haiku_rag_slim/haiku/rag/utils.py
+++ b/haiku_rag_slim/haiku/rag/utils.py
@@ -1,3 +1,4 @@
+import math
 import sys
 from datetime import UTC, datetime
 from importlib import metadata
@@ -14,6 +15,16 @@ if TYPE_CHECKING:
     from haiku.rag.config.models import AppConfig, ModelConfig
 
 
+def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
+    """Compute cosine similarity between two vectors."""
+    dot_product = sum(a * b for a, b in zip(vec1, vec2))
+    norm1 = math.sqrt(sum(a * a for a in vec1))
+    norm2 = math.sqrt(sum(b * b for b in vec2))
+    if norm1 == 0 or norm2 == 0:
+        return 0.0
+    return dot_product / (norm1 * norm2)
+
+
 def parse_datetime(s: str) -> datetime:
     """Parse a datetime string into a datetime object.
 
diff --git a/haiku_rag_slim/pyproject.toml b/haiku_rag_slim/pyproject.toml
index 5ba9eac3..6838b81d 100644
--- a/haiku_rag_slim/pyproject.toml
+++ b/haiku_rag_slim/pyproject.toml
@@ -24,6 +24,7 @@ classifiers = [
 dependencies = [
     "cachetools>=5.5.0",
     "docling-core==2.65.1",
+    "haiku.skills>=0.4.2",
     "httpx>=0.28.1",
     "jsonpatch>=1.33",
     "lancedb==0.29.2",
@@ -57,6 +58,10 @@ mistral = ["pydantic-ai-slim[mistral]"]
 bedrock = ["pydantic-ai-slim[bedrock]"]
 vertexai = ["pydantic-ai-slim[vertexai]"]
 
+[project.entry-points."haiku.skills"]
+rag = "haiku.rag.skills.rag:create_skill"
+rag-rlm = "haiku.rag.skills.rlm:create_skill"
+
 [project.scripts]
 haiku-rag = "haiku.rag.cli:cli"
 
diff --git a/mkdocs.yml b/mkdocs.yml
index ba20c134..681e49d0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -74,6 +74,10 @@ nav:
       - Agents:
           - agents/index.md
           - RLM Agent: agents/rlm.md
+      - Skills:
+          - skills/index.md
+          - RAG: skills/rag.md
+          - RLM: skills/rlm.md
       - Toolsets: tools.md
       - Applications: apps.md
       - Server: server.md
diff --git a/pyproject.toml b/pyproject.toml
index c8c9c9cc..dec0a341 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -131,6 +131,10 @@ filterwarnings = ["error", "ignore::UserWarning", "ignore::DeprecationWarning"]
 
 [tool.coverage.run]
 source = ["haiku_rag_slim"]
+omit = [
+    "haiku_rag_slim/haiku/rag/chat/*",
+    "haiku_rag_slim/haiku/rag/inspector/*",
+]
 
 [tool.coverage.report]
 show_missing = true
diff --git a/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py
deleted file mode 100644
index 659ad1ff..00000000
--- a/tests/agents/chat/test_chat_agent.py
+++ /dev/null
@@ -1,1627 +0,0 @@
-from pathlib import Path
-
-import pytest
-from ag_ui.core import StateDeltaEvent
-
-from haiku.rag.agents.chat import (
-    AGUI_STATE_KEY,
-    ChatDeps,
-    ChatSessionState,
-    create_chat_agent,
-    prepare_chat_context,
-    run_chat_agent,
-)
-from haiku.rag.agents.chat.context import _summarization_tasks
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.client import HaikuRAG
-from haiku.rag.config import Config
-from haiku.rag.tools import ToolContext
-from haiku.rag.tools.qa import MAX_QA_HISTORY, QAHistoryEntry
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionContext, SessionState
-
-
-def extract_state_from_result(result, state_key: str = AGUI_STATE_KEY) -> dict | None:
-    """Extract emitted state from agent result's tool return metadata.
-
-    Applies the JSON Patch delta to an empty state to get the final state.
-    """
-    import jsonpatch
-
-    for message in result.all_messages():
-        if hasattr(message, "parts"):
-            for part in message.parts:
-                if hasattr(part, "metadata") and part.metadata:
-                    for meta in part.metadata:
-                        if isinstance(meta, StateDeltaEvent):
-                            empty_state = {
-                                state_key: ChatSessionState().model_dump(mode="json")
-                            }
-                            patched = jsonpatch.apply_patch(empty_state, meta.delta)
-                            return patched.get(state_key)
-    return None
-
-
-@pytest.fixture(scope="module")
-def vcr_cassette_dir():
-    return str(Path(__file__).parent.parent.parent / "cassettes" / "test_chat_agent")
-
-
-def test_create_chat_agent(temp_db_path):
-    """Test that create_chat_agent returns a properly configured agent."""
-    agent = create_chat_agent(Config)
-    assert agent is not None
-    assert agent.name == "chat_agent" or agent.name is None
-
-
-def test_chat_deps_initialization(temp_db_path):
-    """Test ChatDeps can be initialized with required fields."""
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    assert deps.config is Config
-    assert deps.client is client
-    assert deps.tool_context is context
-    client.close()
-
-
-def test_chat_deps_is_agent_deps(temp_db_path):
-    """Test ChatDeps is a subclass of AgentDeps."""
-    from haiku.rag.tools.deps import AgentDeps
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-    assert isinstance(deps, AgentDeps)
-    client.close()
-
-
-def test_agui_state_key_constant():
-    """Test AGUI_STATE_KEY is exported with correct value."""
-    assert AGUI_STATE_KEY == "haiku.rag.chat"
-
-
-def test_chat_deps_state_setter_none(temp_db_path):
-    """Test ChatDeps.state setter handles None gracefully."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    context.register(QA_SESSION_NAMESPACE, QASessionState())
-    context.register(SESSION_NAMESPACE, SessionState())
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    # Setting state to None should be a no-op
-    deps.state = None
-
-    # State should remain unchanged
-    qa_session_state = context.get(QA_SESSION_NAMESPACE)
-    assert isinstance(qa_session_state, QASessionState)
-    assert qa_session_state.session_context is None
-    client.close()
-
-
-def test_chat_deps_state_setter_handles_initial_context(temp_db_path):
-    """Test ChatDeps.state setter transfers initial_context to qa_session_state."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    context.state_key = AGUI_STATE_KEY
-    # Register QASessionState (normally done by prepare_chat_context)
-    context.register(QA_SESSION_NAMESPACE, QASessionState())
-    context.register(SESSION_NAMESPACE, SessionState())
-
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    # Client sends initial_context with no session_context
-    incoming_state = {
-        AGUI_STATE_KEY: {
-            "initial_context": "Background info about the project",
-            "session_context": None,
-            "qa_history": [],
-            "citations": [],
-            "document_filter": [],
-            "citation_registry": {},
-        }
-    }
-
-    deps.state = incoming_state
-
-    # initial_context should be copied to qa_session_state.session_context
-    qa_session_state = context.get(QA_SESSION_NAMESPACE)
-    assert isinstance(qa_session_state, QASessionState)
-    assert qa_session_state.session_context is not None
-    assert (
-        qa_session_state.session_context.summary == "Background info about the project"
-    )
-    client.close()
-
-
-def test_chat_deps_state_setter_parses_session_context_dict(temp_db_path):
-    """Test ChatDeps.state setter parses session_context dict and extracts summary."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    context.state_key = AGUI_STATE_KEY
-    context.register(QA_SESSION_NAMESPACE, QASessionState())
-    context.register(SESSION_NAMESPACE, SessionState())
-
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    # Client sends session_context as a dict (as it comes from JSON)
-    incoming_state = {
-        AGUI_STATE_KEY: {
-            "session_context": {
-                "summary": "Previous conversation summary",
-                "last_updated": "2025-01-27T12:00:00",
-            },
-            "qa_history": [],
-            "citations": [],
-            "document_filter": [],
-            "citation_registry": {},
-        }
-    }
-
-    deps.state = incoming_state
-
-    # session_context dict should be parsed into SessionContext
-    qa_session_state = context.get(QA_SESSION_NAMESPACE)
-    assert isinstance(qa_session_state, QASessionState)
-    assert qa_session_state.session_context is not None
-    assert isinstance(qa_session_state.session_context, SessionContext)
-    assert qa_session_state.session_context.summary == "Previous conversation summary"
-    client.close()
-
-
-def test_chat_deps_state_setter_preserves_server_session_context(temp_db_path):
-    """Test that server's session_context is preferred over client's stale value."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    context.state_key = AGUI_STATE_KEY
-    qa_state = QASessionState()
-    qa_state.session_context = SessionContext(
-        summary="Fresh summary from background summarizer"
-    )
-    context.register(QA_SESSION_NAMESPACE, qa_state)
-    context.register(SESSION_NAMESPACE, SessionState())
-
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    # Client sends stale session_context
-    incoming_state = {
-        AGUI_STATE_KEY: {
-            "session_context": {
-                "summary": "Stale summary from client",
-                "last_updated": "2025-01-27T12:00:00",
-            },
-            "qa_history": [],
-            "citations": [],
-            "document_filter": [],
-            "citation_registry": {},
-        }
-    }
-
-    deps.state = incoming_state
-
-    # Server's fresher session_context should be preserved
-    qa_session_state = context.get(QA_SESSION_NAMESPACE)
-    assert isinstance(qa_session_state, QASessionState)
-    assert qa_session_state.session_context is not None
-    assert (
-        qa_session_state.session_context.summary
-        == "Fresh summary from background summarizer"
-    )
-    client.close()
-
-
-def test_trigger_background_summarization_no_qa_history(temp_db_path):
-    """Test trigger_background_summarization returns early when qa_history is empty."""
-    from haiku.rag.agents.chat.agent import trigger_background_summarization
-    from haiku.rag.agents.chat.context import _summarization_tasks
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    context.register(QA_SESSION_NAMESPACE, QASessionState())
-    context.register(SESSION_NAMESPACE, SessionState())
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-
-    tasks_before = len(_summarization_tasks)
-    trigger_background_summarization(deps)
-    assert len(_summarization_tasks) == tasks_before
-    client.close()
-
-
-def test_chat_session_state():
-    """Test ChatSessionState model."""
-    state = ChatSessionState()
-    assert state.citations == []
-    assert state.qa_history == []
-
-
-def test_citation():
-    """Test Citation model."""
-    citation = Citation(
-        index=1,
-        document_id="doc-123",
-        chunk_id="chunk-456",
-        document_uri="test.md",
-        document_title="Test Document",
-        page_numbers=[1, 2],
-        headings=["Section 1"],
-        content="Test content",
-    )
-    assert citation.index == 1
-    assert citation.document_id == "doc-123"
-    assert citation.chunk_id == "chunk-456"
-    assert citation.content == "Test content"
-
-
-def test_qa_response():
-    """Test QAHistoryEntry model."""
-    citation = Citation(
-        index=1,
-        document_id="doc-123",
-        chunk_id="chunk-456",
-        document_uri="test.md",
-        document_title="Test Document",
-        content="Test content",
-    )
-    qa = QAHistoryEntry(
-        question="What is this?",
-        answer="This is a test",
-        confidence=0.95,
-        citations=[citation],
-    )
-    assert qa.question == "What is this?"
-    assert qa.answer == "This is a test"
-    assert qa.confidence == 0.95
-    assert len(qa.citations) == 1
-    assert qa.sources == ["Test Document"]
-
-
-def test_qa_response_sources_with_uri_fallback():
-    """Test QAHistoryEntry.sources falls back to URI when title is None."""
-    citation = Citation(
-        index=1,
-        document_id="doc-123",
-        chunk_id="chunk-456",
-        document_uri="test.md",
-        document_title=None,
-        content="Test content",
-    )
-    qa = QAHistoryEntry(
-        question="What is this?",
-        answer="This is a test",
-        citations=[citation],
-    )
-    assert qa.sources == ["test.md"]
-
-
-def test_qa_response_to_search_answer():
-    """Test QAHistoryEntry.to_search_answer() converts to SearchAnswer for research graph."""
-    citation = Citation(
-        index=1,
-        document_id="doc-123",
-        chunk_id="chunk-456",
-        document_uri="test.md",
-        document_title="Test Document",
-        content="Test content",
-    )
-    qa = QAHistoryEntry(
-        question="What is the answer?",
-        answer="The answer is 42",
-        confidence=0.95,
-        citations=[citation],
-    )
-
-    search_answer = qa.to_search_answer()
-
-    assert search_answer.query == "What is the answer?"
-    assert search_answer.answer == "The answer is 42"
-    assert search_answer.confidence == 0.95
-    assert search_answer.cited_chunks == ["chunk-456"]
-    assert len(search_answer.citations) == 1
-    assert search_answer.citations[0].chunk_id == "chunk-456"
-
-
-# DocLayNet content for testing
-DOCLAYNET_CLASS_LABELS = """
-DocLayNet Dataset - Class Labels
-
-DocLayNet defines 11 distinct class labels for document layout analysis:
-1. Caption - Text describing figures or tables
-2. Footnote - Notes at the bottom of pages
-3. Formula - Mathematical expressions
-4. List-item - Items in bulleted or numbered lists
-5. Page-footer - Footer content on pages
-6. Page-header - Header content on pages
-7. Picture - Images and diagrams
-8. Section-header - Headings for document sections
-9. Table - Tabular data
-10. Text - Regular paragraph text (highest count: 510,377 instances)
-11. Title - Document titles
-
-The Text class has the highest count with 510,377 instances in the dataset.
-"""
-
-DOCLAYNET_ANNOTATION = """
-DocLayNet Dataset - Annotation Process
-
-The annotation process was organized into 4 phases:
-- Phase 1: Data selection and preparation by a small team of experts
-- Phase 2: Label selection and guideline definition
-- Phase 3: Annotation by 40 dedicated annotators
-- Phase 4: Quality control and continuous supervision
-
-The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-"""
-
-DOCLAYNET_DATA_SOURCES = """
-DocLayNet Dataset - Data Sources
-
-The data sources for DocLayNet include:
-- Publication repositories such as arXiv
-- Government offices and official documents
-- Company websites and corporate reports
-- Data directory services for financial reports
-- Patent documents
-
-Scanned documents were excluded to avoid rotation and skewing issues.
-"""
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_run_chat_agent(allow_model_requests, temp_db_path):
-    """Test run_chat_agent returns agent output string."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        output = await run_chat_agent(agent, deps, "Search for class labels")
-        assert isinstance(output, str)
-        assert len(output) > 0
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_search_tool(allow_model_requests, temp_db_path):
-    """Test the chat agent's search tool functionality."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add test documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_ANNOTATION,
-            uri="doclaynet-annotation",
-            title="DocLayNet Annotation",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask something that should trigger the search tool
-        result = await agent.run(
-            "Search for documents about class labels",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        assert len(result.output) > 0
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_search_tool_with_filter(allow_model_requests, temp_db_path):
-    """Test the chat agent's search tool with document filter."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add test documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_DATA_SOURCES,
-            uri="doclaynet-sources",
-            title="DocLayNet Sources",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to search within a specific document
-        result = await agent.run(
-            "Search for information about class labels in the DocLayNet Class Labels document",
-            deps=deps,
-        )
-
-        assert result.output is not None
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_get_document_tool(allow_model_requests, temp_db_path):
-    """Test the chat agent's get_document tool."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add a test document
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to get a specific document
-        result = await agent.run(
-            "Get me the DocLayNet Class Labels document",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        # The response should contain info about the document
-        assert "DocLayNet" in result.output or "class" in result.output.lower()
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_get_document_not_found(allow_model_requests, temp_db_path):
-    """Test the chat agent's get_document tool when document is not found."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask for a document that doesn't exist
-        result = await agent.run(
-            "Get me the nonexistent document",
-            deps=deps,
-        )
-
-        assert result.output is not None
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path):
-    """Test that the ask tool is called and can add citations to session state."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add a document with specific content
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask a question that should use the ask tool
-        result = await agent.run(
-            "What is the highest count class in the DocLayNet dataset?",
-            deps=deps,
-        )
-
-        assert result.output is not None
-
-        # Verify the agent used the ask tool by checking for tool calls
-        tool_calls = [
-            part
-            for msg in result.all_messages()
-            if hasattr(msg, "parts")
-            for part in msg.parts
-            if hasattr(part, "tool_name") and part.tool_name == "ask"
-        ]
-        assert len(tool_calls) >= 1, "Expected ask tool to be called"
-
-        # Session state should be registered (citations may or may not be present
-        # depending on whether the research graph found relevant evidence)
-        session_state = context.get(SESSION_NAMESPACE)
-        assert isinstance(session_state, SessionState)
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_ask_triggers_background_summarization(
-    allow_model_requests, temp_db_path
-):
-    """Test that the ask tool triggers background session context summarization.
-
-    Patches the internal trigger in run_qa_core to avoid concurrent HTTP calls
-    that break VCR cassette replay ordering. Triggers summarization explicitly
-    after the agent run completes.
-    """
-    from unittest.mock import patch
-
-    from haiku.rag.agents.chat.agent import trigger_background_summarization
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Patch internal trigger to avoid concurrent HTTP calls during VCR
-        with patch("haiku.rag.agents.chat.agent._trigger_summarization"):
-            result = await agent.run(
-                "What is the highest count class in the DocLayNet dataset?",
-                deps=deps,
-            )
-
-        assert result.output is not None
-
-        # Trigger summarization explicitly (sequential, deterministic)
-        trigger_background_summarization(deps)
-
-        # Wait for background task to complete
-        qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session_state is not None
-        key = id(qa_session_state)
-        if key in _summarization_tasks:
-            await _summarization_tasks[key]
-
-        # Verify session_context was populated by background task
-        assert qa_session_state.session_context is not None
-        assert qa_session_state.session_context.summary != ""
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_multi_turn_with_context(allow_model_requests, temp_db_path):
-    """Test multi-turn conversation with initial context, summarization, and prior recall.
-
-    Exercises the full conversation flow:
-    1. Initial context is transferred to session context
-    2. First question triggers background summarization
-    3. Second related question uses prior answer recall and updated session context
-    4. Both qa_history entries are present after two turns
-
-    The ask tool internally fires background summarization (concurrent HTTP calls)
-    which causes VCR cassette mismatches. We patch it to a no-op and trigger
-    summarization explicitly after each turn to keep HTTP ordering deterministic.
-    """
-    from unittest.mock import patch
-
-    from haiku.rag.agents.chat.agent import trigger_background_summarization
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_ANNOTATION,
-            uri="doclaynet-annotation",
-            title="DocLayNet Annotation",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Set initial state with initial_context (mimicking AG-UI client)
-        deps.state = {
-            AGUI_STATE_KEY: {
-                "initial_context": "The user is researching the DocLayNet dataset for a paper on document layout analysis.",
-                "session_context": None,
-                "qa_history": [],
-                "citations": [],
-                "document_filter": [],
-                "citation_registry": {},
-            }
-        }
-
-        # initial_context should be transferred to QASessionState
-        qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session is not None
-        assert qa_session.session_context is not None
-        assert (
-            qa_session.session_context.summary
-            == "The user is researching the DocLayNet dataset for a paper on document layout analysis."
-        )
-
-        # Patch the internal summarization trigger in the ask tool to avoid
-        # concurrent HTTP calls that break VCR cassette replay ordering.
-        with patch(
-            "haiku.rag.agents.chat.agent._trigger_summarization",
-        ):
-            # First question about class labels
-            result1 = await agent.run(
-                "What are the class labels defined in DocLayNet?",
-                deps=deps,
-            )
-        assert result1.output is not None
-
-        # Trigger summarization explicitly (sequential, no concurrency)
-        trigger_background_summarization(deps)
-        key = id(qa_session)
-        if key in _summarization_tasks:
-            await _summarization_tasks[key]
-
-        assert qa_session.session_context is not None
-        assert qa_session.session_context.summary != ""
-
-        # qa_history should have one entry
-        qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session is not None
-        assert len(qa_session.qa_history) >= 1
-
-        # Second related question - uses prior answers and updated session context
-        with patch(
-            "haiku.rag.agents.chat.agent._trigger_summarization",
-        ):
-            result2 = await agent.run(
-                "How were the annotations created and how many annotators were involved?",
-                deps=deps,
-                message_history=result1.all_messages(),
-            )
-        assert result2.output is not None
-
-        # Trigger summarization explicitly
-        trigger_background_summarization(deps)
-        qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session is not None
-        key = id(qa_session)
-        if key in _summarization_tasks:
-            await _summarization_tasks[key]
-
-        # qa_history should have two entries
-        assert len(qa_session.qa_history) >= 2
-
-        # Session context should be updated with newer summary
-        assert qa_session.session_context is not None
-        assert qa_session.session_context.summary != ""
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_ask_with_prior_answer_retrieval(
-    allow_model_requests, temp_db_path
-):
-    """Test that ask tool retrieves relevant prior answers from qa_history.
-
-    This exercises the prior answer retrieval logic:
-    1. First ask populates qa_history with question_embedding
-    2. Second similar ask should find the prior answer via embedding similarity
-    """
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps1 = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # First ask - establishes qa_history
-        result1 = await agent.run(
-            "What are the class labels in DocLayNet?",
-            deps=deps1,
-        )
-        assert result1.output is not None
-
-        # Check that session state has citations after first call
-        session_state = context.get(SESSION_NAMESPACE)
-        assert isinstance(session_state, SessionState)
-        # Citations might be 0 if the answer came from prior context
-        assert len(session_state.citations) >= 0
-
-        # Second ask - similar question triggers prior answer retrieval
-        result2 = await agent.run(
-            "Tell me about DocLayNet class labels",
-            deps=deps1,
-        )
-        assert result2.output is not None
-
-        # The QA session state should have history entries
-        from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-        qa_session = context.get(QA_SESSION_NAMESPACE)
-        assert isinstance(qa_session, QASessionState)
-        # After two asks, we should have entries in qa_history
-        assert len(qa_session.qa_history) >= 1
-
-
-def test_fifo_limit_enforcement():
-    """Test that FIFO limit enforcement logic works correctly.
-
-    This tests the FIFO trimming logic used in the ask() tool:
-    if len(qa_history) > MAX_QA_HISTORY:
-        qa_history = qa_history[-MAX_QA_HISTORY:]
-    """
-    # Create a session state with MAX_QA_HISTORY + 1 entries
-    qa_history = [
-        QAHistoryEntry(
-            question=f"Question {i}",
-            answer=f"Answer {i}",
-            confidence=0.9,
-        )
-        for i in range(MAX_QA_HISTORY + 1)
-    ]
-
-    session_state = ChatSessionState(
-        qa_history=qa_history,
-    )
-
-    # Simulate the FIFO enforcement from agent.py
-    if len(session_state.qa_history) > MAX_QA_HISTORY:
-        session_state.qa_history = session_state.qa_history[-MAX_QA_HISTORY:]
-
-    # History should be trimmed to MAX_QA_HISTORY
-    assert len(session_state.qa_history) == MAX_QA_HISTORY
-    # The first entry should now be "Question 1" (Question 0 was dropped)
-    assert session_state.qa_history[0].question == "Question 1"
-    # The last entry should be the last added question
-    assert session_state.qa_history[-1].question == f"Question {MAX_QA_HISTORY}"
-
-
-def test_chat_session_state_document_filter():
-    """Test ChatSessionState with document_filter."""
-    state = ChatSessionState(
-        document_filter=["doc1.pdf", "doc2.pdf"],
-    )
-    assert state.document_filter == ["doc1.pdf", "doc2.pdf"]
-
-
-def test_chat_session_state_document_filter_default_empty():
-    """Test ChatSessionState document_filter defaults to empty list."""
-    state = ChatSessionState()
-    assert state.document_filter == []
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_chat_agent_search_with_session_filter(
-    allow_model_requests, temp_db_path
-):
-    """Test that session document_filter restricts search results."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add two distinct documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_DATA_SOURCES,
-            uri="doclaynet-sources",
-            title="DocLayNet Sources",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-
-        # Set session filter to only include the labels document
-        session_state = context.get(SESSION_NAMESPACE)
-        assert isinstance(session_state, SessionState)
-        session_state.document_filter = ["DocLayNet Class Labels"]
-
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Search should only return results from the filtered document
-        result = await agent.run(
-            "Search for information about DocLayNet",
-            deps=deps,
-        )
-
-        assert result.output is not None
-
-        # Check that citations in context are only from the filtered document
-        session_state = context.get(SESSION_NAMESPACE)
-        assert isinstance(session_state, SessionState)
-        # If citations were added, they should only be from the labels document
-        for citation in session_state.citations:
-            assert "labels" in citation.document_uri.lower() or "Labels" in (
-                citation.document_title or ""
-            )
-
-
-def test_ask_tool_citation_registry_logic():
-    """Test the citation index assignment logic used by the ask tool.
-
-    Verifies that:
-    1. First chunk gets index 1
-    2. Second unique chunk gets index 2
-    3. Same chunk_id always gets same index
-    4. Indices don't reset between calls
-    """
-    session_state = SessionState()
-
-    # Simulate first ask tool building citations
-    first_ask_chunks = ["chunk-a", "chunk-b"]
-    first_citations = []
-    for chunk_id in first_ask_chunks:
-        index = session_state.get_or_assign_index(chunk_id)
-        first_citations.append(
-            Citation(
-                index=index,
-                document_id="doc-1",
-                chunk_id=chunk_id,
-                document_uri="test.md",
-                content="test",
-            )
-        )
-
-    assert first_citations[0].index == 1
-    assert first_citations[1].index == 2
-
-    # Simulate second ask tool - overlapping chunk_id should keep same index
-    second_ask_chunks = ["chunk-b", "chunk-c"]  # chunk-b was in first ask
-    second_citations = []
-    for chunk_id in second_ask_chunks:
-        index = session_state.get_or_assign_index(chunk_id)
-        second_citations.append(
-            Citation(
-                index=index,
-                document_id="doc-1",
-                chunk_id=chunk_id,
-                document_uri="test.md",
-                content="test",
-            )
-        )
-
-    # chunk-b should have same index as before
-    assert second_citations[0].index == 2
-    # chunk-c is new, gets next index
-    assert second_citations[1].index == 3
-
-    # Registry should have all three chunks
-    assert len(session_state.citation_registry) == 3
-    assert session_state.citation_registry == {"chunk-a": 1, "chunk-b": 2, "chunk-c": 3}
-
-
-def test_search_tool_citation_registry_logic():
-    """Test the citation index assignment logic used by the search tool.
-
-    Verifies that search and ask tools share the same registry,
-    maintaining stable indices across different tool calls.
-    """
-    session_state = SessionState()
-
-    # Simulate ask tool first (assigns indices 1, 2)
-    for chunk_id in ["chunk-a", "chunk-b"]:
-        session_state.get_or_assign_index(chunk_id)
-
-    # Simulate search tool returning overlapping + new chunks
-    search_chunks = ["chunk-b", "chunk-c", "chunk-d"]  # chunk-b already exists
-    search_citations = []
-    for chunk_id in search_chunks:
-        index = session_state.get_or_assign_index(chunk_id)
-        search_citations.append(
-            Citation(
-                index=index,
-                document_id="doc-1",
-                chunk_id=chunk_id,
-                document_uri="test.md",
-                content="test",
-            )
-        )
-
-    # chunk-b should have same index as assigned by ask (2)
-    assert search_citations[0].index == 2
-    # New chunks get incrementing indices
-    assert search_citations[1].index == 3
-    assert search_citations[2].index == 4
-
-    # Registry should have all four chunks
-    assert len(session_state.citation_registry) == 4
-    assert session_state.citation_registry == {
-        "chunk-a": 1,
-        "chunk-b": 2,
-        "chunk-c": 3,
-        "chunk-d": 4,
-    }
-
-
-# =============================================================================
-# Prior Answer Recall Tests
-# =============================================================================
-
-
-def test_cosine_similarity_identical_vectors():
-    """Test cosine similarity returns 1.0 for identical vectors."""
-    from haiku.rag.tools.qa import _cosine_similarity
-
-    vec = [1.0, 2.0, 3.0]
-    assert _cosine_similarity(vec, vec) == pytest.approx(1.0)
-
-
-def test_cosine_similarity_orthogonal_vectors():
-    """Test cosine similarity returns 0.0 for orthogonal vectors."""
-    from haiku.rag.tools.qa import _cosine_similarity
-
-    vec1 = [1.0, 0.0, 0.0]
-    vec2 = [0.0, 1.0, 0.0]
-    assert _cosine_similarity(vec1, vec2) == pytest.approx(0.0)
-
-
-def test_cosine_similarity_opposite_vectors():
-    """Test cosine similarity returns -1.0 for opposite vectors."""
-    from haiku.rag.tools.qa import _cosine_similarity
-
-    vec1 = [1.0, 2.0, 3.0]
-    vec2 = [-1.0, -2.0, -3.0]
-    assert _cosine_similarity(vec1, vec2) == pytest.approx(-1.0)
-
-
-def test_cosine_similarity_zero_vector():
-    """Test cosine similarity handles zero vectors gracefully."""
-    from haiku.rag.tools.qa import _cosine_similarity
-
-    vec = [1.0, 2.0, 3.0]
-    zero = [0.0, 0.0, 0.0]
-    assert _cosine_similarity(vec, zero) == 0.0
-    assert _cosine_similarity(zero, vec) == 0.0
-    assert _cosine_similarity(zero, zero) == 0.0
-
-
-def test_prior_answer_relevance_threshold_constant():
-    """Test PRIOR_ANSWER_RELEVANCE_THRESHOLD is set to expected value."""
-    from haiku.rag.tools.qa import PRIOR_ANSWER_RELEVANCE_THRESHOLD
-
-    assert PRIOR_ANSWER_RELEVANCE_THRESHOLD == 0.7
-
-
-def test_prior_answer_matching_above_threshold():
-    """Test that similar questions (above threshold) are matched."""
-    from haiku.rag.tools.qa import (
-        PRIOR_ANSWER_RELEVANCE_THRESHOLD,
-        _cosine_similarity,
-    )
-
-    # Simulate two nearly identical question embeddings
-    question_embedding = [0.5, 0.5, 0.5, 0.5]
-    prior_embedding = [0.51, 0.49, 0.5, 0.5]  # Very similar
-
-    similarity = _cosine_similarity(question_embedding, prior_embedding)
-    assert similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD
-
-
-def test_prior_answer_matching_below_threshold():
-    """Test that dissimilar questions (below threshold) are not matched."""
-    from haiku.rag.tools.qa import (
-        PRIOR_ANSWER_RELEVANCE_THRESHOLD,
-        _cosine_similarity,
-    )
-
-    # Simulate two different question embeddings
-    question_embedding = [1.0, 0.0, 0.0, 0.0]
-    prior_embedding = [0.0, 1.0, 0.0, 0.0]  # Orthogonal = very different
-
-    similarity = _cosine_similarity(question_embedding, prior_embedding)
-    assert similarity < PRIOR_ANSWER_RELEVANCE_THRESHOLD
-
-
-def test_qa_response_embedding_cache():
-    """Test that QAHistoryEntry stores and retrieves question_embedding correctly."""
-    embedding = [0.1, 0.2, 0.3, 0.4]
-    qa = QAHistoryEntry(
-        question="What is X?",
-        answer="X is Y.",
-        confidence=0.9,
-        question_embedding=embedding,
-    )
-
-    assert qa.question_embedding == embedding
-    # Embedding should be excluded from serialization (AG-UI state)
-    serialized = qa.model_dump()
-    assert "question_embedding" not in serialized
-
-
-def test_qa_response_embedding_default_none():
-    """Test that QAHistoryEntry.question_embedding defaults to None."""
-    qa = QAHistoryEntry(
-        question="What is X?",
-        answer="X is Y.",
-        confidence=0.9,
-    )
-
-    assert qa.question_embedding is None
-
-
-# =============================================================================
-# Background Task Cancellation Tests
-# =============================================================================
-
-
-@pytest.mark.asyncio
-async def test_summarization_task_cancellation():
-    """Test that new summarization tasks cancel previous ones for same state object."""
-    import asyncio
-
-    from haiku.rag.agents.chat.context import _summarization_tasks
-
-    # Clear any existing tasks
-    _summarization_tasks.clear()
-
-    key = 12345  # Simulates id(qa_session_state)
-
-    # Create a slow task that simulates summarization
-    async def slow_task():
-        await asyncio.sleep(10)  # Would take 10 seconds
-
-    # Start first task
-    task1 = asyncio.create_task(slow_task())
-    _summarization_tasks[key] = task1
-
-    # Simulate what happens when second ask comes in - cancel first task
-    if key in _summarization_tasks:
-        _summarization_tasks[key].cancel()
-
-    # Yield to let cancellation propagate
-    await asyncio.sleep(0)
-
-    # Start second task
-    task2 = asyncio.create_task(slow_task())
-    _summarization_tasks[key] = task2
-
-    # First task should be cancelled
-    assert task1.cancelled() or task1.done()
-
-    # Second task should be running
-    assert not task2.done()
-
-    # Cleanup
-    task2.cancel()
-    try:
-        await task2
-    except asyncio.CancelledError:
-        pass
-    _summarization_tasks.clear()
-
-
-# =============================================================================
-# list_documents Tool Tests
-# =============================================================================
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_list_documents_basic(allow_model_requests, temp_db_path):
-    """Test that list_documents tool returns available documents."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add test documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_ANNOTATION,
-            uri="doclaynet-annotation",
-            title="DocLayNet Annotation",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to list documents
-        result = await agent.run(
-            "What documents are available in the knowledge base?",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        # Should mention both documents
-        assert "DocLayNet" in result.output
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_list_documents_with_session_filter(allow_model_requests, temp_db_path):
-    """Test that list_documents respects session document_filter."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add test documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_DATA_SOURCES,
-            uri="doclaynet-sources",
-            title="DocLayNet Sources",
-        )
-
-        # Set session filter to only include the labels document
-        context = ToolContext()
-        context.register(
-            SESSION_NAMESPACE,
-            SessionState(document_filter=["DocLayNet Class Labels"]),
-        )
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to list documents - should only show filtered documents
-        result = await agent.run(
-            "Show me what documents are available",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        # Should only mention the Labels document, not Sources
-        assert "Labels" in result.output or "labels" in result.output
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_list_documents_pagination(allow_model_requests, temp_db_path):
-    """Test that list_documents supports pagination via limit/offset."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add multiple test documents
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-        await client.create_document(
-            content=DOCLAYNET_ANNOTATION,
-            uri="doclaynet-annotation",
-            title="DocLayNet Annotation",
-        )
-        await client.create_document(
-            content=DOCLAYNET_DATA_SOURCES,
-            uri="doclaynet-sources",
-            title="DocLayNet Sources",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to list first 2 documents
-        result = await agent.run(
-            "List the first 2 documents available",
-            deps=deps,
-        )
-
-        assert result.output is not None
-
-
-# =============================================================================
-# summarize_document Tool Tests
-# =============================================================================
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_summarize_document_found(allow_model_requests, temp_db_path):
-    """Test that summarize_document generates a summary for a found document."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Add a test document
-        await client.create_document(
-            content=DOCLAYNET_CLASS_LABELS,
-            uri="doclaynet-labels",
-            title="DocLayNet Class Labels",
-        )
-
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to summarize a specific document
-        result = await agent.run(
-            "Summarize the DocLayNet Class Labels document",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        # Should contain summary content about class labels
-        assert len(result.output) > 50
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_summarize_document_not_found(allow_model_requests, temp_db_path):
-    """Test that summarize_document handles not found documents gracefully."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        context = ToolContext()
-        prepare_chat_context(context)
-        agent = create_chat_agent(Config)
-        deps = ChatDeps(
-            config=Config,
-            client=client,
-            tool_context=context,
-        )
-
-        # Ask to summarize a document that doesn't exist
-        result = await agent.run(
-            "Summarize the nonexistent document",
-            deps=deps,
-        )
-
-        assert result.output is not None
-        # Should indicate the document wasn't found
-
-
-# =============================================================================
-# count_documents Tests
-# =============================================================================
-
-
-@pytest.mark.asyncio
-@pytest.mark.vcr()
-async def test_count_documents(temp_db_path):
-    """Test count_documents method."""
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        # Empty database
-        assert await client.count_documents() == 0
-
-        # Add documents
-        await client.create_document(content="Doc 1", uri="test/doc1.pdf")
-        await client.create_document(content="Doc 2", uri="test/doc2.pdf")
-        await client.create_document(content="Doc 3", uri="other/doc3.txt")
-
-        # Count all
-        assert await client.count_documents() == 3
-
-        # Count with filter
-        assert await client.count_documents(filter="uri LIKE '%.pdf'") == 2
-        assert await client.count_documents(filter="uri LIKE '%.txt'") == 1
-
-
-def test_citation_index_fallback_without_session_state():
-    """Test that citation indices fall back to sequential numbering without session_state.
-
-    This tests the fallback branch in the ask and search tools when
-    ctx.deps.session_state is None.
-    """
-    # Simulate the fallback logic from agent.py lines 281-285
-    citation_infos = []
-    session_state = None  # No session state
-
-    # Simulate processing citations without session_state
-    chunk_ids = ["chunk-a", "chunk-b", "chunk-c"]
-    for chunk_id in chunk_ids:
-        if session_state is not None:
-            index = session_state.get_or_assign_index(chunk_id)
-        else:
-            index = len(citation_infos) + 1
-        citation_infos.append(
-            Citation(
-                index=index,
-                document_id="doc-1",
-                chunk_id=chunk_id,
-                document_uri="test.md",
-                content="test",
-            )
-        )
-
-    # Without session_state, indices are simple sequential numbers
-    assert citation_infos[0].index == 1
-    assert citation_infos[1].index == 2
-    assert citation_infos[2].index == 3
-
-
-@pytest.mark.asyncio
-async def test_summarization_task_cleanup_on_completion():
-    """Test that completed tasks are cleaned up from _summarization_tasks."""
-    import asyncio
-
-    from haiku.rag.agents.chat.context import _summarization_tasks
-
-    _summarization_tasks.clear()
-
-    key = 67890  # Simulates id(qa_session_state)
-
-    # Create a fast task
-    async def fast_task():
-        await asyncio.sleep(0.01)
-
-    task = asyncio.create_task(fast_task())
-    _summarization_tasks[key] = task
-    task.add_done_callback(lambda t: _summarization_tasks.pop(key, None))
-
-    # Wait for completion
-    await task
-
-    # Task should be cleaned up
-    assert key not in _summarization_tasks
-
-
-@pytest.mark.asyncio
-async def test_ask_tool_returns_qa_result_when_no_state_delta(temp_db_path):
-    """Test ask tool falls through to QAResult when state delta is empty.
-
-    When run_qa_core produces no state changes (client_snapshot == new_snapshot),
-    compute_combined_state_delta returns None and the tool returns QAResult directly.
-    """
-    from unittest.mock import AsyncMock, patch
-
-    from pydantic_ai import Agent
-    from pydantic_ai.models.test import TestModel
-
-    from haiku.rag.tools.models import QAResult
-    from haiku.rag.tools.qa import create_qa_toolset
-
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        context = ToolContext()
-        prepare_chat_context(context, features=["search", "qa"])
-        context.state_key = AGUI_STATE_KEY
-
-        deps = ChatDeps(config=Config, client=client, tool_context=context)
-        toolset = create_qa_toolset(Config)
-        agent = Agent(
-            TestModel(call_tools=["ask"]),
-            deps_type=ChatDeps,
-            toolsets=[toolset],  # ty: ignore[invalid-argument-type]
-        )
-
-        # Mock run_qa_core to return a result WITHOUT modifying any state.
-        # Since client_snapshot == new_snapshot, the delta is None.
-        mock_result = QAResult(
-            question="test", answer="The answer", confidence=0.9, citations=[]
-        )
-        with patch(
-            "haiku.rag.tools.qa.run_qa_core",
-            new_callable=AsyncMock,
-            return_value=mock_result,
-        ):
-            result = await agent.run(
-                "test question",
-                deps=deps,  # ty: ignore[invalid-argument-type]
-            )
-
-        # The tool should have returned the QAResult (not a ToolReturn)
-        assert "The answer" in result.output
-
-
-@pytest.mark.asyncio
-async def test_ask_tool_delta_without_citations(temp_db_path):
-    """Test ask tool emits StateDeltaEvent without citation sources when citations are empty.
-
-    When the QA result has no citations but state DID change,
-    the tool returns a ToolReturn with a delta but no 'Sources:' line.
-    """
-    from unittest.mock import AsyncMock, patch
-
-    from pydantic_ai import Agent
-    from pydantic_ai.models.test import TestModel
-
-    from haiku.rag.tools.models import QAResult
-    from haiku.rag.tools.qa import (
-        QA_SESSION_NAMESPACE,
-        QASessionState,
-        create_qa_toolset,
-    )
-
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        context = ToolContext()
-        prepare_chat_context(context, features=["search", "qa"])
-        context.state_key = AGUI_STATE_KEY
-
-        deps = ChatDeps(config=Config, client=client, tool_context=context)
-        toolset = create_qa_toolset(Config)
-        agent = Agent(
-            TestModel(call_tools=["ask"]),
-            deps_type=ChatDeps,
-            toolsets=[toolset],  # ty: ignore[invalid-argument-type]
-        )
-
-        mock_result = QAResult(
-            question="test", answer="No citations answer", confidence=0.9, citations=[]
-        )
-
-        async def mock_qa_core(*args, **kwargs):
-            # Simulate state mutation (qa_history entry) but no citations
-            ctx = kwargs.get("context")
-            if ctx is not None:
-                qa_state = ctx.get(QA_SESSION_NAMESPACE, QASessionState)
-                if qa_state is not None:
-                    qa_state.qa_history.append(
-                        QAHistoryEntry(question="test", answer="No citations answer")
-                    )
-            return mock_result
-
-        with patch(
-            "haiku.rag.tools.qa.run_qa_core",
-            new_callable=AsyncMock,
-            side_effect=mock_qa_core,
-        ):
-            result = await agent.run(
-                "test question",
-                deps=deps,  # ty: ignore[invalid-argument-type]
-            )
-
-        # Should have the answer but NO "Sources:" line
-        assert "No citations answer" in result.output
-        assert "Sources:" not in result.output
-
-        # Should have emitted a StateDeltaEvent (qa_history changed)
-        state = extract_state_from_result(result)
-        assert state is not None
-        assert len(state.get("qa_history", [])) > 0
-
-
-@pytest.mark.asyncio
-async def test_ask_tool_delta_with_citations(temp_db_path):
-    """Test ask tool emits StateDeltaEvent with 'Sources:' line when citations are present.
-
-    When the QA result has citations and state changed, the tool returns
-    a ToolReturn with a delta and appended 'Sources: [1] [2]' line.
-    """
-    from unittest.mock import AsyncMock, patch
-
-    from pydantic_ai import Agent
-    from pydantic_ai.models.test import TestModel
-
-    from haiku.rag.tools.models import QAResult
-    from haiku.rag.tools.qa import (
-        QA_SESSION_NAMESPACE,
-        QASessionState,
-        create_qa_toolset,
-    )
-
-    async with HaikuRAG(temp_db_path, create=True) as client:
-        context = ToolContext()
-        prepare_chat_context(context, features=["search", "qa"])
-        context.state_key = AGUI_STATE_KEY
-
-        deps = ChatDeps(config=Config, client=client, tool_context=context)
-        toolset = create_qa_toolset(Config)
-        agent = Agent(
-            TestModel(call_tools=["ask"]),
-            deps_type=ChatDeps,
-            toolsets=[toolset],  # ty: ignore[invalid-argument-type]
-        )
-
-        citations = [
-            Citation(
-                index=1,
-                document_id="doc-1",
-                chunk_id="chunk-a",
-                document_uri="test.md",
-                document_title="Test",
-                content="content",
-            ),
-            Citation(
-                index=2,
-                document_id="doc-1",
-                chunk_id="chunk-b",
-                document_uri="test.md",
-                document_title="Test",
-                content="content",
-            ),
-        ]
-        mock_result = QAResult(
-            question="test",
-            answer="Cited answer",
-            confidence=0.95,
-            citations=citations,
-        )
-
-        async def mock_qa_core(*args, **kwargs):
-            ctx = kwargs.get("context")
-            if ctx is not None:
-                qa_state = ctx.get(QA_SESSION_NAMESPACE, QASessionState)
-                if qa_state is not None:
-                    qa_state.qa_history.append(
-                        QAHistoryEntry(
-                            question="test",
-                            answer="Cited answer",
-                            citations=citations,
-                        )
-                    )
-            return mock_result
-
-        with patch(
-            "haiku.rag.tools.qa.run_qa_core",
-            new_callable=AsyncMock,
-            side_effect=mock_qa_core,
-        ):
-            result = await agent.run(
-                "test question",
-                deps=deps,  # ty: ignore[invalid-argument-type]
-            )
-
-        # Should have the answer WITH "Sources:" line
-        assert "Cited answer" in result.output
-        assert "Sources:" in result.output
-        assert "[1]" in result.output
-        assert "[2]" in result.output
-
-        # Should have emitted a StateDeltaEvent
-        state = extract_state_from_result(result)
-        assert state is not None
diff --git a/tests/agents/chat/test_context.py b/tests/agents/chat/test_context.py
deleted file mode 100644
index c87fbf95..00000000
--- a/tests/agents/chat/test_context.py
+++ /dev/null
@@ -1,336 +0,0 @@
-from datetime import datetime
-from pathlib import Path
-
-import pytest
-
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.config import Config
-from haiku.rag.tools.qa import QAHistoryEntry
-from haiku.rag.tools.session import SessionContext
-
-
-@pytest.fixture(scope="module")
-def vcr_cassette_dir():
-    return str(Path(__file__).parent.parent.parent / "cassettes" / "test_chat_context")
-
-
-class TestSessionContext:
-    """Tests for SessionContext model."""
-
-    def test_session_context_creation_empty(self):
-        """Test SessionContext can be created with defaults."""
-        ctx = SessionContext()
-        assert ctx.summary == ""
-        assert ctx.last_updated is None
-
-    def test_session_context_creation_with_values(self):
-        """Test SessionContext can be created with provided values."""
-        now = datetime.now()
-        ctx = SessionContext(
-            summary="User discussed authentication patterns.",
-            last_updated=now,
-        )
-        assert ctx.summary == "User discussed authentication patterns."
-        assert ctx.last_updated == now
-
-    def test_session_context_serialization_roundtrip(self):
-        """Test SessionContext serializes and deserializes correctly."""
-        now = datetime.now()
-        original = SessionContext(
-            summary="Test summary with facts.",
-            last_updated=now,
-        )
-        # Serialize to dict
-        data = original.model_dump()
-        # Deserialize back
-        restored = SessionContext(**data)
-
-        assert restored.summary == original.summary
-        assert restored.last_updated == original.last_updated
-
-
-class TestSummarizeSession:
-    """Tests for summarize_session function."""
-
-    @pytest.mark.asyncio
-    async def test_summarize_session_empty_history(self):
-        """Test summarize_session with empty qa_history returns empty string."""
-        from haiku.rag.agents.chat.context import summarize_session
-
-        result = await summarize_session(qa_history=[], config=Config)
-        assert result == ""
-
-    @pytest.mark.asyncio
-    @pytest.mark.vcr()
-    async def test_summarize_session_single_entry(
-        self, allow_model_requests, temp_db_path
-    ):
-        """Test summarize_session with a single qa entry."""
-        from haiku.rag.agents.chat.context import summarize_session
-
-        qa_history = [
-            QAHistoryEntry(
-                question="What is the authentication method?",
-                answer="The API uses JWT tokens for authentication.",
-                confidence=0.95,
-                citations=[
-                    Citation(
-                        index=1,
-                        document_id="doc-1",
-                        chunk_id="chunk-1",
-                        document_uri="auth-guide.md",
-                        document_title="Auth Guide",
-                        content="JWT token details...",
-                    )
-                ],
-            )
-        ]
-
-        result = await summarize_session(qa_history=qa_history, config=Config)
-
-        # Should produce a non-empty summary
-        assert len(result) > 0
-        # Summary should mention authentication or JWT
-        assert "authentication" in result.lower() or "jwt" in result.lower()
-
-    @pytest.mark.asyncio
-    @pytest.mark.vcr()
-    async def test_summarize_session_multiple_entries(
-        self, allow_model_requests, temp_db_path
-    ):
-        """Test summarize_session with multiple qa entries produces consolidated summary."""
-        from haiku.rag.agents.chat.context import summarize_session
-
-        qa_history = [
-            QAHistoryEntry(
-                question="What is the authentication method?",
-                answer="The API uses JWT tokens for authentication.",
-                confidence=0.95,
-                citations=[
-                    Citation(
-                        index=1,
-                        document_id="doc-1",
-                        chunk_id="chunk-1",
-                        document_uri="auth-guide.md",
-                        document_title="Auth Guide",
-                        content="JWT token details...",
-                    )
-                ],
-            ),
-            QAHistoryEntry(
-                question="What is the rate limit?",
-                answer="Rate limiting is set to 100 requests per minute.",
-                confidence=0.9,
-                citations=[
-                    Citation(
-                        index=1,
-                        document_id="doc-2",
-                        chunk_id="chunk-2",
-                        document_uri="api-reference.md",
-                        document_title="API Reference",
-                        content="Rate limit config...",
-                    )
-                ],
-            ),
-            QAHistoryEntry(
-                question="How do I refresh tokens?",
-                answer="Use the /refresh endpoint with your refresh token.",
-                confidence=0.85,
-                citations=[
-                    Citation(
-                        index=1,
-                        document_id="doc-1",
-                        chunk_id="chunk-3",
-                        document_uri="auth-guide.md",
-                        document_title="Auth Guide",
-                        content="Token refresh...",
-                    )
-                ],
-            ),
-        ]
-
-        result = await summarize_session(qa_history=qa_history, config=Config)
-
-        # Should produce a non-empty summary
-        assert len(result) > 0
-        # Summary should contain structured sections
-        result_lower = result.lower()
-        assert "key facts" in result_lower or "established" in result_lower
-        assert "documents" in result_lower or "sources" in result_lower
-
-    @pytest.mark.asyncio
-    @pytest.mark.vcr()
-    async def test_summarize_session_with_current_context(self, allow_model_requests):
-        """Test summarize_session incorporates current_context into the summary."""
-        from haiku.rag.agents.chat.context import summarize_session
-
-        qa_history = [
-            QAHistoryEntry(
-                question="What's the rate limit?",
-                answer="100 requests per minute.",
-                confidence=0.9,
-            )
-        ]
-
-        # Provide current_context (e.g., previous summary)
-        current_context = "Focus on Python APIs. User is building a web application."
-
-        result = await summarize_session(
-            qa_history=qa_history,
-            config=Config,
-            current_context=current_context,
-        )
-
-        # Summary should be non-empty and ideally incorporate context about Python/web
-        assert len(result) > 0
-        # The context about "Python" or "web application" should influence the summary
-        result_lower = result.lower()
-        assert (
-            "rate" in result_lower or "limit" in result_lower or "100" in result_lower
-        )
-
-
-class TestUpdateSessionContext:
-    """Tests for update_session_context function."""
-
-    @pytest.mark.asyncio
-    @pytest.mark.vcr()
-    async def test_update_session_context_returns_context(
-        self, allow_model_requests, temp_db_path
-    ):
-        """Test update_session_context returns a populated SessionContext."""
-        from haiku.rag.agents.chat.context import update_session_context
-
-        qa_history = [
-            QAHistoryEntry(
-                question="What is the authentication method?",
-                answer="The API uses JWT tokens.",
-                confidence=0.95,
-            )
-        ]
-
-        result = await update_session_context(
-            qa_history=qa_history,
-            config=Config,
-        )
-
-        assert result.summary != ""
-        assert result.last_updated is not None
-
-    @pytest.mark.asyncio
-    async def test_update_session_context_with_empty_history(self):
-        """Test update_session_context with empty history returns empty summary."""
-        from haiku.rag.agents.chat.context import update_session_context
-
-        result = await update_session_context(
-            qa_history=[],
-            config=Config,
-        )
-
-        assert result.summary == ""
-
-
-class TestTriggerBackgroundSummarization:
-    """Tests for trigger_background_summarization."""
-
-    def test_trigger_with_empty_qa_history(self):
-        """trigger_background_summarization returns early with empty qa_history."""
-        from haiku.rag.agents.chat.context import (
-            _summarization_tasks,
-            trigger_background_summarization,
-        )
-        from haiku.rag.tools.qa import QASessionState
-
-        tasks_before = len(_summarization_tasks)
-
-        qa_session_state = QASessionState()
-        assert len(qa_session_state.qa_history) == 0
-
-        trigger_background_summarization(qa_session_state, config=Config)
-
-        # No new task should have been created
-        assert len(_summarization_tasks) == tasks_before
-
-    @pytest.mark.asyncio
-    async def test_trigger_cancels_existing_task(self):
-        """Second trigger cancels the previous background task."""
-        import asyncio
-        from unittest.mock import patch
-
-        from haiku.rag.agents.chat.context import (
-            _summarization_tasks,
-            trigger_background_summarization,
-        )
-        from haiku.rag.tools.qa import QAHistoryEntry, QASessionState
-
-        _summarization_tasks.clear()
-
-        qa_session_state = QASessionState(
-            qa_history=[QAHistoryEntry(question="Q1", answer="A1", confidence=0.9)]
-        )
-
-        # Patch _update_context_background to be a slow coroutine
-        async def slow_background(*args, **kwargs):
-            await asyncio.sleep(10)
-
-        with patch(
-            "haiku.rag.agents.chat.context._update_context_background",
-            new=slow_background,
-        ):
-            # First trigger creates a task
-            trigger_background_summarization(qa_session_state, config=Config)
-            key = id(qa_session_state)
-            assert key in _summarization_tasks
-            first_task = _summarization_tasks[key]
-
-            # Second trigger should cancel the first
-            trigger_background_summarization(qa_session_state, config=Config)
-            await asyncio.sleep(0)  # Let cancellation propagate
-            assert first_task.cancelled() or first_task.done()
-
-            # Cleanup
-            if key in _summarization_tasks:
-                _summarization_tasks[key].cancel()
-                try:
-                    await _summarization_tasks[key]
-                except asyncio.CancelledError:
-                    pass
-            _summarization_tasks.clear()
-
-
-class TestUpdateSessionContextPassesCurrentContext:
-    """Tests for update_session_context current_context forwarding."""
-
-    @pytest.mark.asyncio
-    async def test_update_session_context_passes_current_context(self):
-        """Test update_session_context passes current_context to summarizer."""
-        from unittest.mock import patch
-
-        from haiku.rag.agents.chat.context import update_session_context
-
-        qa_history = [
-            QAHistoryEntry(
-                question="What is JWT?",
-                answer="JSON Web Token for authentication.",
-                confidence=0.95,
-            )
-        ]
-
-        captured_current_context = []
-
-        async def mock_summarize(qa_history, config, current_context=None):
-            captured_current_context.append(current_context)
-            return "Mocked summary"
-
-        with patch(
-            "haiku.rag.agents.chat.context.summarize_session",
-            new=mock_summarize,
-        ):
-            await update_session_context(
-                qa_history=qa_history,
-                config=Config,
-                current_context="Previous session summary",
-            )
-
-        assert len(captured_current_context) == 1
-        assert captured_current_context[0] == "Previous session summary"
diff --git a/tests/agents/chat/test_features.py b/tests/agents/chat/test_features.py
deleted file mode 100644
index a438cd7e..00000000
--- a/tests/agents/chat/test_features.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from pydantic_ai import FunctionToolset
-
-from haiku.rag.agents.chat.agent import (
-    DEFAULT_FEATURES,
-    FEATURE_ANALYSIS,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_SEARCH,
-    ChatDeps,
-    create_chat_agent,
-    prepare_chat_context,
-)
-from haiku.rag.agents.chat.prompts import build_chat_prompt
-from haiku.rag.client import HaikuRAG
-from haiku.rag.config import Config
-from haiku.rag.tools.context import ToolContext
-from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-
-def _count_function_toolsets(agent) -> int:
-    """Count FunctionToolset instances in an agent (excludes internal toolsets)."""
-    return sum(1 for t in agent.toolsets if type(t) is FunctionToolset)
-
-
-# =============================================================================
-# Feature Selection Tests
-# =============================================================================
-
-
-def test_default_features(temp_db_path):
-    """Default features create search + document + qa toolsets and register both states."""
-    context = ToolContext()
-    prepare_chat_context(context)
-    agent = create_chat_agent(Config)
-
-    # Should have 3 toolsets (search, document, qa)
-    assert _count_function_toolsets(agent) == 3
-
-    # Both SessionState and QASessionState should be registered
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is not None
-
-
-def test_search_only(temp_db_path):
-    """features=["search"] creates only search toolset, no QASessionState."""
-    context = ToolContext()
-    prepare_chat_context(context, features=[FEATURE_SEARCH])
-    agent = create_chat_agent(Config, features=[FEATURE_SEARCH])
-
-    assert _count_function_toolsets(agent) == 1
-
-    # SessionState always registered, but QASessionState should NOT be
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is None
-
-
-def test_search_and_documents(temp_db_path):
-    """features=["search", "documents"] creates both toolsets, no QASessionState."""
-    context = ToolContext()
-    prepare_chat_context(context, features=[FEATURE_SEARCH, FEATURE_DOCUMENTS])
-    agent = create_chat_agent(Config, features=[FEATURE_SEARCH, FEATURE_DOCUMENTS])
-
-    assert _count_function_toolsets(agent) == 2
-
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is None
-
-
-def test_all_features(temp_db_path):
-    """All four features create four toolsets."""
-    context = ToolContext()
-    prepare_chat_context(
-        context,
-        features=[FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA, FEATURE_ANALYSIS],
-    )
-    agent = create_chat_agent(
-        Config,
-        features=[FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA, FEATURE_ANALYSIS],
-    )
-
-    assert _count_function_toolsets(agent) == 4
-
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is not None
-
-
-def test_no_qa_skips_qa_session_state(temp_db_path):
-    """Without QA feature, QASessionState is not registered."""
-    context = ToolContext()
-    prepare_chat_context(context, features=[FEATURE_SEARCH, FEATURE_DOCUMENTS])
-
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is None
-
-
-def test_chat_deps_state_without_qa(temp_db_path):
-    """ChatDeps.state getter omits qa_history/session_context when QASessionState absent."""
-    from haiku.rag.agents.chat.state import AGUI_STATE_KEY
-
-    client = HaikuRAG(temp_db_path, create=True)
-    context = ToolContext()
-    prepare_chat_context(context, features=[FEATURE_SEARCH])
-
-    deps = ChatDeps(config=Config, client=client, tool_context=context)
-    state = deps.state
-
-    # State is wrapped under the AGUI state key
-    assert AGUI_STATE_KEY in state
-    inner = state[AGUI_STATE_KEY]
-
-    # SessionState fields should be present
-    assert "document_filter" in inner
-    assert "citation_registry" in inner
-    assert "citations" in inner
-    # QA fields should NOT be present
-    assert "qa_history" not in inner
-    assert "session_context" not in inner
-    client.close()
-
-
-# =============================================================================
-# Prompt Composition Tests
-# =============================================================================
-
-
-def test_build_chat_prompt_default():
-    """Default features produce prompt mentioning all standard tools."""
-    prompt = build_chat_prompt(DEFAULT_FEATURES)
-
-    assert "list_documents" in prompt
-    assert "get_document" in prompt
-    assert "summarize_document" in prompt
-    assert "ask" in prompt
-    assert "search" in prompt
-    assert "analyze" not in prompt
-
-
-def test_build_chat_prompt_search_only():
-    """Search-only prompt doesn't mention ask or document tools."""
-    prompt = build_chat_prompt([FEATURE_SEARCH])
-
-    assert "search" in prompt
-    assert '"ask"' not in prompt
-    assert '"list_documents"' not in prompt
-    assert '"get_document"' not in prompt
-    assert '"summarize_document"' not in prompt
-
-
-def test_build_chat_prompt_includes_analysis():
-    """Analysis feature adds analyze guidance to prompt."""
-    prompt = build_chat_prompt(
-        [FEATURE_SEARCH, FEATURE_QA, FEATURE_DOCUMENTS, FEATURE_ANALYSIS]
-    )
-
-    assert "analyze" in prompt
-    assert "search" in prompt
-    assert "ask" in prompt
-
-
-def test_build_chat_prompt_with_preamble():
-    """Custom preamble replaces the default identity section."""
-    custom = "You are a custom assistant."
-    prompt = build_chat_prompt(DEFAULT_FEATURES, preamble=custom)
-
-    assert prompt.startswith(custom)
-    # Tool guidance should still be appended
-    assert "search" in prompt
-    assert "ask" in prompt
-    # Default identity should NOT be present
-    assert "haiku.rag" not in prompt
-
-
-def test_build_chat_prompt_without_preamble_uses_default():
-    """Without preamble, the default identity section is used."""
-    prompt = build_chat_prompt(DEFAULT_FEATURES)
-
-    assert "haiku.rag" in prompt
-
-
-def test_create_chat_agent_with_preamble():
-    """create_chat_agent passes preamble through to build_chat_prompt."""
-    custom = "You are a domain expert."
-    agent = create_chat_agent(Config, preamble=custom)
-
-    assert agent is not None
-    # _instructions is the internal list of instruction strings/callables
-    assert any(
-        custom in instr for instr in agent._instructions if isinstance(instr, str)
-    )
diff --git a/tests/agents/chat/test_state.py b/tests/agents/chat/test_state.py
deleted file mode 100644
index 19220c87..00000000
--- a/tests/agents/chat/test_state.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from haiku.rag.agents.chat.state import ChatSessionState
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.tools.session import SessionContext, SessionState
-
-
-def test_max_qa_history_constant():
-    """Test MAX_QA_HISTORY constant value."""
-    from haiku.rag.tools.qa import MAX_QA_HISTORY
-
-    assert MAX_QA_HISTORY == 50
-
-
-def test_citation_registry_index_assignment():
-    """Test get_or_assign_index basic index assignment behavior.
-
-    Verifies:
-    - First chunk gets index 1
-    - Second unique chunk gets index 2
-    - Same chunk_id always returns same index
-    """
-    session_state = SessionState()
-
-    # First chunk gets index 1
-    index1 = session_state.get_or_assign_index("chunk-abc")
-    assert index1 == 1
-
-    # Second unique chunk gets index 2
-    index2 = session_state.get_or_assign_index("chunk-def")
-    assert index2 == 2
-
-    # Same chunk_id returns same index (not incremented)
-    index1_again = session_state.get_or_assign_index("chunk-abc")
-    assert index1_again == 1
-
-
-def test_citation_registry_stability():
-    """Test citation indices are stable across multiple calls in any order."""
-    session_state = SessionState()
-
-    # First round assigns indices 1, 2, 3
-    idx_a = session_state.get_or_assign_index("chunk-a")
-    idx_b = session_state.get_or_assign_index("chunk-b")
-    idx_c = session_state.get_or_assign_index("chunk-c")
-
-    # Second round - existing chunks keep their indices regardless of order
-    assert session_state.get_or_assign_index("chunk-b") == idx_b
-    assert session_state.get_or_assign_index("chunk-a") == idx_a
-    assert session_state.get_or_assign_index("chunk-c") == idx_c
-
-    # New chunk gets next index
-    idx_d = session_state.get_or_assign_index("chunk-d")
-    assert idx_d == 4
-
-
-def test_citation_registry_serialization_roundtrip():
-    """Test citation_registry serializes and deserializes correctly for AG-UI state."""
-    # Create state and assign indices
-    original = ChatSessionState()
-    original.citation_registry = {"chunk-a": 1, "chunk-b": 2}
-
-    # Serialize
-    state_dict = original.model_dump()
-    assert "citation_registry" in state_dict
-    assert state_dict["citation_registry"] == {"chunk-a": 1, "chunk-b": 2}
-
-    # Deserialize (simulating AG-UI state restoration)
-    restored = ChatSessionState.model_validate(state_dict)
-    assert restored.citation_registry == {"chunk-a": 1, "chunk-b": 2}
-
-
-def test_chat_session_state_initial_context_default_none():
-    """Initial context should default to None."""
-    state = ChatSessionState()
-    assert state.initial_context is None
-
-
-def test_chat_session_state_initial_context_preserved():
-    """Explicit initial_context should be preserved."""
-    state = ChatSessionState(initial_context="Background info about the project")
-    assert state.initial_context == "Background info about the project"
-
-
-def test_chat_session_state_initial_context_serialization():
-    """initial_context should serialize and deserialize correctly."""
-    state = ChatSessionState(
-        initial_context="User is working on authentication",
-    )
-    state_dict = state.model_dump()
-    assert state_dict["initial_context"] == "User is working on authentication"
-
-    restored = ChatSessionState.model_validate(state_dict)
-    assert restored.initial_context == "User is working on authentication"
-
-
-def test_chat_session_state_model_dump_json_serializes_datetime():
-    """model_dump(mode='json') should serialize datetime to ISO string.
-
-    Agent tools use model_dump(mode='json') when creating StateSnapshotEvent
-    to ensure datetime fields are JSON-serializable for external clients
-    persisting AG-UI state to database JSON columns.
-    """
-    from datetime import datetime
-
-    session_state = ChatSessionState(
-        session_context=SessionContext(
-            summary="Test summary",
-            last_updated=datetime(2025, 1, 27, 12, 0, 0),
-        ),
-    )
-
-    # This is how agent.py creates snapshots for StateSnapshotEvent
-    snapshot = session_state.model_dump(mode="json")
-
-    # datetime should be serialized as ISO string, not datetime object
-    assert isinstance(snapshot["session_context"]["last_updated"], str)
-    assert snapshot["session_context"]["last_updated"] == "2025-01-27T12:00:00"
-
-
-def test_chat_session_state_citations_history_default():
-    """citations_history defaults to empty list."""
-    state = ChatSessionState()
-    assert state.citations_history == []
-
-
-def test_chat_session_state_citations_history_roundtrip():
-    """citations_history serializes and deserializes correctly."""
-    citation = Citation(
-        index=1,
-        document_id="d1",
-        chunk_id="c1",
-        document_uri="test://doc",
-        document_title="Doc",
-        page_numbers=[],
-        headings=None,
-        content="content",
-    )
-    state = ChatSessionState(citations_history=[[citation]])
-    data = state.model_dump(mode="json")
-    restored = ChatSessionState.model_validate(data)
-    assert len(restored.citations_history) == 1
-    assert restored.citations_history[0][0].chunk_id == "c1"
diff --git a/tests/agents/research/test_models.py b/tests/agents/research/test_models.py
index c20a72b4..148f06ca 100644
--- a/tests/agents/research/test_models.py
+++ b/tests/agents/research/test_models.py
@@ -25,7 +25,7 @@ class TestCitation:
         assert citation.index is None
 
     def test_citation_with_index(self):
-        """Test Citation can be created with index (chat agent use case)."""
+        """Test Citation can be created with index (chat use case)."""
         citation = Citation(
             index=1,
             document_id="doc-1",
diff --git a/tests/agents/research/test_research_graph.py b/tests/agents/research/test_research_graph.py
index c08e9693..9a989653 100644
--- a/tests/agents/research/test_research_graph.py
+++ b/tests/agents/research/test_research_graph.py
@@ -68,64 +68,15 @@ def test_iterative_plan_result_model():
     assert continue_result.next_question == "What are the specific requirements?"
 
 
-# =============================================================================
-# Conversational Graph Tests
-# =============================================================================
-
-
-def test_build_research_graph_conversational_mode_returns_graph():
-    """Test build_research_graph with output_mode='conversational' returns a valid Graph instance."""
+def test_build_research_graph_returns_graph():
+    """Test build_research_graph returns a valid Graph instance."""
     from pydantic_graph.beta import Graph
 
-    graph = build_research_graph(output_mode="conversational")
+    graph = build_research_graph()
     assert graph is not None
     assert isinstance(graph, Graph)
 
 
-def test_build_research_graph_report_mode_returns_graph():
-    """Test build_research_graph with output_mode='report' returns a valid Graph instance."""
-    from pydantic_graph.beta import Graph
-
-    graph = build_research_graph(output_mode="report")
-    assert graph is not None
-    assert isinstance(graph, Graph)
-
-
-def test_conversational_answer_model():
-    """Test ConversationalAnswer model can be created with all fields."""
-    from haiku.rag.agents.research.models import Citation, ConversationalAnswer
-
-    citation = Citation(
-        index=1,
-        document_id="doc-1",
-        chunk_id="chunk-1",
-        document_uri="test.md",
-        document_title="Test Doc",
-        content="Test content",
-    )
-
-    answer = ConversationalAnswer(
-        answer="The answer is 42.",
-        citations=[citation],
-        confidence=0.95,
-    )
-
-    assert answer.answer == "The answer is 42."
-    assert len(answer.citations) == 1
-    assert answer.confidence == 0.95
-
-
-def test_conversational_answer_default_values():
-    """Test ConversationalAnswer uses correct default values."""
-    from haiku.rag.agents.research.models import ConversationalAnswer
-
-    answer = ConversationalAnswer(answer="Just the answer.")
-
-    assert answer.answer == "Just the answer."
-    assert answer.citations == []
-    assert answer.confidence == 1.0
-
-
 def test_format_context_for_prompt_basic():
     """Test format_context_for_prompt with basic context."""
     from haiku.rag.agents.research.dependencies import ResearchContext
@@ -138,22 +89,6 @@ def test_format_context_for_prompt_basic():
     assert "What is X?" in result
 
 
-def test_format_context_for_prompt_with_session_context():
-    """Test format_context_for_prompt includes session_context as background."""
-    from haiku.rag.agents.research.dependencies import ResearchContext
-    from haiku.rag.agents.research.graph import format_context_for_prompt
-
-    context = ResearchContext(
-        original_question="What is Y?",
-        session_context="Previous discussion about topic Z.",
-    )
-    result = format_context_for_prompt(context)
-
-    assert "<background>" in result
-    assert "Previous discussion" in result
-    assert "What is Y?" in result
-
-
 def test_format_context_for_prompt_with_prior_answers():
     """Test format_context_for_prompt includes prior_answers."""
     from haiku.rag.agents.research.dependencies import ResearchContext
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml
deleted file mode 100644
index e8bf4cfd..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml
+++ /dev/null
@@ -1,1311 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5339'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What is the highest count class in the DocLayNet dataset?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
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-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
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-      content-type:
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-        index: 0
-        message:
-          content: ''
-          reasoning: Need ask.
-          role: assistant
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-              name: ask
-            id: call_k66a34yj
-            index: 0
-            type: function
-      created: 1770037789
-      id: chatcmpl-710
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 45
-        prompt_tokens: 1055
-        total_tokens: 1100
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
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-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
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-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
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-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
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-
-          <context>
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-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
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-                type: boolean
-              next_question:
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-                - type: string
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-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
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-            type: object
-        type: function
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-          reasoning: 'We need to propose first question to investigate. The answer: "What is the highest count class in the
-            DocLayNet dataset?" So first step may be to get class distribution stats. So question: "What are the class names
-            and their respective document counts in the DocLayNet dataset?" That will let us identify highest count class.'
-          role: assistant
-          tool_calls:
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-                in the DocLayNet dataset?","reasoning":"We need to retrieve the class distribution to identify which class
-                has the highest count."}'
-              name: final_result
-            id: call_bq73y6jw
-            index: 0
-            type: function
-      created: 1770037791
-      id: chatcmpl-880
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-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
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-        prompt_tokens: 374
-        total_tokens: 507
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
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-      accept-encoding:
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-      connection:
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-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
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-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class names and their respective document counts in the DocLayNet dataset?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
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-            type: object
-        type: function
-      - function:
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-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
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-      content-type:
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-      choices:
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-        index: 0
-        message:
-          content: ''
-          reasoning: Need search.
-          role: assistant
-          tool_calls:
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-              arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}'
-              name: search_and_answer
-            id: call_n08pybym
-            index: 0
-            type: function
-      created: 1770037792
-      id: chatcmpl-47
-      model: gpt-oss
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-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 39
-        prompt_tokens: 632
-        total_tokens: 671
-    status:
-      code: 200
-      message: OK
-- request:
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-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
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-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet dataset class names document counts
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 10
-        total_tokens: 10
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3745'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class names and their respective document counts in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}'
-            name: search_and_answer
-          id: call_n08pybym
-          type: function
-      - content: |-
-          [b40c5a39-4b39-4da9-b431-2fcfe2e5944b] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_n08pybym
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '789'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "**Query**: What are the class names and their respective document counts in the DocLayNet dataset?\n\n**Answer**:
-            \ \nDocLayNet defines 11 distinct class labels for document layout analysis (see the source list). However, the
-            retrieved document does not provide the specific number of documents for each class. Thus, the class names are
-            known, but their document counts are not available from the retrieved content.  \n\n**Cited chunks**:  \n- b40c5a39-4b39-4da9-b431-2fcfe2e5944b
-            \ \n\n**Confidence**: 0.4"
-          role: assistant
-      created: 1770037796
-      id: chatcmpl-453
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 129
-        prompt_tokens: 823
-        total_tokens: 952
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4411'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class names and their respective document counts in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}'
-            name: search_and_answer
-          id: call_n08pybym
-          type: function
-      - content: |-
-          [b40c5a39-4b39-4da9-b431-2fcfe2e5944b] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_n08pybym
-      - content: "**Query**: What are the class names and their respective document counts in the DocLayNet dataset?\n\n**Answer**:
-          \ \nDocLayNet defines 11 distinct class labels for document layout analysis (see the source list). However, the
-          retrieved document does not provide the specific number of documents for each class. Thus, the class names are known,
-          but their document counts are not available from the retrieved content.  \n\n**Cited chunks**:  \n- b40c5a39-4b39-4da9-b431-2fcfe2e5944b
-          \ \n\n**Confidence**: 0.4"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '979'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to return in function call final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines the following 11 class labels for document layout analysis:\n- Caption\n-
-                Footnote\n- Formula\n- List‑item\n- Page‑footer\n- Page‑header\n- Picture\n- Section‑header\n- Table\n(The
-                retrieved source lists these labels, but does not provide the document count for each class.)","cited_chunks":["b40c5a39-4b39-4da9-b431-2fcfe2e5944b"],"confidence":0.4,"query":"What
-                are the class names and their respective document counts in the DocLayNet dataset?"}'
-              name: final_result
-            id: call_njvex9t3
-            index: 0
-            type: function
-      created: 1770037800
-      id: chatcmpl-580
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 160
-        prompt_tokens: 977
-        total_tokens: 1137
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3072'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is the highest count class in the DocLayNet dataset?</question>
-            <prior_answers>
-              <item>
-                <question>What are the class names and their respective document counts in the DocLayNet dataset?</question>
-                <answer>DocLayNet defines the following 11 class labels for document layout analysis:
-          - Caption
-          - Footnote
-          - Formula
-          - List‑item
-          - Page‑footer
-          - Page‑header
-          - Picture
-          - Section‑header
-          - Table
-          (The retrieved source lists these labels, but does not provide the document count for each class.)</answer>
-                <confidence>0.4</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '753'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need answer with citations but we have no evidence about counts. So cannot determine. Should say cannot
-            find.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I couldn''t locate any reliable source that lists the document counts for each class
-                in the DocLayNet dataset, so I’m unable to say which class has the highest count.","citations":[],"confidence":0.4}'
-              name: final_result
-            id: call_nfwmamn7
-            index: 0
-            type: function
-      created: 1770037803
-      id: chatcmpl-137
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 88
-        prompt_tokens: 509
-        total_tokens: 597
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5840'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What is the highest count class in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need ask.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}'
-            name: ask
-          id: call_k66a34yj
-          type: function
-      - content: |-
-          I couldn't locate any reliable source that lists the document counts for each class in the DocLayNet dataset, so I’m unable to say which class has the highest count.
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_k66a34yj
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '587'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I’m sorry, but I couldn’t find a reliable source that lists the class frequencies for the DocLayNet dataset,
-            so I don’t have the information on which class has the highest count. If you come across a specific document or
-            figure that shares those numbers, let me know and I can help interpret it!
-          role: assistant
-      created: 1770037806
-      id: chatcmpl-124
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 67
-        prompt_tokens: 1153
-        total_tokens: 1220
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_triggers_background_summarization.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_triggers_background_summarization.yaml
deleted file mode 100644
index a9ba7a0e..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_ask_triggers_background_summarization.yaml
+++ /dev/null
@@ -1,1668 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5237'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What is the highest count class in the DocLayNet dataset?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
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-                type: string
-            required:
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-            type: object
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-        type: function
-      - function:
-          description: |-
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-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
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-                description: The document title or URI to summarize
-                type: string
-            required:
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-            type: object
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-        index: 0
-        message:
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-          role: assistant
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-              name: ask
-            id: call_784vb9ov
-            index: 0
-            type: function
-      created: 1769804682
-      id: chatcmpl-17
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 45
-        prompt_tokens: 1033
-        total_tokens: 1078
-    status:
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-      message: OK
-- request:
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-      - content: |-
-          You are the research orchestrator planning the investigation.
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-          If a <background> section is provided, use it to understand the conversation context.
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-
-          For simple questions, investigate them directly. For composite or complex questions,
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-
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-          - Include concrete entities, scope, and any qualifiers
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-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
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-          description: Output from iterative planning step.
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-            First investigate the dataset classes distribution. So question: "What are the class labels and their sample counts
-            in the DocLayNet dataset?" Provide reasoning.'
-          role: assistant
-          tool_calls:
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-                dataset?","reasoning":"Starting with an overview of the dataset’s class taxonomy and frequency distribution
-                is necessary to determine which class has the highest count."}'
-              name: final_result
-            id: call_jwthcz93
-            index: 0
-            type: function
-      created: 1769804687
-      id: chatcmpl-176
-      model: gpt-oss
-      object: chat.completion
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-        prompt_tokens: 374
-        total_tokens: 496
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
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-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
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-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
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-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class labels and their sample counts in the DocLayNet dataset?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
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-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
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-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
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-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
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-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need query: "DocLayNet class labels sample counts".'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet class labels sample counts"}'
-              name: search_and_answer
-            id: call_l0sn6eh0
-            index: 0
-            type: function
-      created: 1769804691
-      id: chatcmpl-351
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 47
-        prompt_tokens: 631
-        total_tokens: 678
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
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-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet class labels sample counts
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 9
-        total_tokens: 9
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3764'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class labels and their sample counts in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need query: "DocLayNet class labels sample counts".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet class labels sample counts"}'
-            name: search_and_answer
-          id: call_l0sn6eh0
-          type: function
-      - content: |-
-          [a25c9330-02e8-460c-bb79-6f9ac60076b7] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_l0sn6eh0
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '527'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need sample counts. Need another search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet sample counts per class"}'
-              name: search_and_answer
-            id: call_r91mdtdu
-            index: 0
-            type: function
-      created: 1769804694
-      id: chatcmpl-366
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 43
-        prompt_tokens: 829
-        total_tokens: 872
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '103'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet sample counts per class
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 9
-        total_tokens: 9
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4647'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class labels and their sample counts in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need query: "DocLayNet class labels sample counts".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet class labels sample counts"}'
-            name: search_and_answer
-          id: call_l0sn6eh0
-          type: function
-      - content: |-
-          [a25c9330-02e8-460c-bb79-6f9ac60076b7] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_l0sn6eh0
-      - content: |-
-          <think>
-          Need sample counts. Need another search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet sample counts per class"}'
-            name: search_and_answer
-          id: call_r91mdtdu
-          type: function
-      - content: |-
-          [a25c9330-02e8-460c-bb79-6f9ac60076b7] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_r91mdtdu
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '459'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I’m sorry, but I couldn’t find the requested sample‑count information for each class in the DocLayNet dataset.
-          reasoning: No counts. Likely not in KB. Can't answer.
-          role: assistant
-      created: 1769804696
-      id: chatcmpl-114
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 47
-        prompt_tokens: 1023
-        total_tokens: 1070
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4990'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the class labels and their sample counts in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need query: "DocLayNet class labels sample counts".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet class labels sample counts"}'
-            name: search_and_answer
-          id: call_l0sn6eh0
-          type: function
-      - content: |-
-          [a25c9330-02e8-460c-bb79-6f9ac60076b7] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_l0sn6eh0
-      - content: |-
-          <think>
-          Need sample counts. Need another search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet sample counts per class"}'
-            name: search_and_answer
-          id: call_r91mdtdu
-          type: function
-      - content: |-
-          [a25c9330-02e8-460c-bb79-6f9ac60076b7] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_r91mdtdu
-      - content: |-
-          <think>
-          No counts. Likely not in KB. Can't answer.
-          </think>
-
-          I’m sorry, but I couldn’t find the requested sample‑count information for each class in the DocLayNet dataset.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '716'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: I must output using final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I’m sorry, but I couldn’t find the requested sample‑count information for each class
-                in the DocLayNet dataset.","cited_chunks":[],"confidence":0,"query":"What are the class labels and their sample
-                counts in the DocLayNet dataset?"}'
-              name: final_result
-            id: call_n0223bk9
-            index: 0
-            type: function
-      created: 1769804700
-      id: chatcmpl-270
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 84
-        prompt_tokens: 1095
-        total_tokens: 1179
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2858'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is the highest count class in the DocLayNet dataset?</question>
-            <prior_answers>
-              <item>
-                <question>What are the class labels and their sample counts in the DocLayNet dataset?</question>
-                <answer>I’m sorry, but I couldn’t find the requested sample‑count information for each class in the DocLayNet dataset.</answer>
-                <confidence>0.0</confidence>
-                <source>null</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1027'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need to answer: highest count class in DocLayNet dataset. We need evidence. Likely class with most
-            samples. According to datasets, LayoutXNet etc. I recall DocLayNet has 6 categories: title, heading, paragraph,
-            list, figure, table. Highest is paragraph or title? Quick memory: Titles fewer. Paragraph maybe most. Let''s answer
-            paragraph. We''ll note citation? but none. We''ll answer.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"The class with the most instances in the DocLayNet dataset is **Paragraph**. It appears
-                in the largest number of annotated segments compared to the other layout classes.","citations":[],"confidence":0.7}'
-              name: final_result
-            id: call_d41bj6u4
-            index: 0
-            type: function
-      created: 1769804706
-      id: chatcmpl-54
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 151
-        prompt_tokens: 457
-        total_tokens: 608
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1680'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What is the highest count class in the DocLayNet dataset?
-          **Answer** (confidence: 70%):
-          The class with the most instances in the DocLayNet dataset is **Paragraph**. It appears in the largest number of annotated segments compared to the other layout classes.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '728'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            ## Summary
-
-            - **Key Facts Established**
-              - In the **DocLayNet** dataset, the layout class that appears most frequently is **Paragraph**.
-
-            - **Documents Referenced**
-              - None cited directly; claim based on internal dataset statistics.
-
-            - **Current Focus**
-              - The user is investigating class distribution within the DocLayNet dataset, specifically identifying the highest count class.
-          reasoning: Need to summarize. No prior context.
-          role: assistant
-      created: 1769804710
-      id: chatcmpl-228
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 96
-        prompt_tokens: 382
-        total_tokens: 478
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5733'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What is the highest count class in the DocLayNet dataset?
-        role: user
-      - content: |-
-          <think>
-          Need ask.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"What is the highest count class in the DocLayNet dataset?"}'
-            name: ask
-          id: call_784vb9ov
-          type: function
-      - content: The class with the most instances in the DocLayNet dataset is **Paragraph**. It appears in the largest number
-          of annotated segments compared to the other layout classes.
-        role: tool
-        tool_call_id: call_784vb9ov
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '361'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: The class with the highest count in the DocLayNet dataset is **Paragraph**.
-          role: assistant
-      created: 1769804713
-      id: chatcmpl-332
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 21
-        prompt_tokens: 1127
-        total_tokens: 1148
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_prior_answer_retrieval.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_prior_answer_retrieval.yaml
deleted file mode 100644
index 4f6a93c4..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_prior_answer_retrieval.yaml
+++ /dev/null
@@ -1,2945 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5219'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What are the class labels in DocLayNet?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
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-        type: function
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-              name: ask
-            id: call_05x5qgod
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-            type: function
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-        prompt_tokens: 1030
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-    status:
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-- request:
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-          You are the research orchestrator planning the investigation.
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-
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-            That''s the question. Our first investigative question should be to clarify dataset and label set. So ask: "What
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-            index: 0
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-      created: 1769804740
-      id: chatcmpl-396
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-      message: OK
-- request:
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-          3. If needed, perform follow-up searches with different keywords (max 3 total).
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-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
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-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
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-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the specific class labels defined in the DocLayNet dataset for document layout elements?
-        role: user
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-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
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-                - type: 'null'
-                default: null
-              query:
-                type: string
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-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
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-                  type: string
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-                default: 1.0
-                description: Confidence score for this answer (0-1)
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-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
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-            id: call_zm3su6ln
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-    status:
-      code: 200
-      message: OK
-- request:
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-      content-type:
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-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
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-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 11
-        total_tokens: 11
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3764'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the specific class labels defined in the DocLayNet dataset for document layout elements?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet dataset class labels document layout elements"}'
-            name: search_and_answer
-          id: call_zm3su6ln
-          type: function
-      - content: |-
-          [1837e787-843a-4d5c-a855-974c3c7d99d1] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_zm3su6ln
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '752'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- **query**: What are the specific class labels defined in the DocLayNet dataset for document layout elements?
-            \ \n- **answer**: DocLayNet defines **11 distinct class labels** for document layout analysis:  \n\n  1. Caption
-            \ \n  2. Footnote  \n  3. Formula  \n  4. List‑item  \n  5. Page‑footer  \n  6. Page‑header  \n  7. Picture  \n
-            \ 8. Section‑header  \n  9. Table  \n\n- **cited_chunks**: [\"1837e787-843a-4d5c-a855-974c3c7d99d1\"]  \n- **confidence**:
-            0.95"
-          role: assistant
-      created: 1769804756
-      id: chatcmpl-85
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 155
-        prompt_tokens: 825
-        total_tokens: 980
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4394'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the specific class labels defined in the DocLayNet dataset for document layout elements?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet dataset class labels document layout elements"}'
-            name: search_and_answer
-          id: call_zm3su6ln
-          type: function
-      - content: |-
-          [1837e787-843a-4d5c-a855-974c3c7d99d1] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_zm3su6ln
-      - content: "- **query**: What are the specific class labels defined in the DocLayNet dataset for document layout elements?
-          \ \n- **answer**: DocLayNet defines **11 distinct class labels** for document layout analysis:  \n\n  1. Caption
-          \ \n  2. Footnote  \n  3. Formula  \n  4. List‑item  \n  5. Page‑footer  \n  6. Page‑header  \n  7. Picture  \n
-          \ 8. Section‑header  \n  9. Table  \n\n- **cited_chunks**: [\"1837e787-843a-4d5c-a855-974c3c7d99d1\"]  \n- **confidence**:
-          0.95"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '847'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to return via final_result tool.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote,
-                Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.","cited_chunks":["1837e787-843a-4d5c-a855-974c3c7d99d1"],"confidence":0.95,"query":"What
-                are the specific class labels defined in the DocLayNet dataset for document layout elements?"}'
-              name: final_result
-            id: call_8usp77i6
-            index: 0
-            type: function
-      created: 1769804761
-      id: chatcmpl-375
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 128
-        prompt_tokens: 1005
-        total_tokens: 1133
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2942'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What are the class labels in DocLayNet?</question>
-            <prior_answers>
-              <item>
-                <question>What are the specific class labels defined in the DocLayNet dataset for document layout elements?</question>
-                <answer>DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.</answer>
-                <confidence>0.95</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '784'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to answer with citation possibly. Prior answer gives 9 labels but question asks what are class labels
-            in DocLayNet. Provide list.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote,
-                Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.","citations":[],"confidence":1}'
-              name: final_result
-            id: call_mqo3aqcz
-            index: 0
-            type: function
-      created: 1769804765
-      id: chatcmpl-141
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 102
-        prompt_tokens: 478
-        total_tokens: 580
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1708'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What are the class labels in DocLayNet?
-          **Answer** (confidence: 100%):
-          DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.
-          **Sources:** DocLayNet Class Labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1046'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            # Current Session Summary
-
-            ## Key Facts Established
-            - **DocLayNet Class Labels**: The dataset defines 11 distinct class labels for document layout analysis:
-              - Caption
-              - Footnote
-              - Formula
-              - List‑item
-              - Page‑footer
-              - Page‑header
-              - Picture
-              - Section‑header
-              - Table
-
-            ## Documents Referenced
-            - **DocLayNet Class Labels** – Source listing the 11 labels; likely provides detailed descriptions and example annotations for each class.
-
-            ## Current Focus
-            - The user is inquiring about the specific class labels used in the DocLayNet dataset, presumably to understand the dataset’s annotation schema or to prepare for tasks involving document layout classification.
-          reasoning: Need summary with key facts established etc.
-          role: assistant
-      created: 1769804772
-      id: chatcmpl-130
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 168
-        prompt_tokens: 396
-        total_tokens: 564
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What are the class labels in DocLayNet?
-        role: user
-      - content: |-
-          <think>
-          We need ask.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"What are the class labels in DocLayNet?"}'
-            name: ask
-          id: call_05x5qgod
-          type: function
-      - content: |-
-          DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_05x5qgod
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1036'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            DocLayNet defines **9** distinct class labels for document layout analysis:
-
-            | Class | Description |
-            |-------|-------------|
-            | **Caption** | Text associated with an image or figure. |
-            | **Footnote** | Notes or references at the bottom of a page. |
-            | **Formula** | Mathematical expressions or equations. |
-            | **List‑item** | Items within a numbered or bulleted list. |
-            | **Page‑footer** | Content that appears in the footer of a page. |
-            | **Page‑header** | Content that appears in the header of a page. |
-            | **Picture** | Images or illustrations. |
-            | **Section‑header** | Title or heading of a section. |
-            | **Table** | Tabular data. |
-
-            These categories are used to label regions in scanned documents for layout‑aware processing.
-          role: assistant
-      created: 1769804780
-      id: chatcmpl-197
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 175
-        prompt_tokens: 1135
-        total_tokens: 1310
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5216'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Tell me about DocLayNet class labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '487'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need ask.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"document_name":null,"question":"DocLayNet class labels"}'
-              name: ask
-            id: call_1h78avcz
-            index: 0
-            type: function
-      created: 1769804781
-      id: chatcmpl-764
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 36
-        prompt_tokens: 1028
-        total_tokens: 1064
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '92'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet class labels
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: xrJAuccNEDwf3106c2G2PAU0Iroq8JE9n0RWPaYfXjwgNq48GCcdO+dCJjym5PM8TXsDO2qOLjzfBfm81/JivYnrCz38n4C8DMDlO81mtLu49oC8fl48us8GszwLpgI9mo5HvHEbqrwspri8pyS2vCjAFTz8wTw9dwGTPBfk+Ly12Tw9ngtfuwpCgztk1ly8qJXbu9vyvbtQ4PI7naZSvS9Grjz/q4C7DCxJPBlm/TsiIN07woR7vEKcCzxHwv27SEgzvUKr4rtDlfQ7JahLPHH5vrz0Ubm8gJiFPQCXMzvD5b08Wvrou3AGFbyMsfg8dzuwu31UCjvOadW8mWTuvB3uNrz+UJ68tcQ/PLp/oLx/KYg8TlV0vCT8Or2S1pE8zEygvLbGKTzvhEM8QSMBvcr5OLwOlqc8HQ3Hug1L8zzIPvg7XzFYvGEwC7qZnys9oOpou9oolTt7ogo9KXULPGMqFr2S9ws9pB3cOuurtzXLLIS8cnPeOlI8BbvRvYs8Yq6fvD+3hrznAR68Rkc0u6p2Q7zE65q8takFPfoOWLuAr9k7sW/CvJ9niLw+Vdq7xYa5uhGpPDzMq9A5swEuvNbKJrywPeg7dpiIO/4fZ7w0lyy8H2UkPftDgjw/edA8jYMOvKIX+TvyzoC8S43xOlJdKjxYVym9UXmztyypoLxuMrU8NdMEuEXBPTyhoai8FS71PL4Js7wKWoU7DjQjPP1dGrwc2uI733/gOVF24zwli5S8E/9EO8e7LLupsZY8E6ODvGwOZb3WfIS8BkL+vKXigTwem827xDgVPIRcl7wYpqQ8+q5XuwAHzjtIjBA9vnyZvFKaKzitO/U7ZRoTO0wtQLrSfdQ8wY3RvD/Q2Tz1OkE8TR9YPEi6Gjy7NQO8km2Kuy8K2rz0QeQ7nqGIvGo3DryQeFw4D5aYvFktibzLnuC7onAGvBDDlry9ptk8LDw3PBJVCz2hW9E7HmwGO0umDDxCSKy7g4YaPPq3SLy1wJI8n9n/O1WBgjqHbgM74wy9vGJkDzx2Mg+8k36MulOcOrwRR++7+oqJvCr/eTwTEuk7h+AjPCaxeDzw9TC8YHwZvG4FTryrs5g79K+8vLuSGzzfXH+8kSN1u1GSm7z2/JK8wkebvKjcYzznpok8TfjIvJdESLwyL/k8bGRnO4UWgbvDhCO891MbvFfoAjyDeZC83OejO5+LXjvok4a8Wud/PFuHkrxQHEs88YuaPJ8DMbw9Qb67ztIIPFN7kTwPAqG8yMR+uyDRajzZlD6845/iPAVmhbzLPUa8DpQJPE86e7y0dgq84g/iup7I77xKVq+8Gj+gvPMrG7uli4I7wKMYPfvdy7xhW7C8+CIgvFm/zbzNahq95q02vIlBNLzaJoa7BQobvCz5YLwEJzq83zeFu3iyCTyAqdw7x6mcvGhA6rsHjIo7QkEQPSyf/7n1OZ075vXVO658qDy8ara8o3FsPGU/VDxQp606cZ3UPKyxGrxrRW67L93vvKLB8rqpXQa79tQ+vPJK1jzR2JK7lt+JO0EhXjxM2c48Kk2EvDw7tjwP1J28RY61vIslVTxbH0I882kEvJDHsDtTY1y81/1kvGsEuDtRmNO669KjPHmvNryt07w8LD5lu2GaXryo6ag8+nFwPHGSRDyEUEi86f/oumvxADxIlqM84azmvJv8OLuz0G46TMPsvOAawrzniAS8bToyveqy5bxAhC27Y0Xauz6k+TsLv908mgnBPAHcCj1ZAJq8YNpsO7vQED0uwjS9WWI9vGm+rbo2+8o7PATUuyx4/jyJNnU7Uu0WPDRyurz4PBg8XrWiPLCEv7wWOfK8bdkTu+K2TLzrWgC8i/t9vDZKCrnAgV27QfTWvPwqdbxZi9C6ppcuuoFBzDtaKHa8fsexOwnpCTzGNLu8eAbdvAQThbzN+TK7eZecPMaTAL3Qf9+7X73kvOnijTwctBM9E7chvBR2eDu6q886pp0iParqcTy7+F87YYy3vGdbxbusFnO8Qcziu+oUBbxsrOw7EP9WPDKlvzpQTbg8OYcZvbRh6juOorC7WTSYvJYJ3LuxkzK8vbtVO0RMTT3Xk+Q7UwspvH/wbb3hHxM9PB+GOiMq1DzNKwk9X5dOvZ1NjbyxpwC8wxgAvapvzrwr0Iw8Ecc8vdug17zK1MY8Qqb3OlD7wzs/X+K81Ky9uzhwTDxOFWu8XLsqvf6r8TvUWSA8q5bSvIjygDu0GnI6X1EMvJm9Gr1ObCc80ZrzOoEWJDvVMY08WMU3PCaT3jyorZe890AQvclXsrsQTPo8bLINPHM6YD145Y287y0DvPnlP7zayQu8I+n8ORbF1byWuZc843cROruw+7r00Ww8HPI0vQOshLySBuc7Ib7YPNe27TwwlaW8p2uOvLXFLToef1275ZvVu27I57s27xs6Ut6rus3yk7pFkki9VweSPKcUrb3KQr08wboqOzzOZbxbLk08o5O2vGu0hbxsZHi8IScTvSGa+jy/leq7uWSuvJZZibwiHYk6Kt+pPHaE7TwBSxM8tgVdPN9+Ybw1m1s8DVaBPO1DsDx5dso8y/8WPOI4rjzvt9085/EFPRV0ODysp7S8u7IxvIK5Az10QaW8LWDdvJusMjxZaIs81SWoPFEGlDxxLcy8VfmnvP42IzrQ5Lm8VWpqvFANoDzzRE28bHWFvBCMzjyni807KzRNO6avVrzGtIs8JNZZPD0ioTy4a4C8Ci4SvZeusDxkkrm80DAquyEaCbtRQZk7DAi7PLJvdrys1gW8KAcMvOZmGLyuUG+7QyRPPHP/4zw0SBW88Y/9Oh1SNzz5zXq7xXZaPJdELTxRzVM7Hi3nvG3c6DsWwSo8bLjyO8jyaDzrPTY863lHO6AjUjwI8R88I3NDPNuUqbmIFAG9y1VAPDDldTtex/G80UBIvIXB4DumlQC99xQJPE+AZrxGf2W7PcdoPGSxprx2viM9B67QO40dITx/IT29R1w/PO3+tTzo2fE7RN7XO3giDbwiNxu7wgocvJ3f6rx3kJK7ZcScvPaYL7qWpg48p2gqvDghZTznVoI8OXAzu62/uLoVABC9NexLPFEQVTyPbaE77A20vH2Gz7t9mD07iYrYuSwPcDwnm308M8y2PJQqFrwVEAi5+1xpPJPOrTkj2vO7KF6AvFs89bu/SaO8GBQJvcKbZLnhIhG8DFCwPO0g5Do9mF+8brzOPPXW27tsYJc7NaVlPJwaETtwR7e82XuTuxZViTyXmy25qrKUPHzzkzzdjOc7rvrCvB78k7uV31i8596Qu2/84LvUNAu9gk8LPBhrVbtbf508gVnhullaB73eAri8b14AvRhxHLtDYbe7B3uDvAPvV7yQZEM9vNR7Olx11bzQmKK72sRUvT412jye+IG63b02vIVFNj3HgK48IRFiPKGWTrvqrjk92kffvMJ8Tr2NcBW93n8SOrFYAbtF/ZM7vka4PLRJSbx2LIQ8OxTqu70SzrsXSuM88Oq/uy7fBjy/niU8AJFNvGLQe7xSyvO8ctMoPYkVhLyiyY08nCcDveO/8bv1fTE7TPucvHqBV7wxprw8r2nAPJXYgbwXRYE8dCTWvIZ4OTsIsd47LPWBPE+oCLxhBE08texpPJ+Q8jv3ZB643goXvMElK7xwisw72QEZvM+qpDvpdHE802sYvAhB8Ly/U3O8UocVPd9WCLu8qgk9dWvXPPwlPTxTX0K8mMeqvCHwizmF0CC63k67O2UH6bzunDe8sp5tPG6SBDzbqIW6DbEAPZJNszzXZJg81uySu5WiHr0c1SQ7VIOAvMSqVrzH4N+7yV11PGZ597sMvKS8tmuzOzbwgTw/MOa85yqHOo+J6rx1Z9o7C6jEO3eYrjzMgIO88AOMPdeAHLwC8R28hFwAvHPJsLsGege9Qs7iOxqW0jxF2TA8GRIDvfqFpTtlRwQ9TLyFO2yfFTwyV6A8ycM/vB/LQTwuOx+9e2qIO1wbwTy9Jpg8BNwHO7HaXTqEr147M9R3vMXkzDt9D0E8HSNdPEOwu7k0PqQ8dpJ2PFHyXrtkZPO8Yw24u3eCHzugHpm8LdGTvLlmCT1WXP47rpaiutdhWzzZ2oS8XjSnO9PR6zvqBQS8/aBIPaXS9rvcrj27PaojvHjL0DypTqe7jdbFvDuo87z7db87sgkJvAIt5DreUE28Tc7Bu/HLnbzC+u+7T7cIvcury7vWokC8Bajeu1DRZjwMC4w7CByTvLNtzDyHq3G7/NIQvVLx3LvD57U8s9vbu28QhTzI3L88wcwAvXELEz2bprY85zCjO4ZZdLyv1OA8xmTLOiBZzbySuaU6Gu27PEXyULwoi4m8DFtHPRBUN7wWBew6o244uXMWpjw5f1Y8gH6uOwvmGjxlAXw7odzbO1QGDj3p4jK7i0QFPeimqDvo4gi7wn29PMMgbbxZXnY8M42VPLXY7zrZx6s8h+oWvTVR37wNYYC8Q7CcuyklML10NAs9AGEQvNyzQDtrlXk7FKPJvOPB5jsVMXm6amkOvNtlrDyx/5I9j1sHPYLUrjvv1wO8fdJkujHeNj03KfU7vewjOz+tSrxHvIs8jUWkvBI1jbw17AO8HoTUu1xPiDnxo6g7SAwFvTvC4ru4AYW96XjjPLXRy7l8Rd87s+wdPJ2xZT1BpV68xCyvvNkspTs/Jzk7rpLou14jNzxZ7p68/k6mPC188TwIgZK8WHiAPDLk4rw8Onk7WyuiPGSTojv0V968Bq88PAeigzp4fp47YIF9u/nLxzzto0C8j8qqvCfV6jw3tJA6Zcrcu6hlCzws3xS8jSAIPare1DxaMtQ7GWqbvP6vrTuwbKM8h8fvvPw27LzU1ue89vRbPIUrBrtGLl+90n4hPFGg6DkzSsg7u2ggvXSOurt0pe07l9REvFzt2byn7NK4Xx8XPUSkdbzw7SS8o03YvCmA9by50967j8gFvRXohDwwE+W7OEnIPMkwrjwoOD28ynInPCzaAjycZYE8vLCSvBcpALwWvKA890YEPRmBYzzp5108cEAiO4Gm9DsyxDy8iOj/O/MjML1GlDA8Jh5HvDJ8ErwZyem7KaxAvEWKUrxJBJc76TvEu93zBDzL9Os5Vj2LOyUlgDyFHg+9hf1FPNWUVDwnYd07GLy7PEffjLxyPi68HoAHPAXy1buU9B278IcEvEhmKLu4glK7FJEUPIXncTslJts8+QX1O5K3Qry2c2q86ftmvE/8Xjzt8K47hjGjuosPK70OV587RGIuPPnFzjtZwHw7idJlvH1qcTux5yw8oddzu4widjxeS0M9DHAMPJfkgzxSFAw8tN11O5B5bL3JgOM8Pz/4Oq3cjrylp0i8wB/RvAFn/TwTWe26DrPEPB6cI70NKIS8JM0hvLPvu7kZoA661ODGvKdAsrx5Ttg7su7mPGNQcbwEAUA8utgMvN1u/joHYas705U0PDq5uDy8czO7nRLWPNkS7zwEBS680jcdPbgAL7p4lOg83KhovSzsiTwQynU8aKwvPMegSrw5y/m7NUBSu/ynvbxJt0s7VE8RPeYbiTygU7y81WmTvARmJzxIqme6b+QQvI+w1rsyFv66M0UePJXOUzw8lC67Hvz3OyZE6Lx+hLu89ZKOPCi9szuADoe8iUCjPIYnSDzSgAK8pyJgPEvtSb0F6rq7vPhmvWMfdbsniwk8CCCxu32RSTwqlsO8VskxPSaZgLz6Zd27+6beu+hrBL25XZu8kL48vSmCkrzb4g666GWLvBASDbtbBzC8qeRwvDXRBbtQPTw8cxjROw3hz7oXnAE6OSbOPHsVlTwYW3o8W/CpPHuhBLw7pQY9BDb2u9Ou5rwg9Oa7PvicvDNCj7tLg4m8w8kovEqbl7ziug+8ycXXvGbqg7xFPsE8xHbHO8OPqTs83r48WxE1vc5X/TwH4pO8Kv9tPGARurvTuxO8v9OOvObqEL0PL1G8N/0BvSXQPDx+UfI77eZevJZJAj2GVkE7VImXuxezxDzk6IK84QdVvABt3Twamia94DZKPHkOajz9Y4i8JhymPCs9TDxzVLi8KDHCO3lDxjz7vMe8KpSwvHDsmbwji788GeiwvDrrlTwyBKU8l5P+uyBQOz2DwBE8c1eFPNBA5rx4BUc7qoOZPBWi5Dyvx/y6gAdHPO1yIDx47JY8/mK+PFFJIzx7Ceu79awHPJ4GKLgPZki8eod3vMNXlTv4nby7uGP2u96AmztCuwY8o2CrungYzbx92z0817rkPCE5uLcSuqU7dDUDPNRKQ7xrJry77webvN4L37vKbLm7H9EtPfb/jLwgihK9958IPf4j3TpQ3tw87SBzu+CNKD3mSBA92Dr8OznxJDxQfQC9dLyHPIhakbxGaaa6XNpLuzMjmL10lca8yLNzOzicQr1+tcm7iUzMvIv4RrsDnYw7tMq6vCJ9RDvEpKW73HNhPXTYJDyTZr87Fj/GvGFIiTtbuC28T4xbPIu/Az08Yze8F888PA6jAr1s5Ia88a2zumMjtDvP9IK8FesQvV0cnbzVS088KK0jPc+zGD3K73M7MnvhPHvJAj1xBWE7/ne4O0laJb1ak0E8lf6OvCcwGry2+608NdTMug1fBjuzETU75Tz6PKnFjbzfQjM9Lq4AvM+ntzvPwKU8dYi5u18dP7x9wmm9x4ljPIUrA732HXK87KcXu59ywrumJuw73LbKu069l7yAAlQ9CxLPvJYcDzwqDsu8uIZbPH9e1zw+6eu6u+LdPJ5o/rsXb6+8CsNBPCYc1TxKlQC9UHbJPK/RmTwGq0e8HDDzvN0u3jvtYLC7WRd9vOJaQL0GaO08nLKKvMSBvTvJ6gg7X53Huz0LEjpWQQ69R0GMPGDp47vBcg+9lRiAPMtsUTtHu2q9VhuIvGGCJr2Z9nk7OH4NO2DPxbto6gQ8g2/buhtZFb3EPlI8SYksPFpBjzxXPvQ7WLLhvFVyxzxLIZo7GrCcu5QSArsRlx48B/8OvQJkvLsUvrS7mb4xPNJyFb0Aycq8VYbvvJBHxbw6OEC80YUqvP3Idzw/ba04kUskvC+o/zsipC27q2QGOzuhGDwfb2i8f9ygumF5Xrq3v3s8JS7UOy23FD0Hy3W8uMA8Ow4/1Dzp6Tg75UQxPVFAN7yjMUi9T6fVvCvh7ryuPCs8YqUTu7CTKTs/8229qiEIPMyHz7wNJxy9wUB8OYIm7zvrG+w6vVqOPGCVvTyQObU8znVXPO8yJDxX2Uy7t0QjPEkECrv0gkC8gYblPAawp7uUHQA8zb+Pu0k/2DyQQYo8NinUvNjVSzzEUak8AftPO2bqprzyeZS8xR2LvAV2qjw7SS686CL/PM0LCL32SJy81UWeO5GNFzsmOdc8dkAWuzf8vjyWiig98yBKvTmOjDyQIJK7C8JMPL+bv7vRVse7cBbnvK5YGz3HLFs8mhsqvUgBDz280Bg8a1zzu1CUBDsSZ7O4psiuu6z/L70KoMw8YkxmuzfmH7zL5MM8swvLPGFw97xjMqu8Jz2/vOE1OD1kiBO9Ih+WvMQBkzt/o9W8qPHSPPRWhLzWSzu8WwP8uyAzyTyOHSy9TFwAvShrb7xqSJE72UNOvC4qdjwuxXu7Q2rBOwbrQLuynIO8J1pBvAKmmLyjf1u8wHCAO+qEbjxBHNQ6K11UPKs8yDxloqM7KF4vvKCwhryWqac8TTA4vClhHrzZYgq7PJb8OXsFlLq64so8H7aDPORwhbyIlPo7NhghPR/gjzwyQMW7dBHPPAalz7zvzAW9nldMPBxedbyzZMS58gwyPNxiubzI5a+6u6E1PVX0cbztqru8Hp7QvHNyfbz5aU28OvnhvCxeyjxAIEU8swOiuxu+vrw2JbU8HpZ0PFavvrwWXg07hyC2vOAQnjxy65u7RVb4PK8Awruaxra8n90PPRfka7q3EQE8WkjsPLr5GjxXDfS7hruqO44wjDyAM9C8u9ioPObyhryJxpC8YvvtvHV3XrpmHzK9igSFPKy8Z7zK4Vu8d2ivu2fwmrziG2a8G+xcOzObXzv5k0E8aQSOPCEIZLx9KV68d8SNPNQP9jwyzMS8z0WavDhFdTvmV8U64nhKvNwn5rlpLqs7fKzjvA/W9juFjaC8aOcju1tGyjvzgjA9idgkO7XDXbx1tYA8PpbPPNDQgzuMcgA8zaODvD8tsbzD9Ge7foWbu6dqLTsO6jo9dfGzvFC3tTzCNYI8p9+UO/GXtzoxyUG77Pd9vB+ckDwx6Ic7mMLxO2aB4juAPBu8PNWcvKKcBD34mPc6uqY5vSp+Rjx/4uc8Cm0VOT5WmjzrLj88IEZBuofCiD07YTe9f3mTuXpJjbz01zE7P09IvEHT+TsIipm7xMSLPMXj1rzrv3E7nF0pPJH0Mrt0u9u6w990PB3fILz73wU8A/xFPNvIi7vtksA82eV+ugXcYLyo+hS8nsPAOxKKJjxE2+A8lwOtPFS1rLzLDTs7ylqfO5WZ8Lv0sk07F+2IPGTlXjwBvqK8/tQZvGvlZTwgSAk9V67XPMlVsrykw6m8fjHTu02ISz20pzW9TncDvGaRCjzwVRO9KcdBvBgZ2ruLx5o6mitZvJwX9jtrGP08XRY+vG6+k7zPKKU7SJ1zPHsXJj3zr6U8wvZGPQLyODx96ja8KTgmvGoldzxFxPk8WdFJu5sV/TtLsI081YkmPN7ONj1BwK68wkxhux5tCrzERma8UrsYOkVXtbz03Qg9k7UGPEsoC72Tjao8xRDEvEX50zyEuDi9cTGbvHnbI73Ar1I4Ak5HPGnAkjvPUuU7Qv6uPCcClzt7R/E8LdGuPHQTjTwD7aE8Y+8mvMXRoDzy5VG8XyeFPJI+hLr1Qr25qzniPMe3Ejy0HWC8y4+fPAal3TtI4QI851/lvCQq5ztSNvK7kZAQvc2cFz2n01g8FnO/PIiBkruCPwa9QAttuyD9ury1B/+7KGYIPPYkC731eF28CRDiPDWBWrzG19G8wyM7ux0llbzZP+K7Z2LdO4QtODxbKrc8Ff9yO+RHODz/vRE8380zPUhQpzxTAQ+9kPj6uqyYRLs3HGm8qmqsPE2vSjuVluK8lzkkOyKsTrue/gi8W7GzvJpUJbwbbmA7vAk8vJeYJTx8wbW8t0cGveRYgLt6jSI9DfQSvH9nyTt2k1g8Pc5mPFiG2DwK6xI8zs60vL8bCTv209U7AK1QPOKFLD2oPIY8+vbyuxuyKDzlQgs7ZEVavMcpkTzNtcE7IfTIPAbFS7w6PGy8Ck2ku78MErxIEvS8G7m3vCPd2bu700K7WjyUO06WQDz9z6m8VJvqO+xusjzvBQ89tR68O37ObbumY8A6qbooPHPw9Tu5iQy9cBq1O9JxbLkOxim7s7jSu0fDBTycDSO7vYOyvNnkvrwYBmy8wb8QvPLJj7wdzC+95mguPGCDGjkOtsy8CTNEvCzS4TwH/GW8hfP5PJ5hODyNB1i8x0oXPESLr7rsaaG8oE8IvZ8UR7zsa5G88/E9vdLaODzmW0Y8ju74O1elt7vlJda7IDtgu+1aZLw+ZWO9Jr9EPCLEUjyJzyC9F/SlvPLKtjzaGwO9OAUOPAaiEL2o2bE8TBfju1FLMLxz5x68wmGOvLyU1DzSaQs9h6ugOroU0Lzmqx48VvLUOnpQ1zw9SzI8w2JVO9tGyroKsD48luCCPDJEIb3IrDQ7H00KPAGNXDsCyc28JmWmvCZdRr04IY47hYDevF7lU7rrkZq8+o9tu1+WALxkBvi7t/SqvFOkyjpMHoq8QYEVvRdOwrvA8LO8ZQafPIYeGb1doRE9stAtvI7fSzws1q+8bxhUvB9/vDxyStU6r+gdPJa6s7uD/M47+VsLvcBcqDyti6E8RB7Cuo2EE7v5Dwu81s2WPJrWSjuPbw28aRmuPLizxruuFAG9Shs2vDBVDr2Jwwq8J3wnPHn4uTzETIO8mbF4PJrZv7sKSKO8M271vKWzm7zSBIY8wifsO+qiCr1+zlw85NcTPbD0lzwk8627v1zRPF8NUDtLDQE87vMYO6ToF7zERPk8l9XBPKz6bDyAD6U850MkPfPfIT2L0O28QbUyu1Lk5TvwRco8OtM8O7dXmbtiZW27gJldPN1d2DyYYmG8M8dXPMcNF7weXwO9vHU6vTLnYjzuVR+7+nSfPG3vqTySTpY7hu5MO3Lu8bvIGI48tbwAvUSkADyZkKe7ZbTTulpOuzztOTQ8L83BPMOiCzv4Exe9VjuHuyPxmTxJ5rM7xtZWPPuhFbyXu2m7mLX/u+yvFbxi2PA71MDTu083nztLL+m8R+S9ubPROzwyBUi7S/WCPO+eqLyzvAA9JUA8PC5qG7xnnIO8zaDfPO+YBDp+ZhY7FfEdPO3KpLxS4V28a8JQPOETozxWRU270L6VvM33O7zHM+O6NE8EPVsLMTtsuby7sPbZu6g4Er0GGeG7sGFUPLztUjy60Q67p7TpvClHbb2Q6vW7V1y1O620GT0+P8y8rY7RO+H1vLyoOye8cYGWu7SkJTvzMFi8xgf0PBybtbsh3BM8ooFXPMuUALwkHGK91kFMPC0XVbsH8l88mkrqO/2tNjxzTJm62qYQPcYE8bwwOey8aBw8PDZWLzz5rQY84rdRu9+m/7zHzfY5u/6HPGzJIz1ILaU8H5+xO7zXODy3phC8FhlwvDExKz3q9Zm764Zpu2cOF7ydD3a8RWMGOmMoHr3g//O7sFSVvFauML2PgHG7Y7DHu2hRHL2vk6C8JQeQO1h9mzwD2wS74NJlvJv7LTxmX2K8QYV+O1wg0zyw1Wo8pG/kvIhs/zt9YI87c+CcuxTjCDf5xsW8vM9BO5tRUb2sFeY8Rwv6PA7mDjwYYj29YN1jO+cm1jxeNvK8sCsePNQGET3SDo26obBCPAe03LsfGIu7tqOHO+S8rbtXzwW8TbpIO1AFe7xzK4U80cC7vFZ5PbyyQWO9iEi/vLxg+zzaCok69xoiPN+1qTmM7uu86WKyPMlh2bu4Cpy8Ewp9PK1KKb1mNGW7CnGTPCgn6DxrIx67GjN/vFQRHTxgy4680uWVO8HVbrvTF6w8KurQvKIIILxBYMy6b99qus9KwLtwELM7GWP3OXg7Rj1poyI8EMy0u3O3y7rSJjg8X3sBvD/NK72MR1u87cx0PHlEgDtdsoQ88QkfPdgBFD2e1SY7OgBLuz5UEbqqfS28aEoAvCg59rvmWwm8+/DFPMN73bxUMbY6+1W0PMgEVryw25c7vTHiuuAHr7wqn6a88rwlvNZS1LkSKkM7C+sxOxoWDTxlaYO89QrwO+fFO7u0eY08afpvPAEOk7wzaOI84fmqO2bIf7xi0TS8NErEO0bX5rylPea8aXZcvFGM17vsQ3I6jLAIvFbFbDobQbi8O2fbPFVLHT1+aYQ8cnBEPEh6XDyGw4i8W7uWPNKsZztP8ki8qhNDPKM1zjr+U8A7hY0CPdSelzpx/6m7aQ1TPPMnhrwsJly8djjWvKMjl7x63SQ9mxiavGcYTb0WQn88y3DtPMOb1Dv2kDc8I/rTvFERSzwaMNu7W+YCveegVL3WB9O75q+Xu4vqETu7uqm8q9CNO3UHH7zvpC888eu6vF5PWzw/KRe72d7SObFZYjzNXwG7XYIHut5MDb2o5NA8JXu+vMfjsLzzceG5VxsdO3b8wzyTTiU9NWBCPBMJCLwcXAY9TRIZvfA8Tzx8N2y71w9TPL6pR71BaWC83HevvMNYLL2Fpbg8l2ORvF9uOzxocSi8xEMBvFr9Wj393oy8eDztPMHitzyIFPS8ayaIPJpizrwKjJg8iWBTvPEfJT2koDY8pesgvEi6qbuaoVS7e5ImPTw67DrEPTE7FvSvvD1ctzyDCZU8Io5bPHpXmDy+ByA8rVwbO0ALlTu9LaY72l0xO3JW1buEcAA9MtyWuyIhAL3Yaqk7ZI4mvHsMRzwQPay88CuiupdsR73fZqg79/ZzvHOUx7w2zK28Lz6dPFRW8LtlZDo8yASXvBkJ+7tW9sI6Lo8yPOI8qzyTjKk7uZkBvT1RpjzdFwK7LQ69OgR2oTymbIi8TiAzOzQZJD3TUYi84IyHu5PmUD0e9No6G7d0O7y767sBuge8fkw5u04dwLw5CBO8OXPzPDXTH7zuO+y8D44yPJLCijt7XO48b40SPZsgVrzvM5c8XzMIvGiQv7vMqCq91N4Qve7uvLyW1+M7M/6xPK+HMLwREU09L/S9O5GJQTyirSI89ze3vN46Dj3I6PG89neAuwXsmDwoyVU74KB4u+OIRLytPxC9w9OTPOlqyDzV0Ky81aaQOyA8Q7wlqAk8RSp4PPW41juolH27jx8ovVBj2LyrPwq8qc2YPBUA0jzwLCs7ehaWPMUIIrtMtME8NdVOvB/GprsCFNK8e1u3vKSMT7zkJb48CxOlvE5EXrkNiSi95ScQvP01YbwoDNw6GzQJPL1nczz053o8a2R8vOcsET10QmK8wY5Ou+VYbDyabPC6DleGOn7NErzwEqI70WbuvCtFGLzluiK9PgPyuuKPD7zlXZU8aZqUPDylkLs9xJK8KV0CvEg+ATxMFqu8wy+uPEZ68LvPica8fJkwPMffEzxpeH+8RgCXPIVliLyys9657aR6PBtXULuVOFo7/2mePBI2rDxx8/u8GoquPO3ZvLsz3qo87uftPDR1JLyTviQ9oBSxPJLLUjxiTvA80q5OPHw0Czwgewm8m4bTugwZLj3ggqG8gvMPOx0DB7sYLe48VVlsOwO7i7zaP3Q8zlkoPUQ7izwhBv08QoWNuln7i7u3B2+73xPuPJi49Du5Iyc8mig6PO40ZLx8e1g7zcWKO3c9ZTwFSYQ82X6lvIa8Tzx0K6i8oBisvKlOkrt2QcO8nTvru7lRYTtIQ1k8jcXCvIN8gDxS/Se7/WU3vB5xrbt1mRQ9iEbRu4GuvbzEEi+8gWnWu9ZzK7ym3Ki8/lgTu6T92byD+w09PK8mvZU3cTy2r9q8tuP5O9HLgbw7yCs8SGclPY2p5zuX8hU7BE88u+YayDuTCqm7w7Truq0Xlby1lXM8PHoIvY3l1ziiP8K7vjQ6PULZTbrmthW8bDijO3yXY7tZsZU8ZJi/vMGj5jwjaZe7snq5PPFFP7q3iw49oLwsPPQQe7vk3oU8F/Z+O2Ve1Lp1Mqc7ryXGu3b3druuoas8e7xMvb74CboUotC8L2jQuyBtuTszFAS99CsjvZYV2zxgjnw8pUGpPP8zuzt5ilS8iGIFueLdq7zCxqa8QFJqvETlJjx7Vhm9FlF3PAOl+rw25TA9ROjTunlFqjmNxGo8e4NrvPD3FbwUJ447hbQZPYQ35LydJJE8nWK7u0uAijuVEQm8bC/5u2txvTjtjQy9s+rDPHTFsjxdJH68vQjhPAich7qh26Y56NrlvHLUhzy+3qk7Ot6TvOqui7wJjTm8Zz3DO/WGGrxGLV67zk8buikLGjwup6c88z46u5hKWjxc3AA8O2jDOyCxMTuarbA8a2uaO56Lg7zFrVO7G5AjvJIznrtYWaI8XR3FvJLRQ7y7bF+8uM+UvCG7Qjvnz2g7JIU1vPx/Ljui6UG8Sp2yu//rRTqYcYy8bJLYu0xiqDxd7kg84o2PvMJ1hzsyuyU8T9oUPBKw+bxsKZi8hxrlOw==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 7
-        total_tokens: 7
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '109'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - What are the class labels in DocLayNet?
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 12
-        total_tokens: 12
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2941'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator evaluating gathered evidence.
-
-          You have access to context that may include:
-          - <background>: Domain context for the conversation
-          - <prior_answers>: Previous Q&A pairs with confidence scores
-
-          Your task:
-          1. Review the provided evidence carefully
-          2. Assess whether it sufficiently answers the original question
-          3. Decide whether to continue research or synthesize
-
-          Decision criteria:
-          - Set is_complete=True if the evidence adequately answers the question
-          - Set is_complete=False with a next_question if important gaps remain
-
-          If not complete, propose exactly ONE high-value follow-up question in next_question:
-          - Focus on the most critical gap not covered by prior_answers
-          - The question must be standalone and self-contained
-          - Avoid repeating questions that have already been answered
-          - Include concrete entities, scope, and any qualifiers
-
-          Provide brief reasoning explaining your decision.
-        role: system
-      - content: |-
-          Review the gathered evidence and decide whether to continue or synthesize.
-
-          <context>
-            <background># Current Session Summary
-
-          ## Key Facts Established
-          - **DocLayNet Class Labels**: The dataset defines 11 distinct class labels for document layout analysis:
-            - Caption
-            - Footnote
-            - Formula
-            - List‑item
-            - Page‑footer
-            - Page‑header
-            - Picture
-            - Section‑header
-            - Table
-
-          ## Documents Referenced
-          - **DocLayNet Class Labels** – Source listing the 11 labels; likely provides detailed descriptions and example annotations for each class.
-
-          ## Current Focus
-          - The user is inquiring about the specific class labels used in the DocLayNet dataset, presumably to understand the dataset’s annotation schema or to prepare for tasks involving document layout classification.</background>
-            <question>DocLayNet class labels</question>
-            <prior_answers>
-              <item>
-                <question>What are the class labels in DocLayNet?</question>
-                <answer>DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.</answer>
-                <confidence>1.0</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '968'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We have evidence listing 11 labels but earlier list missing one? They listed 9, but earlier mention 11.
-            Need check. Prior answer lists 9 labels. Might missing some. Need verify. So continue? Provide follow-up to confirm
-            full list of 11 labels.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What are the complete 11 class labels defined in the DocLayNet
-                dataset, including any labels not mentioned in prior answers?","reasoning":"The prior answer lists only nine
-                labels; the background mentions eleven. The missing two labels need confirmation."}'
-              name: final_result
-            id: call_bdu5c278
-            index: 0
-            type: function
-      created: 1769804788
-      id: chatcmpl-193
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 134
-        prompt_tokens: 628
-        total_tokens: 762
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2914'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the complete 11 class labels defined in the DocLayNet dataset, including any labels not mentioned
-          in prior answers?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '494'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"DocLayNet 11 class labels","limit":5}'
-              name: search_and_answer
-            id: call_3808in7z
-            index: 0
-            type: function
-      created: 1769804790
-      id: chatcmpl-740
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 39
-        prompt_tokens: 640
-        total_tokens: 679
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '95'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet 11 class labels
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 10
-        total_tokens: 10
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3764'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the complete 11 class labels defined in the DocLayNet dataset, including any labels not mentioned
-          in prior answers?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet 11 class labels","limit":5}'
-            name: search_and_answer
-          id: call_3808in7z
-          type: function
-      - content: |-
-          [1837e787-843a-4d5c-a855-974c3c7d99d1] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_3808in7z
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1136'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "**query:** What are the complete 11 class labels defined in the DocLayNet dataset, including any labels
-            not mentioned in prior answers?\n\n**answer:** DocLayNet defines 11 distinct class labels for document layout
-            analysis:\n\n1. Caption – Text describing figures or tables  \n2. Footnote – Notes at the bottom of pages  \n3.
-            Formula – Mathematical expressions  \n4. List‑item – Items in bulleted or numbered lists  \n5. Page‑footer – Footer
-            content on pages  \n6. Page‑header – Header content on pages  \n7. Picture – Images and diagrams  \n8. Section‑header
-            – Headings for document sections  \n9. Table – Tabular data  \n\nThe remaining two labels are omitted in the retrieved
-            snippet; they are not provided in the current source.  \n\n**cited_chunks:** [\"1837e787-843a-4d5c-a855-974c3c7d99d1\"]
-            \ \n\n**confidence:** 0.82"
-          role: assistant
-      created: 1769804798
-      id: chatcmpl-336
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 202
-        prompt_tokens: 831
-        total_tokens: 1033
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4776'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the complete 11 class labels defined in the DocLayNet dataset, including any labels not mentioned
-          in prior answers?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet 11 class labels","limit":5}'
-            name: search_and_answer
-          id: call_3808in7z
-          type: function
-      - content: |-
-          [1837e787-843a-4d5c-a855-974c3c7d99d1] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_3808in7z
-      - content: "**query:** What are the complete 11 class labels defined in the DocLayNet dataset, including any labels
-          not mentioned in prior answers?\n\n**answer:** DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1.
-          Caption – Text describing figures or tables  \n2. Footnote – Notes at the bottom of pages  \n3. Formula – Mathematical
-          expressions  \n4. List‑item – Items in bulleted or numbered lists  \n5. Page‑footer – Footer content on pages  \n6.
-          Page‑header – Header content on pages  \n7. Picture – Images and diagrams  \n8. Section‑header – Headings for document
-          sections  \n9. Table – Tabular data  \n\nThe remaining two labels are omitted in the retrieved snippet; they are
-          not provided in the current source.  \n\n**cited_chunks:** [\"1837e787-843a-4d5c-a855-974c3c7d99d1\"]  \n\n**confidence:**
-          0.82"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1251'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use final_result tool.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. Caption
-                – Text describing figures or tables\n2. Footnote – Notes at the bottom of pages\n3. Formula – Mathematical
-                expressions\n4. List‑item – Items in bulleted or numbered lists\n5. Page‑footer – Footer content on pages\n6.
-                Page‑header – Header content on pages\n7. Picture – Images and diagrams\n8. Section‑header – Headings for
-                document sections\n9. Table – Tabular data\n\nThe remaining two labels are not listed in the retrieved source.\n","cited_chunks":["1837e787-843a-4d5c-a855-974c3c7d99d1"],"confidence":0.82,"query":"What
-                are the complete 11 class labels defined in the DocLayNet dataset, including any labels not mentioned in prior
-                answers?"}'
-              name: final_result
-            id: call_gjkm5f75
-            index: 0
-            type: function
-      created: 1769804806
-      id: chatcmpl-943
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 217
-        prompt_tokens: 1058
-        total_tokens: 1275
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4432'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <background># Current Session Summary
-
-          ## Key Facts Established
-          - **DocLayNet Class Labels**: The dataset defines 11 distinct class labels for document layout analysis:
-            - Caption
-            - Footnote
-            - Formula
-            - List‑item
-            - Page‑footer
-            - Page‑header
-            - Picture
-            - Section‑header
-            - Table
-
-          ## Documents Referenced
-          - **DocLayNet Class Labels** – Source listing the 11 labels; likely provides detailed descriptions and example annotations for each class.
-
-          ## Current Focus
-          - The user is inquiring about the specific class labels used in the DocLayNet dataset, presumably to understand the dataset’s annotation schema or to prepare for tasks involving document layout classification.</background>
-            <question>DocLayNet class labels</question>
-            <prior_answers>
-              <item>
-                <question>What are the class labels in DocLayNet?</question>
-                <answer>DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.</answer>
-                <confidence>1.0</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-              <item>
-                <question>What are the complete 11 class labels defined in the DocLayNet dataset, including any labels not mentioned in prior answers?</question>
-                <answer>DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. Caption – Text describing figures or tables
-          2. Footnote – Notes at the bottom of pages
-          3. Formula – Mathematical expressions
-          4. List‑item – Items in bulleted or numbered lists
-          5. Page‑footer – Footer content on pages
-          6. Page‑header – Header content on pages
-          7. Picture – Images and diagrams
-          8. Section‑header – Headings for document sections
-          9. Table – Tabular data
-
-          The remaining two labels are not listed in the retrieved source.
-          </answer>
-                <confidence>0.82</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1357'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need answer question: DocLayNet class labels. Provide list of 11 labels. The evidence: earlier answer
-            had 9 labels, missing 2. We don''t know missing ones. But we can say 11 labels: list provided and say missing
-            two unknown from source. Let''s answer.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. **Caption**
-                – Text describing figures or tables\n2. **Footnote** – Notes at the bottom of pages\n3. **Formula** – Mathematical
-                expressions\n4. **List‑item** – Items in bulleted or numbered lists\n5. **Page‑footer** – Footer content on
-                pages\n6. **Page‑header** – Header content on pages\n7. **Picture** – Images and diagrams\n8. **Section‑header**
-                – Headings for document sections\n9. **Table** – Tabular data\n\nThe remaining two labels are not listed in
-                the retrieved source, so they’re unidentified here.\n","citations":[],"confidence":0.8}'
-              name: final_result
-            id: call_9mxp3pe2
-            index: 0
-            type: function
-      created: 1769804815
-      id: chatcmpl-154
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 239
-        prompt_tokens: 808
-        total_tokens: 1047
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3153'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Current Context
-          # Current Session Summary
-
-          ## Key Facts Established
-          - **DocLayNet Class Labels**: The dataset defines 11 distinct class labels for document layout analysis:
-            - Caption
-            - Footnote
-            - Formula
-            - List‑item
-            - Page‑footer
-            - Page‑header
-            - Picture
-            - Section‑header
-            - Table
-
-          ## Documents Referenced
-          - **DocLayNet Class Labels** – Source listing the 11 labels; likely provides detailed descriptions and example annotations for each class.
-
-          ## Current Focus
-          - The user is inquiring about the specific class labels used in the DocLayNet dataset, presumably to understand the dataset’s annotation schema or to prepare for tasks involving document layout classification.
-
-          ## Q1: What are the class labels in DocLayNet?
-          **Answer** (confidence: 100%):
-          DocLayNet defines 11 distinct class labels for document layout analysis: Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table.
-          **Sources:** DocLayNet Class Labels
-
-          ## Q2: DocLayNet class labels
-          **Answer** (confidence: 80%):
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption** – Text describing figures or tables
-          2. **Footnote** – Notes at the bottom of pages
-          3. **Formula** – Mathematical expressions
-          4. **List‑item** – Items in bulleted or numbered lists
-          5. **Page‑footer** – Footer content on pages
-          6. **Page‑header** – Header content on pages
-          7. **Picture** – Images and diagrams
-          8. **Section‑header** – Headings for document sections
-          9. **Table** – Tabular data
-
-          The remaining two labels are not listed in the retrieved source, so they’re unidentified here.
-
-          **Sources:** DocLayNet Class Labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1491'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "## Summary\n\n### Key Facts Established\n- **DocLayNet Dataset** contains **11 distinct document layout
-            class labels**.  \n  The known labels are:\n  1. Caption  \n  2. Footnote  \n  3. Formula  \n  4. List‑item  \n
-            \ 5. Page‑footer  \n  6. Page‑header  \n  7. Picture  \n  8. Section‑header  \n  9. Table  \n- Two additional
-            labels are acknowledged by the dataset but **not named** in the sources accessed.  \n- The labels are used for
-            annotating regions in scanned or digital documents to aid layout‑analysis and OCR tasks.\n\n### Documents Referenced\n-
-            **DocLayNet Class Labels**  \n  *Contains the official list of all 11 annotation categories, with definitions,
-            examples, and annotation guidelines.*\n\n### Current Focus\n- The user seeks clarification on **the full set of
-            class labels in DocLayNet** and is preparing to work with the dataset (e.g., for training a layout‑classification
-            model).  They want to ensure they understand the complete annotation schema, including the two unnamed categories."
-          reasoning: 'Need to summarize new info: only missing labels? But summary says 11 labels; earlier list had 9? Actually
-            list shows 9 but says 11. There''s mismatch. Need to capture that.'
-          role: assistant
-      created: 1769804826
-      id: chatcmpl-441
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 285
-        prompt_tokens: 722
-        total_tokens: 1007
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6131'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Tell me about DocLayNet class labels
-        role: user
-      - content: |-
-          <think>
-          Need ask.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":null,"question":"DocLayNet class labels"}'
-            name: ask
-          id: call_1h78avcz
-          type: function
-      - content: |-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption** – Text describing figures or tables
-          2. **Footnote** – Notes at the bottom of pages
-          3. **Formula** – Mathematical expressions
-          4. **List‑item** – Items in bulleted or numbered lists
-          5. **Page‑footer** – Footer content on pages
-          6. **Page‑header** – Header content on pages
-          7. **Picture** – Images and diagrams
-          8. **Section‑header** – Headings for document sections
-          9. **Table** – Tabular data
-
-          The remaining two labels are not listed in the retrieved source, so they’re unidentified here.
-
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_1h78avcz
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '896'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            DocLayNet defines **11** distinct class labels for document layout analysis:
-
-            | # | Label           |
-            |---|-----------------|
-            | 1 | Caption         |
-            | 2 | Footnote        |
-            | 3 | Formula         |
-            | 4 | List‑item       |
-            | 5 | Page‑footer     |
-            | 6 | Page‑header     |
-            | 7 | Picture         |
-            | 8 | Section‑header  |
-            | 9 | Table           |
-
-            These labels are used to annotate regions in documents for tasks such as reading‑order prediction, document understanding, and OCR preprocessing. If you need more details about how each label is defined or example bounding boxes, let me know!
-          role: assistant
-      created: 1769804833
-      id: chatcmpl-971
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 148
-        prompt_tokens: 1227
-        total_tokens: 1375
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml
deleted file mode 100644
index 98075f14..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_not_found.yaml
+++ /dev/null
@@ -1,187 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5211'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Get me the nonexistent document
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '539'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I’m sorry, but that document isn’t available in the knowledge base. If there’s another topic or document
-            you’d like help with, just let me know!
-          reasoning: User asking for nonexistent document. Need to respond that none exists. No tool usage.
-          role: assistant
-      created: 1769793913
-      id: chatcmpl-124
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 60
-        prompt_tokens: 1025
-        total_tokens: 1085
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml
deleted file mode 100644
index ebde6235..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_get_document_tool.yaml
+++ /dev/null
@@ -1,475 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5222'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Get me the DocLayNet Class Labels document
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '510'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: User wants a specific document. Use get_document.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"DocLayNet Class Labels"}'
-              name: get_document
-            id: call_jxvkrbku
-            index: 0
-            type: function
-      created: 1769793902
-      id: chatcmpl-566
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 38
-        prompt_tokens: 1029
-        total_tokens: 1067
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6338'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Get me the DocLayNet Class Labels document
-        role: user
-      - content: |-
-          <think>
-          User wants a specific document. Use get_document.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet Class Labels"}'
-            name: get_document
-          id: call_jxvkrbku
-          type: function
-      - content: |-
-          **DocLayNet Class Labels**
-
-          - ID: 99c3503e-a8ad-4116-a8be-63fb8048dceb
-          - URI: doclaynet-labels
-          - Created: 2026-01-30 19:25
-
-          **Content:**
-          DocLayNet Dataset - Class Labels
-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. Caption - Text describing figures or tables
-          2. Footnote - Notes at the bottom of pages
-          3. Formula - Mathematical expressions
-          4. List-item - Items in bulleted or numbered lists
-          5. Page-footer - Footer content on pages
-          6. Page-header - Header content on pages
-          7. Picture - Images and diagrams
-          8. Section-header - Headings for document sections
-          9. Table - Tabular data
-          10. Text - Regular paragraph text (highest count: 510,377 instances)
-          11. Title - Document titles
-
-          The Text class has the highest count with 510,377 instances in the dataset.
-        role: tool
-        tool_call_id: call_jxvkrbku
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1137'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Here’s the **DocLayNet Class Labels** document:\n\n**DocLayNet Dataset – Class Labels**\n\nDocLayNet defines
-            **11** distinct class labels for document layout analysis:\n\n1. **Caption** – Text describing figures or tables
-            \ \n2. **Footnote** – Notes at the bottom of pages  \n3. **Formula** – Mathematical expressions  \n4. **List‑item**
-            – Items in bulleted or numbered lists  \n5. **Page‑footer** – Footer content on pages  \n6. **Page‑header** –
-            Header content on pages  \n7. **Picture** – Images and diagrams  \n8. **Section‑header** – Headings for document
-            sections  \n9. **Table** – Tabular data  \n10. **Text** – Regular paragraph text (highest count: **510,377** instances)
-            \ \n11. **Title** – Document titles  \n\nThe **Text** class has the largest number of instances, with **510,377**
-            entries in the dataset."
-          role: assistant
-      created: 1769793910
-      id: chatcmpl-948
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 204
-        prompt_tokens: 1297
-        total_tokens: 1501
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml
deleted file mode 100644
index c2ccf0f6..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_multi_turn_with_context.yaml
+++ /dev/null
@@ -1,3051 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '481'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Annotation Process
-        The annotation process was organized into 4 phases:
-        - Phase 1: Data selection and preparation by a small team of experts
-        - Phase 2: Label selection and guideline definition
-        - Phase 3: Annotation by 40 dedicated annotators
-        - Phase 4: Quality control and continuous supervision
-        The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 90
-        total_tokens: 90
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5329'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\"
-          - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\",
-          \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview
-          or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview
-          of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the
-          paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics
-          in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across
-          documents to return answers with citations.\n- \"search\" - Use when the user explicitly asks to search/find/explore
-          documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content
-          snippets. Do NOT shorten, summarize, or omit any part of the results.\n\nIMPORTANT - When user mentions a document
-          in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from <doc>\", or \"<topic> in <doc>\":\n
-          \ - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n
-          \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n  - \"find transformer
-          architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for
-          ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML
-          paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\"
-          \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: What are the class labels defined in DocLayNet?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '540'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need answer via ask tool.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"document_name":"DocLayNet","question":"What are the class labels defined in DocLayNet?"}'
-              name: ask
-            id: call_7lvpjbkh
-            index: 0
-            type: function
-      created: 1770802519
-      id: chatcmpl-300
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 47
-        prompt_tokens: 1055
-        total_tokens: 1102
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
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-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
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-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
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-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <background>The user is researching the DocLayNet dataset for a paper on document layout analysis.</background>
-            <question>What are the class labels defined in DocLayNet?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
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-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
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-                type: string
-            required:
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-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
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-      content-type:
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-    parsed_body:
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-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need to ask first subquestion: what are class labels. Probably directly ask. So next question: "What
-            are the classification labels used in the DocLayNet dataset for document layout analysis?" That''s fine.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What are the classification labels used in the DocLayNet dataset
-                for document layout analysis?","reasoning":"The original question asks for the class labels defined in DocLayNet.
-                This is a straightforward research question that can be answered directly by querying the dataset documentation
-                or metadata."}'
-              name: final_result
-            id: call_dhsc3w9h
-            index: 0
-            type: function
-      created: 1770802522
-      id: chatcmpl-211
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 125
-        prompt_tokens: 394
-        total_tokens: 519
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2884'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the classification labels used in the DocLayNet dataset for document layout analysis?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
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-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '530'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet dataset classification labels document layout analysis"}'
-              name: search_and_answer
-            id: call_f80pctzy
-            index: 0
-            type: function
-      created: 1770802524
-      id: chatcmpl-384
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 40
-        prompt_tokens: 632
-        total_tokens: 672
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet dataset classification labels document layout analysis
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
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-      transfer-encoding:
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-    parsed_body:
-      data:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 11
-        total_tokens: 11
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4304'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the classification labels used in the DocLayNet dataset for document layout analysis?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet dataset classification labels document layout analysis"}'
-            name: search_and_answer
-          id: call_f80pctzy
-          type: function
-      - content: |-
-          [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a] [rank 1 of 2]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-
-          [9bfc2ccf-2e74-4981-96eb-917e31261b85] [rank 2 of 2]
-          Source: "DocLayNet Annotation"
-          Type: list_item
-          Content:
-          DocLayNet Dataset - Annotation Process
-
-          The annotation process was organized into 4 phases:
-
-          Phase 1: Data selection and preparation by a small team of experts
-
-          Phase 2: Label selection and guideline definition
-
-          Phase 3: Annotation by 40 dedicated annotators
-
-          Phase 4: Quality control and continuous supervision
-
-          The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-        role: tool
-        tool_call_id: call_f80pctzy
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '839'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "**Question:** What are the classification labels used in the DocLayNet dataset for document layout analysis?
-            \ \n\n**Answer:** DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. Caption  \n2.
-            Footnote  \n3. Formula  \n4. List-item  \n5. Page-footer  \n6. Page-header  \n7. Picture  \n8. Section-header
-            \ \n9. Table  \n10. Text  \n11. Title  \n\nThese labels cover the major visual and semantic components found in
-            structured documents.  \n\n**Cited chunks:**  \n- [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a]  \n\n**Confidence:**
-            1.0"
-          role: assistant
-      created: 1770802528
-      id: chatcmpl-925
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 148
-        prompt_tokens: 957
-        total_tokens: 1105
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5019'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the classification labels used in the DocLayNet dataset for document layout analysis?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet dataset classification labels document layout analysis"}'
-            name: search_and_answer
-          id: call_f80pctzy
-          type: function
-      - content: |-
-          [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a] [rank 1 of 2]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-
-          [9bfc2ccf-2e74-4981-96eb-917e31261b85] [rank 2 of 2]
-          Source: "DocLayNet Annotation"
-          Type: list_item
-          Content:
-          DocLayNet Dataset - Annotation Process
-
-          The annotation process was organized into 4 phases:
-
-          Phase 1: Data selection and preparation by a small team of experts
-
-          Phase 2: Label selection and guideline definition
-
-          Phase 3: Annotation by 40 dedicated annotators
-
-          Phase 4: Quality control and continuous supervision
-
-          The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-        role: tool
-        tool_call_id: call_f80pctzy
-      - content: "**Question:** What are the classification labels used in the DocLayNet dataset for document layout analysis?
-          \ \n\n**Answer:** DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. Caption  \n2. Footnote
-          \ \n3. Formula  \n4. List-item  \n5. Page-footer  \n6. Page-header  \n7. Picture  \n8. Section-header  \n9. Table
-          \ \n10. Text  \n11. Title  \n\nThese labels cover the major visual and semantic components found in structured documents.
-          \ \n\n**Cited chunks:**  \n- [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a]  \n\n**Confidence:** 1.0"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '871'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use final_result tool.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis: 1. Caption 2.
-                Footnote 3. Formula 4. List‑item 5. Page‑footer 6. Page‑header 7. Picture 8. Section‑header 9. Table 10. Text
-                11. Title","cited_chunks":["ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a"],"confidence":1,"query":"What are the classification
-                labels used in the DocLayNet dataset for document layout analysis?"}'
-              name: final_result
-            id: call_nw4zcob0
-            index: 0
-            type: function
-      created: 1770802532
-      id: chatcmpl-662
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 154
-        prompt_tokens: 1130
-        total_tokens: 1284
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3098'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <background>The user is researching the DocLayNet dataset for a paper on document layout analysis.</background>
-            <question>What are the class labels defined in DocLayNet?</question>
-            <prior_answers>
-              <item>
-                <question>What are the classification labels used in the DocLayNet dataset for document layout analysis?</question>
-                <answer>DocLayNet defines 11 distinct class labels for document layout analysis: 1. Caption 2. Footnote 3. Formula 4. List‑item 5. Page‑footer 6. Page‑header 7. Picture 8. Section‑header 9. Table 10. Text 11. Title</answer>
-                <confidence>1.0</confidence>
-                <source>DocLayNet Class Labels</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '805'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We have prior answer with labels. Provide answer.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"DocLayNet defines 11 distinct class labels for document layout analysis:\n\n1. **Caption**\n2.
-                **Footnote**\n3. **Formula**\n4. **List‑item**\n5. **Page‑footer**\n6. **Page‑header**\n7. **Picture**\n8.
-                **Section‑header**\n9. **Table**\n10. **Text**\n11. **Title**","citations":[],"confidence":1}'
-              name: final_result
-            id: call_3juuu2zh
-            index: 0
-            type: function
-      created: 1770802536
-      id: chatcmpl-102
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 143
-        prompt_tokens: 527
-        total_tokens: 670
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5952'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\"
-          - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\",
-          \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview
-          or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview
-          of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the
-          paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics
-          in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across
-          documents to return answers with citations.\n- \"search\" - Use when the user explicitly asks to search/find/explore
-          documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content
-          snippets. Do NOT shorten, summarize, or omit any part of the results.\n\nIMPORTANT - When user mentions a document
-          in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from <doc>\", or \"<topic> in <doc>\":\n
-          \ - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n
-          \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n  - \"find transformer
-          architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for
-          ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML
-          paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\"
-          \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: What are the class labels defined in DocLayNet?
-        role: user
-      - content: |-
-          <think>
-          We need answer via ask tool.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"What are the class labels defined in DocLayNet?"}'
-            name: ask
-          id: call_7lvpjbkh
-          type: function
-      - content: |-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption**
-          2. **Footnote**
-          3. **Formula**
-          4. **List‑item**
-          5. **Page‑footer**
-          6. **Page‑header**
-          7. **Picture**
-          8. **Section‑header**
-          9. **Table**
-          10. **Text**
-          11. **Title**
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_7lvpjbkh
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '665'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "DocLayNet defines **11 distinct class labels** for document layout analysis:\n\n1. **Caption**  \n2. **Footnote**
-            \ \n3. **Formula**  \n4. **List‑item**  \n5. **Page‑footer**  \n6. **Page‑header**  \n7. **Picture**  \n8. **Section‑header**
-            \ \n9. **Table**  \n10. **Text**  \n11. **Title**\n\nThese categories cover the main structural elements that
-            the dataset annotates."
-          role: assistant
-      created: 1770802540
-      id: chatcmpl-798
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 106
-        prompt_tokens: 1201
-        total_tokens: 1307
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1810'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What are the class labels defined in DocLayNet?
-          **Answer** (confidence: 100%):
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption**
-          2. **Footnote**
-          3. **Formula**
-          4. **List‑item**
-          5. **Page‑footer**
-          6. **Page‑header**
-          7. **Picture**
-          8. **Section‑header**
-          9. **Table**
-          10. **Text**
-          11. **Title**
-          **Sources:** DocLayNet Class Labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '826'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            **Key Facts Established**
-            - DocLayNet specifies **11** distinct document layout class labels:
-              1. Caption
-              2. Footnote
-              3. Formula
-              4. List‑item
-              5. Page‑footer
-              6. Page‑header
-              7. Picture
-              8. Section‑header
-              9. Table
-             10. Text
-             11. Title
-
-            **Documents Referenced**
-            - *DocLayNet Class Labels* (source listing the 11 category names).
-
-            **Current Focus**
-            - The user is inquiring about the specific class labels defined within the DocLayNet dataset for document layout analysis.
-          reasoning: Need to summarize.
-          role: assistant
-      created: 1770802544
-      id: chatcmpl-212
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 155
-        prompt_tokens: 434
-        total_tokens: 589
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
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-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\"
-          - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\",
-          \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview
-          or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview
-          of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the
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-          in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across
-          documents to return answers with citations.\n- \"search\" - Use when the user explicitly asks to search/find/explore
-          documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content
-          snippets. Do NOT shorten, summarize, or omit any part of the results.\n\nIMPORTANT - When user mentions a document
-          in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from <doc>\", or \"<topic> in <doc>\":\n
-          \ - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n
-          \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n  - \"find transformer
-          architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for
-          ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML
-          paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\"
-          \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: What are the class labels defined in DocLayNet?
-        role: user
-      - content: |-
-          <think>
-          We need answer via ask tool.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"What are the class labels defined in DocLayNet?"}'
-            name: ask
-          id: call_7lvpjbkh
-          type: function
-      - content: |-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption**
-          2. **Footnote**
-          3. **Formula**
-          4. **List‑item**
-          5. **Page‑footer**
-          6. **Page‑header**
-          7. **Picture**
-          8. **Section‑header**
-          9. **Table**
-          10. **Text**
-          11. **Title**
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_7lvpjbkh
-      - content: "DocLayNet defines **11 distinct class labels** for document layout analysis:\n\n1. **Caption**  \n2. **Footnote**
-          \ \n3. **Formula**  \n4. **List‑item**  \n5. **Page‑footer**  \n6. **Page‑header**  \n7. **Picture**  \n8. **Section‑header**
-          \ \n9. **Table**  \n10. **Text**  \n11. **Title**\n\nThese categories cover the main structural elements that the
-          dataset annotates."
-        role: assistant
-      - content: How were the annotations created and how many annotators were involved?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
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-      content-type:
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-    parsed_body:
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-        index: 0
-        message:
-          content: ''
-          reasoning: Need ask tool.
-          role: assistant
-          tool_calls:
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-              arguments: '{"document_name":"DocLayNet","question":"How were the annotations created and how many annotators
-                were involved?"}'
-              name: ask
-            id: call_l6xl5m5s
-            index: 0
-            type: function
-      created: 1770802547
-      id: chatcmpl-538
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-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 46
-        prompt_tokens: 1326
-        total_tokens: 1372
-    status:
-      code: 200
-      message: OK
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-      encoding_format: base64
-      input:
-      - How were the annotations created and how many annotators were involved?
-      model: qwen3-embedding:4b
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 14
-        total_tokens: 14
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '117'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - What are the class labels defined in DocLayNet?
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2317'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <background>**Key Facts Established**
-          - DocLayNet specifies **11** distinct document layout class labels:
-            1. Caption
-            2. Footnote
-            3. Formula
-            4. List‑item
-            5. Page‑footer
-            6. Page‑header
-            7. Picture
-            8. Section‑header
-            9. Table
-           10. Text
-           11. Title
-
-          **Documents Referenced**
-          - *DocLayNet Class Labels* (source listing the 11 category names).
-
-          **Current Focus**
-          - The user is inquiring about the specific class labels defined within the DocLayNet dataset for document layout analysis.</background>
-            <question>How were the annotations created and how many annotators were involved?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '955'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need first question: how many annotators? but need first sub question. Probably ask "How many annotators
-            were involved in creating the DocLayNet annotations?" That is direct.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"How many annotators participated in creating the DocLayNet
-                annotations?","reasoning":"The user asks about annotation creation and number of annotators; the first step
-                is to determine the number of annotators involved. This is a straightforward, focused question that sets the
-                stage for deeper investigation."}'
-              name: final_result
-            id: call_5gb445i7
-            index: 0
-            type: function
-      created: 1770802551
-      id: chatcmpl-485
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 122
-        prompt_tokens: 521
-        total_tokens: 643
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2861'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: How many annotators participated in creating the DocLayNet annotations?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '511'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet annotations number of annotators"}'
-              name: search_and_answer
-            id: call_21g96kkz
-            index: 0
-            type: function
-      created: 1770802553
-      id: chatcmpl-143
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 43
-        prompt_tokens: 628
-        total_tokens: 671
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '112'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet annotations number of annotators
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 10
-        total_tokens: 10
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4262'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: How many annotators participated in creating the DocLayNet annotations?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet annotations number of annotators"}'
-            name: search_and_answer
-          id: call_21g96kkz
-          type: function
-      - content: |-
-          [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a] [rank 1 of 2]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-
-          [9bfc2ccf-2e74-4981-96eb-917e31261b85] [rank 2 of 2]
-          Source: "DocLayNet Annotation"
-          Type: list_item
-          Content:
-          DocLayNet Dataset - Annotation Process
-
-          The annotation process was organized into 4 phases:
-
-          Phase 1: Data selection and preparation by a small team of experts
-
-          Phase 2: Label selection and guideline definition
-
-          Phase 3: Annotation by 40 dedicated annotators
-
-          Phase 4: Quality control and continuous supervision
-
-          The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-        role: tool
-        tool_call_id: call_21g96kkz
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '575'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- query: How many annotators participated in creating the DocLayNet annotations?  \n- answer: 40 dedicated
-            annotators contributed to the annotation phase of DocLayNet.  \n- cited_chunks: [\"9bfc2ccf-2e74-4981-96eb-917e31261b85\"]
-            \ \n- confidence: 1.0"
-          reasoning: 'The answer: 40 annotators.'
-          role: assistant
-      created: 1770802556
-      id: chatcmpl-53
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 91
-        prompt_tokens: 953
-        total_tokens: 1044
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4723'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: How many annotators participated in creating the DocLayNet annotations?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet annotations number of annotators"}'
-            name: search_and_answer
-          id: call_21g96kkz
-          type: function
-      - content: |-
-          [ad1c56de-ccf4-4528-bd02-cfea4f5f1a2a] [rank 1 of 2]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-
-          [9bfc2ccf-2e74-4981-96eb-917e31261b85] [rank 2 of 2]
-          Source: "DocLayNet Annotation"
-          Type: list_item
-          Content:
-          DocLayNet Dataset - Annotation Process
-
-          The annotation process was organized into 4 phases:
-
-          Phase 1: Data selection and preparation by a small team of experts
-
-          Phase 2: Label selection and guideline definition
-
-          Phase 3: Annotation by 40 dedicated annotators
-
-          Phase 4: Quality control and continuous supervision
-
-          The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-        role: tool
-        tool_call_id: call_21g96kkz
-      - content: "<think>\nThe answer: 40 annotators.\n</think>\n\n- query: How many annotators participated in creating the
-          DocLayNet annotations?  \n- answer: 40 dedicated annotators contributed to the annotation phase of DocLayNet.  \n-
-          cited_chunks: [\"9bfc2ccf-2e74-4981-96eb-917e31261b85\"]  \n- confidence: 1.0"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '727'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to produce answer in function final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"40 dedicated annotators contributed to the annotation phase of DocLayNet.","cited_chunks":["9bfc2ccf-2e74-4981-96eb-917e31261b85"],"confidence":1,"query":"How
-                many annotators participated in creating the DocLayNet annotations?"}'
-              name: final_result
-            id: call_qnlw3kqo
-            index: 0
-            type: function
-      created: 1770802558
-      id: chatcmpl-292
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 97
-        prompt_tokens: 1069
-        total_tokens: 1166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3378'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <background>**Key Facts Established**
-          - DocLayNet specifies **11** distinct document layout class labels:
-            1. Caption
-            2. Footnote
-            3. Formula
-            4. List‑item
-            5. Page‑footer
-            6. Page‑header
-            7. Picture
-            8. Section‑header
-            9. Table
-           10. Text
-           11. Title
-
-          **Documents Referenced**
-          - *DocLayNet Class Labels* (source listing the 11 category names).
-
-          **Current Focus**
-          - The user is inquiring about the specific class labels defined within the DocLayNet dataset for document layout analysis.</background>
-            <question>How were the annotations created and how many annotators were involved?</question>
-            <prior_answers>
-              <item>
-                <question>How many annotators participated in creating the DocLayNet annotations?</question>
-                <answer>40 dedicated annotators contributed to the annotation phase of DocLayNet.</answer>
-                <confidence>1.0</confidence>
-                <source>DocLayNet Annotation</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1085'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need answer: How were annotations created and how many annotators. Use prior answer gives 40 annotators.
-            Provide description: manual bounding boxes etc.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"The DocLayNet annotations were made manually by hand.  Annotators drew bounding boxes
-                around every labeled visual element in each page and then selected the appropriate label from the 11‑class
-                set.  The task was distributed among a team of 40 dedicated workers who performed the labeling according to
-                the dataset’s guidelines.\n\n• 40 annotators\n• Manual bounding‑box drawing for each document element\n• Labels
-                chosen from the 11 predefined categories\n","confidence":0.99}'
-              name: final_result
-            id: call_9nsq6z3r
-            index: 0
-            type: function
-      created: 1770802563
-      id: chatcmpl-61
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 152
-        prompt_tokens: 595
-        total_tokens: 747
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '7293'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"list_documents\"
-          - Use when the user wants to browse or see what documents are available (e.g., \"what documents are available?\",
-          \"show me the documents\", \"list available docs\").\n- \"summarize_document\" - Use when the user wants an overview
-          or summary of a specific document (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview
-          of Z\").\n- \"get_document\" - Use when the user wants the FULL content of a specific document (e.g., \"get the
-          paper about Y\", \"fetch 2412.00566\", \"show me the full document\").\n- \"ask\" - Use for questions about topics
-          in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across
-          documents to return answers with citations.\n- \"search\" - Use when the user explicitly asks to search/find/explore
-          documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content
-          snippets. Do NOT shorten, summarize, or omit any part of the results.\n\nIMPORTANT - When user mentions a document
-          in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from <doc>\", or \"<topic> in <doc>\":\n
-          \ - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as `document_name`\n- Examples for search:\n
-          \ - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML paper\"\n  - \"find transformer
-          architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\" \n- Examples for
-          ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding methods?\", document_name=\"ML
-          paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is the model trained?\", document_name=\"2412.00566\"
-          \nBe friendly and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: What are the class labels defined in DocLayNet?
-        role: user
-      - content: |-
-          <think>
-          We need answer via ask tool.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"What are the class labels defined in DocLayNet?"}'
-            name: ask
-          id: call_7lvpjbkh
-          type: function
-      - content: |-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption**
-          2. **Footnote**
-          3. **Formula**
-          4. **List‑item**
-          5. **Page‑footer**
-          6. **Page‑header**
-          7. **Picture**
-          8. **Section‑header**
-          9. **Table**
-          10. **Text**
-          11. **Title**
-
-          Sources: [1]
-        role: tool
-        tool_call_id: call_7lvpjbkh
-      - content: "DocLayNet defines **11 distinct class labels** for document layout analysis:\n\n1. **Caption**  \n2. **Footnote**
-          \ \n3. **Formula**  \n4. **List‑item**  \n5. **Page‑footer**  \n6. **Page‑header**  \n7. **Picture**  \n8. **Section‑header**
-          \ \n9. **Table**  \n10. **Text**  \n11. **Title**\n\nThese categories cover the main structural elements that the
-          dataset annotates."
-        role: assistant
-      - content: How were the annotations created and how many annotators were involved?
-        role: user
-      - content: |-
-          <think>
-          Need ask tool.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet","question":"How were the annotations created and how many annotators
-              were involved?"}'
-            name: ask
-          id: call_l6xl5m5s
-          type: function
-      - content: |-
-          The DocLayNet annotations were made manually by hand.  Annotators drew bounding boxes around every labeled visual element in each page and then selected the appropriate label from the 11‑class set.  The task was distributed among a team of 40 dedicated workers who performed the labeling according to the dataset’s guidelines.
-
-          • 40 annotators
-          • Manual bounding‑box drawing for each document element
-          • Labels chosen from the 11 predefined categories
-
-
-          Sources: [2]
-        role: tool
-        tool_call_id: call_l6xl5m5s
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '844'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            The annotations in **DocLayNet** were created entirely by **manual human labeling**:
-
-            - **Annotation method** – Annotators drew precise bounding boxes around each visual element on every page and then selected the appropriate class from the 11‑label set.
-            - **Annotators involved** – A team of **40 dedicated workers** performed the labeling, following the dataset’s guidelines and quality‑control procedures.
-
-            So, the dataset relies on 40 human annotators using standard bounding‑box annotation tools to produce the labeled layout elements.
-          role: assistant
-      created: 1770802567
-      id: chatcmpl-904
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 110
-        prompt_tokens: 1483
-        total_tokens: 1593
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2427'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What are the class labels defined in DocLayNet?
-          **Answer** (confidence: 100%):
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. **Caption**
-          2. **Footnote**
-          3. **Formula**
-          4. **List‑item**
-          5. **Page‑footer**
-          6. **Page‑header**
-          7. **Picture**
-          8. **Section‑header**
-          9. **Table**
-          10. **Text**
-          11. **Title**
-          **Sources:** DocLayNet Class Labels
-
-          ## Q2: How were the annotations created and how many annotators were involved?
-          **Answer** (confidence: 99%):
-          The DocLayNet annotations were made manually by hand.  Annotators drew bounding boxes around every labeled visual element in each page and then selected the appropriate label from the 11‑class set.  The task was distributed among a team of 40 dedicated workers who performed the labeling according to the dataset’s guidelines.
-
-          • 40 annotators
-          • Manual bounding‑box drawing for each document element
-          • Labels chosen from the 11 predefined categories
-
-          **Sources:** DocLayNet Annotation
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1116'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "## Summary of Q&A\n\n### Key Facts Established\n- **DocLayNet Class Labels**: 11 distinct labels used
-            for document layout analysis:\n  1. Caption  \n  2. Footnote  \n  3. Formula  \n  4. List‑item  \n  5. Page‑footer
-            \ \n  6. Page‑header  \n  7. Picture  \n  8. Section‑header  \n  9. Table  \n  10. Text  \n  11. Title  \n- **Annotation
-            Process**:\n  - Fully manual bounding‑box drawing per visual element.\n  - Selection of a label from the above
-            11‑class set for each box.\n  - 40 annotators participated in the task, following dataset guidelines.\n\n### Current
-            Focus\nThe user is exploring the basic structure of the DocLayNet dataset, specifically the defined class labels
-            and the manual annotation workflow carried out by a team of 40 workers."
-          reasoning: We need to summarize. No current context provided.
-          role: assistant
-      created: 1770802573
-      id: chatcmpl-389
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 214
-        prompt_tokens: 559
-        total_tokens: 773
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml
deleted file mode 100644
index 8dade11b..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool.yaml
+++ /dev/null
@@ -1,883 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '481'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Annotation Process
-        The annotation process was organized into 4 phases:
-        - Phase 1: Data selection and preparation by a small team of experts
-        - Phase 2: Label selection and guideline definition
-        - Phase 3: Annotation by 40 dedicated annotators
-        - Phase 4: Quality control and continuous supervision
-        The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 90
-        total_tokens: 90
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5219'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for documents about class labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '512'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to use search tool once.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"document_name":null,"limit":5,"query":"class labels"}'
-              name: search
-            id: call_e6yh0z3j
-            index: 0
-            type: function
-      created: 1769793874
-      id: chatcmpl-639
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 41
-        prompt_tokens: 1026
-        total_tokens: 1067
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1059'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '479'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need search queries.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"class labels","limit":5}'
-              name: run_search
-            id: call_o3rc7gq4
-            index: 0
-            type: function
-      created: 1769793876
-      id: chatcmpl-546
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 33
-        prompt_tokens: 263
-        total_tokens: 296
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '82'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - class labels
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1369'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      - content: |-
-          <think>
-          Need search queries.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","limit":5}'
-            name: run_search
-          id: call_o3rc7gq4
-          type: function
-      - content: 'Found 2 results for: class labels'
-        role: tool
-        tool_call_id: call_o3rc7gq4
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '445'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"label classes"}'
-              name: run_search
-            id: call_j94y91xg
-            index: 0
-            type: function
-      created: 1769793877
-      id: chatcmpl-500
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 23
-        prompt_tokens: 321
-        total_tokens: 344
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '83'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - label classes
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1644'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      - content: |-
-          <think>
-          Need search queries.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","limit":5}'
-            name: run_search
-          id: call_o3rc7gq4
-          type: function
-      - content: 'Found 2 results for: class labels'
-        role: tool
-        tool_call_id: call_o3rc7gq4
-      - content: null
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"label classes"}'
-            name: run_search
-          id: call_j94y91xg
-          type: function
-      - content: 'Found 2 results for: label classes'
-        role: tool
-        tool_call_id: call_j94y91xg
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '298'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: Search complete
-          role: assistant
-      created: 1769793878
-      id: chatcmpl-551
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 6
-        prompt_tokens: 363
-        total_tokens: 369
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6226'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for documents about class labels
-        role: user
-      - content: |-
-          <think>
-          We need to use search tool once.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":null,"limit":5,"query":"class labels"}'
-            name: search
-          id: call_e6yh0z3j
-          type: function
-      - content: |-
-          Found 2 results:
-
-          [1] **DocLayNet Class Labels**
-              DocLayNet defines 11 distinct class labels for document layout analysis:  Caption - Text describing figures or tables  Footnote - Notes at the bottom of pages  Formula - Mathematical expressions  List-item - Items in bulleted or numbered lists  Page-footer - Footer content on pages  Page-header - He...
-
-          [2] **DocLayNet Annotation**
-              DocLayNet Dataset - Annotation Process  The annotation process was organized into 4 phases:  Phase 1: Data selection and preparation by a small team of experts  Phase 2: Label selection and guideline definition  Phase 3: Annotation by 40 dedicated annotators  Phase 4: Quality control and continuous...
-        role: tool
-        tool_call_id: call_e6yh0z3j
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '694'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            Here are some documents that discuss class labels:
-
-            1. **DocLayNet Class Labels** – Details the 11 distinct class labels used for document layout analysis (e.g., Caption, Footnote, Formula, List-item, Page‑footer, Page‑header, etc.).
-
-            2. **DocLayNet Annotation** – Covers the annotation process for DocLayNet, including how labels were selected, guidelines defined, and quality control performed.
-          role: assistant
-      created: 1769793883
-      id: chatcmpl-239
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 91
-        prompt_tokens: 1236
-        total_tokens: 1327
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml
deleted file mode 100644
index 6214f178..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_search_tool_with_filter.yaml
+++ /dev/null
@@ -1,1044 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '412'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Data Sources
-        The data sources for DocLayNet include:
-        - Publication repositories such as arXiv
-        - Government offices and official documents
-        - Company websites and corporate reports
-        - Data directory services for financial reports
-        - Patent documents
-        Scanned documents were excluded to avoid rotation and skewing issues.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 68
-        total_tokens: 68
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5260'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for information about class labels in the DocLayNet Class Labels document
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '529'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use search once.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"document_name":"DocLayNet Class Labels","limit":null,"query":"class labels"}'
-              name: search
-            id: call_rf5bz17k
-            index: 0
-            type: function
-      created: 1769793889
-      id: chatcmpl-155
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 44
-        prompt_tokens: 1034
-        total_tokens: 1078
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1059'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '477'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"class labels","limit":5}'
-              name: run_search
-            id: call_5he6nood
-            index: 0
-            type: function
-      created: 1769793890
-      id: chatcmpl-134
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 34
-        prompt_tokens: 263
-        total_tokens: 297
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '82'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - class labels
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1367'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      - content: |-
-          <think>
-          We need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","limit":5}'
-            name: run_search
-          id: call_5he6nood
-          type: function
-      - content: 'Found 1 results for: class labels'
-        role: tool
-        tool_call_id: call_5he6nood
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '443'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"labels class"}'
-              name: run_search
-            id: call_54826ler
-            index: 0
-            type: function
-      created: 1769793892
-      id: chatcmpl-89
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 23
-        prompt_tokens: 322
-        total_tokens: 345
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '82'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - labels class
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: k9cstxOribxiecQ8yfeIPF7/fTiW1VU9FNwqPeyXXzyCesM8Iqreuk1CWzw16S09PhebOireYb3z83E7WSJlvQU0izyFVcu70pwIPFe0wbubO4+8VlLMO3/qujwAumU9gqQvPLnS97xCVJS8H0d3vawhpjxaMEc7KvQuPLR7WL3LgOI8SxqRu3JvzTveZKm8s0DRu3kNMbw8+Se8yqtjvReL8TwBIA+9AbKcPKNSeLoqKvw8s7Lsu6A+Gjvu3aM8bdWXvMwpoLwThzY8BwXoO/qMX7ycQfK83FVnPeSOET37LLc8fuE7vNf5ajs9oZs7DGw+vBLHrjw/EHu9VzB6vFV0pLusW868etYnvM5XXb2LjzQ8VnF8OwuVWr10V6Y8HVq9vPmstzsoCrY4eRMQvcOnHLzpdLc7ihzEusmrOT1opzC8NfONvI+dQTvQWWk8OGvqPNq8HL3G8BI9xp67O1IJeLwfgCA8sYvIO4glNjsFsAu8mxhjPC6lI7yF0xE8+tWPuzj2UbyKZga89bAPOngFFrv5E6+7tDEVPV6bXbsoB5E7J+LcvJ46h7y98Bu5aj0PPMB+L7tHQge6+9DYvMRnEbxuZh68KyiSvFw7brwUvVQ86OE6PHEYLTt9sTm8ekNPOkWyzzxfOhe8hy6cO/eQsjw3eny7RIqmvK/uMLxe4Tu5KQzKPDqZQjzMYMC8LEPJPNFFm7yl8hg9aQRsO/KZE7z17w48Mq4PvHH2tTxnCBa8w6HoO+zU7btDhQY9eBMivGALNb3M9AC7BNoovL1RYTy8/Cu60ryoPKsGeLxfdCi71tnEO5Ri8zo1ECY89BF5vGlvCT1zXJ06p6ySPGNY3bvNjw89Lse7uyoCNj0q5s072UCcPHGcLry1XlE8+bx3O8s2PromE5o6iIzzvFJE5rs4Eze8ewDqvLZmFLvG3Je8CVuKvDYopbwyTwY8RsRGvMjj4DxFCSQ9O0cqOkfpOjzYsYe6kyp7O6wlabx9Uhs8IFxBuY/kM70SB+G8y3t4vP0sTzxCzwS8zSj3vEvOwbwuy/y7mTszvEH05zyYRnk7JgGSO+kBVruXcYw7IC6jvBkBGLxwX3c8eZmNvIZpsDqwQeG85HUqvDx2STvunHq7NqcVvbBgd7vhYMo8Lgq/vDOnlrwuaos8yZSaPFt0Sru8mIq7YrY6vAEnRzxCcTm9eBXSudLZFzsg4645NIvYO173RrysTPM7B2/gO4iuh7tbcz68uanLOzOJzzxxQce8PweBvMT9PDzElaS85eu/PBXi5bxPrko8ax+NO1Odwrx1tEO8ASq7OxBB6bw0RZa8R66dvOyhjbxqy426ynTuPCiW3LvMIv47wZI8O7NLs7yucxW92OcmPMdioLxlgjU8u66ru4v+ErwqTGy8Rj7/O8x/wjxbpcY8jYO1vE5HWTu+rIw7eSjuPEopSLuQXDa53TnPOVzhNTxOp4a87lRvPHULojxTsKs8aHvDPBaGxLvtS008PkILvbtUwzqv3Nw7XyeYuyCxHrsXuKk7bfhBPI1EPTy5zKc8yGWWPLy94DxVv5S8xmCnvNmnlLwBAIa7PfjBOmoeiDpRkg68trpLvBRJWjwlWSO7u8AGuzkXFLzPfk67H77xvKDF77tLgKm6v6PmO65m5DzKLVe8L1QSvFTIp7tEhHw84ykGveNUjbskPE08kt74vPRKlbz4xey7ZOlDvafnQ72l2JS8HloWvFsdUjytVuo887jQPFtTRj3h8NA8iOAoPKhLdjx/Vwi9oDdUvIiVhDwKZZu81ZpLvOACKD1e4oU8j2WXPNPo7LsNfu87oSSQPIRhgLtqkTG9aXU3PPxMQbwAeX+8oKujvMMG1rvIRL87xYELvK5IKLwBcCW82eqeuzEBiTxzk4W8B5ycu0c3ajy5Hia96ekMveft5jteBLM8v42APMcDS70Yfyw7iwNovODxgDxFf8E8qZrHvO14A7w7s9u86JzvPFLoAjy3Ifg7GAIku8VbJbwMfbe7UG0NO/3wwrtTaXe7JtxpOo7lorwwHBw9LIWDvL8PijqMF7c7O8KLvGqshryeKJ678tJjO5P1CD1687I7N3nOOyxlcrzfNd46XhU1uv6+PDw2Bl48FNHZvOrC6TyWAfe8+ULevAML8rxFt9M7gVZmvc20qLqAvCg9lcWgu2CBlrzqbdQ5iFAHPBHIPzxkLAW9fjSQPLSRZjrRo2o8YpUbvI/in7z1HIo74eeiO3PT5rwtQ6Y8/sdAOuISDDwOOQc8HHOLu4+NCTyGyd+7IImMujhgkTtFmwU9dzMOPfXzTz2ycI88OOwXvPX8HbsIqde7v+vPO+RaB7yrIhk9X/6bu2mmfzxWRdM7BPkYvUqJqrx+mbw8iatiPEZV0rgGw8u8CIYCvCSkxrswmr08iyjIu5mjs7u9LGA8nAQBO4V5hbxHWvG8PBshPNC/6b1XLj08XlYLPdP2QbwJGhC8Bca8vBJprbx+iwW8wKqovBeAIj3hEaa86b7RvPE0t7zMpkC8KhfzPFLavzxCXNy7unY0PJG4o7pTdYw8fP0ZvMlh1rpmtAc97QotPFyvlzz61Wo8DP0KPQuvBbyGsf27DrnZuw/VzjxhZWC8YJe+vD67RTyfU4w8vN/EO7qaZjuAOee8IdBevOcnjzwWJOi8Dg2pO7sCVT2lxRO8/4nwvK10Vj0lRYK62h8DPJjWpLnCzZM8GWu7PB0pCD2k/0W6Enk0vcRGAjthNPA7uia2vEWSR7wiq6o8MhngPKO727ytH9g71yw8PJdx9Lwbpee8J/YhPGJVmjxWOGm8PcM+vCNhILs84IA86eO9PKVhKLzIraQ8IF0DvXwbVzw9WkU8yiEtPD6QmDzaGI68YMUsPJJwYbwhuog8aMwVO8rWTTx1Un686JZDu6qSVjvpHEi9g1+zOxBsgLuL/IC8dBk4PJ5Lpbyq0Ze7rPbvPHBXg7uJuws9hvk6PFeXsrvj7Ke8jI1qPLxoDbsRgZ28yL0QvLLcO7zGdMi8mnqiPG7x4rx0ASo8WqkgvCVuBLxfItA8z7Kru2a6RbzqinE7zQq6uzNkZLyKI1G9kgenu4XxxjsbZ8g8xTKBu8CqX7veJ4C8HdiavC8VijyN7qc7r0OkOyXxKTy8XYi8qui5O2Sp3zu640+8ULbau5uIC70vHqG8m/8JO6KxqzvWXh486zavPMRhmrwyhgO7rNoIPRshA7zfk/G7h9CCPHWKgzzCMAG9t74SPBTx4jvddva7OMmZvG2CPzys24Q7POEmvWYUPbx0S9g7y8jyvJX4CLwhclS9cMpFu28sB7x6H5y71FO/uchTPL31CU87Rd5FvM1K5bsMBRu9/IKdvOSkY7zlwtM8oH0PPBpmjrwSFiQ8Z9QevUJt7zzHeRk7WDq4PKkfqjyOW+481nSGPCMF5jut86Q849HGvKYASb2h3i47YXODPFrsETvzzwq8Bs67PMIJOTusbaE7EsydPFTWbrs1nDq76dzwOCr6MjwwkoQ82giKvKnhbrygXyO8RRVpPNxUXL3zBZs6OWk3vcMbITyOCIQ8KdLDvImFEr29wx277K6ku/gC6bv+EIG7lC/hvFLRGTwVdWI8YRjpPJj47LwhKlo8LeAaPGCczTxHWT68KWmpvM2fjbszS1K8ZGgQuwqusDy3s3o8+SpgPNLPxrxpvYw8jWWzPBl8zjsKeXc8PjiYPNUzrDv5t5+7rv3BvK/n27sKU0Y8180jO3PfHjyhP7+8lcdSPWK1Ibo4aJU7ajCkPPg5yDwgK+Q8LnsMufLtxLyE/4O6KEOyvFfYE7yZKom85r4Nuk20QrxP/sa88Vz7PORVdTwOvuq80pivvOsY8rxUdwE931RXPC+RcDwEmqa7T39mPcHP9Ts+Mt67xZgVu183PjyjtwG9ctJlu0jsMDsuIyM8JWwHvV3M6jtHBOA7GmOdvFT7GDwLfkA9FsglPB8GqjxpZ4q8id9dvIBKKTz3kQM8CBkFvHix2jtAk7s8X9bkvDdLSjwYLhK8fvB1PKm8/Lsl1Pg8xo0hPSPR3jvo2VC8Ir4KPOTcBbshq0i8XOXUvCt/FT3gDcw7LDBdu9tnyzy2hxQ82YM9vJijOTuzS7C6h9e5PNd6DrsKpka8J9GuvAT+iDxIeoi80xKSvO7xcrw3q4o7DReouy70sbypEqm89XUMvbJRN72tMFm8Mg4pveq0iDzIUyi7SMQhu+StprwTrg27SISfPHhTMjwx9KM6OFhTvTPIJDsQs3k8DBIiPBV+Mrw2RBw94O8kvHeTrTynRym3qcjFPORSgjuBGVk8Vj8JPDhXSLzfxZC7Q869Oxuikzwv2pq7kdNCPSEl+LzBW3k7BoajPIkqZDx/gB487F8rPEGkmLutlzY8X/FLPMABEj1yovI7rZqcuyfLQTym97o7cP3MPNVOGTxkGgm8CFGSPJWSzbriPMQ8/XX9uwQDf7xTaym99TAIu3ksKrtHfJQ8yPyTvMiccDoDu9u7FceEOfiILTsojy88Y4Jcuy2vnLuLtHw9TAagupDWWzwh1cQ6IkucPKbZSD2BYsC6za4nvIVPWrxdlie6LXiovKn1nbyZYwa7aHCLOwWbEL3fSHQ8hoXXvBKHUjt7z6u9HvGRPCf0szyOgQ09gZS6uz6ddD1w51+8REcyvNQG6jprVy+7k9Cyu1OkFT2QrpS8hI80Pc6R/DuBme67BlEkvNY57TocZiw8coKsOwBux7vzUDW9AmaWPPyRuDzsV4o8fjY8ux2iPruz1Qy77MM/vVcytzzMiR87SWEuPOUSFjztHQK9Hss8PPkOET3wi9w8BcCavPLiWrzl7767eCJ1PAMxoLyFSqy8N0TFPLfbQbzuSWm9ELYlu+TEQLs0M+c8kPiZvGg7CTwP6cY5WhNfvLrgIr3mtM685ssBPTCfJ7oMcK2820IzvN8X+7zl5VC9bZI+vedQ+zwMIoE649DKPJd0PDuyGa28xkXwOknqk7pJ7Ps8fYVTPH4knjtvduk7o5G4uxWyUT3wJpA8jHpXPJv1jTxYlCc86gCJuqT/zTvK7767d5MguxAjHb0TNoe8MbCPu0pxWLzfzcE7aM+MO/S+Dj1zhSE8mHv0PHiyBDuN99q8T4zjPCVvBD2HGd47hFvCOZJAzLz81i88LylCPA2bSDsUObu7nRXKvNDtEDp00IO655B6unSNmrzT7f48LsquPGA6ZDyhq528YQQbPGMWp7oGvpu8XNpgPDmodL16Mhc7ZisqvIm33jzI4Vu7K9FbvIHIV7y347c8M3OsOy8V0zyvFCM9zjGhPLHRyjwgtsq8iNZGu43G1bz7SS67CusIPDgq8LzUYcG7yBIFvJt+DT3HCpe8ZrMrPFYYN71UN4O8jlyGOmfw3TtckUo8jiGnvDXRLTwaUJA7wlkDPM6hErz+8048oGLPu2oPCLw77xw8zr3/u+FvUTzEN4G84h6Yu3+maTy9xVG8Pn1HPCY6wzxCEwM9m5AJvQSYFDwbmDI1nzDOO3BScLzUz5u5/0CrvDh8Hr0zyJO7coaaO3j187tvfY68B+aLvMBCwLuJr/c7+RjVu4ilbTzC/am7pOIRvMf8jznK/XI8NJhEPFC21Ly16nO8QBscupqB4zzy5JC80MNnu/lpEz2ZkxY8HRwCPELZhLznrYC8KQscvcsxkDyjrxM8F3f3vOMCczzqkZ28tx5EPMwA37sNam08SKGKvAbTYLxfo4C8Jif/vOl8ebkNdvq8NRiYux/P4rhqT9C7j2rDvPVYcLu7p2G8rHVjPNFu2DxIZZQ8Yuq+PI9ePT0f+gC7zC2yu5jCl7yzfDg9t1+zvJrlG7wUUHA8jAxDvFLHDDzD3KS7PaKKuxl8mLxZHFS84H36vN57Q7wB2Vw8z4PtPKgwBTyuU0k8623YvE60Aj29yRu8S1SePCO/5rsafjK8Itm5u8K8Gb3iueu7aWGNvBZat7xDmiU82Tu0vHxwmzvVC3I8e3SGPBmLUT2w7xu8s873uyUljDwqXr284CRVPGXjgTwvNUM8t9LxPL71gbvk+ei8P3FsOlelQDztfIK8EAC+vBfHhrtl5H08FSGPuwapMzwCNjQ8w28YPLGlaD1dw9+7G3g5PJAYG7sG+IE5iZTrO6TQFT3FX1S7JmrUu/ZA9Tzy6Z48QrcAva6KRjwMUGQ71Hm8POzy+zuohUm8yLs6vVcY2DtYblq7K3fhOx5wsjuAjXC8N8d4PE8nt7zcFus83vzmPFOfB7xnM/67rfgXPd244LtTfwQ8H18ovdOMPzxrRey6PKfdPEOWXjuBbQq9BU+yPJUqeDtOoiw80uZSuzw6Xjuqj0E9FXQXuyBHrDvH9928yLMPOz5+sLyYKCQ8cKZZvDTnDr1OQgC8SJXLu3kt5rycYiA7h3IdveqiNbxyqjy7ggQuvGrNDzzgLuK8v0tlPb4hQTwg6mY8erxSvIG/lzx+1GS8xrLEuy463Tt1Kly8XHCdPC4LAb0hFai74O6XvJaxgjzY2V68POflu+SwyrtQuji6XGytPEiyGTwmVS88HqnaPJfsazvGw4w8fEG2PAL/r7w1ZsM814mrvETfujvqokc8juGwuxneRzyDVRo8Mn9+PBDplTt68AE9rFPXOTc7rzt8r8k8z1ofuS+ot7ygEj+90GmGOVhUFrulWFG8wzEwPODcBbuxKpQ7xfFHvGhdQjz5PEk9BiECPJRhZrzT4am86PWbOa6cGj1TlR48QvetO/Dkz7oP5787qDa5uVS3Aj0slyA7u19VvLrVOz28gKK7c2wMvWpZsrtYh9K8LtZoO4FtqLzZ0Nc8dxWDvLqazDpis7W6fnNmvKGzazvxgQS90CuSPO2JWryUCXS9pG7HPCrVRjzhX+i8OAJMurYfKb3HTDm8slVJPLqTK7ylKUI8UGgkO3UFcLxdi7U8f/2cu4NFeTzOD187owcEvedi/rq+Ase4czpKvPvUOrwHcky7tDgxu1buLbu06M+78ZEOPUjbH72kg1y8Rcy+vNsiCb1AX+S7FMAGvAnFRjxvsJ08s/QRvNM9QDyorYK6WgKWO+sgPjtblUi8YWnUO4CVY7w5eO88hc7Sux+fDz38jP47O4P6OhPIzzxwy7c7f2lKPI7AqLxvPCe9qwpzvDes87ztUII8soL7OkHPUbsjPDu9f07TOieS27t0She9bJ2/uvMx2TtwQba7NPoEPWvaBbxsc6k7d7+gPKUEFTuyHMu87lcHPOZHbDzUUqS7cPs1PJ/YLTyZjQi937MRvOMmujylpzC811bLvJ9puDoXQyg8U0OlPPmcG7zLsha8DK3nO3loLT3sXJY7JCLIPA6erLy+MFa8uFmuurVZHLya4sg8K1ubumupEz184hI8SQU5vVovHzz90GW7bgluuO9Gg7z0GIG8sOsYvYDd+TwS3fE8f83XvO8sujxVMjG6soi2vOc/4jxiofg7fkWCuB1V+rxNKzE9za91u1NpQLz384Q82pYiPcjqL72ifQ29b/YfvOHDAD294se8FvcIvUe+n7xjUrk6J8NwPK2GibxxWzO85yrlu3gfAj2qjwA8y45evAGoTLza+He7mKj+vKjN+zxv8dc7NHOdO9I5tzk3l1E8nLzCvMPzHLxdc5g6Ez9Nu8clhzq0PKE79akdPXj6/jzRNxC7kYcDPEmQDrwOmX487ViAvNU9a7xak5072J0QvAiAJLtVz+s8+d6wPMvoQrw4as27W1+kPEmMjTzfvcy774b+OwtFnLyULz68XMw6vEREabwLN7A6d3cSPPu6h7swDde8/sP/PJb94rzc4Ye7Zl0LvRostLuKD6i8k2m2vCKQEj2+9nU7b4mou4wGljuN6I86HX1kPNHHn7sAslw8Z5E7vPCKIjz6x6y6cE9tPLVUDTz2c528dG2XPJS0drwQ2rE89d4EPaqEIDxxQoG87hKgulAVeTw2lQe9yqI2PYu37ruEptC7w2mavMilOLxbUQi9m+5CPM2Dd7rmw4S8w6GGOzCc47tispy868YuPHu1hrslpi89dk+BOxRxW7xILP06cgxeuyjBDj28K+e8GyjLOoPiYrs1R5k8KZWzvLaAVrvathq7f8DcOzKHATrTVDi5owzQO0a9bTu+c8U8w9UJOypSrrzCc5A8WwZevGCu0Lw87xm8yo0vvOXI7rxhR+g6XL3XvDNDxjkopN48898gPH+6+TyJNzg7BLAAu+xSSzzhWh684WsAvVYAsDvQn9o83XikPIEReDoHgBK8WXVAvCUXqjyArEE8RRU8vdnohTybYqs8pFAePEKIkjzBxI482Q7cO91cTz2JfYK8MGiSPBA7v7xGARm85n4qOyMlmjyfoHk760YCO6aDmLyJPVA8xvDQPKD5Czz/I7W8bsASPJ4MZzyt6x06UbsOvOx8WjwLmUI7nhwDvaHnh7wwS7i8WWc6PDPkVDwQJaY7rGE/uL3XkrzwSAm7AjC1vPXxqTyCfaO7rYtRup9lhDxCiUy83EASOr3wbDyX9zE8TtY7PGxQl7zIpYy88zrFO3b1Jz1kY1a9boSTvMk14Lp1rvS824VZvGZbDryEI+08I8r7Oti/PjqQzhs91MVyuzL96Lu2bJM7J5AQPG/Rxzwc7Jk8GwXUPOUw7jtp5728u83svEL4TjwSqds8PXBmPPZSNzyGUYo8d7fcO2PrPD2ZxLK7S0tUvK5kbLxAe7+8GdbwO/MGAr0DVMU8MW9/uzENorzN8co7IFGouyYfkDzxu5a7eZSyvGza+DuSRyw8qtBbPFx4ejwi4fM70Z2yughkSDx6y8o8KyknPO0cFru3Yyw8YFBTvJXqbDxSk566XnqQPDAoJLwUo3+8C48kPb7MDTwhnNO8BgUIPIHD5LvmhiK8KipdvOXeKbzplOC6tC4RvRYTWzytlRw9JkTmPIuYGrwmXgW9DQbZOeSAaLwGQJG8VLWZvJguIr2N8by7bgmePMUNYbx7Tma8B6lqOYo8SLreBgm8mQ48vG0WDDxh4mA8htkOvAvcAD0SFzc8CuyrPI0V1jzy7fu8d8X7u9rCPbw40FO8QZmlPLszczwQrD68VVnQO7FAe7vlrgc8Gr0uvJhHzrvfcqg8dKU0PGkbvzy6lOW8lublvHaNHrzPvyQ9E1Upu6OkezvJAEi882q8O1AVnTydeRc8uNbhvLHSoDsSciQ7oegTO1XThTxtgLA8XarHOxudgTuiR3O8KR8RvCbMAz1eOnO8ej4ru+jxD73X+9m8/QNtvAlvgTw47yO8dhAovNU/tTwB5qQ8qw7cu+HOYrvmb5S8veiIPKBCOD3lkOE8uw6jPLzh/zzchWS8nL/JuyMxv7wOwIW8ypnBPAQZojwM9g68EMiDOiYfnDzl9SY8xBMjvPq2Pb3xToS67P8/u8o6FruDN9e8AmN+PDZEIrzd8kS8vmqDvCoP6jygycw6PnKuPJQKJj3EMMe8rBapPC64hryEgVu9lXrZvHGpizsPmBO9toWsvFw1yTyBNt48KzXHPLP5mTp10UG8aecBPLNoJTyhAe28mSEhPHzojzxomSu9CgoEO0AT4jxyTKi8mBCqPNRWk7wyQDE7ILPKvINkD72U74q8MXx6uxYIkTzKt5k8A+/VvOFG17xOopQ7KJ09vOHUBz1TzhM9YH+BuyBHEzy/g4e8MmgQPFr9rLzTPwQ8JUkBvIoO/DvYEnY5/3IQu/I4tbyv9xY8pSTWvLqtpzsCyyG8HnZIPPuEmLybrpI8o8nzvMBKkrw/HIO81EREvah6crxDqwq9cvdnPJ7qQb0I/PU8RRsWOq4+7Drd7U28nRmIvN6PyzxP3ga9F5+Suq7SnrzwW+A7XrVVu4Rz8DrLMaW3muGIO5LAqTs62vw7Z2/BO+LWJTuQ0fG7tpraPH18tbpe2WC8Iq0kvPMiBr1DFQW81mbIPOMgoDtwnWS8lFecO1rc47tm1IS8TvHJus6fMrkLDym8Q7v/On4eEr0UqwG9IskEPa/yUjztkgo8YzZAO7HDCLujReA78xUiOsS+QTi3DgY9ftPWPI52vzzpWUU9LCwsPe/Q4LtggeS89SDqOnWi7TwuVjw83x2iO6Qm/jo6NVu7ojASPfcqNzzcilq8bwixPONLfrxB8hW9t2ZEvT64GTtRazw85dY/vBj5IjzvqvS8l0pRvMVODbvO1He5GiVFvDhvPj0o5wq8xSmIvGfIFz0oqRE9uZxDPB0xhDzDmJ68BrVCvGa6Lj0pBOY6jfoxvOJphbxkJgw9W45quxVXobzTRMs8F+98vB8vZzm7xbK7HdTnuxwgmztCOJA89ReZPA3/v7t5xCw8WYwaPPhPlLxkiOW8wpQkPFbHYLw2Elc8zEZJPGZkmLzI8B682J4luyr/oDpul387C+dyvIRrGL0Qc5i8aFXJOxQaNDyJYHs5O2KMvOOVmrx3KLg89brFPJmlxjs9qEe8pqKyvF/dML1OsEA7iiltu/hFMD3uZ6m8/r2ePJP4jztULXW8TF25PKzpNr1nd6W84TCpu5cGsTzNO0g7cye9PDwauDoA/Ee9GJwLPAGFTrwz2LM7cz09PLn7xjydNa+7OkP+OvrAMLxD8wK9KcsRPaN2EDwv26a6aQUGPGf+Ebz2ppk8zSVrPAvBAz1W5iQ9ttDNvIsGy7vlCnQ8t2KxPIqBJz1vG8q8K5vkux+2Gr0M3D47tvrCvGoL7rwt+Qu8E29NvFXodr1nM1U8ZIZsvHdSB7wKPIG81ouyPFljBjs/K6+8dqCgvE37EbwDb4+732tIPIt2FDz+OxC7+ojfvGZYgTubqAW8tpaPvEUWKDwlu/+8RmgNPdUPILy1vqY8d70TPWfcKznhE/68hSv2uZ8lbTwfIje7hSTfPP8Ts7qxKre5WXPuu4NbNLxPCvO8m9JcPB00WDvHqS07n5roPCDii7tz7ay6t1ZxuyTDjLwkiSi9TowQO3VImDwUIJW8bZWyu2WMsLyuYee8iZHFPPYVYTx6boC8t0OrvCh2Dr0Ljra8Xl8fO1fVFD368ay79DfkvOCwCDyuQtO7U4SWPGaPsbtgUZ87Z1zcvHyatLykw8W8EEMBvWuQortFtQo8wyYzvGHgVD2lwYQ78zeDvJvepLtacd079T2tvBlmQr1NGk+8orAfPLRWvLwjS2A8Fmd4PKQn1zx77p08Ix6HPJkTXTzVCLi8qRaNPKWmUryi+gq9UpNmOxm6x7zwYaa7liWxPGJSubwms2s85ctgPDga/ruGcq28jmOlOpIOXjxXU+08hfpbPEOtGjy/F+y7m0dCOkQMrzyCMJe6P2n0vKVs4ryXGcy73cqfOud92LzSKUC8S9IvO8OH8LyHLgC9qFeQvIfU7bt3NFw845FTvNfKCbxbbxu7AqKnPH16ujxIU+Y8PNVSPAu+GzypfBK8JEXHPIMd67pj/tm8n+Xbu6nhkTxxLK68z084PUmeKTxdJYw7iHeGPOXZbrwxEWI6LX8KPE4Gobx/De08tGlVPFb4I73l3NY7om9vPOZqt7tGqWY8zCKuvPGa8jwMBUO8Fvvsu7m42rzLVOS78l7yOmbUdzpZOiG9XB9tvLDlbbyQ6887R3oxve2fdTxpMaQ7FK26PP5qIzvpAB+7ECY9O3C4CL3oWgE9yJEUvZtuPzvXMgG76vyKO6ZKSj0grgE97BsZPcktxbxzgT49x1s5vflYrzw13zs8krJ3O71Z/Lt//cm8dowpOstzRb06ycO633oPvCa6GTzC6hK9VrZ+u1LhTT3gRDG9Dz43u5urpjrk0Ai9wiO6O3n2drwvgyU8O6JEu5cOzTw4vc48S/mgu+KwTbz/czM7pde/PMW4BDyZ5hE8qMfvuhmPjTy8oHC7vbavO2agYzxnttw8B+c7PDlTcTw6LE66UMYXucG7arzosOs8VluvPI3c1rshoKa8YwIoOttKyTuMXC+8ivz/u9dD0rxk9/k84dsavDp/T7xLz0i4BBU6PE/oNTxvihs8rdTUvEnTObznhsM8N72GuwzoETzb+aI8MFHGvOZAEj2gNBg7B7yTPA9kbzyVVso7n8B5O2fgSTzlwKy83ljhuSYWtTwYTUu8NxtovPoaG7x/EAi7jOlZPJpet7ojx2c7tA1APVWLZrwz/su8LpFGPM0O9DuCwgY8U/65PEFkmLxiNqU8PgVAvByaX7zBijW9XTb9vO43CzwAR8M7AdeFPJBqiLyJYHc8BLi7PD8dsbtKJ1a7cxvevORjmTzuAMW8qFKAPC4IDD0F5DM8hUJMPBFcujxjvhu9hf4RPPwRHzzaNn28T2sRvPSgC72xhRI8lPxFu+HNaLv2z268NEwAvcKvyrzVg4+8jVL5PLj9Hj3zsu07lj+BPA/2GTxW5aq7XKfeuwzS8Dta0P68eQO+vBkgibx+7oc7KyIcvYhEmTt//rq8B3mqvO4Wxbs+hIM8toeUvByG7DzaDhQ8Q+zCu2rkBz1nJsA6WTQAvSdpAz0L18m85JR1ObflJTs53Qc8ODBpvFYkhLxMOye9zotEvAuBhTw+2JY8Y2TuPJx6jTxsxTi8v9GVvBVIqzwCySC77DEPPesa87qnvZO8s5GkPJiZhDx1MQg8/r8LPAe0gLypRm689KOZuejtlLwAARe6Ioy8uiSxSTz0+f67SAWTPJFzKLwiDfs7fmDsO6fH8bsub7Q85rmUPCgn8jv7Aw097G0ZPEd7Xj3/8lK7MU7nvPSlJT063+C8YakEOywkhLt9vBI8hWuEPH5Clrz0yAM9Rp79PK0TkroZ79g79nB7Oyx1Ozz/UAc80MJ6PN/9ejv3Tmq70xb/O3yVFryE/5q7EIaJvOg0yjy4ge47CQUOvIfrGjyCD5G8cVmivJu+OTwGI1+8EDNVvIBxQzzQyM08IncrvOMdIDw4ok+8mIgZPPFsGz3E5sQ8IOSkvIpytLssX2G8h5mrvK+ky7ppS2i8lp7UOy+86rsThYg8BMv8vB/2sDzwmtS8dpRKvPuqHLwfqeo7/jhKPWdm1TwaJhQ6LxQbOxWMhjtiy0w8KfVrvCi40rvwPLM71JDDO9sAQbsAkge8n9xlPIm0wTv2Eg48yQ5VuRJ43Lo36bo86y4Svd6Cpjt9dxs886yOPNP8JDvHrJo8DnrSPDc6p7v0nVg8q1ETPJoBqTfKFpe7Lhltu6bvIbsg3087BMRsu17vKrugp9m7cMTuO6kCgLxqKAe9BID8uzHi6jyEqgO8lejKPHPDerv5XXg6GBaxOc/UH7wO2VW8smIkvACgprw1BCK8NrUZPGZuSLxQO048jdgkO89DobwFwj47ybuXvBQevTqT7rW71nXqPFYbtbzabKE80QiduzWFujyIzES8zlvsu8wBorxHhje7312RPA5WOTyd4C68zeogPE8oKTzq9r87EYAQvUe6CD36VJK7utZnvD7KEDuCMRG9mjrGPE3lhLzJlyc8LAJwO5VSODvMBb87hpftO2/PDLynrme7Zzeeu40vZTwQAyw8A3HIPImFtbtJVR27oji6Olcdo7zijw88HiIYvE/uabxWhQ88oiCNvMlCqLupx4c89dhhvICeALwGbdW8jr+lvF8hQrzPW6K82aFGvPPL0Lv4W8e7Qlx3uUPwzbzt+xc8FJWMO8xnf7t8AXm8dVkTvA==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1640'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      - content: |-
-          <think>
-          We need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","limit":5}'
-            name: run_search
-          id: call_5he6nood
-          type: function
-      - content: 'Found 1 results for: class labels'
-        role: tool
-        tool_call_id: call_5he6nood
-      - content: null
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"labels class"}'
-            name: run_search
-          id: call_54826ler
-          type: function
-      - content: 'Found 1 results for: labels class'
-        role: tool
-        tool_call_id: call_54826ler
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '445'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"label classes"}'
-              name: run_search
-            id: call_ddn7y42u
-            index: 0
-            type: function
-      created: 1769793893
-      id: chatcmpl-482
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 23
-        prompt_tokens: 364
-        total_tokens: 387
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '83'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - label classes
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1915'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: class labels
-        role: user
-      - content: |-
-          <think>
-          We need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","limit":5}'
-            name: run_search
-          id: call_5he6nood
-          type: function
-      - content: 'Found 1 results for: class labels'
-        role: tool
-        tool_call_id: call_5he6nood
-      - content: null
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"labels class"}'
-            name: run_search
-          id: call_54826ler
-          type: function
-      - content: 'Found 1 results for: labels class'
-        role: tool
-        tool_call_id: call_54826ler
-      - content: null
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"label classes"}'
-            name: run_search
-          id: call_ddn7y42u
-          type: function
-      - content: 'Found 1 results for: label classes'
-        role: tool
-        tool_call_id: call_ddn7y42u
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '298'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: Search complete
-          role: assistant
-      created: 1769793894
-      id: chatcmpl-973
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 6
-        prompt_tokens: 406
-        total_tokens: 412
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5944'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for information about class labels in the DocLayNet Class Labels document
-        role: user
-      - content: |-
-          <think>
-          Need to use search once.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":"DocLayNet Class Labels","limit":null,"query":"class labels"}'
-            name: search
-          id: call_rf5bz17k
-          type: function
-      - content: |-
-          Found 1 results:
-
-          [1] **DocLayNet Class Labels**
-              DocLayNet defines 11 distinct class labels for document layout analysis:  Caption - Text describing figures or tables  Footnote - Notes at the bottom of pages  Formula - Mathematical expressions  List-item - Items in bulleted or numbered lists  Page-footer - Footer content on pages  Page-header - He...
-        role: tool
-        tool_call_id: call_rf5bz17k
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '724'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "**DocLayNet Class Labels** defines 11 distinct class labels for document layout analysis: \n- Caption
-            – Text describing figures or tables  \n- Footnote – Notes at the bottom of pages  \n- Formula – Mathematical expressions
-            \ \n- List‑item – Items in bulleted or numbered lists  \n- Page‑footer – Footer content on pages  \n- Page‑header
-            – Header content on pages  \n- ... (additional labels included in the full document)"
-          role: assistant
-      created: 1769793899
-      id: chatcmpl-579
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 93
-        prompt_tokens: 1171
-        total_tokens: 1264
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_search_with_session_filter.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_search_with_session_filter.yaml
deleted file mode 100644
index 076f1392..00000000
--- a/tests/cassettes/test_chat_agent/test_chat_agent_search_with_session_filter.yaml
+++ /dev/null
@@ -1,880 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '412'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Data Sources
-        The data sources for DocLayNet include:
-        - Publication repositories such as arXiv
-        - Government offices and official documents
-        - Company websites and corporate reports
-        - Data directory services for financial reports
-        - Patent documents
-        Scanned documents were excluded to avoid rotation and skewing issues.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 68
-        total_tokens: 68
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5218'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for information about DocLayNet
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '509'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use search tool once.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"document_name":null,"limit":null,"query":"DocLayNet"}'
-              name: search
-            id: call_qg1ku4jk
-            index: 0
-            type: function
-      created: 1769794108
-      id: chatcmpl-107
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 41
-        prompt_tokens: 1027
-        total_tokens: 1068
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1056'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: DocLayNet
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '504'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need two searches: original and one alternative.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"DocLayNet","limit":5}'
-              name: run_search
-            id: call_3xwcatjd
-            index: 0
-            type: function
-      created: 1769794109
-      id: chatcmpl-742
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 39
-        prompt_tokens: 264
-        total_tokens: 303
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '79'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 5
-        total_tokens: 5
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1388'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: DocLayNet
-        role: user
-      - content: |-
-          <think>
-          Need two searches: original and one alternative.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet","limit":5}'
-            name: run_search
-          id: call_3xwcatjd
-          type: function
-      - content: 'Found 1 results for: DocLayNet'
-        role: tool
-        tool_call_id: call_3xwcatjd
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '448'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet layout"}'
-              name: run_search
-            id: call_2nfaz0ow
-            index: 0
-            type: function
-      created: 1769794110
-      id: chatcmpl-971
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 25
-        prompt_tokens: 329
-        total_tokens: 354
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '86'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet layout
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 6
-        total_tokens: 6
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1669'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search query optimizer. You MUST use the run_search tool to execute searches.
-
-          For each user request:
-          1. Use the run_search tool with the original query
-          2. Use run_search again with 1-2 alternative keyword queries
-          3. Keep all queries SHORT (2-5 words)
-          4. After all tool calls complete, respond "Search complete"
-
-          You can optionally specify a limit parameter (default 5).
-
-          IMPORTANT: You must make actual tool calls. Do not output "run_search(...)" as text.
-        role: system
-      - content: DocLayNet
-        role: user
-      - content: |-
-          <think>
-          Need two searches: original and one alternative.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet","limit":5}'
-            name: run_search
-          id: call_3xwcatjd
-          type: function
-      - content: 'Found 1 results for: DocLayNet'
-        role: tool
-        tool_call_id: call_3xwcatjd
-      - content: null
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet layout"}'
-            name: run_search
-          id: call_2nfaz0ow
-          type: function
-      - content: 'Found 1 results for: DocLayNet layout'
-        role: tool
-        tool_call_id: call_2nfaz0ow
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Run a single search query against the knowledge base.
-          name: run_search
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to fetch (default: 5)'
-              query:
-                description: The search query
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '298'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: Search complete
-          role: assistant
-      created: 1769794111
-      id: chatcmpl-419
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 6
-        prompt_tokens: 375
-        total_tokens: 381
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5882'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Search for information about DocLayNet
-        role: user
-      - content: |-
-          <think>
-          Need to use search tool once.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"document_name":null,"limit":null,"query":"DocLayNet"}'
-            name: search
-          id: call_qg1ku4jk
-          type: function
-      - content: |-
-          Found 1 results:
-
-          [1] **DocLayNet Class Labels**
-              DocLayNet defines 11 distinct class labels for document layout analysis:  Caption - Text describing figures or tables  Footnote - Notes at the bottom of pages  Formula - Mathematical expressions  List-item - Items in bulleted or numbered lists  Page-footer - Footer content on pages  Page-header - He...
-        role: tool
-        tool_call_id: call_qg1ku4jk
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '783'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Here are the search results for “DocLayNet”:\n\n**[1] DocLayNet Class Labels**  \nDocLayNet defines 11
-            distinct class labels for document layout analysis:  \n\n- Caption – text describing figures or tables  \n- Footnote
-            – notes at the bottom of pages  \n- Formula – mathematical expressions  \n- List-item – items in bulleted or numbered
-            lists  \n- Page-footer – footer content on pages  \n- Page-header – header content on pages  \n\n(And three additional
-            labels not listed here.)"
-          role: assistant
-      created: 1769794118
-      id: chatcmpl-23
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 107
-        prompt_tokens: 1162
-        total_tokens: 1269
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_count_documents.yaml b/tests/cassettes/test_chat_agent/test_count_documents.yaml
deleted file mode 100644
index b7fcb7e2..00000000
--- a/tests/cassettes/test_chat_agent/test_count_documents.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '75'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Doc 1
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 4
-        total_tokens: 4
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '75'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Doc 2
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 4
-        total_tokens: 4
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '75'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Doc 3
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 4
-        total_tokens: 4
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml b/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml
deleted file mode 100644
index ee77e6b5..00000000
--- a/tests/cassettes/test_chat_agent/test_list_documents_basic.yaml
+++ /dev/null
@@ -1,476 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '481'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Annotation Process
-        The annotation process was organized into 4 phases:
-        - Phase 1: Data selection and preparation by a small team of experts
-        - Phase 2: Label selection and guideline definition
-        - Phase 3: Annotation by 40 dedicated annotators
-        - Phase 4: Quality control and continuous supervision
-        The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 90
-        total_tokens: 90
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4848'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What documents are available in the knowledge base?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '470'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need list_documents.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":10,"offset":0}'
-              name: list_documents
-            id: call_e3klcjfu
-            index: 0
-            type: function
-      created: 1769523738
-      id: chatcmpl-179
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 35
-        prompt_tokens: 956
-        total_tokens: 991
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5290'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: What documents are available in the knowledge base?
-        role: user
-      - content: |-
-          <think>
-          Need list_documents.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":10,"offset":0}'
-            name: list_documents
-          id: call_e3klcjfu
-          type: function
-      - content: |-
-          Found 2 document(s):
-
-          - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27)
-          - **DocLayNet Annotation** (URI: doclaynet-annotation, Created: 2026-01-27)
-        role: tool
-        tool_call_id: call_e3klcjfu
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '509'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Here are the documents available in the knowledge base:\n\n- **DocLayNet Class Labels** (URI: `doclaynet-labels`,
-            Created: 2026‑01‑27)  \n- **DocLayNet Annotation** (URI: `doclaynet-annotation`, Created: 2026‑01‑27)"
-          role: assistant
-      created: 1769523739
-      id: chatcmpl-793
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 70
-        prompt_tokens: 1064
-        total_tokens: 1134
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml b/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml
deleted file mode 100644
index 0abdf7dc..00000000
--- a/tests/cassettes/test_chat_agent/test_list_documents_pagination.yaml
+++ /dev/null
@@ -1,524 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '481'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Annotation Process
-        The annotation process was organized into 4 phases:
-        - Phase 1: Data selection and preparation by a small team of experts
-        - Phase 2: Label selection and guideline definition
-        - Phase 3: Annotation by 40 dedicated annotators
-        - Phase 4: Quality control and continuous supervision
-        The Corpus Conversion Service (CCS) was used for annotation, providing a visual interface.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 90
-        total_tokens: 90
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '412'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Data Sources
-        The data sources for DocLayNet include:
-        - Publication repositories such as arXiv
-        - Government offices and official documents
-        - Company websites and corporate reports
-        - Data directory services for financial reports
-        - Patent documents
-        Scanned documents were excluded to avoid rotation and skewing issues.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 68
-        total_tokens: 68
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4833'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: List the first 2 documents available
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '472'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need list_documents.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":2,"offset":0}'
-              name: list_documents
-            id: call_tsw9p0as
-            index: 0
-            type: function
-      created: 1769523751
-      id: chatcmpl-975
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 33
-        prompt_tokens: 954
-        total_tokens: 987
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5277'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: List the first 2 documents available
-        role: user
-      - content: |-
-          <think>
-          We need list_documents.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":2,"offset":0}'
-            name: list_documents
-          id: call_tsw9p0as
-          type: function
-      - content: |-
-          Found 2 document(s):
-
-          - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27)
-          - **DocLayNet Annotation** (URI: doclaynet-annotation, Created: 2026-01-27)
-        role: tool
-        tool_call_id: call_tsw9p0as
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '453'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Here are the first two documents available:\n\n1. **DocLayNet Class Labels** – URI: `doclaynet-labels`
-            \ \n2. **DocLayNet Annotation** – URI: `doclaynet-annotation`"
-          role: assistant
-      created: 1769523752
-      id: chatcmpl-973
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 50
-        prompt_tokens: 1063
-        total_tokens: 1113
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml b/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml
deleted file mode 100644
index 4df19677..00000000
--- a/tests/cassettes/test_chat_agent/test_list_documents_with_session_filter.yaml
+++ /dev/null
@@ -1,480 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '412'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Data Sources
-        The data sources for DocLayNet include:
-        - Publication repositories such as arXiv
-        - Government offices and official documents
-        - Company websites and corporate reports
-        - Data directory services for financial reports
-        - Patent documents
-        Scanned documents were excluded to avoid rotation and skewing issues.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 68
-        total_tokens: 68
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4833'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Show me what documents are available
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '503'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: User wants list of documents. Use list_documents.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":null,"offset":null}'
-              name: list_documents
-            id: call_6004erad
-            index: 0
-            type: function
-      created: 1769523744
-      id: chatcmpl-41
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 38
-        prompt_tokens: 953
-        total_tokens: 991
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5232'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Show me what documents are available
-        role: user
-      - content: |-
-          <think>
-          User wants list of documents. Use list_documents.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":null,"offset":null}'
-            name: list_documents
-          id: call_6004erad
-          type: function
-      - content: |-
-          Found 1 document(s):
-
-          - **DocLayNet Class Labels** (URI: doclaynet-labels, Created: 2026-01-27)
-        role: tool
-        tool_call_id: call_6004erad
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '513'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            Here’s what’s available in the knowledge base:
-
-            - **DocLayNet Class Labels** (URI: **doclaynet-labels**, Created: 2026-01-27)
-
-            Let me know if you’d like to see the content of this document or have any other questions!
-          role: assistant
-      created: 1769523745
-      id: chatcmpl-378
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 62
-        prompt_tokens: 1041
-        total_tokens: 1103
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_run_chat_agent.yaml b/tests/cassettes/test_chat_agent/test_run_chat_agent.yaml
deleted file mode 100644
index 5b1bf86c..00000000
--- a/tests/cassettes/test_chat_agent/test_run_chat_agent.yaml
+++ /dev/null
@@ -1,488 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5372'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"search\"
-          - Use when the user explicitly asks to search, find, or explore documents. Handles multi-query expansion internally
-          and returns matching passages with surrounding context.\n- \"list_documents\" - Use when the user wants to browse
-          or see what documents are available (e.g., \"what documents are available?\", \"show me the documents\", \"list
-          available docs\").\n- \"summarize_document\" - Use when the user wants an overview or summary of a specific document
-          (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview of Z\").\n- \"get_document\" - Use
-          when the user wants the FULL content of a specific document (e.g., \"get the paper about Y\", \"fetch 2412.00566\",
-          \"show me the full document\").\n- \"ask\" - Use for questions about topics in the knowledge base. Searches across
-          documents and returns answers with citations. Prior answers are recalled to avoid redundant work.\n\nIMPORTANT -
-          When user mentions a document in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from
-          <doc>\", or \"<topic> in <doc>\":\n  - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as
-          `document_name`\n- Examples for search:\n  - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML
-          paper\"\n  - \"find transformer architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\"
-          \n- Examples for ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding
-          methods?\", document_name=\"ML paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is
-          the model trained?\", document_name=\"2412.00566\" \nAfter calling search, copy the ENTIRE tool response to your
-          output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.\nBe friendly
-          and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: Search for class labels
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '505'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use search tool once.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"class labels","filter":null,"limit":null}'
-              name: search
-            id: call_5ipxdc53
-            index: 0
-            type: function
-      created: 1770983496
-      id: chatcmpl-160
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 39
-        prompt_tokens: 1057
-        total_tokens: 1096
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '82'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - class labels
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 3
-        total_tokens: 3
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6032'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: "You are a helpful research assistant powered by haiku.rag, a knowledge base system.\n\nYou have access to
-          a knowledge base of documents. Use your tools to search and answer questions.\n\nCRITICAL RULES:\n1. For greetings
-          or casual chat: respond directly WITHOUT using any tools\n2. NEVER call the same tool multiple times for a single
-          user message\n3. NEVER make up information - always use tools to get facts from the knowledge base\n4. For questions:
-          Use the \"ask\" tool EXACTLY ONCE - it automatically uses prior conversation context\n5. For searches: Use the \"search\"
-          tool EXACTLY ONCE - it handles multi-query expansion internally\n\nHow to decide which tool to use:\n- \"search\"
-          - Use when the user explicitly asks to search, find, or explore documents. Handles multi-query expansion internally
-          and returns matching passages with surrounding context.\n- \"list_documents\" - Use when the user wants to browse
-          or see what documents are available (e.g., \"what documents are available?\", \"show me the documents\", \"list
-          available docs\").\n- \"summarize_document\" - Use when the user wants an overview or summary of a specific document
-          (e.g., \"summarize document X\", \"what does Y cover?\", \"give me an overview of Z\").\n- \"get_document\" - Use
-          when the user wants the FULL content of a specific document (e.g., \"get the paper about Y\", \"fetch 2412.00566\",
-          \"show me the full document\").\n- \"ask\" - Use for questions about topics in the knowledge base. Searches across
-          documents and returns answers with citations. Prior answers are recalled to avoid redundant work.\n\nIMPORTANT -
-          When user mentions a document in search/ask:\n- If user says \"search in <doc>\", \"find in <doc>\", \"answer from
-          <doc>\", or \"<topic> in <doc>\":\n  - Extract the TOPIC as `query`/`question`\n  - Extract the DOCUMENT NAME as
-          `document_name`\n- Examples for search:\n  - \"search for embeddings in the ML paper\" → query=\"embeddings\", document_name=\"ML
-          paper\"\n  - \"find transformer architecture in 2412.00566\" → query=\"transformer architecture\", document_name=\"2412.00566\"
-          \n- Examples for ask:\n  - \"what does the ML paper say about embeddings?\" → question=\"what are the embedding
-          methods?\", document_name=\"ML paper\"\n  - \"answer from 2412.00566 about model training\" → question=\"how is
-          the model trained?\", document_name=\"2412.00566\" \nAfter calling search, copy the ENTIRE tool response to your
-          output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.\nBe friendly
-          and conversational. When you use the \"ask\" tool, summarize the key findings for the user."
-        role: system
-      - content: Search for class labels
-        role: user
-      - content: |-
-          <think>
-          Need to use search tool once.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"class labels","filter":null,"limit":null}'
-            name: search
-          id: call_5ipxdc53
-          type: function
-      - content: |-
-          Found 1 results:
-
-          [1] **DocLayNet Class Labels**
-              DocLayNet defines 11 distinct class labels for document layout analysis:  Caption - Text describing figures or tables  Footnote - Notes at the bottom of pages  Formula - Mathematical expressions  List-item - Items in bulleted or numbered lists  Page-footer - Footer content on pages  Page-header - He...
-        role: tool
-        tool_call_id: call_5ipxdc53
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            <summary>Search the knowledge base for relevant documents.</summary>
-            <returns>
-            <description>Formatted search results with content and metadata.</description>
-            </returns>
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              filter:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional SQL WHERE clause to filter documents.
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: from config).'
-              query:
-                description: The search query (what to search for).
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>List available documents in the knowledge base.</summary>
-            <returns>
-            <description>Paginated list of documents with metadata.</description>
-            </returns>
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              page:
-                default: 1
-                description: 'Page number (default: 1, 50 documents per page)'
-                type: integer
-            type: object
-        type: function
-      - function:
-          description: |-
-            <summary>Retrieve a specific document by title or URI.</summary>
-            <returns>
-            <description>Document content and metadata, or not found message.</description>
-            </returns>
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Generate a summary of a specific document.</summary>
-            <returns>
-            <description>Generated summary or not found message.</description>
-            </returns>
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize.
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            <summary>Answer a question using the knowledge base.
-
-            Uses a research graph for searching and synthesizing answers.</summary>
-            <returns>
-            <description>QAResult with answer, confidence, and citations.</description>
-            </returns>
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within.
-              question:
-                description: The question to answer.
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1054'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Here’s what the knowledge base says about **class labels**:\n\n**DocLayNet Class Labels**\n\n> DocLayNet
-            defines 11 distinct class labels for document layout analysis:\n> - **Caption** – Text describing figures or tables
-            \ \n> - **Footnote** – Notes at the bottom of pages  \n> - **Formula** – Mathematical expressions  \n> - **List-item**
-            – Items in bulleted or numbered lists  \n> - **Page-footer** – Footer content on pages  \n> - **Page-header**
-            – Header content on pages  \n> - *(the list continues with the remaining labels, but the excerpt ends here)*  \n\nThat’s
-            all the relevant information I have on class labels in the current documents. Let me know if you’d like more details
-            or have another query!"
-          role: assistant
-      created: 1770983500
-      id: chatcmpl-782
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 161
-        prompt_tokens: 1190
-        total_tokens: 1351
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml b/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml
deleted file mode 100644
index 1ed2a175..00000000
--- a/tests/cassettes/test_chat_agent/test_summarize_document_found.yaml
+++ /dev/null
@@ -1,563 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '730'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - |-
-        DocLayNet Dataset - Class Labels
-        DocLayNet defines 11 distinct class labels for document layout analysis:
-        1. Caption - Text describing figures or tables
-        2. Footnote - Notes at the bottom of pages
-        3. Formula - Mathematical expressions
-        4. List-item - Items in bulleted or numbered lists
-        5. Page-footer - Footer content on pages
-        6. Page-header - Header content on pages
-        7. Picture - Images and diagrams
-        8. Section-header - Headings for document sections
-        9. Table - Tabular data
-        10. Text - Regular paragraph text (highest count: 510,377 instances)
-        11. Title - Document titles
-        The Text class has the highest count with 510,377 instances in the dataset.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 166
-        total_tokens: 166
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5379'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Summarize the DocLayNet Class Labels document
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '491'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need summarize_document.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"DocLayNet Class Labels"}'
-              name: summarize_document
-            id: call_080b2wi5
-            index: 0
-            type: function
-      created: 1769523888
-      id: chatcmpl-174
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 34
-        prompt_tokens: 1043
-        total_tokens: 1077
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1250'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a concise summary of the document content provided below.
-
-          Start with a one-sentence overview, then list the main topics covered, and highlight any key findings or conclusions.
-
-          Guidelines:
-          - Be 3-5 sentences for short documents, 5-8 for longer ones
-          - Focus on factual content and key information
-          - Do not include meta-commentary like "This document discusses..." or "The document covers..."
-          - Do not speculate beyond what's in the content
-
-          Document content:
-          DocLayNet Dataset - Class Labels
-
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          1. Caption - Text describing figures or tables
-          2. Footnote - Notes at the bottom of pages
-          3. Formula - Mathematical expressions
-          4. List-item - Items in bulleted or numbered lists
-          5. Page-footer - Footer content on pages
-          6. Page-header - Header content on pages
-          7. Picture - Images and diagrams
-          8. Section-header - Headings for document sections
-          9. Table - Tabular data
-          10. Text - Regular paragraph text (highest count: 510,377 instances)
-          11. Title - Document titles
-
-          The Text class has the highest count with 510,377 instances in the dataset.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '759'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "DocLayNet lists 11 distinct layout classes for documents, including captions, footnotes, formulas, list
-            items, page headers and footers, pictures, section headers, tables, titles, and regular paragraph text.  \nThe
-            classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table,
-            Text, and Title.  \nThe Text class is the most common, comprising 510,377 instances in the dataset."
-          reasoning: Need concise summary 3-5 sentences.
-          role: assistant
-      created: 1769523890
-      id: chatcmpl-181
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 114
-        prompt_tokens: 316
-        total_tokens: 430
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '6132'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Summarize the DocLayNet Class Labels document
-        role: user
-      - content: |-
-          <think>
-          Need summarize_document.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet Class Labels"}'
-            name: summarize_document
-          id: call_080b2wi5
-          type: function
-      - content: "**Summary of DocLayNet Class Labels:**\n\nDocLayNet lists 11 distinct layout classes for documents, including
-          captions, footnotes, formulas, list items, page headers and footers, pictures, section headers, tables, titles,
-          and regular paragraph text.  \nThe classes are: Caption, Footnote, Formula, List-item, Page-footer, Page-header,
-          Picture, Section-header, Table, Text, and Title.  \nThe Text class is the most common, comprising 510,377 instances
-          in the dataset."
-        role: tool
-        tool_call_id: call_080b2wi5
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1334'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            **DocLayNet Class Labels**
-
-            DocLayNet categorizes document elements into **11 layout classes**:
-
-            | Class | Brief Description |
-            |-------|-------------------|
-            | **Caption** | Labeling of captions (e.g., of figures or tables). |
-            | **Footnote** | Footnote text appearing at the bottom of a page. |
-            | **Formula** | Mathematical or chemical formulas. |
-            | **List‑item** | Items within bulleted or numbered lists. |
-            | **Page‑footer** | Footer content that appears on every page. |
-            | **Page‑header** | Header content that appears on every page. |
-            | **Picture** | Images embedded in the document. |
-            | **Section‑header** | Headers that introduce document sections. |
-            | **Table** | Tabular data structures. |
-            | **Text** | Standard, continuous paragraph text. |
-            | **Title** | The main title or subtitle of the document. |
-
-            These labels are used to annotate the layout of documents in the DocLayNet dataset, enabling researchers to train models for document understanding tasks such as layout analysis, OCR, and information extraction.
-          role: assistant
-      created: 1769523896
-      id: chatcmpl-959
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 234
-        prompt_tokens: 1201
-        total_tokens: 1435
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml b/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml
deleted file mode 100644
index 53a0670e..00000000
--- a/tests/cassettes/test_chat_agent/test_summarize_document_not_found.yaml
+++ /dev/null
@@ -1,196 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5368'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
-
-          You have access to a knowledge base of documents. Use your tools to search and answer questions.
-
-          CRITICAL RULES:
-          1. For greetings or casual chat: respond directly WITHOUT using any tools
-          2. For questions: Use the "ask" tool EXACTLY ONCE - it automatically uses prior conversation context
-          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
-          4. NEVER call the same tool multiple times for a single user message
-          5. NEVER make up information - always use tools to get facts from the knowledge base
-
-          How to decide which tool to use:
-          - "list_documents" - Use when the user wants to browse or see what documents are available (e.g., "what documents are available?", "show me the documents", "list available docs").
-          - "summarize_document" - Use when the user wants an overview or summary of a specific document (e.g., "summarize document X", "what does Y cover?", "give me an overview of Z").
-          - "get_document" - Use when the user wants the FULL content of a specific document (e.g., "get the paper about Y", "fetch 2412.00566", "show me the full document").
-          - "ask" - Use for questions about topics in the knowledge base. It automatically finds relevant prior answers from conversation history and searches across documents to return answers with citations.
-          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
-
-          IMPORTANT - When user mentions a document in search/ask:
-          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
-            - Extract the TOPIC as `query`/`question`
-            - Extract the DOCUMENT NAME as `document_name`
-          - Examples for search:
-            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
-            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
-          - Examples for ask:
-            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
-            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
-
-          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
-        role: system
-      - content: Summarize the nonexistent document
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Use this when you need to find documents or explore the knowledge base.
-            Results are displayed to the user - just list the titles found.
-          name: search
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: 'Number of results to return (default: 5)'
-              query:
-                description: The search query (what to search for)
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: |-
-            Answer a specific question using the knowledge base.
-
-            Use this for direct questions that need a focused answer with citations.
-            Uses a research graph for planning, searching, and synthesis.
-          name: ask
-          parameters:
-            additionalProperties: false
-            properties:
-              document_name:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
-              question:
-                description: The question to answer
-                type: string
-            required:
-            - question
-            type: object
-        type: function
-      - function:
-          description: |-
-            List available documents in the knowledge base.
-
-            Use this when the user wants to browse or see what documents are available.
-          name: list_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Maximum number of documents to return
-              offset:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-                description: Number of documents to skip (for pagination)
-            type: object
-        type: function
-      - function:
-          description: |-
-            Retrieve a specific document by title or URI.
-
-            Use this when the user wants to fetch/get/retrieve a specific document.
-          name: get_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to look up
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-      - function:
-          description: |-
-            Generate a summary of a specific document.
-
-            Use this when the user wants an overview or summary of a document's content.
-          name: summarize_document
-          parameters:
-            additionalProperties: false
-            properties:
-              query:
-                description: The document title or URI to summarize
-                type: string
-            required:
-            - query
-            type: object
-          strict: true
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '655'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I’m sorry, but I couldn’t find a document with that name. If you have the exact title or a related keyword,
-            let me know and I’ll try again.
-          reasoning: User asks to summarize nonexistent document. According to rule, for summary use summarize_document tool,
-            but if document doesn't exist? We must search? Likely we respond that document not found. No tool needed.
-          role: assistant
-      created: 1769523898
-      id: chatcmpl-69
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 86
-        prompt_tokens: 1039
-        total_tokens: 1125
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_multiple_entries.yaml b/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_multiple_entries.yaml
deleted file mode 100644
index 5a5f7c8f..00000000
--- a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_multiple_entries.yaml
+++ /dev/null
@@ -1,94 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1493'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs, produce a structured summary that captures key information for future context.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What is the authentication method?
-          **Answer** (confidence: 95%):
-          The API uses JWT tokens for authentication.
-          **Sources:** Auth Guide
-
-          ## Q2: What is the rate limit?
-          **Answer** (confidence: 90%):
-          Rate limiting is set to 100 requests per minute.
-          **Sources:** API Reference
-
-          ## Q3: How do I refresh tokens?
-          **Answer** (confidence: 85%):
-          Use the /refresh endpoint with your refresh token.
-          **Sources:** Auth Guide
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '865'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            ## Key Facts Established
-            - **Authentication method**: JWT tokens.
-            - **Rate limit**: 100 requests per minute.
-            - **Token refresh**: use `/refresh` endpoint with a refresh token.
-
-            ## Documents Referenced
-            - **Auth Guide** – contains details on JWT usage, token issuance, and refresh mechanism.
-            - **API Reference** – includes rate limiting policy and endpoint descriptions.
-
-            ## Current Focus
-            The user is currently gathering foundational API usage details, specifically authentication methods, rate limits, and token refresh procedures.
-          reasoning: We need to summarize.
-          role: assistant
-      created: 1769007514
-      id: chatcmpl-760
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 121
-        prompt_tokens: 365
-        total_tokens: 486
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_single_entry.yaml b/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_single_entry.yaml
deleted file mode 100644
index 7e11185d..00000000
--- a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_single_entry.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1207'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs, produce a structured summary that captures key information for future context.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What is the authentication method?
-          **Answer** (confidence: 95%):
-          The API uses JWT tokens for authentication.
-          **Sources:** Auth Guide
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '575'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            **Key Facts Established**
-            - The API uses **JWT tokens** for authentication.
-
-            **Documents Referenced**
-            - **Auth Guide** – Provides details on JWT usage for this API.
-
-            **Current Focus**
-            - Understanding the authentication method employed by the API.
-          reasoning: We need to summarize.
-          role: assistant
-      created: 1769007512
-      id: chatcmpl-739
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 65
-        prompt_tokens: 292
-        total_tokens: 357
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_with_current_context.yaml b/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_with_current_context.yaml
deleted file mode 100644
index df3a653d..00000000
--- a/tests/cassettes/test_chat_context/TestSummarizeSession.test_summarize_session_with_current_context.yaml
+++ /dev/null
@@ -1,89 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1581'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs (and optionally existing context), produce a structured summary that captures key information for future context.
-
-          If a "Current Context" section is provided at the start of the input, incorporate that context into your summary. This might be initial background context from the user or a previous summary - build upon it rather than discard it.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - When building on existing context, merge new information with prior context
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Current Context
-          Focus on Python APIs. User is building a web application.
-
-          ## Q1: What's the rate limit?
-          **Answer** (confidence: 90%):
-          100 requests per minute.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '682'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            ## Summary
-
-            - **Key Facts Established**
-              - The user is building a web application and is focused on Python APIs.
-              - The relevant rate limit is **100 requests per minute** (confidence 90%).
-
-            - **Documents Referenced**
-              - None cited in this exchange.
-
-            - **Current Focus**
-              - Understanding and managing API rate limits for the Python-based web application.
-          reasoning: We need summary.
-          role: assistant
-      created: 1769164539
-      id: chatcmpl-369
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 91
-        prompt_tokens: 362
-        total_tokens: 453
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_chat_context/TestUpdateSessionContext.test_update_session_context_returns_context.yaml b/tests/cassettes/test_chat_context/TestUpdateSessionContext.test_update_session_context_returns_context.yaml
deleted file mode 100644
index 088560ec..00000000
--- a/tests/cassettes/test_chat_context/TestUpdateSessionContext.test_update_session_context_returns_context.yaml
+++ /dev/null
@@ -1,80 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1163'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a session summarizer. Given a conversation history of Q&A pairs, produce a structured summary that captures key information for future context.
-
-          Your summary should be concise (aim for 500-1500 tokens) and include:
-
-          1. **Key Facts Established** - Specific facts, data, or conclusions learned during the conversation
-          2. **Documents Referenced** - Documents or sources that were cited, with brief notes on what they contain
-          3. **Current Focus** - What topic or question thread the user is currently exploring
-
-          Rules:
-          - Extract only high-signal information that would help answer follow-up questions
-          - Omit small talk, greetings, or low-confidence answers
-          - Use bullet points for clarity
-          - Keep technical details but compress verbose explanations
-          - Preserve document names/titles when mentioned in sources
-
-          Output the summary directly in markdown format. Do not include meta-commentary about the summary itself.
-        role: system
-      - content: |
-          ## Q1: What is the authentication method?
-          **Answer** (confidence: 95%):
-          The API uses JWT tokens.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '559'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: |-
-            ## Key Facts Established
-            - The API authentication method is **JWT tokens** (high confidence 95%).
-
-            ## Documents Referenced
-            - *None provided*.
-
-            ## Current Focus
-            - The user is exploring details related to **API authentication mechanisms**.
-          reasoning: We need summary.
-          role: assistant
-      created: 1769007530
-      id: chatcmpl-975
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 64
-        prompt_tokens: 284
-        total_tokens: 348
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestAskTool.test_ask_with_tool_context_returns_tool_return.yaml b/tests/cassettes/test_qa_tools/TestAskTool.test_ask_with_tool_context_returns_tool_return.yaml
deleted file mode 100644
index 36d4f514..00000000
--- a/tests/cassettes/test_qa_tools/TestAskTool.test_ask_with_tool_context_returns_tool_return.yaml
+++ /dev/null
@@ -1,840 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '831'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need first sub-question. Is simple: definition of Python. So ask: "What is the Python programming language?"'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What is the Python programming language?","reasoning":"The
-                original question is a general inquiry about Python. To start the investigation, I propose a direct, focused
-                sub-question that asks for a clear definition of Python as a programming language."}'
-              name: final_result
-            id: call_p8ab8c7u
-            index: 0
-            type: function
-      created: 1770981737
-      id: chatcmpl-105
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 99
-        prompt_tokens: 365
-        total_tokens: 464
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2830'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the Python programming language?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '507'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"Python programming language definition","limit":3}'
-              name: search_and_answer
-            id: call_zueyemxn
-            index: 0
-            type: function
-      created: 1770981739
-      id: chatcmpl-557
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 36
-        prompt_tokens: 622
-        total_tokens: 658
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '108'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python programming language definition
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 5
-        total_tokens: 5
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3309'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"Python programming language definition","limit":3}'
-            name: search_and_answer
-          id: call_zueyemxn
-          type: function
-      - content: |-
-          [fde33f29-9966-4af2-89bb-a99276e3bf28] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_zueyemxn
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '558'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- **query**: What is the Python programming language?  \n- **answer**: Python is a programming language
-            that is widely used for web development.  \n- **cited_chunks**: [\"fde33f29-9966-4af2-89bb-a99276e3bf28\"]  \n-
-            **confidence**: 0.6"
-          reasoning: Only one result. Use it.
-          role: assistant
-      created: 1770981741
-      id: chatcmpl-122
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 89
-        prompt_tokens: 733
-        total_tokens: 822
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3753'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"Python programming language definition","limit":3}'
-            name: search_and_answer
-          id: call_zueyemxn
-          type: function
-      - content: |-
-          [fde33f29-9966-4af2-89bb-a99276e3bf28] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_zueyemxn
-      - content: "<think>\nOnly one result. Use it.\n</think>\n\n- **query**: What is the Python programming language?  \n-
-          **answer**: Python is a programming language that is widely used for web development.  \n- **cited_chunks**: [\"fde33f29-9966-4af2-89bb-a99276e3bf28\"]
-          \ \n- **confidence**: 0.6"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '677'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to return via final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language that is widely used for web development.","cited_chunks":["fde33f29-9966-4af2-89bb-a99276e3bf28"],"confidence":0.6,"query":"What
-                is the Python programming language?"}'
-              name: final_result
-            id: call_rwgn9if5
-            index: 0
-            type: function
-      created: 1770981744
-      id: chatcmpl-377
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 85
-        prompt_tokens: 847
-        total_tokens: 932
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2746'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>What is the Python programming language?</question>
-                <answer>Python is a programming language that is widely used for web development.</answer>
-                <confidence>0.6</confidence>
-                <source>Python Guide</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '717'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to answer "What is Python?" based on evidence. Only prior answer says Python is a programming
-            language widely used for web development. No other evidence. So answer accordingly.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language that is widely used for web development.","confidence":0.6}'
-              name: final_result
-            id: call_p1sr48zo
-            index: 0
-            type: function
-      created: 1770981746
-      id: chatcmpl-912
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 77
-        prompt_tokens: 430
-        total_tokens: 507
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestAskTool.test_ask_without_tool_context.yaml b/tests/cassettes/test_qa_tools/TestAskTool.test_ask_without_tool_context.yaml
deleted file mode 100644
index 21966adc..00000000
--- a/tests/cassettes/test_qa_tools/TestAskTool.test_ask_without_tool_context.yaml
+++ /dev/null
@@ -1,1083 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '926'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need to start investigation. Simple question: define Python. Maybe ask: "What is the definition of Python
-            as a programming language?" Let''s output.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What is the definition and primary purpose of the Python programming
-                language?","reasoning":"The user asks ''What is Python?'' which is a general question. To start the investigation,
-                we formulate a focused sub-question that seeks a clear definition and purpose of Python, which will guide
-                further research."}'
-              name: final_result
-            id: call_0nnda0yu
-            index: 0
-            type: function
-      created: 1770981713
-      id: chatcmpl-757
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 116
-        prompt_tokens: 365
-        total_tokens: 481
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2868'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and primary purpose of the Python programming language?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '523'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":3,"query":"definition primary purpose Python programming language"}'
-              name: search_and_answer
-            id: call_6l76j8m7
-            index: 0
-            type: function
-      created: 1770981721
-      id: chatcmpl-532
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 38
-        prompt_tokens: 628
-        total_tokens: 666
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '124'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - definition primary purpose Python programming language
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 7
-        total_tokens: 7
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3363'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and primary purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"definition primary purpose Python programming language"}'
-            name: search_and_answer
-          id: call_6l76j8m7
-          type: function
-      - content: |-
-          [784c5dd3-2cff-4ed2-88d2-932992529175] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_6l76j8m7
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '563'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Only one result, vague. Ask follow up maybe another keyword.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":3,"query":"definition of Python programming language purpose"}'
-              name: search_and_answer
-            id: call_ffmplnh1
-            index: 0
-            type: function
-      created: 1770981727
-      id: chatcmpl-480
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 47
-        prompt_tokens: 741
-        total_tokens: 788
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '119'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - definition of Python programming language purpose
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 7
-        total_tokens: 7
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3898'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and primary purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"definition primary purpose Python programming language"}'
-            name: search_and_answer
-          id: call_6l76j8m7
-          type: function
-      - content: |-
-          [784c5dd3-2cff-4ed2-88d2-932992529175] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_6l76j8m7
-      - content: |-
-          <think>
-          Only one result, vague. Ask follow up maybe another keyword.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"definition of Python programming language purpose"}'
-            name: search_and_answer
-          id: call_ffmplnh1
-          type: function
-      - content: |-
-          [784c5dd3-2cff-4ed2-88d2-932992529175] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_ffmplnh1
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '370'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I could not find a detailed definition and primary purpose in the retrieved documents.
-          role: assistant
-      created: 1770981728
-      id: chatcmpl-922
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 19
-        prompt_tokens: 863
-        total_tokens: 882
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4146'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and primary purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"definition primary purpose Python programming language"}'
-            name: search_and_answer
-          id: call_6l76j8m7
-          type: function
-      - content: |-
-          [784c5dd3-2cff-4ed2-88d2-932992529175] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_6l76j8m7
-      - content: |-
-          <think>
-          Only one result, vague. Ask follow up maybe another keyword.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"definition of Python programming language purpose"}'
-            name: search_and_answer
-          id: call_ffmplnh1
-          type: function
-      - content: |-
-          [784c5dd3-2cff-4ed2-88d2-932992529175] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_ffmplnh1
-      - content: I could not find a detailed definition and primary purpose in the retrieved documents.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '683'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to output using final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I could not find a detailed definition and primary purpose of Python in the retrieved
-                documents.","cited_chunks":[],"confidence":0.2,"query":"definition and primary purpose of Python programming
-                language"}'
-              name: final_result
-            id: call_lhu4se0a
-            index: 0
-            type: function
-      created: 1770981730
-      id: chatcmpl-738
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 68
-        prompt_tokens: 907
-        total_tokens: 975
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2782'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>definition and primary purpose of Python programming language</question>
-                <answer>I could not find a detailed definition and primary purpose of Python in the retrieved documents.</answer>
-                <confidence>0.2</confidence>
-                <source>null</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '700'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We have no evidence aside from prior answer being none. So answer acknowledging lack of evidence.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language, but I couldn''t locate a detailed definition or its
-                primary purpose from the sources I reviewed.","citations":[],"confidence":0.3}'
-              name: final_result
-            id: call_f1iawyjt
-            index: 0
-            type: function
-      created: 1770981732
-      id: chatcmpl-906
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 73
-        prompt_tokens: 433
-        total_tokens: 506
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_fifo_limit.yaml b/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_fifo_limit.yaml
deleted file mode 100644
index cbcca31b..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_fifo_limit.yaml
+++ /dev/null
@@ -1,1133 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '88'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - One more question?
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 5
-        total_tokens: 5
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '357'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Q0
-      - Q1
-      - Q2
-      - Q3
-      - Q4
-      - Q5
-      - Q6
-      - Q7
-      - Q8
-      - Q9
-      - Q10
-      - Q11
-      - Q12
-      - Q13
-      - Q14
-      - Q15
-      - Q16
-      - Q17
-      - Q18
-      - Q19
-      - Q20
-      - Q21
-      - Q22
-      - Q23
-      - Q24
-      - Q25
-      - Q26
-      - Q27
-      - Q28
-      - Q29
-      - Q30
-      - Q31
-      - Q32
-      - Q33
-      - Q34
-      - Q35
-      - Q36
-      - Q37
-      - Q38
-      - Q39
-      - Q40
-      - Q41
-      - Q42
-      - Q43
-      - Q44
-      - Q45
-      - Q46
-      - Q47
-      - Q48
-      - Q49
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      - embedding: a0K4uG5KzjpICnM8yDCEPHFzL7q1XYs9fciiPQbSaTv9n+c8W0gQPLsnajsk/wq9G+mHO4MwjL12yZI9qfGIOw/R7zxrDnA6vS/zu4vUx7s6kn28qrNPPYakJjzSpCy9naTmu2bAJrxxAfe8g1/uvTeHlDwvvMw8D6O5vMeQ8bwVv6g9o1alOykLvDv2ZTy83GgbvBFGFrwZ4si6VMUyPKzrtjwuGi+9B+WtPDdHWjxVYEI7PH4AvYsdJTxuPSg74F3YOxwMBL2fGSw8LbrsO7wmxzrFOMK83nbmOyUM/bwHLXQ9TDn+u5whE73LB568wfhdOslHIzzaPuC8FQf4up/GHruBPqW8nhI0vD9QUL3nczQ8EQuUO6TapbsJM0Y8LoxMvGi1WLq2fzc8iS0EvUVDqLslWhg90AUevGwWvzw0vTq7l4j1u8gyKTyBS2i80xEhPVnGfTz+ZkI86VqSuitEDL12pSy8xBqzPLEVwTyi5Q68vKpBPJCTPTxGxZU7NbNBvGVRtbyhHqG8wzmhPLyqHbxUupA7d70kPEokz7txHV28hqGovJtrT7yIkpu8yac2u5UGMLuhZEC7ezbxu3ZghzvrVly9QGBMOlMHv7s9C507fJc+PP3V0jvI19e7wnE9u/MwoDwSj5s7CwdVPDNBujuLyho9w029vACEjTrak0q8yvOmPLm2/TwawkW8IiWlvNBHILwmnse8DZIoPBOe+rviy1i7HJOcu2GBiDxiADe8IiCAPPiLVzxRvAW8AgpgvLcPxLy89r470iaNvG6xlDzuDg28JIyyPDQ4R7xARa07H1qaPLQhuLvE8uE8ht/hu1SqojwikoY8+1XcPHiAUbxnnD08+9v5OoyfPT0gusk7d8QyPJP8iDs18Ji6ezHevIoZ2LzPYyu8DW9bvK/ZILw4NA+8XgLxvN75K7s/CNm8UqxbvKKEL7tOAlE8SE0evCsc5rt8ig09YniIOyIP7TuNg3881iUku55nuzvJFyc8kds2u+cBB72VhBo8aD+4OtGQxrupco27KODtvJboBr17CLo7E529OzsB2DwgDhg7/OMVvIwAg7sSrwC8pCnfu8qX7ztxo4u8xP5qvAqkMDwYNNi8+WRCPGHOHDx4bLW8KL7MvEtdvzsBghy8mJ6pvH+ggLyB8KA8tLk0PUwNIDzcjTM7kpitvLH3c7vsdza9uPkvPIJWBjxHflO7hPFJPITNFjzth7Y8yt/APHkFtrvJSWe8HWSLvMdKwjtt3fe8eRCQvCjlaDycLos8XKMBvHk4WrwYqsI72SmaPNCruLwCi9K8zI/OOhbZmLyqVo+8UI9GvDMdCr1pQ1+70WgiPB4yBL3XFtk8YNrvO0WvHrw8g3q7f2YSvB4DK7xPDiS7RhWAu3BKhrysZKU7TJEZtsj22js1rOI8XwVEO270RjzY0Jy8wznTPClKBLuysKq6EHG7u5yuCTyMlp68MR4gPGh+pjuMRXE8wxREPd2jdrz59I46rluKu+1oejzxuRa7PgTUOx+V/zqN/6A8Qf24vOXpajuwmjM8apqLvJ19iDxhFY26DbHYvL5x4brfbB08qCqNvAI+5DvK1cG8XNF1O5L7X7xjN/w6WQ6DO5CUOzy025s8DctCu8XvoLq+WOQ5jvG1vPzBhbwSHH68upRLOikS0jp2DW+8yjhJvSZMt7teopI7bYwMvR5P+bslVMC7CG6BvThZubyoSqu83F3Mu6ztzzvCrQM92xzuPMgqzzwxIRk9FpvBvPC7ljxVYim9r3o4uz+3QTzoCTa62ECTvCVSEj3v9zA8z0h+PJ09RTx3SwQ8tA3vPMsDF7xJq/C8AOS8O/lLiDtBRO+7Y+tNvYketbzPrNM6rdt4vNLo3jwOPgy9l0GzvH7/kTzfNMe5WdWIPI+lTTxI4+O7mKuqvFWErDsTqDm8fP8MPPFxzDvavci8aq/yu6B5LT0UZV+8zEOavBVIyTtRgZO8Hrs5PMymNbyC4nS57mgzPNFXSbxXuJa8NnRXvG5ZljwjBXQ86LtCvJyqUbzVbpM8LuiJvFnCAz2COji8yhJPO+SLFTzJRhY7QVSDvHx6YjyeflI8EfIQvLhqe7y/JY08opGVO5BeFjzqF5Y7ve5EvOqNHLmhRtW8AO45vehGTbzjy3q7KSYzPPeLITlxnB497+YRu8f8urq7p/c6ZzddPNblKrv4PWg7xQ+NPOHtrzxAPdi67eervI7MzLutKVw7S7huu2XrWLxkpOS7vY21POTUdTweqJG7m40tuzG0oDwYeCk8EVmXPArd17rMc2k8qR4KPaefGT1In2485xeAPBb6PzyiwwK811egO/ByCb1MOce7TQ+8u9n1Cz01xT08FP2EvZSOmLqcxBa305wDO0dSHz0PmWG8CXfDvBcAe7y4YOi7wVUhvAwF8bzRsqE8sPuKPDkzi7zr9+G8ItN/uzue870tJYI8Xw9HPDITyrx59d28aEa3O86agTo7tuy7/KhfOxGO0Tw12A+9D+XKvEUb57vXZFE8U4WnvHKcDD3pPhc87x5vu5yygzs81lI9WxFPPaXBrTzDjIo8pjCPvHfdj7nvvrY8hiu1PP6qwjzW0B25VC1EuoBjIDwuzJc8/ruSuwZ8obvN5kY8yfYnvPVB5zyEGFu9bcqnPCSbgzyGyG+8PP4RPc9HUTxjzM27X/7GvMN7yzwEBPW6k/UiOn+cmzsnTji5MgajPJc2VTwdchm8YakkvdsU8DxfqAq7Zu1fvM+xt7xJWt48B1wGu9aSmbwmdYs8XQQbPfPRsrz5y2e8bJT8PHmcqzzT+Je8CrKqO1UwsTzJvr07UqhWPB5dnLzdJik9GNhsvYLQgLn4MG08rpMlPGwQCTtNKR47HE2JvHvrfrxHi+U74Ptvu+/2XTxIjTY81KNcuwFRabzl9Fi96fIMPZBF6LnCkAq7h5wNPSO1gbwt95I7KlhZPfiV5rrjmjA9bEZ5vHKabjr/T+s7ewYBvReLVbyj8Ik8s6iUPKlSEr3YvFW8Ol13PPCVEbzO/tS8eyNKOy0yKTyOwHc8tvetutQGZbuY7hC9H1dkO1LIHTzMxqm84XtlvC5y/buC2iW8y/VhO+ztF70an6Y8Q/+6u1yjozzu6s07/qYOPXT/1Dyebi28OahMPDSiZjySMhW6c9tYPHGGrLxFcNg79GiYvAhDXrwXs+k7ngNLPe9ztbyz/G46FA8CO0MKpLzqErc7udpWPF7/BD3cFhS8D4UYPFqlBbxK7Ry8hfU4vNXcjTwgfLo8LMxAvcnj0ryQ1i481WMqvK71hzlGRCy9x3+fvLx83bxAmKY6Fo7AvDXAHL2ApC298SsmvJJxqrs4fKe8fpGzvHFvAbzbO748o3ztvAQoazrOSf+86zwovX+dGD1p3So896SHu5ZbrTzyz8U8t//ZPNBgwjpQpyY92eH1vKQN8LypbRM8PlGeui3qRjyWTTC5lNaQPKha9rvbvL67BZGpPBdwgLtdpQU8RErlvJdeGj2Ppsu7DtuxvNXxuzhyCZA7W/EmPIwRMb2f1OK6utFKvaDepjrYXIY9y396vNa8+bw0MQm8AaNKuiGj3LurgwA8TCAFvZK4mTx9pFe8aq0xPOl5Ar3qAkQ7j0NqvBPqSD3g+Iw7nCfzO3Ib5TzhTVe8in1YPMj/vjtDu9w8tIL+O+Z047wcgEU8Ji8zPDvrJjs6roU7bJdYu3yyjDulED+99DBevEu/PLt0aXs8LgaYuxjj+Tzfx8a7/O8HPbv3ijzayAg9bjevPASvFj2Te2K5cQGQvKQRlbwaoz+81E0uvMnNdbx1Pve8x1GhO163Hrse+Uo8HCuMOxbOxjtSdDa9KYucvHgFzTkatqc8xHc5PG5kPLtcxwI89BeRPO+9ETxFJki8ZWCNt3C4qDwkt9a7jYDIPL3E77yUbso7i5w1vNqj0rvsnEc8/vysO/PVkrtWu5o8phNSvLp0Dj05XVS9pi4wvaL1/jsKVsy7bsf8O/rAQzwfbJS5tkwIveI4xLtxFPg7ZnI/O0dWNjxxRMQ8wW4qOxEZUrqpRzS8IdUnvD9zCTvieZ+84IWKO0s+rzvtMi290FKcvDprKTzCtjq8LSDNPGslbTt1IpU6gtiYPE4IQTsHjTa9qSkYvYER3jwG6hq8zNIiPED3RLyWKyS82jFnvC4Jq7xpuc67RtYHvY7ZjDpkTqy8g7kbvRxNGbw3Yg09UagKvUPQhLxXXCA731nfu6H5E7wpN507g4j4vEOGiryOASC8i4v1vNNMGTxz/k89YmOeujjnFj27xoA7CuOCO+/CgDlossc80j0munGZGb0X91G7RlXxOwbEgrwOvHi8/1rnPJ8wZbybAaU8kEPtPFoJ+DyBr6g8bo0/vGsFEbyd+dE8N1hPvIyTsDwK15K8L5WsOGWnhryWGL67dzWAvL4ohzsl+Qq8pVFeukJmurxeJCo8m5JNvVU+BL09/8+7mydtPDxcnzsjjxM9Q0Psuy3gUrs92I08Up+NPMxhYrtCwqK8Y64lPfGdyTog54c9m5E5vCaCyDxvy7I76cHGPK/3szzYJIy8l2mAvK3lhLx+Ahc7ofXcvLw3+7uULte8InhoPEkEbLskGoK8Z0sSvFf3pLwM4+e8s0CsPPulgTxGZQQ9uI1TPNzFyzsbBgQ8Nk59OwzGw7xMfx68cU2GugMWDj1430682QlBPUDo2zyp71i8o0f6OTLw1jwOV1U7F8uNOgjhmbpLdAG6N2maPJHToTyXjHQ8v9CNu+Raw7tbrXI8LN36vHexBz244bW8bQiRvK362DybpLO7Yr50u6quCT3tuJW70sktvfa+hLp4A4G65VO5uor0Fb1UovW8tiaxPJvKdbuq6Ee9pc2pO8MdBTwtKr27csSMvOG4sjo67ds8ro4hut6CfL1kE++77mopPb/G0rzuD/K7bfJkuz8wOr0gkyg8TDJKvfBFMztsyg+8PR8Fu+QBKrxdFju7it7uO/nCDz3OdnY80sSAu8QPubp0+Zw7Xx+SOyZYdz2fiRw8oMfLPGOkBz2nbaM7VhiXPMFFzTwndHC8DeMOvMC+7Tv7bIY7+IFWvKN8IbwiSYM7N4grPKIKJ7wO2MY7n09JvKSXATzKprW8f6aEO97qoDxPVfY7AEsQPAEnQ7w+lpY6eXDju0POmbwCVA+8DYuJOjRhJryYxoO7tJx7O6SyQDunnSA9ycKAPWp88jxKIwO9bp5mPLzQurpr/8O7EqWMu3j2ML0nigQ8yckDvY5JoDzy79m8AStuPDEjArz/5Vm75ZPiPBC1HrwC3yA9wAUrvESyELyqEoE8Hl75O5W6dLyc4sI8OoEcPAnSEL3iqhG82ERmvOooGD0DkJ0888RVPIv1LLztjle8Z6cevK0ccjxd5na8xR8cvLjARLwNRck5akanOrOTn7xWNsk7kXw5O2VPE7wvMto49Ul2PG9oAD3+LRa9RHOkPEXdUTvzFHq7A5LrPN5GWTwwmDI8HR1SvH4+STv69YE7jrHAu1KTkDuYY0M8zijaPEW+1Tv16Ii8lvgwPDF48zu8Xm+8gm9yO0DJdjwJZ+E8IwKIOzRtFb0q4EI8NVxlOWgD8Tz9+Za8xj0VPI7xDby57og88yVwu0GcozzL33g8oiO4PNRoujwe+ca8YePMvJuMfbuScAy84E64vCWvGjzLafk6mvdNvCMA4zt7twu9I5AbPUxRibsyPXY77XuKPEukObwgAwO8vCObuiUSz7wRohe9vh1evUzigLyYVMm637B+vPVZ2jvGZgS9LPfWPHrHYDxPEVq8II+sPCJswDzXLhI84gkivDMU9LomICQ9MGFavMYUHL23xN05j/uFPBCQtLtGCKA8TK/OPDA2wrtzb1A8Gs/uux7+t7zv+ZK8ol/API7a7LvuSaI8VQelu9UqFztWle27qEo9PIYHmjuhf1O8PMmkvEoOTrzxKcW8cin1vOp3kzvsAY88hzOlvNperzuFHoa8T7DWOzzzgDzLJZA8IWh6vJfjmzsNh/o61LHLPESuOTqF4ES8wvlJPXMQhTxP09O8FL8tvPvfqjyd0cE7Z8n1vEDCiDvPbiy7sga3OzR/W7v9Vs48hK4JvQgAxDz1dug7zwMVu5F8C709d5E7rNPHvArHzDyej568fMwbvA0VNDy1hNM8H3ajvMr2bzxpPCe8yvJrO8+Wv7yNvwA9OBaqvAlYMj0uoDi8T1vGPJlZr7swDZi8hg5tu9r8JL0GFC4840UaPH8af7wjrOA8szrfPJfoa7x3DBk7L/LxvAsw3zxlLKC6zmP1PJQvHr0ObaC8iydivDCfwjyfyRw8mgD2u1Jzgzw0uJU8reoJvda5PrwD+Y87mVgpPFPgi7xBwrw7u5WSvOTMLL3kkBi9pb/bvJ3UxrytkDI8U6WRvHN4h7yt5Lw8nHclu1CDcTzjLmu7eLJCPWbcZTwT+wI8QA7NPFLAjTx3+8e8U5sDPKtFlzwjHgS9a6inPObl17sm1gS9WUIpvKZW8rtiG8e84LDtu6Az8bvymHq85vwLPOfwcjqPzOm7AUNzPBEwUzuoDSA9bwYePWNkCL1hKYU7TXeSuxhbi7ycWuk7D+/vPMVM9zsu4Ws8pqd5vJRU4zuFfTo9QQSOO0h+r7zrbYo8HR5KO0c6v7w4ky25J5uTvKVaBbygfJK81y2Vu6Oqh7xem+48MdMCvLf5hzvT0uU8QKkWvVSDn7wr4Sy9FFrfvHkjHTwt/Go8i7RxO1OiizqB4d47BbKKPNjtQzwgvnU9DZUVvZjwST03nsi7qDiCu8Oo67xAfGS87gmhO2ruTLyHcUM8fLOcPGzzpjzlkoa8/heFvKCq47zV5QO9zxFHPdCFhLwKyf68kUVvPJm+DzwZySO8f31kvNBM8bpwuj28meS7PE6rEr3jsxA77qCkvMLfhrx/qyo88lhEPN5sarxuBW08bHcWvYhiMz3c7gs8DmDJvGAOSrrv91I8ROqTvLdZNbsCvAo8JRtwunYug7tpPQy8F7QwvRpTGzpMXqu89FFyvEZ2Nrw4vjQ8w+PNPGpcIDyoVOk6O7nnu7iuLbsd3ku8TQC2PJpFnLwrWdU8AH9GvJTytzsj/LC641iAvKEXYztz/hE8QP0hPeRcg7zUd8e7S+Gvug3817x/ZXE8+mKUPGzMMLtO5De9SvuJPPQoDjwNIIy8FJaWPIItvLvaats8F5AZPdUHPLwPg4k8SXkwuRU2KLwe0Km8CRQ2PA+g+jyRutW8palGPJDovrscBAa8tKUzPKBznzx+KHQ8LOnROjaXUzwH+Ns82osSPXufKLxDgyA7ZrPxPBzxqTuvbSa7KeTNuruUj7rejLO85rsOO+Q8fLsO6bU7yVjuvPgdOz3QG9y3JhSsvCKrCz0es4I82otzPA2u3rxDle27W4YevLVINTwg+40836sJvbtxoTxiMcS8fwRdvB7uijxovws9XbT5u3NI3rxdxJc6O1eyu+FUL7zvnqs8AooOOwVWrLyquvO8ReU1vGrdKz3BksK83hEYvV9nLryUZrW83KWzPMC4RbzAIVi8mk+6PHeyEj15c467DkMGPGB0tDv367E8bkKFvLSfuDzvYNO7caTxu716zDll54m7ruoLvM8nzbyjXOC7i1Q/PGmeiDzlue86+75suwApCD2JBM+8ZPN9vL3BNzyqaBc8cnOJvFcunLyGSfI7T6AtPGncJrxG5108Wpu+O368BruwlGQ7pyEEPW+gNryBWvQ8ub7FvGKcszsUIBI7TxcnPTqklrxXUgq6/XPTPHpdurxfAQk7vrVtPZqHmLzE7IW8X7U/vC3QZDsPiAS9itSbvPR+ibsOV3+8p4YDPF3JuLvqoXk8SK8GPVYl47ysQym7H2eXvE3U5rh3Vd88lYtOPPfAYDzMzye8gzb6u8CKrztwkX+8oUORPERw+TzOfYI7HHocvbJkKzzLSb68rAA2PVgeFjto2Z288/oLvWrtKzo/bFK85EBFPENd1ruBWbM8WXAfO8mJZjwAQni8byOUPIO6Hbtwczc8isBuvHtRg7tmvQm9HyT/O7xwJTzpm5W8Ge3CPHAqgzyZAMO8WxVaO1fzsjxxiTy8n4VsPOlMOL23zQ28l7kwPFmZgbuugu070JLQO9YkDjwFpgE8Az8AOzWsVLzmIQc9aoMBvPxexbwo88c8V4P1vOyNAT1fnrQ8V8kJuueqTTwuqPo7Sc7svJD9Uzw5rEo8V1fAvKUxnjpCGuK7ghwlPEpkaTyPJ468wd4HvdMEwDxcb9o7GMGqvMyD3Dx3xag8MzeYPJn5gDsmdkQ8S0ykO9IhizwVgzu86d1eO1KRC7zp6oi7NMKSu8oaljwveMG7Slc8u/W1Br3EkhI8QG+APH2KlDyhWm28EJmJuohlfjzazRg817I0OnVjFzw0Ho07lG9VvC4Gt7ucnJy84MusOwvFkrxm9TE85uAZPZW3Ebw8nZa8YZNevDkodTsucFA86nuOO/6QMLxHV6m8uup9vBQYKzw4hgw9NXZQPLvSCzyd1jy88fqiOh3WkTzxYQG9YbowvO81MzweLJu8okmTvEc6NjpYDcE8C911PDGRgTuVUxY70QU4PNCDSrzUMGW8NrRAPXqyi7lb7+g7QcuhPN/5rjuNABG7fAacvDTSCjyNtcI8AKKzu8FlgLzrfQ28JIlgvHB+tTzwJAU8GFmDu3VUp7x7iei89PW0vDIzV7w2ZqM8AI0Ou5l+ubzcKS28BJSyvEWEAT3es/G8SnPfvIZGsDyxXR48KZUavNRBeTwT62m8s8cGPVQhHbyhjqs7A6NvO9QaUbsJOwc85uJkvFr1HTxK4Cy8ntwmvFeUvbyiQcK8toRPPbhQM7rc2P68VGfhO4jJvLx65927D2efvArrlLyTRz885RX1vFGQFzzDJQU9Z9mKvHrGeLxgsHe8gKW9PLvlgbx7F3m83u0MPEJB47xNR1S8oHPMPMu7Xrz7MRe9GMlTOgVCxTwkzcW8TCx8O/lD9TuTqbA8CiPuvDonXzzAGou7aeDBPJR+pruE7Oi8SY2rO/PcnDx4+JS8oEuUu1Ze0ru0at+8CrNBPHmbebvgkMm7MPzzvHa6gLxD7Ue7i+L+PAcBzTtX37a73svhut1NnzyCM9y6DR7DuhWpLzy4jx67njiZPG+G/TsrZV48E+m0vGwMjzw/sFW8hOUePAH87jyDwDg7kaZXPE0QMT1WmLg8UosjvJ+H3zwNf0O9cY2IuzfbCb1hGtS71Dp9PNbKr7xrZr282SR4O74d3DygRQW9MfTXusZDTzvtOD88PKEwOyYzLT3NR6c7eI1AvLgzujncp7k8hhEFu7+sqLw8Yii9ygv5PFK8NTu/1Xk8tsDOvIKHGztE5x88/3OlvFZvmrweoZm87XRzvNl0kLxq1J+8EGdFPKtbtbxHCUU69rObO1ssNT2/zAq75CeGPP96ED1v1Bi7Pme9O8PhHT2Gx668NaJAvKHHmTu6+5w8jDE4vEyIzrsXjR675f9oPPtpT7sVYAo8aSbtO0t5ELscrQq8L0ASPTSeiTuQRRi9cYunPJZu9TxTGmi8yVTHvDNWm7vmDsI8VROaOGqmqbzhq8E8fV57PI+D9TyihXo84UpaPOpwOzwUirU8vThcO/yfDz3E+N88OF2BO7aJsLyh/ya8KcSQO4GxnrrPyYk7vFEuvNoBULzSAxq8v6PUuwAT17tyUXi7nL2svNtxorwU56A8r2mpvE6HFry6xTq6q2wQvNmjhTsUjzq9ou+HvPy5xDzjNXC8+aXLPFZjPb0cJmo8xxliu3eqzjzGGao8HJehvPq6Az077jy8LJGOPAsuOLzE6y08LJlJvKHF4zwc2Ui6h8sDuwUiG7zUSom8Oo/1vArqArz9OKm8p/yGPBKuJz0Vgjo8FHqPvBhL3Lx+YOI7+MnQPIAoCD3J1DQ8wKUVumNivbx2ur+8FxaqOn4/HTrTgpu8hxkFPHaFNDjUQE+8EzdxPCGWlzzgzyQ8PRXgu/jGeDxGHoA7X2HbPI9+ZjqJ2AQ9kOIBPU2shbpFALE7zrMhPUjzeTs70MO84UQ5vAloAz2cyMM8NZBiO/mhuzpCA8A5r98SPK33rzzA83m8qcu9O2Ka3rwm3fg7ME0KvYG3Zjw+eGM73f21uwDt4bxb7sY7Lq39vAiPXzwFupy78EFKOb+JEjuJLBe84hjBu4uxSD17hw88TfN6PBJpnjw3FXu8ixPYuy3eyTz1qPk8Szvtu4RTwjti0Q09o2gPPBCYSbw8YBy6l+htPAS4BbtFQLq8p9/dPF3457tsF7G7odhNPNYBiDsUrQm7TW27PH2EC7z8WF8740lpPIAn/btUUIA82wv4Oz4PNbySPH+8x5kAu3lSMTwyJ3E7tiDBvDlg1Dsorf28CTsdPJLqrjtLjq08UQGYu1J8TLxpGVE8xvTKutggoDzJxag7J1MmvVFYVr3Azo68MRhjPMLnxDyvtNC8Bk7HPIpWybwXe4k7j3z5uxctFrxnqIU7XhlIPBJvzjy4GOg75syAu7ArmLwaSNK83EHpOq6nGrz/eAi8IY+KOhkl5jyNf+c8XGZ3O9Rnnjrm7CA8JUoSPZdLxbsteYq8opnDvP8hgjxio6g8lBSNuwiM/Du7Wfc8QySxPOyhNrre2lm8I0tTPL6fvjxkjKm8ll/KvDCcOr0wCBQ8R626O9BNtrwBwNA6XHW4PBjwhr2DmwA86tnHvHNK+rwXKr66M9jfOzvzyjyA/2O88hvAu1pEFLsPJIg7OaRyvCotqbsMsv67h3RdvNFLwbuxoZW8TNkdvBRr1rtwt7s8uXU9OVHbibzVprc8HxM1PEHwobzIJhm9/2jjvMG3LbtcYnK86L0svPjPQ7zKvYE8h4MsPVymMbxu1DC8joRlvHQffDviPEw8V90DPWHIA7tYGnI8MWdPPF/GbbxTvCa9O0j5vJF2FDxeaxG9Cu8lvAHOGzz/+s+8c2IOPT9Mjjw3t+q8sLboPCBAvLtev3K8FhwQPEksMjso9Yk62VATvb2kxLuuoxS9SzFAPMF4h7xuBSe82Gbdu5z40TxuD1a7vGTPvFozBjzmi7G8AAXyO6cABD064bK83K+cvGHp3bxLrVK8moROvERXCL1Y9lO8pkkaPKpn/TuN3u47vZSWPHY7TTyLA/i7eYKlOiQXkzxZFCy8+WaKO12U3zsNpnq7CrrcPKm1DLwWu/+7mceyPDw9OzyWxYQ4i+hCPJyPPbyDMEy8oiYNPA4D0Twm8Ha7h0cEOvKPBTxBlCi8KDWCPGw+vjyzYNg7YMXMOx6qt7wMl4g8h8WBvGD+KrzIk3W6hHcLPJqJvbzDykm7csEHvJJG6jvb97Y8VK/8u3+HzjyfGYC8N7VUO8HZjjz4zIK7caN/PG4CIzxodgY6KGwnO/ryrTtmtaU7BpeHvJ4cAjwj9Zs7iJYau4+UFz0pHzu8Jv6JPIXFnblE/W+8hUP2PLWCKr2bA/U8Zk/nPCRAqLwanK6802PZOk1kljz9ChC7q66yvFdrirwJBuM6qT7PvC33ML35E5s8rzXKvGm5vjvFOOC8t6FPvC4XJT1EYP27noDivCdRqrw5vQG8kyxtOj2+/jorwLY7mnWYO7bPHL0vCsY84J2CvB4M0rv/GOK8JxVrvKhtJz1RluA86NFvPHFxlbzv7iY9uOhZvHimg7ySMuY7Nb4FPf7TQDzB6jW9AuaePGsXg7wQM5U825xEu1BB2Ty8KcO8ASuEvED0BzyW0gO9luGIvPOkt7toODm8XitSvDWjBr17GOA8ltqBvPMrIbzpCFg6ZRSkvLlBJr0DdkS8pa2yPFfx2zzKBZU7NT6kvFk6GzywHsc7N9ZXPK/q8jysT+a8L2KWPLJK6jsomAw8r57TPPKC6ryQEww9sCwbPZLIf7yjamo7LiXEO/aHrzwoCf07/fLyuy0az7yHHoW8dsiBup2fpjy/1a28gPiMvGHwgrzKqc46IQRMvfL5yrsppZ67wJQrupHxCTwqUJw8fFDcu749KjwCByo7BpHPvMtkazychT07raeXu8Li5Dx3n7a6osPkO+5PRTt6o0i92NqpvFSKFr2q+GG8ePZ+ulOKB72UTa872H1wPC7x0TswrJW88aMPu7BYu7vjWH+7w3CPvB9opjtz4je8BC7CuwLiVLxjJii9m1GgvDjHOzvWQoA8mAXUPMqKZjxoWZ88hWSTPDEuuzxS1zw8KXfMvHxQwzvwZrY8Ooq7vKWOEzy3+s88IFibPGoKtDzcS3s6/4CnPOeZtTxe7668Ac62u8KC67yQak87bCZKvGl18jlwIi071LmcvHFFcrwkJp67qvE+PBXqETx1lS08DZ/OvF/uCr10rYk77cAwPBZHAjw1M4y83IaCvAhhljybSzW9ZhU9vKCKmbvFnoG7hQKavFEl/buFOlm89/EGvbf7yTyN7fA6NDfJO4V8MDxuENI7AhyjvCy9i7gZCxG9iqIgPYcfLzzGFCO8yM2ru4NjJbyjBw+8s4V3O/j7GTyfyWO8dYKkPJd1LzwP0sQ6immcvLESQ7wxnBC9JqKKu+D0f7yasbe8utfeOr8dmjwUOwM9deh8PEwcVDzti4i8Iz/rPCUo+LnNz4i8jlyTvJWvibsXS3e8Jy2XPHTJuLyYMv+7eA6APNVlN7wkt8y6GLSIOVuN/DyDqvo8nUc5vEK01jxNA0U8mf7eu0wsGLlHuuG7E/KOO7xghzyVw9c7734MvHcEQby1Ny4948QDPWbj/LtS6jY6GulzOr9W6Ls7Elk8KSvUPAe6U7xpX5U7BlkmO8u40rw4wLE7bnx6vJkag7xKzk47SXVEvJJC1Lq8+Ho7odqIvPIrZDyBSCU5Tp88PA+2Az0ZJo48VPyUvGqdzrsYkNm8622zPK52RTyh7wE9QTi4vE5+Nr0Zhhm84T4cvbwSpTpPh0o8NYQeOpK9frylv8s8BTCGvASEyTyxaHm8hLoyu2uh0bwjHt+6vU4SPfbWors/RbK8RMQ3PMyrvLtwz+Y724mXPBQHvjtvQ8G7pJpBOxEjGT3IoU48MXX2O+hePbsosGk7BiiVOgcjPbw+7ja8Y35RPO56rTz925k7lc0uO6Tzxbv6SSU86uJjPLQ7WTwN7bE8dZozPGPdNbzWDuq6SBY9OmSnlTunk8u4B1vyOqfJzzvOb/68t4zquxB3DD3e9p68tSJXvAS9kLwuAnS7J73iPFudVbtRxmY762akO6o7kbkKXYe4KB4Kvcna/7sC/Be8NPzMO91wJry/BZQ8RfSdPJRSejylWiS8Bqvmu1Cs0bxQ3YM7gUTCO916R7xvyvM8o1s3O/bOyLtKWj+8NRiTvJCjfTwcZsC7nddiOVv9zbsl/fu6gQPDO8C3FbxWaqg8L/mTvOHbOzzCBSM8OAs3vOhQGTxdSXy69nYnPOmSobyFhQg85DyNu+8YDLz9z6e87LMZu2KrXjyFhgO7jEkXO7r0JL2n3rY7iO+WumAkEbxv7MK7JTXGOiZHNjt+eYC8jwxLPLM1L7vLOb48twLLOvudWLxkuya8XhwOuwQzVDu3rVQ7j3Myu56TsLuExc48Wxuwuy/ZULxpZcK7FSznOtEb2zviZ/08nzdDPE84NDvkv1I8W8g/vA==
-        index: 1
-        object: embedding
-      - embedding: J8XUuEGuWTyMP5U8iZesO7C4RbotAH495NSjPXRlPTwP++082z1VOvkkODwPwCK81DlxOztDZL0ZNqM9U84YPGsXajwI6JA6OQZLvPvz7rtzCpK8YaFgPf2ILzZvtxS9EVWgvPUeTLyEegC9Ot3GvVa1dTsBVAM9b8/CvIM/0LxPmr09kfHDO+rQ8zulVDu8Ses4vBbJ5rsZ83K8oF/7OwD/zTz7aQ+9pBgDPeNLPjwJC1I8Qy0YvPsNMzwOVHG8r6Txux+d9Lw5pTg8Gu4OPONGUrxAFt68Oli4PJJVCr3Zq1E98Lm8uzM9CL1iWB290EEAN9YO5zusoxe9MuJOvHtaBrrcno28dlykul6uQ719mhA82hhSOyAbNrw+3KE82zWrvDVPcbv1LvQ8ZI7wvNYoA7zWoUQ93cRFvCYyoDw2tEu84Todu2QrGjwFWD28K8IrPR67QTxj8MU8nKenu/NB5rwFuVC81JjXPEVFwjzn9MS7AtELujQNcjy6txe7ElOIvOtgiLxnNpS8ntiiPPYYmbxwKZ87wK2RO0I0UrwvN4w7cYjbvJ8UOLzg9jW8EQ8mvAamlzlK4kW5JBxfu86dmzwF1xa99YltuopEKLxnfKc8OWuRPC4XzztCAA+6qK3Ou0QUxzwP/Lk7E9ylPHwSWDyUICM9CR5tvHIKWzttPDK8/UrBPPKYBT1pWUW85Y/Ourt8frws5uO7AaMgPOcOJLwytR+8SNfeulxf0zx8QQ28g6CSO1pHXTzwdGi80MuHvF3XA7wigso7waSIOuFeIzywvCm8to0dPNHVmLrUTR484l6OPKbTNLxGYOk8Zehnu0+tozw3jpI8HLy+PJY2a7zMiAo8sQYIPDX3ND0iVEc7EkuGPMCR+rquTsg7oZ2+vEABurxjZYi7gpHMvIu1dLyrd+a7RSoTvZKe2juGy9K8pyQfPPDFjjnbzzA8+oRcvC7Jv7ydkb08m8tpO6ih3zsU0YA8KqaFuozUejscXBQ8UXgmO8tsA73qxgw844o5u/1eXjrISUO8yAW1vAU/3rxkOu68Of8pvLlTyzwzctQ7GWWtu/UhDLxztyK8QFDyO1kYvbthUim8uZ86vLWCODtWWLS8wWW6PA2G2LpbBtu8SfjcvOV+vDvndYK89eSCvL4xhbyB1408BDM3PaLIHDzccmo7joycvKiUDDyzTSi9586QO9hbWDxFxaq78N6+PCPS4TsTYak8x2q3PFP3H7yt3ma8CjbEuzqxkTyW4Ri9SsOZvA2AozyMpbU7gDJTvJu/bbwD8cu6Nk6OPJw/QLwR/MK8Pg3duyr2S7zmvLi8cfFhvD7u1byGiII7QyZuPElB2LwvhaE7CSQnPNRHW7yhjh28BRePvJFdyDpJLxq8lJ2EOjK0mrxjeTY7Q5uQuqj7BzzM9xw98Yj+uf4iXDyT2zW88wPzPEUshrsGKqe6JRsgvAkcrDzJzOm8onVQPIpVGTuVNrc8QTw2PTXhqryLzxA8rO/bvBeQkTukbnQ6QWsfOzxI+jsotr08xKTsvEWoTjsuj9E6FsS+vLu6PjxbAjo7jsqJu7Xj2joKHZ47UT+WvDJIg7vF+gm9dnuFPHkyhbtMb8c7IOE3O1nmhDx3Yqg8L4ZJuxcyBjy9F4S7omvqvIUyXbw4Tai8rl6KuwUdJzwzG6I7BmBJvbm94LuXSXU8iivvvOlJuLtXth28CiJcvdN437yrrWa8B4i2O51gD7xjYcU8SRvnPHcjRzxZ2Ps85/XqvDeWmTwytxC9vjVlu4jjg7oZymy7TT6LvJa/+TzhfCQ8hS3pO2G5Kzyuto48+5QEPQlm/ry/chW9I4lJu/QM1DwP5pe76vsMvb9m0bwCARy8czmMvLQV7DzfOtK87BGrvEkXrzyMq/a7u4KJPALblDw3qbC6kGGavI2tzzpXuAm8/aZUO1zsYLxnTyK8zLLRu7V5Kz0pdMa86XitvBdZlTz6KvS8/pDbPKBAhLweIvk6tPJGPManLLyhl1q8G5cLvEBtTTzRHKQ8pzgyvPdPU7wr9+48Z/WDvHYQrzzSequ8Bju4PG6iHzqpm1Q8+ClrvKbXUTzL3vU7T+RYvGkUBL0myA89kt6TO2kmYDzKHQ07T9ogvGcKYLx6iJ28UlVPvYbb/bzqLZK8hjy0Oy0LBLx6vB0988gcuzKVjrvy48i7yC1nO8tP2rpjr/o74xOAO1vT0jxFgKS7dh4xvOpTHLyfbJ48ab03PBFEKbxS+Aq7fvOoPMIlADzkVy86hQcNvFXgmjxSAhs8VGjdPCAFw7urAM87cyy5PO36Bj16swE8Dgq7PEqssDzfD7K5ULQ1vE53E700u6I5bN4ovCZHDD1Siq48EIhGvaTR4Lk/g8a7XH0BuhpTzDz4GYC8Ney4vPkhJLxXxZA7kPB4vPKsEL1gkwc8myxLPA6MNrx9VY68LSQAvJSh6L3NMZE88cbTPE9iKby4exm9uvPmu2S2EbtLCX+7i+9uvAMr+Tx1ySy90q0CvXfPD7z8Dz08r19uvOZYCD0xRDs8wLxbutTliDyjQDs9AvEZPQD4yTxpKd48ZUouvCVeCjywsGg8x9uNPHgMrTxs5h48CwiEvO5VvDmQ8Pw8Ws0lvN4hJbsnU3g8p+dZu5BO1zxtoWS9dKuNPOH/sTzptxy8dJ7ePB4j9LuGSKo63YgLO/3eBT3fbqs7xTt+Oi9vtLtF6g88w0bXO8RoTjxk8h27L10Mvcq7FT2eCmS8dOEZvN7vMLw64Ag9MM5evIqYV7zdv308S4cDPcVM3rxAGsK8qkbrPLjNmTyUsem8GzqYO8Z/BT1o+7K8Ku8aPLKTizv0+eo8pu5PvcWZXLsp37G7cubQOxd9PTp4Hme6VS7mOnR2fby0dSY8Wwafu1uvsDxeec08YOusO9SBvLx01Ge9XaHLPEL98ztwLeC7aLU3vP8mqLxGeY8762xTPQhe+7rFNA49JQmXvMOarDupWEg8yIT4vA6qHrzcknE88zzpO3Zztbz3p1+8OX7wO9OJcbwyvJa8jBjfuT1DSzomCdG7wQKQvCJgT7w2Dye9Ko30OiR4lzk3cGm8VkqLvFXllzp64YQ8REmQus/c6by4KYw8bC1Ruyyqmjwokug7wPPQPHEwrzs0lt68x3A4OmvpsTsu4Zs71xAMPBuDW7xhDkA8+Kq8vMx/kbx2WZk7+sciPdoDA70to7e8bnKYOzLvgLxFF1u85JL7ulWVDz39fEa8RrqbPAtUkLxsHUG8kEgAPIxx1DxJJwM9pg0yvWN1j7wUrAi8iMkDPGMRzLzIdSO9vVjEvHs/DLxRVsC7v5LNvHA3Ur2Ik4K851ceu6N41rmroK+85gmBvLZJHrwIgfw8TFHivIRONDw9WYG8OKgPve6mQD24fBU8brS0vJTi0jzZRzs8FhmZPOHbZ7yx1ow8Lm4LvboBh7wGkVc7nRe7vPTghzwcwF087wlfO+ycZbyK0K68iBOjPGkIDLvjfZ08vA6uvOkKkjwrJae7cQeovNjytLvrIOS5D35SPCl0gb0VIlC8MILMvM+Qz7yoNHA9iYRKvAfZULybNQ26vAQQvKQEmbyCCpM5/7//vLOeiTtrd2m8I4ovPNtPXLyP+Gw7JHrUu8UQGj0jiAU8MvlCOxwZ6TyycZa7R0RdPL6vmLt77Dg8khbcOp+1YLw95TQ8U8iGPEGfTjw6pbg8bCiJOxKoyDpd1iu9qkXLvEiIvLtH4w08kOAduzaL2jw7LZw7Ja2uPJj1ajzVwt08o7OKPK4DLT1dbVm8/hgXvTbQ8bz1T5i82eyYvNiiJ70/4Bq9J5JIPLYXW7znM4E7qW0APMsXWDzTYxC97G0KvNXL5jsCFCo8mNiKPKc1tjuMWWc8n3X6PB/naDx3yj+8l3QXuDkaPD1kOo06sh0UPah1dryNbXw8go2pvGc8tLmiltc7+2y4uzyV0bu2mD486jEcvA2O6jxv1Gq9X0kPvXdSRzyFPbu7fQEgPLEaTjw5rik8CTMbvUiFLTqFkPw7Ud3zOm8DyjsgaQg9gXCoPB5kFju/ppC8bejRvLKw7jwfjfq8wrOzOwFCOLuNPya9yUnovFDhPzyuklO8n2rRPMnUQrzzgo07mXyKPMVJHjvyIRC96CfRvNveBz2Symk7rqQ7PD3+vrsScq+7Hl7Guhn1Y7y0KYK76dDvvJ6eMrxxy7289NwCvf0157vNqSM9cnu1vPu8mLx6j+87FhHRO1m3T7zi/m66M57HvBo0Zrsn0Bm82C/ZvEgmhDvXtyA9QwE5vLyrBT2R3/47FIQ5PDoUEbwbGP88pTQGOCxJ8bxScOs7CzYPPDFmPbyX04+8kJPJPJEt1zvPSb08oeOZPOzvBz1pMNg8rgF0u3MNaLtPfY08u8kQvHYj1DxJEMK7H6poOrQX8rtkq9g7MjlYvIdJwbuXfJS87WCvu8BexryRDLU8aHJtvbqOB70PPeC8vmSiPFWr8rstQEg9Nv2kvG0WM7kqASu68Lsku5fA0bt6tBi8AsQwPQyalzyo/YA9DnsBvMvP0TxU1JI8alPPPCUUvjy79Y+7/vM+vBY+abxr83c87s+SvJDiTLzzvZO8Jq47PPzaorzbu0O8EErAvASqojumOhC9ctPZPLTN3jywoyk97p1oPEVU0juLQK88alHKu5YtubzzDZu67cZ/uxli2Dw98E28V8tfPUR54zylc2u8RkKPOzwc+jqcGrA6ieUevFpgtTvAMtC7yIu9PDvpkzyoW4w8SUeZvO1otTnNHbA7NRcJvZ+PpDyI0c68YAZ5utsVtTylA1Y7YfW4utVbUj33dXW8GCV/vA8Wh7wMcwm8/ckuvJPD7bxpZwC9rdeOPCx8W7zoQUW9e8N2O6ND8ztWToq7f1OYvB+rGjwGUgU90mc7vHYQZb0XuyY7ZDuCPY1gIr1QSg68C3V7vDGoJ73H55888jIFvUimNTyN6SC6LVeXvHicxjvNiEK8uzIOPM+JtDwxjzQ8XMyFvJrERrvxsJq7TdFyPECWXT2LB7s6WaJVPEiMIT1d5C483nQtPGXQ0jzWJ/e7z1pcvKifNDwGWjQ8/peEu7kfD7zRAD48KYwMu19GZrzDKzA7oMQruwnsoTxAmNS8LrmmPK5BfjxiRoi8B/yiu7u/oLyy7qc71weAvIZeubtcTN25vpbHO8aWU7zhLKe771uvPH6sezwOpxY9XpNyPZx1ID1B0hi9xfjgO0iE/jtzwVK8VdyHO5W+Ob3Uglc8Nb3XvEYc2jxS6Sa9bFgvPO9S5LsFevq7z9G0PLRstrrPH/A8Kq6CvGuM6rtqvq48WImcPHhc57yRvfY8d5AtPHS0zrwzOye8bwaEvOxGIT3pBUQ7Mr50PCVYirxKp6u8FospOUDhYTzoLci7TkU3uo1vDbwbEYE7Mm2NPKX/6bzFCDG7lgxrvL3muLuYBTk8u9u3PEIDrDy2lB29wIR8PBa3KDtISDU8NytQPBBnKTw83e47BaxFvNFRvTvbhUI6QpAzvDbdETynDjY8qkbrPDj1ELzRvr+7RbbQuz/nNTuNEHW7XbrCPOYnMzyiwpo8z5svurQN4rwxwT88qjaGu6XlDD0/KG+8ECaFPE5y+LtxHUY8hkp5O14PhztcTc08t4ihPMm84DzMOcS8Wnihu46ClTq0+Iy7FDPpvPNsdDxMcsk7XtOKvEsDCTz22AW91pQzPQkbLLyKlpw6M/TrO2k2+7sTJAy8AzmPu7ZIYrzEuBe9Ci1hvZoGgLxpPWS8JP7BvL14wDza6hK9S90cPFnGm7sZv6i7PSe9PAAN6TzJKTM8iowmvIyxQ7wjxzk9xzOtvGrPHb0eAiq8+PSBO+NPFTxnUNU8rgqdPOAQSLshxQk7jyqDvBzdCb0895O8b26wPD9fnjtpKLE8h+V9vJUVBDw92Mm7FcqHPMtv2zuRoBy85rzNu1e2g7zt4we9nkP+vJTdODxeckY8t/uKvFwu7jtrsyu7rv+auxV+gzxOT8480XU1vCp4pzys2hI7EDD7PP/4eDpA8p27D9t3Pec4fDwipte8inWnOV3EoDzbUhC8fam3vKH5c7vOrxg7JUdNPDLDz7mjaM48qGEhvW0StTzz/2s8RNMDvLCHCL3NDsw7m2VbvHblwjwA54y8ofu3vCQvOTwJQuc8JH5VvHFkWDwRG/G7JIW0O5EM1LshdPU8JxLEvDtxLD2AKzI79DBJPHne8Lrk7WO8E72muj947rwxXU887S29PItPy7xqC+M8L+mwPH3HqrrgZgc8TKH3vB1jyzxjAmw7t7YKPX/v3Lx3xem8s3eHu8nvtTy3cic8XgZXvM1RHTyrVGE8vKfsvAJpfLvxhuu7i4qQPCH3S7w0B6C7oUXnvLC0Mb2rESO93nQJvWACt7xeIaQ7d7vmvH6mW7zrfA49AqJPuWMzsDxikJo7JGdMPZTkkjwmtmc8i9ckPOzCrzyNK3m8J7/0u9PyQj2VSgq9W2YLPS6RkLzRdu+8FZocO6FsnDpi/rq85Edqu5UKEbxL+Ba8SbDnOyGX2jsYCIq7mP+pPBebBTwRvqo8Mk0APZuxD73pb+E7Nm3su4BReLzD6fo7dLLuPKx++zq5zRc8LyOTvF6WTzwpfSY9qp2KPJINuryka3M8p74AvHB4WLz1UFi8bAnNvEFyHrw300m8pHFTu+sgcLwcUTc8aDALvGN7UDxIJgo9sAQKvYyEAL05WxK9QbjpvB4wWzySg1g8bEmwu0mYPbutX8I6Uvq0PEkP7zyy/Co9xI/GvEdrEj0Heh47cIGiu1SjW7xNf728gtLBOxg8tLwVDqU8IzXKO8ud6TxoysC76qpsvL9cqrwMkBG98g5SPSeuebywrsO8KuN1PKVnZjyNLjG8BLD9vBUajjsCGkK8ysi2PL7TB73GakM8xHOrvNmlibx/OQ08lrgCPEor6ryHAbQ7RfRHvVjqGj0tj4w8ZGnevIZE4TyMaMI7ugKevFVD3LwIlc47aciDPFmElrq9TVq6rF43vRQM5DtZBzC8wDkJvLjg77tURFQ8z1MKPZ4auzuXGR68Zsmtuhio6bvkPzC8I02FPOY0XrzTZpg8ORlvvCXXiDyk/J05ZQP7u5+bTTw1wkU8Cd9IPQCtJrzhbqK8G3Nau675sLzqp6g8GauVPBH+7Tkc5iO9wIKqPIiquzsJrcS70iyzPFara7oCsA49/aQaPV7MBjvk37c80S/5O5MLJLz06C+8YJCVPEkyqjzRqwC9AbfsPLCYkrox2Rm89AW3Oay6kTtnodQ7GvQJvLzK3DtbW6I8nzHbPLXwNrzpiJI5RsMBPfoMH7rmdbm6v0wsvLvf9ruAuKq8W0qcu5GlDrunYRK8apCivFiuTz3XZOo76RiyvK7RAD1286g88hySPJfsF7wcykm7m9S/vNvqZTxiA8E8t1YCvSRn2Tz88Zy8cLqQvGF4aTxe9Lo8sxmAu02av7zMwji7uPeHOtvCLry05dg8DD5PvB7rmrwQak289XFNvNfaIT1ylXm8EIz+vMmrRLzHJJ68G3uzPJa/grxO2vi7P8uuPNo84TznQKi76HFgPHjDhLs3TA080KRivIeA7Tx4l7m7zvMLvPx4DTvY67y7TWmOvBsNs7zyAAm8PpI6PArWrTw7tSs8h/dFPGOnKD2xW1K8L6SPvD8TCztV7GK7SCRvvMuwm7wI9KO7MgiEPCNJ4TvG2dQ822J6O9a0xLvTGXu7rE8DPRc0QrxAmN08S46ovPIWSjxCWwI81qYsPW9tlLzk4mk7sMEFPRrBRLw0ro47NLogPciYuLt8zLu8YUuVvFb1Sjqa0tu86OqOvF2l+bt9T6y7y8jVO26RCrzRfRQ8qHPgPM9h7rxkYeS3tCEWvL4ZEDymmYo8KFMmPGZ6IDytRMm8E222u1BEkjxuHqW8CpFMPPEZkDxZeZQ7MjHVvNc/AjwXQoC8AKlPPW08Bjy/eme8mVEQvSSNgrzzr128Tx8vPC1TmLvFpOc8AD8RPJS1rTzifXW8x2aUPMoxe7rGi8O7pYsbvMtq/7uVzii9fXBKPP55yLqYkqa80k7dPH18lTxRlp+8nMbDu76LvTwSrUM8VQKovFvAKr1Kasi7T0N/PD8bmbzjKbE7fc9fPDZUCDxT/lI6rwh6vAINpLw7iw89HCG6u3Ef4rwSxJE8cxXfvMorzzw+aj885Sh3Op2OOTxFcgs8xwkpvJYctDwcsuQ7YBXcvHfisrsOZwg827WMObP/QTySQSG777/8vMO3tzzwsus7MKZcvFpG/jzOVpM8DxZcPJbbFrrtOxw88KBpO3zX1zxKc9O6at6wuplhCjxB7Qq8NUYVvKVoojweLnO7R1I7O42h2LyFOsg7UyFROx8yCDxYVm28n0f5u7L0LDzOAms8RnXluv4vpzvyXkq7gnKWvHJI0zopfJa8G6vdO6xdlrzf40s8O40JPTJV4rpVPQe8NeW9usj8gjuxU646BHMFvPctzzvbjce8/Kp6uznNXDytxhg9uRXPPCNYrTt4Z468IESZu9FECDypCeq8ZYqAvL/sdjz9xKu8nt6PvG8oBjwPjpI852s1PPuPaDxzBq87vKNtPOoEorvMVoG8LEA2PQUHB7wjgDO7BYjnPFfM1ztRlzs5uPAhvIExjTwYT708PkBZO7GaDbz/CRc804JhvEu2mzyaGq874bf5OnJOMLy0+IG8gy10vLFAjLxuM848OBi1uxw+37znxJy8IeZlvIgikTzl9Du9+PdlvLkaDTweV188rFmtOoFsfzykQ4O8ksYQPS2embtybRk8t0MmO9PhALzyDGg8sFsevMTDgLrIUEy7Mxg+vKyBMbxyml28KPmQPQir0rs69Ai9gy+2OpHMxbveFoy8yAZTu0uyqrwaH/M7uzHWvNmPVDsDRx08p+UGvZOeQLzCFnO7qTKUPLi9dbyodG28/caJuwpjHL3TzAS8tmOdPBX3U7xXw/G8I37tOsJ0PTyNC6S86etKPCfOPzwG9FA8YJjRvN8ooTwU/NC7rlKYPOMT/zsYkae8086FO8+yrjyklKi8kJu5u5SFpLvKcgO9/nywO3gwmLrZD2u8ciWTvNCGvLxg/Ci8DtUNPea2iTzMWHm7tswRvOe4UTwhxIg8r3BLO/tWOjyvfRm8l66WO/RsjDvFT088xbmevITA7zuyZ567A+6EPLwDpTyz9KE8v4QRPMw3QD3tO5U8HVtGu57fJD24hmq9CC8IOvriy7xkHg87it2qPFWAgbwTsIS7syBpPOwKBj3YdNq8HZgiPDLq87teoRw7EGpIu11CAT2e4cA73uYAvBsMkrzMLpk8Neqpuypq5LwhU1W9Tci7PGxpHTxrLts73Fb/vHmjBrwMBZQ8WLqkvJvo0Ly+Yq287XHku9i9mrzlLOG8hpIwPBaT3LxMnEC8hIITO6OWND09cbw7JXBcPA5OCz0Cw/O7bVo8PFfnBj0LzQa9+LP5ukCYwjx0kdM841J2vBg9UDzSjO66yw87PLyFMLwfmyg8z1v9OhtpXrzqMKG8LnQzPfL4BTwzQAq9daGDPBbw6jydJ4e8v3YivHcGZ7xI1fY7kY6Yu3ccA71BtQA9RNkBvJmp3TyXLp48/s96PFBB7DucHcI8Je/3OpEcTT2qA7M8EpNwvAz+GLwALlK8QJiqueBYk7r/t7m72ZQPvLLjHLwxLO68T6Diu8wifDuTxZC8RRoNvcovo7xZ6V88TTfIvCx0/rz48ww8SSeyuyB1Lzxn+xu9I6edvMRh8zxAKaO7ncD1PHauM72/RJA8urHIO2KtUTwzqTI9ut9VvLEDED3zcM28sRcSO1kU07wZ3YU822O4vOT2yDxboCw8wGj3O2hU8Lun82G8T/sBvdRuBbzBAJG8ISfpOg92QD3rJuA7KEnLu7YXfbx8vww7AnTtPLqIHD1UtBC6hEKAOo9QtbzyLb68D8HyO3Lm8rsKHv28kMd9OXuyBLxMWCG8EXbnOzNeYTz55n881xxEPHjhbDz+3G07QQ7CPGFxdLrwSAo9Bg/xPIK+WrtPhXw8j8Q1Pd0eizpddPW8MjYavIf1CT0IyKI807KwuifCHLuy0QC8QpU8O/Vv4zwWaLC8RzcKPHTw3bwT1kq8GwszvXz1EDyhiNK6ZMugOXl2jbxDUgc8q9vZvDZcnTuNEyO8oN+0O0FijDzA5YW7sHkCPLIT4zzLcJA8JlC9PJZyCzx7Ra+7Di/1OjBR9jymffg8vStBPHFmNDwW2u08EH6LPOw6iry9RxU7HInGPP/peDye8w29KHHNPCm0qrvZ9ly8C1OPO3iEaDvH7Zu85Xp+POqFlbzCEVc66pe7PLeL/LptCU88f0OAu3K7UrxGFcG7WShTvNTuirpmtNI6lpMMvefNeDssAa686iiBO4BJkzsSUK07dAN+OQumZrwTX4E8sKGTuwioaDsGJkW7xEknvTX1Q70BUUK8NOG+Osr+pTxuAci8FR/iPJQ8jLzQ4AY8wqDHu2SsKLtl42e8gLiCPKH+lTzG1So8ba+Au0Fx0Lvw5tS8Y4MyPF+Q7bulr0S8jeBnPMuIyzzMFKY82NLJO5PLz7vzL1M6J7/PPHniqbunU828LeSGvO8kSTvvLd48EhoivEs55juWfQw99HlYPGn2K7xs/HW8XvyEPALLhjxZzzW8a1+QvJwf+byf5D48XkxYutmjFrydajM6VtemPPzxdr3cei48hKizvLEE77wa8Nu7zeO3OxP9iDxNtcu7dd2PuzOZlrx5KrA7fUIlvATWE7x+NnK8zc0HvGo6YzvhSWu6GowAPAq2Art0Iwk8DpirPCFs2bwS+JE8s0SJPCeyrrxo4zS99inGvE74yruVBJS80ZJxvFgOCTslY2k8DqsHPfuZLLx72oW80/5rvNW9E7zi9rE8mNeWPHLzmTvYjng8+phzPBLhdbwBmCO9iXoOvbdQSzwAtgS9TpkpvBNeODzwi/2886sMPXHPkDzh3A+9W6yzPFPNyrtPCJC8ftl8PHmNsTwy4Ii8Vv/yvAew+7sjzhu95Uz3O6LlGr0GVPE746C3O59c5jxX6so7qEBKvOpXgTz+dPK75EFGOxaH2DyfUs68OIS2vFNhwryY3Je89XcMOn6HBL3liaa86IJoPLHgazzMWdQ7WcMaPEBcXTxpTMO6+fdbO4bGgzx59zi8qbRtPNlerzoNvCa8oL1sPFS7wLzi3wo7Hz2pPMzwuDwWN/U6EXCSPBdEbLzyg2K8wmdYvKrA4Dx4/I08GYSfu8Xg0Tvafzu8mI29O012hDwCxas8AhwLu8Hy+bwhgaU7ED86vLMQMzroRJm7e3kGu9OEFLyCQ5K7JOEdvF/POTxU85Q8sAECvMl4rzwQh6O8VHYsPGxEZDwdcpu8v4dxPC9DuzzV87o8xqRtO84Ovrs0RnM7vAo4vNogkDyXcDE7X1wHPJJOzjztRcG8f8OmOhrRGzscfEy8I1qoPAltCr36ywo9tLp7PIwWsbwWqCy8XecmOwXd3zs5zie8x5mPvHrneLyxQVi6ZSPXuztD77yGBIA8u5HEvPT5Vjv/vgW9c/gVvIe7Ej3NU9877onpvJPJxLzfmI+8FFGqO6GeojvTaSM7BpU1uzAaCr0X4xI9Xr/OvI+7m7uoacm8W+4qvHkzFT2CDsw8j8+GPEn2lDvdwDY9QIDrvNR9srtWK4K7Jud2PDYJKzwXfS+9sVLJPGls3bw8ib48tf5bvMJEzTyKU/+8qmamvGEPiDusyha9Q4B9OYViY7xY+/m7Uc6lvJgfiLzJ89c81RjtvGt6trvdkiG8pEXUvMgb4Lw4HCK8SjrRPCoWYDv5Mh277NiivPbZADwOO6m7fGGEPNUA1zzdsDu8O2hqO/DlITwYufw6thY2PK8DJL0KPJM8GwotPd3sx7zzkxC8Aqqou15cvjyN2Rm8JazHvG6DnbwSsvG7QveAvGO9azwaPI28G2eMvH0CRLoHiLK8ThFGvREZC7wwfZu5qgIvO+/jIzybQIk8Xz1zOlqRLzz/GcC7H9tIvGIYkTwZ5n46AvgPPBNqAD0yCya83fo9u6WskrvoVym9LUJnvJgjOb0b79O8XuQJPMMW6rzquy+8Ux6DPKuIrbpfWCC852Q9uxLuObqfGaG7gt4dvEK1tjtARRk7RYAiPAkEg7xcIRW9K3jAvDYcJLw448U8XXfBPG9mEzwJkGw8mZuPPDudjDzsFR88mnSVvDMM+Du/24c8HbOnvI41hjyQTY88FOnaO0QVSjysynk8w66dO8Nk2zxID7u8AgXVu2Hg4rxAvt6682/oujRahzqyyAA8hhi6vLhhn7xywwQ8zmPXPOPbUjz1cs88ZHqlvM1OA73nOT08l3kdPAciFjxXXnu8ozOku6x/dzz5wQW9mPctvLTJhbsWDle8SHonvEeVRTsyHNW79h0AvSLBkDys2z48ZVsaPGC9jzwrQfM7wEaDvAx+Mbrz6cS8Wl8QPTUKHLnpDRm8Wq8DvBjigLxfLba6moQHuwlBgjvbUV68NNZ3PH7nVTxi1rm6dt3NvP7by7whWQi9rphgO6UjeryQSR28bNaEOx2N9DyDzKI8RjbUPCHgrjvQcom8GB/LPEdwwLumkRC8wX2hvEbE3LviS8C8vJSIPIVNGb1+Xpq7WxiEPH0jvbrQN9c6ekvNugBnvTxksQ09ZhzBu8l28zzWNb47j4/RuSMZnztSyWA6N4m0O02WBTzg72o8ILX7uxFhWry2Xg094Fr9POtKsbtPSzo8TMD9uaJfgLptScU8Dqa0PCk2vbzzd3o7kOXSOluNzLwaRa87clqEvKiC+7xYNQ68J8YLvFxp+jskqZe5/XMbvAK2oTwrM7U7d0kuPPbGGz1tfao8U1+DvD7Fk7yzbeq8+G8oPLocEzxlVA89sqjSvEjxOb2uwie8G/y+vEJo2TrWBAk8p2D5u3ytILzz17o8ixy3vHzu2zzu7M68dXU0O52p9rwMtyw6I8GxPGJeWbxsDX688aqNPIChbztGtji6T/TRPFto8jskfSY7WBYZu/xYFj1zwgc8u+mtuyayubsjRpk7bVpdu/YSUDugBgS7+Wypu8KxXzyfaAG8YqFPPHqApbzukYo8QgBLPHBHUjx8T9I8znatPE9dt7wBIhY8Pz2vO91iJjy4HZY7uEs7u/H3zjvDU3G8HzVvuwWoKD3Miaa83dWiu1LsRLyyWkw7nTauPIM6ijrF5A88appuPNiY5bvVO1y7I3vEvMotnDo1rZi84CsBPDNA6ruIQto8CfF1PN8SeTsTrO27Iu7Pu5sqT7zzSc07+Wo8PB0lo7vuMr88Nvu1OVrxQbwfA6a8RVsWvM/paTzpghq8v+WkO3qoULwgDlK4sbeku5gqLrteatE8DHKnvPAPszvhf1k6hOOzvNnvA7wcbrO6CDQuPE3Tv7wBDqI5Vce/u6RsiDoZk+m8BOYruwydmTy1e8K6t+9fu7/G9Lzud0W7JYrHu4pbOztaWBY7RnYBPGyxLDyILLy80W8sPCGarLtkJOw8GfCju0rzlrxNgLO7RMjaOtlWjrthuQ46AHdXu3udibyTruk8bRTAugUZgrx8Mzm8NdMovFIPkbquJm08xi40O/ZxK7sqBEE8nPMpvA==
-        index: 2
-        object: embedding
-      - embedding: 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
-        index: 3
-        object: embedding
-      - embedding: 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
-        index: 4
-        object: embedding
-      - embedding: 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
-        index: 5
-        object: embedding
-      - embedding: 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
-        index: 6
-        object: embedding
-      - embedding: 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
-        index: 7
-        object: embedding
-      - embedding: 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
-        index: 8
-        object: embedding
-      - embedding: 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
-        index: 9
-        object: embedding
-      - embedding: 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
-        index: 10
-        object: embedding
-      - embedding: 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
-        index: 11
-        object: embedding
-      - embedding: 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
-        index: 12
-        object: embedding
-      - embedding: A94iuS/O67ok0/k8qIZMvEQZSLpneY89ZayTPaETcDy5g+Y8qKs9u690bLxgcwS8IxEGuyEaPr1sdJE9kdeUPOVrojxmYCC9CwQRPEGU27vV/KO8iLyaPO3ATjw/t7a7eKzkvCpGejx3HuG8uzVvvalJIzxRneI8yFuKvblhA703WLA9xNcqO+vpkztMcEC7VQwmvNVy1rueRQK9mXKpu4JKvDzvuY683dAHPQtl4DtYsRY87CYavEcSWTuLizi8SCgtvE96frwPSgU8d0VQPAoUvTuDUMC8QBElPRicxLwaYW09WKcPvH01L70x06g76mdrOAk1Krt+B+S8rvtovABHC7s4uKO8lx8OvVv0Ub3QOQU8Uc0fuwA6x7vLC+8753OlvPw5BLwOvlY8HQ0JvRQOKbyIPiw9ipOIvJcPgDwbEJo7sI03vD+JWTpWgNS6eXsXPVmJjzwTZbM8dQoEPLNLO71HK/K7I96UPFqzlDxeOpu750D/O/mC7Ds5Qb67QRwmvFqSprzD0RC8Sh9vPJbkg7zasdm7pWOhPCoIxbzhCYO7Kn/cvHNpDryhvpa85JQBvAH3LrvYWvc7oxnFuxe5q7xB5QW9TqJsOs/JEbzdM+w7UA40PUyP4LvKGkA83ck6vARqdTzoz2c82erCPNVyojz1SaE85v6EvM+wtbuFAHW89sBuPCo7ET3SxRC8O0tgvJ8lxLxmwKq7nsDku8suIDxyH7y7Vhl0vONQjTwJ7W28bbFvOyvxjzwhNcQ5ZSeGvOsAN718zgm85oV0Onmy8DnMnDu7ex+vPCCLMLvnCpK6t/RuPPT+PLwAvXk8F6B5uz9Hzzw9U6M8V5kLPZh9TbyYfow80yODPKf15Tw9I4W7EFqAPGTGM7wzWxi6UYq2vE4X07zOTQo8HGgFvSvVgbtSBJ+7M6SdvHleDrt0Nw29eyF8OxILi7y/AAM9DuZqvImcJrxq2qc82IssvMTZnTvoYlU8OG9Gu7J1bDy8k8o8nwWSO/ZJIrxEuAq8moIJvCvTgbulOWI7icrkvMoT0bwApEq7gBpZO6w3Aj3ocUO71HO1u44uXryiqle7f0EPPAdXCTyYyHa8sM+fugdqPjsXq5i8nPULPPusEjyVawq9iMQ0vSDETTxuZN65JGJqvD2jZ7zpPLY8tH0jPFNqrLv0eEI8s06GvBR06DotmGG9Gv4lPGsZ0DuNvWM7rx8tPDizGzz/pjw8YtfBPEi5Vzu8syC8pVhyu0H5dDyCK7S8zhBTvARaPjzTAkA8eIzxu5XX67ormq87+uoKPHxUrbxw+N68NhKlOxO0OLzRhbg6WSiYvAKH97xbV2U722BuPD6U+ryH5uQ8RnILvPbmurwjGfm7aR0zvJuzrzvaU4S74pOEun5qyry5yra6sNcQvPXb4LuSVpc8p856O4Hibju7+kW8dTgDPdwvmLwbavi77bBGOyffizzNkT68eFM/PDLYQTzKQwg8iz0iPenOKbz37aI7DiKdOzWifDz9TCG8VO2UOmG8NDxX6HQ8/DC6vPCgWTwViKs8pWhTvG6HnDoD98W7wdo8vSimBLulBCo8c0i3vFWuRTzSx/S8EQRWuQOFxLuIt008sj3zPNyhJ7uiA7o8SczfuegbxLsbLiS88TzovM1oErzXCJm8D7zaO5pnQDyQmc+6RBYXveUHxTnZzAE9gWbpvL6VKbzI4YY7siOCvb+fKrwAVO67lp5rvEtwqzv059I8Vc04PDajOTzarOk8nyjnvBeJzTw+Mgi9aUP0u3j58btPrAo8WJ/YujMwPT13X048QKFlPEsygLzLxnU7ZHIBPGZtqLtqZYi8LRycuwZJPzys4ri7IMURvSEeiLz5U+S7UoRlvBJCrDzv3cm8Y3xkvTpJmTxWESw8xAs1PIZer7wVwuq8G/blvK38jbvRMiC8/ti8O3eKx7v8sDO8yggNvDRAGj0B8Ya8v2J7vJL6ETzKKY68wUFMPDLfF7z043E8wN4LvFgIyDpKu1y8X7cLvPctuTtZ03w8uoR7vA2YF7w4V5A8x3DKvIZnEz1KHDK79swBOwOlCjvhkGE7BJ2lvEwNnTxZWqY8wVEAvDDCBb36vX+82M3tOssXLjymuiY8HkyLvO702jxnSva8ToQdvQv6wrz7Ase7zG46u1IuR7xXD/g8IRq5uzhzD7zeIBq8CH0aPf/rVbwyCgu8nM2KvO2/hTwYiPY78xm2vKcRNTyCahY8t3DMOmkybrw1+aq73Pc3O+BaRLq9Ogy6Xnegu9I4FD0YRK88su3MO9rPMzoyqqI8ozWuPJdWQz33Qag7QOmFPDbF3bln7AW8t8LPu/w8MrzEonK8iOkQvJMKLj0rMpQ85GsZvd07K7yAui48lrvFu6asCD3M2Y28lluvvCDCRLyrDym8Tt4SPIcHrLxwp9Q8qGOMPG3RqbxMhqW8NMFEvBEk3r2jpaI84R/iPDQYD73Cl468O4TruiGkhDx0xjo8jkSHvGW3uDwEEfu8UcIkvW/F27y7nBk8f0TOvMwhijwUU5g8wIE7u4rfF7vzHwA9ouftPGkIIjzamYc8y2SzuxFRKLyqQZs8BD57usctMj1g9Cm8gLQKvAhQ0zxuJLQ8b/XLvPiIyrykZN+6BJl0vF9WHjzRERK91UVAPBFb8Dyc06c8w8f3PAvE4Tur9RG99GPTOzLpoTzPWVM8qxirvLGsSTzUZUo88OhQPKD6vjwUP568+9M6varuqjyCmgy94yHCO9qylDsh9sg8PRwku2CaRLz57ec7v6/pPAPQpLwPedy7XHyWPL5DRTz9BBi9Ga1EPKL/3DwtUF67ac4wvBMGxbwTHa8848UkvX6r8jgoQp48xAfjPDjygLpTbYG8GlrfuqRKjrzYq/07fhWUu5JxITwfJz88VDqBuro9PrphXYK9egoePXC1iTwYes265TOAOlqWWbzCUKs6NGE6PZ6YQTyFROw8cG6lvNf2xrrlWTa76iXAvApmFjz6hHQ8gT4MPb76lruzkEg6dEmluz0CSrv5F/C87ktIPHqitzv87cc7bNqTu+Ff2jv7Zxy9Ux7PuqKVETxMP6C8X4mCvLziWzwNaxo8niwmvKqHG70laTM849p+ulzkBD0evkQ4w5DkPFFjyju/Esq8cVl/O1pFtDt0Fg08mJL1upP2srygYFg8JlLpvC9+ILx18CA8WoHRPH4ZU7xFkgO8YGOoPF0HhLxDadm82wbDOwJZPz0JGWy8IkgQPLAin7z4dou8hadYPO0ZmzzG/xM9BdsuvSSpo7wy/Ky8SHWcO3FaNrzNWT69MiuHvJhZqrzP/La7+1vzO5dUHr2mQfa8Mpwauqm73DutfqC89+dIvFzBtryeemE86FpSvCnySrsryiG9IMMTvXG9MD3fOGk80KDNvK5bVTylaOo7Vp3WPE/rgbu+PPM8loWvvNSuzbwgmXw8XC6yvKBZN7gNyeG7FU3SPLlvHrxcGUY7TB6sPLW+AL3tdjc8oJAevdezGz0CF1m8l3qIvDF7EjsDjYy7kFulPMzlLb0wxEY7/v8BvfOJL7yS6Sk9ASsWPCHUKDv5VDq8YF3Euw56Vrw9s5c8krA0vVxr3Tv5M+I6dWvPPGO5vrwqbJE7hwLnvHwR7TzK4kg8v9sdvGz6ITxgWWG7WBIHu2EMszmsbtU8spVkO3WXmLy6xxM7k9y4PNhktjwUPq08sRT0u+TzkjwEoUq8lkW+vBOQSjwlLgg9iusgPAuVKz0jP8a78XHWPLdh0jyF3MQ8iqyDO3u9zTw/u+k7xisvvXhVp7wYab+7xjmhu8LUz7yEAmu9B2CovNWenLzXSdE8rIquO8h4Bjzwuii9DTTku+jfq7vihP25tqyPPGr8cjuUGY27n10iPUomZDu0QwW8S/hBvM6CoTxQybC8goLyO0U1uLw5P9K7HkYWOtalxrrklT47k/Ogu+fRPzxbmZ48NlJSu1wHKD1riTu9r6trvK0kczxG+ea7PLQGPR6j/zt3vEw8W/kTvGF/4bvrFwM9tdrzOttQCzyg6k88lNNqPLa1bLrUm5K8sj3lu663XTygv8S8jQt/PIJt/LqCmDa9C8DzvACvyjrAcvq7duCJPOUsUTu5uVg6KmIDPcfIZDxE0ne9Vcs6vdrZ2Tw63YS7H7o2PSli6jthJZi7ffWvO63znzs8ScG7PrTavPf/lzzKDUi8WlsoveaoBjwnkxM9K4cRvYlVgbwFRz48aTnRvKZ5XbxEcog7C3dtvNCjg7wJPfC8MNy+vA2uHjyDtT89loJgPN7NED1Ajxi8OskMPRZ59Lv7Tl08G32cPBr6Ob3fLJE7VXu/PFLzgbx2BYy7aotfPI+sArxGMTI84lTKPC6sNzw9axg976iZvOXsijoME1S8e1L1ux9+Ejwoa+m8IUWAu9r2iLuX6uY6CueCvEux1TuS0xk8P+WduyHfALxKjmE8et4+vXYMF726D1C8o3BdPMkkA7tnTgU9zukavE8lTruMsxu7ssnhOwfXSDpn1IG8mcASPc0lmDw9+WU93LYEuoYXazxA7KM8SofTPM5hmDxaq8+7ZVJpvPAQPLwlVVs8AIogvU/Lh7wVzQG9Er9cPDLPrbxot6Q5iBzhvG1ffrwacxS9Y1LrPK/P0DxOJUg8aIKXu8ijtrtK0rg8j+4NvFt+Frsb6x+54C2evG5x9zyWLLi85DQSPcuI4TyHwoa87uihPKTxNzzP4EQ7zPWNu+zbXrtwmJC75HokO99GdTysd407RqMMPLGnPzzb3WU8oKHivCtlgDypGIe8wluQPLVjPLgsQke6WOhuPIGLAz0hF1y8owQNvVArjLxBp2s8KMqpO/0uFr2pOvO85nsOPYzJkDxyrGm9uAEHOothj7qpL1u8y5WUvOPplLwc7s48L9poPGfYOr1H/JC8IrQrPVUyh7zyucE8xzkJvHj+Kb31aMA8/xwgvSwRoTwjDGe7pSPKu6yclrx2Dp68tWgEOaZgiDzVkaA8M6PkvNdzJbyhc6w61m81O5Z0UT3GJ4m7Sc6BO/SaDz3iJem75KFWPJUpfDyLvMi8eKKVu/LrQbxRCi47V30DvBewlbsje1Q8VpulPGhXSrxVz3g7OKjDu2oxXTt5INK8IdN/PCxTDDyYC/87iaz9u6WZWbwiSlY8QCdnu7Sxgbf1wMS8g+U5PJkAtLxrNRW8gvi7PNECE7o6+BA9EyaDPZHRojzdqma9hjYSu0K0uDuKG1g8wfIXOrJUML1ZcYk8bjPLvCwQujxZBh69qB+BPIoXGbtc8I47jqP1PO6k6DtZCf48cuqou0ICrzudHvi5AvASvCmJKb1XVwI9HJCoPDjwiLwBhwu7iGB/vBB9xTzIwcY7SOKuPHCoy7skcZO8WVqYO6B/5zrXtry85IVlvNb6j7tNER68BwqOPJufp7yosJY8+wGIu8j0VDu4D2a8ZWueuf3NtTwhH/28Wd/WPHgBpbyvOF28oRvVPJtbeLnwoTc7OspXvD2+yTrOM+I8E7J1OxoVG7sMAza6nEGiPATiBrzRq0i8cSUbPHx217y61IG8J9oPO/Ed5DsEx5o8Z1Heuw24Bb1yD7w8TgDLu6qQDD3kiAi1ehbDOUT5t7svBzg5XhDaOnsegrt7O5c8nhuwPALAnTwxM1y8W44fvCYaebzDhLO7WVwgvXWAZjzgtAU7qZ/EvITQxjyiLhG99muyPGxXu7duUM88Ilb8OtWsFLzz/wu80Yu1vEnmSby/lzu9CWZHvQFERjxTpTe7r4kFvfnUsTwPENy8NhnxPGCNzbppQZC8JznJPLWvDT3HLUs8Q0Lnu7qV8rtcJSs9CIghvAMw67wSHpm7B0OoO97lpzqWRMA6j6bHPCLlJ7w5pr484jgNvSKzlryvYv67RKglPc6turu397I8kbYdvMemvjseyJQ7i7qQO2FEKrublGu8cW2gvERASbzPdAO8DYXhu0/MmDt/xRs7Ekt/vLz5EbxeTEi8VUA7O/iByDxFHyE8MbANvRXTCDy/aWc7yR2gPCZkaLs1i1a8qJWQPVu3CTyb9be8o4v3u33FRDx19tG7Gt7uvMwt87q4IIq7O6d4vFanzzo15Ms8KnfDvHOdvTwNlYs7wkZQvA/qM71UwlM8pb/ivFKCpTyA2Lc6XfJtPNf8VzvArRA8YY2+vGCz/zy3AKE7TXmaO7lVzrsyyEU9umx5vCh05TyXb/G8SJOqPJ/Rlzt6Ssu8lfMovDqV0LxhjM88GWQLPKJhHLsIeLk8MfQ3Ol45Sjsl4FM8knu2vBpioDwv9zi7mwjLPOY6hLw8uXm8PXrhu2D4+DwAN6e7PW2lvB9fxrrJjBI9nDvIvOp06DsTCJu8OkmDPPF1OrxpJeE7dKkrvMl0qby5isS8xB1LvKtj/7wIbug7oYU3vKQNJ7xDVgo9BP6tO60CwjwgtHk85ukGPZmVdDytgKw8JHtlPMYLZTzDYlu8E8DAO7VD/Twrl2u83q5NPOIqaLtJd9y8VaQLO81NujwS2iG83UiXvA4ylbz28gK77GGbPBsCfrowdYy8v7JZPHvCNTw07pQ8dicXPWWcWL2FnYS7REthvErBhrxbfTA66bosPYj7bDsHUBo89zYMvMediDyeUCA9e0cmOx9hoLz8Jqo79+5wu9Zldrz0bgy8FRwhvLpdSrxlFku8eMGevEuMCr1w6ec8ab7WvAFJODwDqac8GGm4vG8A2LyykAy9iegwuxcaZjy+d1o8kVMMO5J3JryQ5zI8X5jFPNUjYTzrHxE9FSOfvHboQD1j01W8ZEwWvGA2hrwidO68XXTLOz9j5byeAoK77UDgOjB7lzxmnki7XUiEvJb6przId8a8LLs8PUd4o7wWQgG9vKZlO1GIUjwhxqC8wE+1uwPykrxrRYa8OGFtO0wbr7yoxmg86CzIvFyvTrzGoZA7KieRPLVKHL2dyRk87JNVvfezJz3bQVQ7weEjvRujWzxaJBk8mtP1vOZyUrxVue876uGWu1A1EbskpaO8ymKOvIaAn7w4lBK8yy7RvA8JdLtftnA8TfrtPHiT0zzbFO67P3G0OwDRLrwbySS7K+KaPAK117trAAI9Z7OlvECLAz0Zj128JAkwu9wvOzwEieU7v4gQPYQI/rz/sgq8/z4Cu4+ar7yoWHo8cuGMPBVkzzvfMx29dhUvPaqqQDwM0Py77AK2PG+7abycmx08U7LCPGWOWLxOQ7I8aP/SPNCMZbvkP4u8p705O6cSDT3KgOy8q8MmvHmLwToPnpa8EGTfOxNX3zvdREo8DkCJuXNeKT15l648YTRPPTuIBrxQOB06JHVEPBsENzyVULe6lyYSOzuEhDy/jvS8UkD8OT/syTtsDQo82jbvvCW1zTyL3wM9qIbDvGpHTDy4w7Y85PNvPIWEobu8T5g8vo0gvA5SmzpiJRs8qR02vSmLsDw9s567wokgvKM9gzzeWuw8ytNkOax7oLy7IeM68TzMPE0qMTtHCuU8MSI6PNNhX7zwKbq8V4x4vJ+oQj1x75C8ZhygvMaslby5gsC8QPfdO0p4hLw8f3K82YwmPMxBIj0mhFq7A3bAuzxYFLxOIgg9ooSJuz0D3zzgFCS8vZOgvL/mb7usNGW8evq1vFokj7zIQRu8LNo9PGTtYzzSKwI9FysZPGEswDyqkQy8tT7XvP0qeTurEE88mEKzvJqIsbyRA627TAt4Oh9d5ruKUoE85z7JPCa2o7uML8i7tTrQPOt3Rzv99cc805fJvDYMgjx+rj66I2rGPLMCMLzP58G7REbyPIJipbyOWgM83EgdPS70hrzgJA29+d2GvKfzITytyh69rLbSu1jpCTv06J28hXgVO5fGobuZlBk8L+UwPcgZkrz4Qy88LZcEvcw7wztj0g49tUv8OYoDlTxRLhO9n3+lvKBPwzvBRki8jcgCPAaNsjy9iS88A2aEvLAlbzvd49W86ZcvPeAyFLz5/Mu8wyWgvPx7PzoVUd68Mdipu0J1j7yPpck8vqVNPNAhTTui4Aq87T67PBOCKrpgPpw7JeyJO2WCwTtB/Nm8WFuaPMmgibv2jOy8QULsPFgoITzaBsq7y00QvFRnwTw0NlU8UM5Gu8/EabyO9wK8MAd2PNIUgrxxEIc7Ek/iPMOnCDtZrLk8g8GjvEXmqbwDW/I8N4cluXhCx7tlBJM8M6kCvcuEvjsU6I48f0+vOyZoyDs8j7g7ONxpvAtk0zzHb7w8ZS4YvbbQCrzQfYE8oD2cPHHTpDv39PG7SH21vMpo1Tzgf6073MSCvIrRkzywwKw8EeFhPCJ+ITq/L8w7uIjNu4bERj3fF1+8Su8uu+J7xTu/Spi8HawQu3T15jwC/Ew8KofbOyh8AL1jgLY8BggMPF/HnbvIVH+7gU2+POoIgTxzTPA8hk4bO7bF0Tu3eAA8Ur6nvNVPn7z/kDi8AhCFPP/xibx/y8A88teuPOKTx7y9ZpK8HAW/u1oZArw3aaW7d50kPGO2qzvXNWi8aSaCu4iFgjyRtgs9dPmePDcxmzy5DvW8uv4LPODdJj3WerW8khqVvG4IfDsfHqy8GD2rvIwFsbohm8k8b5SpPA7FETx4AsG7FhuPPFLiorzvDxK80GAzPZ6Dx7sdbFK6Y0ogPFpydDscfxI7hwStvClMrDtP24Y8O4mTPOcRkLxtTDa8yzhwvNP/Rjyeu086e2wPugPjs7zZ+468FhU/u05MOjsfMOQ8MKU9OjbSn7ykrEy8faeHvCkPlzxLNvK8VxDYvNvvlDzLll67IbyNu9PZWDxE8q28K4/EPBhLIrw5tJ+73RSbO8N6wrvgFOY7YQ+Cu6TQDDsgm/m7OrzHujlkrLyphoC89fo8PW31OzwUQhO9/N+8PPQ8ODwFOYO8ZBKHvPwb9bz2m8o8pPs5vRhQsTsLh+Q8RJQRvepeD7yDiKe8/SriPFPhmbwWcLQ50z9FO8OvD73QgZG7MYGEPOQQG7zeigW913/bu0S8GTxmTFq8sVRhPPDHZLt0bhQ81PmtvE6C5DyP/8+8ea6UO+I7ELv6XQW8ro1DPMTaGzur8Ka8+oaaO2TPCzxPu6m8V87+PD41JTrIJa68vU6QvKHN37wKFWm8VreCPLcvsTw/eZI8uQa+OuFhsTxomsU8i49Bu+qxhDtaSJ+6pqr9PO+6jDl/wjU81llQvCuL3jtjyt+7bsdnuizXHz1yo5o8dRO5PGbJNT3Z7dI8kK0su1zHuTzzMSW9O81Nu/80d7yBTJk7VUFnPFCVc7xojw88YkMrOXPOqjz8COO8lGFuvOEzrTyp8Vm8SMbAu2t+nDyPF4y7j6gjvLAGmLogX6I87kGKu8DQe7xuVwO9skDqPJdvizxTmVc8FQvpvOw6TLz8VGI7k/IPvRIDEb3SWqq8lMgavD6hh7gRQ8K8GlQMPS7UwLx7hRC8/6vBuxNXRz0nDTY8Bu/iPD3q5DxSik28YfIqOpqCFT0x2Yi8VIf2u/WRXjwD/GM8rqNvvKs6BDwbKzc8TIQ1PF25OrwWC/c7dPlpPKDUdjs1GkW8wRTAPHn5FTxcx5i8bO1cPF885zxQ54W5c0IWvEDDeTztbK48kBZ/PF5eCb16WEI8c7zzu8K7lDwfG5U8ludXPC/xnDoWhAM8quJKPG4+Fz2wpJs8+3khujiasbyoF/A6fAo9Ot/VVDsO6KU7MqUtvCQiqrz2dKK8daiLvC7uu7zdH0W8eBknvKDRwLwlF5S7IY6xuzT9WbvOApE8bE0JvCZQHTzQdMS800gVvaogszwS9ry8J8t6PFA15ryCcRU8x05MvAAI0jypirE8GCIHvcyUUz3Lwxq8+QZRO5C/RrwnC+k7DmOUvAeGTj3P3ao7dG++uzePvby4ib86JhywvBVzMTvDOCG9RLgFPOUyDz1YZAw8npyHu+iyTrzSLNo7WgOsPEWLSD1cC7865ilnvNM0NLyI4rS8/ccvOl/J3Ttd1xS8MPBRu1vRQbzI2gu8T05qO+u4GDyoy547PutlPCKl8LpUnG864HXTPPdqHTyMMec8SKAAPVm1Rrwl2tU65yMePW1Bazv/zkO8gZ5JvCw2IT0czl08NZFjO2WycTuD7Dm8ChdfPNaHBz2HMny8J1dkvN5+4Lz99Ua8OH0MvQ4KDbvI/0Q8FkK5u+g/lrrQ2Cw83M13vJSeLDzQo6+8QwemuwpbQzzbyTe7enlJPDRIZDz7Vuc8e4usPMOlMjx2Bkm7i1V1u0h6bzw2OPo8ZJ8DPM1dmTw9yqU8kddGvDlUv7x5E787ImPVPNQU07rkzte8UhX0PIrLBDzd3D881gwCPLacFjzmk5W8v/REPOWx1rutvOM6auvkPBNh1bs1Sbm7iVUHPCGRpbxHqpq8AQczvKgVpDzY+/47qxoAvWDHHrwWa+W8sx8fvEobaTuMW9w7oIMtujVYkrmTg+47CvLvO5DokTtDrzW8iSFSvadJjb3oeMe85UjNuhGiJj3G/Ga8XC7JPAvi9rzjMCA8wSQPPHLF3Lzemy+8EhcFPFWvmjyzMpm8B/mBPJ0lELwK3Sm9PKHlPOj9kLwfFM+7GQt3PPSxsTz7MAI8MM9qPMOHdLs/mC488JCvPB515LsFYJK8SzLwvLwUljphVA49tOsVOyOCwTwzxCw9cCCvPMmfDLsZIBO8CPz3O/Q+BDzXm7U7PATBvHH8Vr08py28tvTau2a/U7xDgEa6kBjbOwWUbr2GIyM8c218vNA4K71z6BY7Tfh9Ok2CijxBkPK6m67rvMEGibuM0SI8bSwNu/PMpbvxp946LdTNvMbaGjzRFzK8ztHuOv2YXLv+Tso8k4B5PGFWRbwz/sI86y2pPGJaSrw/ww69VXPBvKhF+zvf3sO87hgKvA/Wy7iYz7c81HLyPK18oryui0A8/w6GO2CEszvkh2k4IJ7VPNYwmryI6fE8N/D+PKFZqrzanC+9RpHIvEnWgTzJtu28oR4uvOH1GTziF0a8bnEHPUUKsTx7nB69dPgxPAa1yLs9B568wUqnPLolejvrUaO7h8wDvfT3nbqhtOK8WhsgPBnnvLxoSAI7vlrFuzuQ3jwOhby8MLu0vI9nljs5z668HgFUPAZZCD29Pp67FnsKvfKGx7zsBEi80Mfhu2Hm97wj84o663uouig9ujwf9bu72uoDPUqdQDzvbKa6JeJBvI9EMzxZOSw7xrpWPBhgJDss04c8pWvAPKubHLtBYZW8aqTVPINovDyqWBG7P/j6O3MxNLxdIgW928t4PJdoZjwS78s8JbIyvHVWXLsIB/i8OAuVPNDJJDwN65Y7lLT1O2oUHLy9o3S7GSwrOpxgxLwEVA07lmZIPK1Gc7y220S8nrYAvNKKCLx3+g88DZTkOS6q0zxFL8K81tTFOh7X4TsJFRe8TEQYPOErP7y9++a7JNo/PCb+urpvJzs8Jm19OiKDvLkX1as818OTPBWOFz3iurC8EIyQPDXP6LvikwC7d4b2PJG2mLzPQyk9B1vLPCd1g7zcRH68EcmjO9H5GDxLpf27cclhPPM+0rtqyGa8MBsHvSaTD73aXYk8Cy3ku1F4sTnL+rO81YBCuyVUIT00gsm7ufWjvBoHCL1ukxA7AS4lu5M5GTxfmKc7lUqnPFiyPL0gyCY98mGevLMrS7xLN6O8H7IEOl182zwWOkE9WB+zPOYAfbwbkgc9SlYTOgesvrykddU6Q01GPeuZDzteQdO8HcI8u2COWrx21Ks80iO+OM+C9zw1NKe8JuWGvAL3Jrub/7G8+ULPvMKV0jrZ2vI4+XoOvc9dKr1V9Og8EqObvENB1TueaxY84I5nvCyaZbwFWGQ6ejjTPGUh7Tt4RIs8uEvuvN1/Czvd4o27KbsOPNBZKj12JAG9DSPTPESM2Du4M6I69DFxPIv96rwv7bA8gDvxPLgqOLyHb+O7O4sgOtWrbDzkX5w62pCNOtdxA71jgKa8ZviBu4TsqjyepQy8VpC3PGO1HLzU7OU4JktEvfZ6TzkISTm8l928vA+hozwIvss80H04vKVLqDq/Vua7PWjeu9zsUDy9SDk83LzBPNp/vzx2oEs70CehvMGgSLr0zFq9QTuTvHg3UL3/IKY66zWVu2G2LrwSkwM85kGhOk4ylTu4Xbe8gkeIuysUzjuOsR08qOPfu+/i3Tv7gwy7qdnou6oRCb2RacW8hg6VvGwQNDrjUlg8upUEPNDOjTwOZ8877QXXPBDjh7stL+c7H8a6utkJCjyKP0s8GlPsvEFqWDxh9ww8pO2OPOa9CT2p/yq9BDYQPGG8dzym3kG9CjOFvOFMsrzVbjs79DMPvL86QDzaWuu7FJMXvYQuI7wQAe67xZbDuy211Dyb5dg8mX8YvSiFrLzcQ7s8yqBxOsiibzqeWSC8GPKPu6l8nzt2NL68S843vBd3VDvkIzO8hI03vJlCATxO3xi8WDj/vDzweDwbnC85jX1PPBLyAjwp68M6MtBMvMSnoDwJQxK9qRppPVA5JDyGgrK8wwglu4vJBzzIeo+8h1MzvHZXsroIHKO7aZXAu++GCzx2WBk67WBEvIkVJLxL9BS9Lkf7uus76DvGlou8LX+cuxz3BTwYIQs8d39/PLTfTTxKIBG83pwPPWA8XTzYjOS7gHDwO2dBVLyyawS9C/RpPHeiKb3kSNM79G68PErgpry8wqU7xeAtPOi3yjwOHbg8FGGzvKBIgDxMcwE8NDmKvL/Tozy53qk7DoxcPDWBCDxT0LQ8lwhHOx7YULxxY/E8Be5bPOvJ8rvIrR08HTabvG2jTrtV6Ps7mgHHO9kIoLz6dvw6uRiwujORnbzBVdi7wqQIvS9qirs1S4m74GFEvK7FpjpT5IM7YdXmvDsn2DuTjNS7W+LnOxibtzzLErM88McMvGBFR7wco+68lb2KPGrL4jsaxAs9luJvvPnDPr2dxfy7fHcNvCegF7yADd66DhvZO7LAt7s9E5E8dFEUvWfLNDwuJUG8zpZbPAJiEb22jaq80El2OwMhETy/zZC8FELaPExJj7wxh9w60i+qPGnFUDwydyM8nS7JPGurDD1SWAA83rY/uggwEby//+07BsaOPO2//rmt1QO81UXtuU9v5Dzv1aK6FTi+PBrUGLzuuk08pVMjPEQ/CzvExGc82dpVu+WygbuOvEG6o/w4uybpDDvSywo8Tx6LOwV1ozzeffS8C/6pvMso8DwMRLy7ZTj0uwszUDvFQW478l2Au9c8H7xa5Is8WO7Pu+1IFLwHRka8LH8QvYTHVjxdLda8yi6rPE3SgLwbGFI8zmYgPAizATyfWUy72p80O5M037yvWac6rLkyPDsoUDzrYRU9N9RBvB2+aTxrB8i7tQWQukEiu7sw3Lo7cLNZPMQgETwPf5C7m4J1OugvyztoSsi7bVd+u4JAWbv9zqy6NFRju3pPLTze7r+7VOVVPKFjbruAkvU6Ch/mu3htk7ryRwC8q5J2O+F/FbyT4Yg7AiFWvIe5CL1sHM67DKwluyzcmrtJEPs7sLTNu6Y3nLsPi6m8zkPsOgcahjygFoY8ZeQEPAg0urylc0O8HAm+u+OljTzG1Pq6WEA/PGN0pLy+Crk8kaEeO3tyQLyLk7U7otnHvNvUPbyRRYs820srPNZX8jsU5nQ8npYqvA==
-        index: 13
-        object: embedding
-      - embedding: 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
-        index: 14
-        object: embedding
-      - embedding: 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
-        index: 15
-        object: embedding
-      - embedding: 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
-        index: 16
-        object: embedding
-      - embedding: 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
-        index: 17
-        object: embedding
-      - embedding: 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
-        index: 18
-        object: embedding
-      - embedding: 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
-        index: 19
-        object: embedding
-      - embedding: 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
-        index: 20
-        object: embedding
-      - embedding: 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
-        index: 21
-        object: embedding
-      - embedding: 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
-        index: 22
-        object: embedding
-      - embedding: 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
-        index: 23
-        object: embedding
-      - embedding: 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
-        index: 24
-        object: embedding
-      - embedding: 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
-        index: 25
-        object: embedding
-      - embedding: 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
-        index: 26
-        object: embedding
-      - embedding: A9bwuGBVIDuf5Dc8A9icOnc4+rlAWXo9o1CjPY6TBzx73/I8GdB8PMkOAbwypVC7omgLO22uL71mZ4g9Yc2HO+cPWDy7aWq8DYwVun3mAbwyP4O8Bw/sPEd9fDzfD4+7hwe4vPGoizwHyNu847OAvW6HlTwhRpw8WyNpvXfs0rzLIqQ91i60PDnzfDtMqkm7QgZovPeNwbt3J0m80+VCuwku7zySD7G8448WPU2gFDz5DJ47wkvkuqfk4jtWKwi8FvZmO3SCmLzg/GA8cvoYOubdkzpSa6u8uylqPBt7kbwJBQk9gkLlu+McE70s/He81CC1OiopmTzwOAS9qg9bvBw9IjoW0368XuOKvGD3CL0SxQQ8k3i9u2HHg7t5s428d3eLvDQyyjr7qag88NUCvdNkIrxvKl49P+W6vE0hqzzFi4W8NJkHu2IPbzwrcYo7Jur5PGhsnTxPiZM80sOwudR0ab1apTO83bfIPKcG7jsu8LM5LkREPHs9CTy9Dw68AyAQvJ0T07xsHo286F55PCFF7btp7SI7HkGAPC5qf7wRy5O8F4fjvESzhLs9mGS8Dp70u94SJjzc6p069kyNuploFjt8jIe8fuaxu2sqCrxMU3A8htsEPb/9wbswTkG7rxb/u9/ShzynaxI8l0qyPHHgZzwJE1Y8Zo2ovMx32LtlgVm7h96wPO5YxjyAMiy8r59ZPC1VkbzghQS8HALDO71NyLkf1Ve8H+nOu05aAD30A0q85wZNO+36AjwjthO8iVeKvKVIxrydT6E7BCqVO4sCFDzzr/u7IyWqPAstHLywPSu8pBCuPAlVjbwX+708w/6NO67A0DzHxW08U/jnPCoCDbyrTnE8HvanPB04VT3vRoS7G/sSPCW8+LoDznU8Fnq6vD49Gbyqdo+39OrCvHeQc7yNvEK8yMz2vODuRbuWaN+8h5x9PIXMw7tf8tQ8UeE9vBsnxryEJ4A7BmnTujPIsDvFrx480zaEu2oYr7t1zYU8ZxMkPNSoqbykbse7j7OWujzzgDov5uQ6AMsBvbwx5LszB868CW6aO37f5zx9DQA7oLEHPKfjpjtJ96u7fLqhPLXJUrvdAN28zcnRu0qGLDyfXp+8CzpNPD3HDbonoaO8gXzYvIDOMjt/bKi7znWlvNOcKrw8TjM83Lp0PJlJKTzF5P87fyTZvPcH1zv+vUW9nAURPOeAZjxRzY47n8xrPGtyAzwJBBy76g6gPONnpLwN3IK8if8evKE6bTywFoq7ZueMvK8xfjzJjfm75uARvHababz2spi66FNIPKnbd7xYdLq84MkLu6czWrx6x2M6MTePvImL3ryX4iQ88E37O2DQEr3vJuU7XLUTvMZLhLwQ9zi8Va2ivK9jhzzS8n8651YOvDhwobzqmzO6KEyPvKnRTrtDwYc8ueSiOnHqxzzAZuo7cjAQPQ4SOrxoG5i8qyPIu9OBhTxT3528N1Bvug4Mzbp46kQ8n38xPazzkbw7v9k7lTxGvNXhYTx7ZPy693x+PKGbMLhVeEQ85FunvBkR2rq4YYw84WErvHwRnjoPp067ryXRvKG2DbtITIg7VNtYvKdiwbniVh293+cwPO2lIjktLYk7SL8xPc0vOTvBSaU8wDQePMDE/DoR1mO8WF6AvEs+xbu1nGm84cIFuTrOlLs/Kii6F/clvWk4UjyoZ9c8d04EvYVmGTuk1Vk7l6BDvTkwBLz0gse7qZZgOxM4g7ng8QM9Ew7KPCGiDbt9Ojg9vYnkvHICUjyictu8Q2eRvNBHFbyE2Tc8qOZWvETQMz34EaA8167eO6bXHbsJyBs819vAPOpLs7zQAz+8+t7ru+duojwVetS7N77GvCB8qLx4QAw8vjaavNePAT1uPde8kncnvWPGFj0S0jO7LS6BPK4YJbolmYy8NSO1vOdNITxJ+HK8m5V5PNgMJ7y+sYq8lqY0vNnqFD3pED68zLhivBdWGTw77Ym8nknKPKo8xLwclrO7g02NvNfOE7xadyi8cAUoO2HBDzw/PF48NMBxvFR3hbzrKdc8wX71vIMPvzwK24i8iCuwPF6DeDtQ3o08DCsNvYrIJTyw7SI8Bs97vFShFL0SxmE8iTe8O8UQVjxj+YM7PXYOvIW5+zqbxEe8e18EvU28Jr2TS3W88N+LO6UQgbyvnLA80cwUOWwIFLuCHQc8axW8PLQOSbw7YBk8lRp+OyzIgjxX6he7wKckvL5fKrtpur48auNlPNqSsrwk0Bo7DItCPLDxnDuIaI67Yae6ulie/jy47Y08/Up5PJ3kpbvQgaw8TmYCPFdX2zyfWbC5dWu/OyX2nDwgQZ65DcJzvNoihbz7Vbk6owY3vBTrSj1WiI08yjVhvVcAFrxgGN87hSAEPMhJnjyicxO85Bn8vDxS+rvdciq8hQbJu1wCuby2WqU8ohm3PKB1u7wZIku8nzm5uzQtob3n1Is7TIgcPX0kyLyWwNK8jvq8OgOC/jvLDVQ8/XjsvDawcDx4Zeu8zsgJvQnP/bxEtuI8UwW9vGtj5TzNCao8Bev4ug8WHTwk71o9jKMoPcM57jxcqpw84d1PPAcM2LsCXmE87p6nPLQKBz2VbhI8YyyovLk2QzwjEp48VlhVvHIUj7x19XA89ddYO2RbAzw1V+a8M9zBO5tnED0lTjI8fJbaPIABXbrh8kG7k4m/u5X4wDyKbJo8cpqbuyDllDvc/bi66I9xPHJiTTwCFny8kUs4vczcpDy2M368aEImuh5KvzpkF3k8Y1nduyTVl7uUwLg8EccIPXzJxrx8g6e8i0D+PHg8njyLlZK8Oy61Olua7TyP1+C7xV53OzPiBjshhMc8of8nvfR62bvP2dO7B25ePArcRbtKNHQ737j+OrLXn7xbx388W5sSPEdyQjxfmQC8VNrcuyU1f7ycS2a9rtn7PNognDzVPsE7DzrBu3UnpLxB8oa8wGxhPd6WKzzI0Rw9mK6Du0vVx7rHdrg7U4PcvMTtCDtehNs8Co8OPasUs7z+RFi8WE2mMzuZjbyn4UG8EKRUvPv9wDsnH+q7bewqvM0sq7uRFhC97cbqO7nubDy+OYu8rzMZvE8+ozwiVIU8F5OSvPWSCL19qZM8w5SZu1RPszwaxZM8LI8nPVpDhjqdStS8eJreO7dkTLz01hM8FHSBO3LBFLw7M/87P/sHvXSa9LxEp/E4udqwPG9S/7wYJTm8T3jHPPqlhLy1S428EzDEOgwQljwH4Mi8ysSPPEo//rxRzkU7KRikuWIilTyesA89k95tvczIHbx0yWy8rf2lPGl/jLxc8Ei9goGqvF6FmbxVoIK7B3MIvW+wML393Li8MlMtvBWJvjt7G428xJ4PvEuBPrwjWxM8PGrXvKtdhTxgp9G8Y9T1vOA7NT32qNg8cCPAvCCCKzyqWJY8mMguPLA8hLwbegY9T17qvKc7PrzFFEE5PJURvWbLUjzIVHc8BP4SO01q77y7dLS7XemEO/UBbLyuBvs7Ff6rvIm4Jj2ALQS8WfyUvGCAu7vyfOa6xXuSPCzObL0uiG68uMtwvAjcr7y8JvI8NTGCu/LcgjrBOQq8oeYLPLN9cLxvNVq5WxMTvVNQAToMRp+7s7HtOyT5Vbz/VwA8x4/Nu4i04zwKNqU8RCSrubjZjTyYV0M6it8WO/kQWTyo2Jo7RUBNvMqijrxAScc8gVe5PJo7EDzb+As9zrFROrNoDzzSthO9rLMRvJrvHzwIdNY8IOKJu/h6+Dw0Tng7jsKbPNNryjxCNCk96NpAumx0IT0wIbK6vFtQvWwB2rxO2qy8HwQ5vBawZ709Sy69CUeGvGQ6WbyTZz88LOqVOzYvNDzYIsy8iZeqO7oKkbsbJUI8CD5zPKgGrjoJeS881mQFPT0+yDwGvlc7NVIBvNePKj2pohw8QweBPHgdxLwK6ow7Rn0AvQNYXryzJZw7rXSdPNwhazqFfa08sk8IuwMLAj1LBEO9GtAXvQEAqTxKuKw7pxyfPC8iETzJ5qA8vEKevNmrjrztLDW73m3wu8cFijwTg5U6w1iIPN/NRTu+85S85Ea5vN6cazxtB6a8o4RQPNyewLzYldq8oT5TvDE7zLuC0J68N/j4Oxrb2rv3b+k7O+eDPGzmQDy9/D29YbkPvfzNejyLdJq7Vo71PPTWIju2Qs27+VpXPAix9busFa46DXygvM1cxLzPHnC6wcMivc7wEbuUmh89HF69vAwCzbxXcJy6ueaAvCGoM7egTZy7RbQNvbpKBrw0DLK8zkP+vNYD8Tz9c089c1XsO+8o5zx/gCk7jKGwPI3yPbzOQcs836OWOyT8OL2u3MQ7MPNyPKSjBb0a9tC88FqEO460CTxpyug8KuKePGVIMD1rIok8ZN7CvEq5DrxXdJs87xo9PO4w5Dy35dy60DasujTOmrwmRQQ6tTa4vIN1ybvdgzm8vk7Du01KxrxkvWQ8/CRWvSRCxrzI3JS8aKSjO8waY7j+DDA9qYTevP2F/zsXyG+7mDb7O5D8oLsBRlO8JQEJPUxpDz0/ZmQ9TFYyPAMDCD0K6wo9cSTPPNwsmjvWwT88XndrO2NSTrxwQsk8MA0NvSsRfLyoawq90KTHPEuu1ry+a4K75P7gvNy05TvSqrm8kq/aPASrAD0RV9U8P1WVu9VzpLpUkNY8Acv8uh03iLwpO+c7DdpavJon2zzX5K68D6svPZ7TqjyLdYK8D6IrPCpQkLybpzs8FnQXPOapDrzJJoc5Up9aPGB5Bz0KtgY995Gau3W7mTrSNdU8BCvovJo1Jjzu/9C8OhpxPJ/qgTwFiXy8c4M+O4eSGj0uih88EbzEvJRexbyG9H04jf89PHDxF72qf/a8N9ahPCHRwjrgPC+9QDpTPFoQ3jrB+jW8uruYvPbA9LtEzBg9YAkVuvAnJr0DWLW7y6tkPVt02rxm5QW7YhRPvOdBD72H0Jg8yeMPveGIgjzHj2m8wUUovDQAibz0CY28HAIePKhIwzz8uLs8I2k7ukwUgLxgETA71IzrOy9FTD02Vii7kBKIPDAzQD0oGSc833vaPJBA1jwoClW8gfJjvABvgLyNtC48sBaUvI8zHbwMw0I8UsIQvInZYTtc8TQ7WZTMuynpJDy836W8K9CmOzob7Tsr34q8nTZhvJmLkbzU/7Q8fCP1un2EcLziepy7CSJGPGIuQbxwZRa7meT8PMyGMTxrrBo9RNhrPSkhLD2Rfm29F0SFvJKmzDvwYLw8w0mPOz25L72qx2o89E4DveW2DT0SmBG9Xt+tPD2Qqzpvnps7c2XKPOnKrbrNzQ49Hz+yvAqgKLz5OJ87lhOMPADagbx44/c8fSdhPBHznbwNlrm8PO4lvb1ClDxW/IE7UmiJPDZiH7zmXNm8f8pnO+lqqTseruu7Upctu5gqQLyq8X85FRpRPFHDBL1s1AU73bkMvAlZyrpbg5W6a6uHPHg0Bz32H9u8TuwCPTh0Ebz+qDQ72Be8PKftjjsk34E7whtOu6nIp7vca6Q8LBKkvNbvGbvu6IA8KucwPMLe/DoWbTy86gOEO/vUu7oZr1u87AiJPFGSNjudhRY9mUwLPElwnrzVEZu7vunLu0ZWNz3oacS77WHHO9GezrrO1R88/lqEu9qRq7vrKx098AJiPJPD0jzpSfW8YR4dPISJNrzBfDq7pOj3vDOfUjzYjBg8cZiRvNuUgjyhlwu97j4iPVvEl7ykph08dZcpPC4ePLw8Wvy7z/6KvHnizDvEGxa9cMgtvYxOj7xj4qe8oWIwvex12jzawua8lOwbPJNE77u+IYS8ObkIPT/nED0Qlik8r9RFO9C967w1eBU9mF5IvPbKCL3NFR+7sbdHPJk3SLu9UgE8bLOSPDSXUbxXzK87SFYTvXTZkbxvtSW8aZ43Pbce8Lt5QVM8bvy7vPigVzt/cyo7QSXjOyp8OjxhtHK8figjvGLEYrz2tr6830FovFNsBzypwJM8gn+qvIunqbwV3pU7hiKevBDimTwZVBs8xveIvAeCQD2LtjW7oTDDPDcOoTv4cgS845IfPeC5JTzNTtO87sOLu9Ky5Dwa35o5WQekvI7duzrhfII7pctJO9LUUrutmV489W17vIIsaDx4DYk8EmsxvJPMH70N+iA8MxChvHiCxjyyCqK8vqQhO2E6vzuv4AM9ojgmvCW2rzxuiyA8RCgBvD0A7Tum7dw8Fda5vP/orjxUU1O8EH1zPAZInbvV5Fm8x9PCvCxb4Ly3YW48d26NPO2HVrw869M8Z3PzO0UBeTyAmQg86VbxvCTr3zxc5wq8F3rQPJrsirwEYga9m4dHO8CdhTwGRg08gbvevPw3sjv/T8Y8cQqVvJZv37tv/rO8/LBxPO4sYbxMZba8mogou6wlqLwSlO+89SxhvJdT6LwAdDI8bR6CvLj5iTyIpPs8HuToO4on7TzQa888SlYKPTBX6DwjJoo8tCsePAWQBTxGD+26w9F8vI2uMz17zxa9CsS5PIRyZ7waA/m846eiO7YjgzwQsgW9AKNDu5PIkrz1k/+7TPFSPCSRCjyBuwu8fTc0PDh9gDrNAi88RxQ0PQOOLL2sCVc8pf+KOyXG6btZRWI8jRbcPNtbtztxQpg8n4WyvODofjyUmxY9vg0bPJlsDL1oRJw8tjKKuqKQb7wN0Gq8evB+vH25X7xVedG8EnTqvEq9rrv80a88K7DSuzTgizyyQuA8LMWivAcB67yndve8a6XwvCpkhzzGMD88jy80vME6VLzbrgw8UUE3PCzMozwKUxk9VsOSuCV2OD0+SDC8m/jruzlBgbwDwPS8kY/KOwlnwryvojQ8gjL+O3KGljxRK5K8VSG6vHDZq7ylUzO9ugnAPGm96by4FNC8Cb4+vCGXWTpbrry8drBrvNdd4TvMXrG8NlaIPMH68ryP6II8suV8vF6cwLwc6Zc6dQhSPDCGDL3VtbY8f/RovRqv7jxUOjA85GHSvDdTrzth84I8MMW0vHQj67ygUhE87aG3PFdUUDw28gy7ZK3MvDEKK7xCvws7m3IVvEawNLzBcYs8zn+gPCcmujxoiQa72gXsOznAHbzCszu8SsNhvK42ZbwGa8I8YMt7vDxzxTxqone8cIsHvPHHmDyMfkY7D2srPWp/iznJM/i8x2SuO/qBAr3uWQ09yLFmPHE89jvfqzS9MtCsPJiWlrx+6ny7xSPPPPaYh7x3/vo8RXitPE78JLujbrA8Q3sIPEq7srx915e8Stqlu/9ujTyTQPy8bXTOO3xeGLzDtB681g9Ku4jfBrxxfu47paOmvKc7pDzvNwA9TbIZPeu4SbyjPY+5tzLEPOpeKTt7dSy8MpTau3k4Vzw1B+W8vMASu75liDzpJtS7wzRrvFFhUz3OsIY8w4iGvARb4zxuUvQ859tWPLBHtrynXmE7bg/ru+7cH7wftJU862oXvXvyljydVWa7WYr3u2ft2TxjjNA8XupTOzokmrzXMvI76rK1PNAm97uKB8U8LEq/uhQkm7xutPG7gXGWvAflSz3XSqO8oDYkvSs/6bxQ1sy8JbuYPC0iq7zxeWS8JKbpPNmerzzlE5K8Tsn1OaWGOTr/e7A89LaqvKNyzzyrCjO8wEZLvGWNKrxY7Ps7HAWPvCbAMrzxCIm8Oqj2O7cJmzzldQQ9HA3lO7VdmzzQSIi83B7LvI/lRby7qt46bJs1vEDmP7zgS7u8et9pPHuVlbywWtQ8g9SLPOpwBDusa268hLXuPC0Yk7ohxPE840HHvJnKxDujbCs8+8YbPQNNl7zgv7q7jKwcPGKe+buMIcU8NtjoPAbc+7tV+ZS8JorNvCn6rLv+nxK9xZWfOyfvczujvAK8ZBcwPB2IUrwKWhM8OvJ8PHU277wiVOc7p+B1vPfNMjx+YYw8Fc9XPDlJ4zwGA+i8qWu2OwbDeDzsp6q8PZSjO/m4qTxgQ2e73W7YvKSLsztWhGq8f+wYPbRFSTzhuIy77O2nvBZKvbkpZiC8yjp0PGZ96juh7iA9AZZ1PItyRzx4YZI72Lv2usuLazwumwI844lvPFiLYbyXCe28kdlWPLtqNbvzX6a8Duj/PLv0ZjvngCq8FP2bu7whDT1HPZM8HA5kvO447LzVG627xtdKPJ66brxy4ZA76/lbPIeK+bsR6Zo8WwaTuy2OA7yvS1s95rdsPKt+m7wY38o8jvf0vHXZFz3ro8o89tuTO4BnJDqnq1m6mH6FvOnUcLtOGoU8pX4TvWLSbbwPjTU6LWriPIHiGTzz34q628Jzu5pSIDwbXqY8o1etu4BnCj0ffsk70swUPJXEljuxgq47dTMgPLJfBD3dQpy84sQAOj2bLTzFWbm8s7NuO37+CD3//ZY7I0pxumc2BL0HoKI8so+4PEliNbsvGM68e6uhu7FsJLnOlao84lU+OnhhbDw7Rg28bSvDvJPWGLyb7Zi8L8RlPNJKvLzdYxc9WjF/PDybXLsTowC8JHu2O2NrwDqclgA52uGavP7eN7pNZ4+8x3/2OgQg3Dy22yY9rvKcPK0FujvkOv68bL+tu/lCeDwT8rC8KJdjvKQvn7sCSpS86VaTO8UUITxata88VoKCO83CmjxxcZ07kheOPJSTsby7P7W88JArPXFur7wfpa279qXoPHkajLu4nPg5PA0Lve2Kqjywj4c8ek/RO7ySrLqOp4a8LVa0vAEyWjzin6O70cS9uuLEPbsJ7qe8KkGtOxN7prv9viE99r4FvGyKxLyG8wq8D1XCvDW2Kzw8mQm9v6UfvNt/hDx8PvW7BRNJvJs1iDvFevA7P1nBPPgu9bsYUQQ8uxdXO9iLerzPcug7W7q+vDQqNzr31Xi8ML2fO2fY0LtNgYu8XmRtPRLQB7x2/9W8sLT+O3L9ALyR7Yy88glFvFrFubzyDKC70jSovAlRPTyGMWQ8DYVgvRp3PbzbyBi8oKz1OwFUJ7uudYu8rpzBOi01Kr1Bn1e87VcFPT1CjLxLvzG9A7BQPNzfg7pTrPW80vUGPCazPTzXMrw8/Q+6vEo6Bz0NFGC8tyCGPGKoETzGiPG8fGcwuWb93TzHsti8p5KxO013cbzgOse8t/q9O8PbkrtTANa8g6Sxu0bDLrztgse86UEXPQO7BD103aE8v4+BvPNboTxzTR27jaxUOypmdTtZ6qa6KqhIPLiLmrt+Cz88ZK0JvK5wx7pTXpK8v+cuO/MFozwRtXg8gOHhPE3oLT321yE7ZnZgPFvdZj0pPlu963P0uUygsbxCTlY7dmWxPAEnYLw0ca48cg+EO4SYXDz229O8kEAoO3zMvTvhY7O79lF2OwE//TxRCVw6nvawO4jB7bxXkDM858OOu/FzgbySWte8L+XxPNBMhDy/UqU8qCrnvDCzo7x0Isc8ZLUOvcSAq7xv/em8rPHPvOPrZLzKbBO9TouQPATYSrwpzka8dJvfPKPZYz2IGYY5hBD0PO0X4zwlxPA7PcoYu90qAT1PqQe9/4RBOOlEwTwNRrE8blRDvPCLiDsnKB48L2m1u/ci17wf6aU8ZXXFO1FBojrvxBC8KA17PML+drtCwwu9ZDVFuzozoTybbvW7GvIwvDqWaDrlZV887bmVu/XjK72EPdk7qkBQvJKMkTwK2ZQ8p6EGunVX/Dv42Sw8vx2gu0YYNT2RDso8lfzFu3HcsbylvXI7AECCOr52qbq8oPu7wn/ru1fglbwrdVy8Qa0+vIEICLyMORK8f+6xvBXuCb1jFBM8XplcuwUWrrwZnW08zIRwvDgogTvN2368sNqcvJ/SHz2yvta8CHcNPRTlML3sOJg8F5nbO7BsqDzzYe08qp7tvIOW3TzxaYe831e3O3DRAb340bM8SByQvGeADD1K16M80nhXO7/WmLzMvK+7RvnvvEQRE7qCNAK9/gA/vPlyDT3BEqA7h92pu8DLUrvidSG7c2SRPD5AOT2Pu4c8htA2OvL+v7wVID28kIetPIEt2LxXI/+81NZNvM44BzumkKC7dBx5PJKW5Dvcf+g6rsudO+IqwjxY6QK7rh/kPN90hjlctAA99H/MPAcPUbtZsjY88hhWPc3yezzukAq9aCAmPHpcCD3yeuA88hcFPNDMl7wZ6Lm7MOZTPCLT2jzGt268Y+OVPA+WTrzTV4S8PgwzvWezi7uCIyA8gagfvFF+T7yeuzo8GZPYvIBANDwyeMK8Z9sFvNTmujwGtmS81Y9KO1zU6zwufJ48PmbVPGmqyrt5BHk6wL1KuhDZxDwQZ608T/VwPJ1JNjwQsvI8b8jmu09CZrwWp4Y8NL4XPEFZkDweCsi8folvOzSih7zLm768JoWHPHdcDDue4a+87RUdu1niO7x/BBc8u/uKPAMOOjwPikq8HcjPuxnvQbyS4To8VSjDvIrXcjzPDCM8cpjhvDNNH7y1AS68OtE+PD5jtrvVcYg8UKIwvG6LArx/epY7BrWSvO/WlLrPlgA8c51GvZzxRb3K2M+85IelvA6qiTsbcd28ihi6PBcZN7zkj8E7WPJOO9KVbLwYtJW8AeYLPG6YIjyvXw27k7NtuhWskrwabr6804WdPGEhEbyUTaK8mdmZO8zmoTzWN4k82p0NPFMQe7soU1C73inqPLRtMTzDFw29scaRvFkL4jtLMdw85WbwOsWTazwq1Ko8kXAzPI8mm7xJCh28bkeJPBv+8TxRvhy88UwqvFBFB71Vnx48WU7Mu52Eibzztfc7dPyYPB8jN73WLrS6y9urvKhx9LyjuQO8nfVZvAn3ijyTXdM7yhpRvLM8QrxXVJQ8dpumvDrvjLz6in68ntK3vAysOTxeRmw8X1OOvM8kIDuFN7g8srK0PIGvHbuvVho9ec7WPBWBlLxG9DG9ZsLUvAShg7y+oji8+psevOHTE7tvYVU8ROcpPVtHJ7x8zXk8oBKHuiieDLzIrC08az64PBGEyLs/hpI8xzv4POWQNrxqDEy9hYvGuz7yyDtXcai8a+aGvPR3uTx3xwK9VulBPf2gZzzq0oa9R8CKO2xQhDr6BvG8fjzuPAOORTyNquG7ye5pvPOQujsfPPO8ao7kOsPgD70ouW27i+c4vAXEzDvEoso66suzvEQNgjwWi9E7g3nHPAGzLD0Y7Hq8rk0RvVAY9Lxy/sW8lk5dO1f55bz2Sy68KJ/oO31MMjwi4tM7Zo7iOlBJCTwAs+u7RiOGvAqIHTyTy5K8noV2PDqCq7sUVCS8IriVPOye2LujtHk8Bf9WPKcCjjvC5yc7GDQ2uwzEJryZMg68+VeZOy9BAD3SQ/k8RRxSPFIDBjygt6+8c+0pPIjU9jsJkuc83pfZO07m7LzPars6NSmcO5ggq7yVEd26lviwPCUHBLzFPwq7moneuylAxjzC9XI7lPuyu7jdkzzYMeG8YgR0OxVOszwzYDi8D8gZPPprIjwC0mc8nb8bvIY4KLw4si28DaP0ut1uKzywTXs4gIIEPJ+4Qz0QWpS8ItkvPDPl3ru22o+7XL7ePJK8Cr1pkAI9/5DXPGCin7xSr627zr0AvBTogbtPSMG8Yg75uevvh7zqPZi8l9F4vOwUqrzTXm07fcyHu7qcHzyy5Yi8872Luynz1jzgNU68zIsyvdzYzLzX0RW6q596PGojQDwKiJI72XBdO/X8O72Ddzk9a6ijvDPn67sfNbS8v4OLPADzhjyqyzs9nrTQPHn7fLyBGg89Hl7Pu2Smu7yt09i750WPPOvH7TvY4zq9ZUZoPB2Sw7zouMU8yIVVvL9fUDzmcMW8SmLlvBmpXTtHneq8EecZPLOUUTtR3qO85jmtvFqCr7wo8Pc8c7nJvGBxRbuCm4W7ZImZvI6yjrznCmc7lu6nPLU/4Dx5AHg8RWWNvAg/5zuypPm60M+jPGoNwzz5sv281TXPPGiY+rrDtjC812y+PFpg87zKNag8fLkCPWyT1rzo3PM7IWboO/EjpTxw3E+8QBe/vMas+rzjyeI73ApHvE+Bg7jIsw48O5m8uz3vLLzYuas71MTyvA1nBbuIZDU568eovF1EBzyz2Vs8avq3OT4xwTuZRsK8bjcbvPay6jtgU6U7wYvEPNVU7Twh0LS7BJ4yvBqlpLyJ1DS9myFuvMM8Sr3u9Ou7NtMAvclCsLxZEnO8B5aKPFiUsDvCU7k718tHvD5vq7suKo077lKdvJqPUTw7S/O5OSX2POv/k7wIvKq8HIuEvCM/aLxcUnU8SOvUPEsKZrwMf/q7u6/QPNhl6Du1CN86o9rzux6yJ7yUKXU8zDVcvDrcwjxcays8H4zOux0ItDz3cFo7s/8uuwCpxDwyD9C8igYeOx8jlLxUKZ08qqctOhRVoTwVMKW6x8AcvT0Eybyqv5k8RMYXO/rV3DxUz6M7nbfOvOE4Rry5K+E8KIWBPKjfXDwpa1S8Lp8lu0DGATyxidi8uA9svBRudzzsgoK87olgutV8Yzzko/o6yWDvvICjrjwDLJE8bg4qPHf8JTy2c7G7uQ1SvI92GLsksrq861wDPfvGxDsDcjI5N0Y2vP9BHLxoUzQ8m6UWPASWHDu8yPG6Maa1NzsIFjx2iJw6UfSHvKdA/7zM9AC9UlEzOz09HLylcDS8maYqvJ2SVzwDL548iNOcPHezjDzg4la8FvEWPVH5wry/juS7moSgO9khQLx49qe8nDufPLiIRr1Gzom8MyiFPOrztbwcjqc8jC9bO64lwDxPkNo87P2LvKbfqTwtGRe5zcorvA4DxTvCGKW8jSC9u5cZTTxo2DU8+kTpO/m9obor3cY8pvn6PI6Ky7tuPhQ8MzQIPKPNvDusnxE89FnbPODgJLw/9E483Nt4u2pWlLyNlZm7bIvMvH3kArzp9ek7uN7ruy/RQDlOgCo8Q+SIvPL6rzwsdhc8bF9NPDmx2TxGyJU7NR+tvM0iA7zB4ia9c/odvPcY4Dta0jM90v14vKYMP70NjeG8MIdPu6i6kLxwWga62onIu/l/fLykG348KjmZvGnzdDy2XOG8wupuPJFz3bxA8Sg7uRLmPOCSLrziB3S8h4GiPLgAjLzEess7J0mSvK/YKjxDVqg7VJy0O885yTw7tRW86JrHOoJO9bt/l2U8y8hBO+R5qbspuju8VfV6vABZqjyQg7W7U3K7PBK0brv2GJY86IOKOzHTXjxGNiE8vEKHOrqArjtYb2I8v5wTu4N3Kzrm/H48cJffu01alDx0wcm81VDvvJgxED11BwW88JAbPI8+xjp6LGY7aGIUPLGMFrxwiHM8P1RVOhx0xLvDnrm6anEYvYK5Mzz/3bO87d2cOxZ5HrxUuug7BBiHPBwTALuqcxI7uoVMvNlDorwli3Q6sBADPAy3bjzFWso8UQbpu5q2GTz/mGm83MH+u8ybUTxWgh48EUjbu1jDfbyiH/o6oKUJu9ZUzLvINw88FvbcvJ8URTyvAQ48aANQvM557DvWVL+6ysw1PIvk57qQVks4eD/2uwVpGjwUmem7whaqPGRaaDtnKpa73R+yO4TRB71MgTW79kOGvM3oC7w/TiG8PFu8u/WrQzzqBky8s80juuxBhLqFvWk8cQs9u50hIr15wBU8/r4sPO5tb7xwM5E8yBF2O0jHzLxEtqg8RjzbO/C6R7xby9q8Sh2jvI5FhrvFPnY74lsAvKT6jrspZ6I7+JBzvA==
-        index: 27
-        object: embedding
-      - embedding: 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
-        index: 28
-        object: embedding
-      - embedding: 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
-        index: 29
-        object: embedding
-      - embedding: 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
-        index: 30
-        object: embedding
-      - embedding: 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
-        index: 31
-        object: embedding
-      - embedding: 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
-        index: 32
-        object: embedding
-      - embedding: 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
-        index: 33
-        object: embedding
-      - embedding: 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
-        index: 34
-        object: embedding
-      - embedding: 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
-        index: 35
-        object: embedding
-      - embedding: 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
-        index: 36
-        object: embedding
-      - embedding: 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
-        index: 37
-        object: embedding
-      - embedding: IJPVuDV5WTzNEK08QiAyvAQYCrrtumM9zDCyPfi3h7tZOgM9DxjlO4IRVrzWX807p2cYO7vEWL2N14Y9mrNjPC8OzDylMIW8xyyAvEpesrvEz5e8eGKHPCfvkjyyOV68XY33u/IfgTwougO9iRKBvVqOuDzegws9vhozvUGxEb23RJA9tf/oO99LuTvh8Sa7sJ97vGz/IbyaRGe8wB+rO/xd5TyGdzC9Z7LMPPqZ0zuKG3k8vVE8vBomCjzGsLK82ac+u73Tk7wVQ6E7EJQ1PFAjCjzFqaC87wW5PCMiwLy3vm09v9sTvKf/r7zIioS8uatTO9mVoTwPDyG9dfmJvMWFsrqbFpy8xVcVvYYYb717BSI8AfL/O6u8yrteRVS8fB3EvO/+F7xjFic8wG/nvAoVGrwpOFk9lvCxvITR7jwHjw67TZKvu4a6uDsOZg47ea3wPHDchzwZ8tc8oojSuyt6GL3roYK7FziBPPKEtjw1CR68CSNOu4hOTzzkgxo7cHUpvMpV0Lx35bO8m3JnPIqpi7wLRG67fE4lO5+irbz//QC8eDvUvLgdSbx0sB+8WqsNvPfJrDu0tIs7C+RDuxpTBbvNNbW85so9utfj37pfwx48UOI1PbO3Nzf7tek7cyQ3u18nsTy1siw8kdo0PHRRgTw+u6Y82AaPvN/mojl3xsK8ftHlPDkDojyOx1O888ibPG5VWLxs6xi8HGN7PMIIOjv5K+G5hdK+vNEjzzzIrPC7m3qGO8hDUDwog4S8q+SVvOBHs7xwyDG7PLDQOn0UrzzCXgm6IBs3PHdFdrt4GqI62KXDPIb5NLyBGAs9KODDu0KL/zycoKQ8EvvePKHbKbym3wo8aj+NPL/eZD2Akv85934uPJ74sDoo5M07/GD7vG5fgLg9xhG79wiqvEI+pLteJQm8ebLTvFTkFrs689y8D/AIPNFdCLx7THg8T3MruZXqr7wUxqg8vi5Eu88+1jsNIZU8O+aOu4FzEryxnVQ8XPQPO9KlBr0TlLG8QDmeOjP6hbtaWQA82IEMvLAn5LwXxI+8GoptPOMJzDww/hm8fIf3u0/0XTzR+0672FK6OwBGDzqPw1+8DIQFvKnG2DujX6K8/YQ3PNQq5jrG5NK8FfezvCIOFDvyxzE7w8fKvCx7abyzUo48gXl6PGPlKTpusy46omCovF2JKjwKQ0m9YFzZOvlSpjulGmu7+TAnPJHCjDv9DQi8C/6zPAdjtLtpACO8CDqJuxRfbDw0Zxq8VC/bvKmWdDwEoSA87TtpPJ8rc7yhsTG8hzEYPOsTc7yJot+8efiyun6ZBrxtk2m7WaiqvEdw/ryQXiU8UdSePMdcCL1lyPk7sa+6uxLhpLy4ItO7fbgWvNsDJjzvOai71HMTvH8utbxxGxA8RaOAO2cSe7gKT708nZ6rO8BWfTzOpEM60LwoPa/Lwrse6JK8P+rIuxBYCjxUiYq8acQIPCgLBjuv5kc8V1okPc9EP7w/9ke7I9QaPM9KYzxwkwW7q/CPuyzxBTxyxpQ887TmvN/WkTygT/I7Z4CgvGGUjjsveoc624MCvCNWMLwTtek72btovB3iQzzOHMi87dksuQrthLtN8/+5+zqaPJnzQrlFzoM8cyTWulWLajsu1ni7pNnkvFvpcry3FIG8oP8OPItcHjzHT986Gz4ZvWAknbtDxfE8v1wEvWmDaLxz+4c8RLE6vfU4Yrz1FtO7QkoVPDgf57odwro8tAK2POBXpjysxBY9hjGAvGdYqzxq6Oa8/fM2vL0wJrzteSk8rskBvC0lXD3l38Q8gn4qPPBGXbsVMEQ8Onw4PYWBpbzlTYC7RzcLvKftqjxkSAU7vngOvB7SxryM1sC6l9GWvETEAT0OWN284RkUvcYi0jxykSs8XKbCPHkYKbq8uLe8WKSNvBlnQzwM3ZK77yWGOpVOF7yA/M28qQ0kvJr1Ij2NPc+8UKKpvGgORTwCsxi94q+gPDEZMrz+vtA7IsnQu5x+R7x2hNe8p7pcvOerdTx3zhY8JSbru/t2Cbx6N4A8iJ67vE3anDy76Hm8hfo4PO7rO7o5UHg8Kx+6vLnjAz3tyas7jHJRuxboiLxnZYM7luLoO3EtbzyFaQS7zTf9u159ULmmPky8+YEVvZR9I70EtEq8870QPP0Eb7z3SgU9Sa4QPPgZbrsJ+NA6IG85PGcNh7sWno078KJ1u5rXsjzVfXg7uh/DvFJ1arvVC806Si80u+SCaLy/Azi8H9+mPKdsuTx4JTe83AnGvDP0zTxLq0a7yinVPPy8GLwniTc83pmzPEwJLD3uhlY7JbisPDPRijvuXoi8oUJZvNpvQryZqJu8DyfjuuK7HD15CsY8wuUfvR6ajLzkhH07IT0tuVSb+Dwc53S8NvTDvAtStbqb4ZC89hoxvHXstrymbJY8tu0RPbGIkbwoR4e62nGjvPdmyr28GFM8QVgrPUMy5Lyzatm8Mc4cu3ayETtQTIa6Q9GHvM3xyDy5yi69mKzivAsY17zJav08KlWlvEVc3Tx23RE8ZZE0u8R5NDvZKCs9bhhDPQ3BzDw17288Fv0ZPK5II7xsCtc7L5D6OzUKAD0Zojo4hUOBuPaPSjvPes48hVicvK6wZLydJhk7uBxWPGcbCT3dQDW9nF60PPFnqzwpb807Ln0ZPSapozvFnA28e2S2OvRh+DxZuhQ8j/V4u8ygtzusSCw8ag3DPBPtPDsnloe8vGVEvcTG8Dze1am87+XnO7Thu7xyUOE833U3vBjHgLyFC627iW1cPZyGoLxldAC8Fi4VPRhMRTw5V8e8a66hPDV01DzsN5E6Z9UmO3tjMTvLbrM8ERQqvccmYbs+yNG7LAeVPN8HojvKq5o8Nv8cPC34XrxpOlo8jh6XvAPllDxr6EE8NxONOdxYVbz7GjG9+3shPSN0Ijt0XwA8hUE3u0m6u7zm/VG8XhxgPdz8vzyjrvo8UgFTvHCUozueZLM7UAwZvRTpwLsC++M8BJsoPfkVJLxDWr68cD0IPPXQurxpILW8nl8MO+FmKbliCk48134dvGJoGTyruTq9ZVcZPA6Q37tRssi8EWVOO1MpHzxc5TQ8ZHmCuzAk/7ynw2A8b5DHu2iH3Ty76Og7OU/NPG2PDDp6PcS8YmMTPFZiCTzKDgI8ubxIPBxaVbx1Khk8R6ynvEtp17xuV787Dk8QPQijjbxKQWc7ETriO0rMiLzLfpe8W8k3PGWtBD1kWZm8Y4AaPMa8sLyTe927z6HququphDw4MyM9UOkivdX5uLyu+D285Me0O9gw5btr2HK9juDnvFNFo7wbCwW8CxYQOmmHCr0dN7W8hEyBvOMwGbsJEtq8JlIhvCCNUbxMr588bID8vNhKpzxbM5q8MZEFvTUjMz36iv25seEAvSz1ijxAd0473KbFPA9mS7pbXQE9Zy4Fvd4VCrxyUE88u4wIvddSkjyyY4U8RYGlPBrDBL2nZA+8l41oO7rtqrxNY4I8HVsLvYOuAz1rXS676rWMvOVgKLwBCpo6pFlturkzZr386/K77a6dvKkNijverhg9zTeluy+airw6l5e8G11qPGD+0LtKLDA6E8S7vMPNuju6Jka6GC5ROvNcgbyR+cI7U4RsvJmfMj1bTps7l2Dlu6TI3jyGx4Q71w5vOwmfezhs43k8yRaJO9EulLyKVsU8JlLOPDWRjDxcW588gF7du16fRDwRhRK9omKQvNaSvbv8z7A84nldOpOU+jzjfQq8En8HPOoj5TzrjAY9f0DfO6lQBT2iweA7YrVSvHif1LyW7CC6T9uivO1yHr3HDH+9TQLmOxcb27sQQEc8JanuO963Cz1CMNa8PWuFOg7Ylry2tN87ovawPPQXU7uqSbs7834QPUa4IjwGLdA6k7Y7vHcrAz3J0bC7UO+pPF0irbz4TVg8jazcvGbqRry9QJM7B4gUPLn1hbwCfzI8v2wJPAX44Tyc5FG9lIOavKJMajxBOrs7GRwnOyW9Vrvou4w80FO7vG0ePTupVYy7ss5iOuR7vDyZ9+c8Sy7rPGyEXTywAr+828GmvEfInDwJ3rm8GQpDPD+HObzjUjK9K5AIvN2BB7wy8A289EaFPBaVzLxe0lU8Q0ybPK9X5TyhgVa9EEwPvX0F1TwppsM7Y/vQPG7EA7yY05e8EvGfPLipU7uRkKi7d4rUvHp1urtWFrq7WjUbvbbQXrw9zAk9Nhw8vZ3DorzBXQE7Q0exvMpc+LtvnpS7tcgKvcZpkbp+R3i8PFz2vFVCmDxeQWM9pYscPBiHEz3lWPC7XqTHPD9vtLukNfQ83XoJPAW9Jb3VR4W7hjP/O+bypby3GBC86U89PHrZlrpsUow8gQmbPKBtzTzJ9/s84vSpu5lBeDvD8SQ86bFtvAQz1jy27sO8OfIBuvkDRLwYGuU71RWovNHPGbxWwu26ALYGvMqCObzBBpo8hRJSvUL41bw+AaW7feYNOYAgDjxGCCg98x8lvewsOrt074k8A/IxPIMW+7smrMy7pE3jPJ130TyJR3U9k4KcvISr5Tup5bk86fG8PCfpzzyVA7s86eXwvGeRoLxzxu07KjHyvE01MrzaExm9Vm4GPRA59LyQrKm6UETQvJUFsroPTRm9gJeHPE0l9DzIaw09XMgHPNOYjTtAWhc83XSiu4t9rry7M827quU8vJvStTzWhPQ64B0oPVQqkDxI5Yi8SFnrO25WnzsRZUM8dnXuN/3bKLsBSgK7IxIOPKHQsTzgzFs8yW6rvBbwKzypPsg7F+ChvAxUzTxFu7C8m0kiPKqknTyT+tO7OpwQPLb2GD06sJG7ikc+vY/fRrwL5Ti8HjrEupmUDb1L9DC95lBrPPvtRbyjlxG97UegPINV6LvYdPw7lti0vGxkH7wGrxM9qjtDO822Kr33LYa89IaLPRZY1rxD/rC7vQfsu2bbFb2YjIw8hIcevW1LhDxjctg7WVV9O3I/jLx0Sc28mEVxO445iTx6/pc8JrkIvATarLxmtbu7oDzRO5aBDT2194+81+pIPKcABz3Dvvs7dPDLPMr7ljz4paO81xP3O8yQujs89IU7lG5QOrH4prxCejw5ukE1vC8ZmrwmMlI8j1lcvA+Hozwv7JS8N7qwO+Q5IjwrW8i7AdJdvKBppbxmYLQ7xcU8vEPUQrs8PeG6n0lFus7i8LuDOR68oHYYPfGt+bvciRI9x+9qPX4vzjxG0c+8R5M5u2sRZLqK9Da8H3B2PLWsSL0QEkM7Mp8gvawRKz0OVBW9Th/gPFmiHjvcm+C7o08ZPaTJ8zvC8eE88Dz9uwYR2DumEMI72uAyvOGd1rxonK08/VqTPPiy6LziDay8pooZvfqZ5jzyxM07Sq+sPIK9nLtpfze8rTcsOcoXtjs4jiK8Iqq8OpWLW7lbwiM8mT8IPNiDDL0FQKo7Ps3cuypEADt137w7620pPETQPTye/ve8Ten2PJJDCrzVgAu8MAlIO7s3yLqHRVW8Aii8vFihaTt7rA888XlTvEsImrsCAQc8rd88PGKBqrvW9IW8FgO8O8G3LbyFIMK8Y+y6O/x/gjtFIBg9D01BPNWwt7yt75c8FPHFuonoQz0cVJi7VZk3vAEcTbur9S28khBTvJQYw7vuo/c8fOa1PPFX0jxuXma8gBLIvMxB6bvtmKW7NQArvVLyhTzORlW8lsPZvEG4gTzcfxK9PEXFPDZKUbxDHOk8lsofO6JQuLwjtuC7wqFpvLuOpbsoqhi9nGxkvX2rFrvVNXO83tEovWx6MTwUIK68d16aPACufrzV8QW8yOL5PJwXJT0FBlc8qv0nvOUPXLxUdzc90D6ju/n8Jb2dbmG8YxYSPI7HLTyuxw4801TVPODxkTt9Qgg8UH2ovIflxLzNYqE7TrXsPEleNbq7F6w8fO99vEVQ3LtYCVO7RIx4PPbC7Ltf6DC8GKRHvA9kL7wirZu8p1EAu1Vb2roYUEu6erVBvI+q0LuOEIa8nqq8vILm+Ty6HpI8xRcTvaEmoTwNpzQ7/MWNPHOZHTz1yHq8cKWSPUs3nTuG9fq8Yw2mvNfrKzyBDBU8zAy7vN/P5TtGVAw8MlQNPG1YgTqM6Z48tePxvPtVrjzCHPM7EoflO3B/Lb0A3ss7uOS7vD5QtDwHdZO8iZytu2zuQzxoz2Q84OaAvBCT1TwRgao7B1V3O7CNu7uWNhk9aq2/vJ2X/zxr+Im8MbfiPCcrpTt+rLm7EFtOO+yqF70liTo86B8APP4Wabz9wcw8OMpkPO64SzuS/+Q7jxYTvbwZEz3AwCS8pMYZPZt84bsL64y8FdpSO+sP5jyIqWU8+UWsvGL6zLvsJdw8S7O3vEkbn7vKvCq822LQPFTqCrzUhrc7+VMVvIwfw7xb6Qm9w2seu2nQxbxyLi67If+MvKxDhruEIwE92M5wuwb+yTxp98A7Ia38PIfU7jxfvw06OKSGPPEDnDoy4am8v5ylvAxZOz2A3du8zVJnPFYoPrw/kcO8/vuuO5TPtTlU9ZC8y8KkvMZfl7wCTzs7iWAYPLFK4Tsuk1q8P/cKPCYfEDzHCbg86960PGZNML1TKkg8nY52vAUOtrwOrg88HDW+PPFpwDta0tA86EE9vDZ0YDz1q0Q9IAeRO+B8x7xWQ5g8Jw8vOyP8irzHBqY74ZJuvPn/VTfdBFG85Lp5vExlmbwAoqg8SQ+RuxVcYDySS4c8Qv34vLTa0by9hDe96IuqvC60FbwdM348rWQMu1udRbzIZ2w8684SPTMQBjwfWzs9cg+lvFAKNz1T+xy8ssN8O7AhObxk5gu9L9P5OcU0d7y7liy7vMqZOxTXXzupKMe8TZoJvF+Ezbzsg/a8Y/EyPQi9HLyBo3m86s6Ju/r8yjz+7tm8zgc7vL0DMrwNSbC8iXvzOuLZv7wDgMk7gpMOvBPXx7wXouw8XS07PGujLL2oRWY8l71vvQpRPD1P83I812OcvBRxZTtsxFw889PcvKLlyrxWIv07QTMTPJkx9TuH4aW8UeJWvKWGaLxDarW79Q24vN02Ezs28BI8tbAJPct+iTyNzZU8FT8RvPV8FLm52My7FW6RO+QrgrywutU8g1wpO1IJvjxirIG8SbqRvCH4uDxv1FM666UAPeyy4bz3c4G8NQstvI5I5by+Tco8pbixPGrucDzATzS9R/0GPSZ/0jsfiK+8Pg9TPLQc17sUmnA8l3XpPK8vArzaCtY7iLG8O9vjj7wBZAG70TLTu0zOUzykGrO8qokbO8YGHrxn9ZK8cJCXOwqPdDuMmR08wQU1u5IOtzy72188+A1BPahtgrzyKCo8RBGWPJYOjzyGNO67KhVPOpbvr7szLKK80yRDvGWY9LouDHc8o/mYvHqtGj3fSKY8gBCpvJuk4zwT/8w8QPSCPEqOXLudGFM8LtctvJTuQjz+wU08DEkGvbRJxjyPFwS8yaifvM9r5zww5Ng8nslau4gt0rz0/wK7D/SBO+FwR7zMPqw8l1oAvG+cBL3kyWe8zhHZvCjSiz2YmWK8wI03vbAlmLx0YE+8xeGyPOlZx7wUcuy7r3WnPBOfmDxV+fW7ZR6DO/o2Tzz2zws9PilivNdEnDy4nXe7bP+QvBETG7zMTAY8TPwqvC2NYrxvcT68C+fpOv9Agjyglxc90GRZvDiy7jzS4py8FBoqvUH3hjxud9W65RIJO90077tQXaa7S73Gu6W+LbxGJKE8U/tTPOo59bvpSIa84OC9PNtV3Lr2hPQ8miK9vFDYVTxwqSw7URTdPHCX8Lz0FhE7wXnaPMcJALwvuZE8wFxSPbUGd7v4TZu8pOJbvLLvULu6hLa8AfRKvKKgDTwXsTq851cNPGggS7wMRH881Fn6PCe+abxAQJI8Zi6qOyrtbTsOhiA98ydBvPW3UTwjppO817lhvIaPETzIT7i82Nq6POveFDzKAga88rmzvMZPoDxvwfm8h20RPaltRTyOwE68lRcTvaMBATx6MYe8KEvZO9gkr7pE/7E8A0XGO9LzFjw0URq8qztFPAiuD7z7uG88A5GaO23XkryjItq8WpcFPW7XUrtZiMe7w1HEPO4ebDsqJbq8dQbbOUSw+jxiWRc802Peuzs4srxOaaS6SzM/OvSw6rvP3qo7a5iePEXPArx1OFM88vVSvHGwI7yEDQA9erbuO9JqSLyCSKo87g8BvbJKOzwgAdw8uepZupDQQDyXNYs8KnOOu1Whqzx1bcw8xM+wvNpoiLyO4oc8+mk3PACQCzwVmgy8NhyKvHhNsDxQ/LM8QFnlvPsHGj16Osk8h2kMvEzU77sr7jQ88l3sOiKBKj05JKC8dJTWu8V9mDxs5rK8yEYNPB9LBT227kY6cDE+PAcLrby0xRg7Lj+mO2rzibxW97e8+xN3PNBOZjxHmIY8rmxTuQg6fro+dgA8W47Qu1+58riyTp680WMoPJV/MbwStc88mvYHPONwu7to9ta8Rv+kuzGDpzuxG2k8uHXOuxV6CLzP/xO8y5v6O/bGnzxvhOQ8RAggPEFqBjyyCOG8B+5wOlMg8jwXGwq9bzLcvGzKKTzpf6y8YCFuvFUTQzxOudI8XjnGPKC/bjzxEFK7uNoQPEmdHrwLG4W8Q8VKPXsefrxsmJG6Mdg9PGUnDzz0m9S6fd0EvbRZejyTLVG7Mp4jPM79b7znnv06/pCUvGNVPTuOWpi7mG7lOu22+rs/LNG8JXabvKzIeTsMGSE9JSNwvBU/wbzePJS8B9DzvOR1jTwkKNm8W4FJvHn7fzyfZzQ6BLNCvEQkmDx0xqu8i7HCPOU7v7uF0Ju6M5xHO8hLEDwzRK48Qxy3u89dnbv4ybq8Fs1KvFioDrmQKzG8cjphPRUApLsgP7S8/G8UPHuY8jsFgYK81oQtvBY8tbyCc8k8h0SmvKz1Zjz7I8Y88gg6vRhF27x0gKY4BU+PPJ7QaLzILvE6Ki3Au6BkKL273MI6w966PELYlLxPfO+8T5ODOyig8DsYAJa851KSPANtqDtOYZ48ZSaTvEI0hzxU3D28/f20PJM++Tvi3NK8hlp3PG4kvzzRmGy8fYQJOz57SbxMVt+81JaqPOBW9bpsV228AwCYvFQtHrwR+Xu8h+oKPVG1dTwuqiE8zdZEPO0cuDzrTZ86ArFFu28aK7wQ++c7Yt98O40nDbtHbrA8WY6FvMKpmDuvQ5M6cbu4Ow1KnTzDM6Y8XpSRPCQqNj13jHg8IZmPO1+QtjzdKiC9/KS3ujyxiLxYzEM8Ik2uPA7qH7zO7b26EG2ZO11/qTygNLi8CHpjvBXvc7te8EQ6X7rVPNqYLj3vZK28MUrsOzmKqrxaV0w8G0RivFJilLyocsW8xkX4PEhqmjxPhrk88NvUvK1NFrw2+4E7X+JWvQscGL0LRwW9GS2yvLwFFrzpxLK8yGOVPIimrbznr7a7NkCOOxWBSj0uKh88A4/ePOLUAD3fQA28R8KiO6aIxzyLNee8XYUDvISRDzyWTJo7dMwzvJLxgbwsMn48GzIbPLMTwLybPj88JuqKPHtJyTpKkE+8DbgMPZQXmDt1jRG9oY8KO6J+wzwko4y8sKnhurOjlTm5zEY8v7EhOxcT8rzR8MQ47J8vvLZdzjwonxc8iEGMPLsZIjytgsY8jvhOOgOgNT01e0Q8YwCNO8mPw7yv/pW7CQ8ku0Ydh7xWfiY8nNKfuwOEy7tAmYW8L/PgvIcAwLt9WQK8VxCVvDTymbzbx608WQSLvFmij7w1lv87JTAdurULhrse7gC9xHslvWqH8DwhQ+C8iKa7PPOYKr2sZLw88Mblu+7dijx8Tg89k9XIvP1r6Txmpui8MmKNOyqqB73EiK47NtqHvKT7Dj3NZbC47fUqvEXuJr35n8u5gorovHsPFDwxZwK9JnygOxtXFT3yYJc6uL8VvKHtz7xnU0U7uZT0PN3yKj343RY87DGHumylk7x7foC8NgxFPFGIrzu2CA69SZv4u48s+zqzhhK85MHIO3RLzTxBFLM8QV7uPHQOBjyqWSS89GSaPOzkU7phZMQ8bdW2PJKoALz5MQE8qUJJPWOcRjxZD9S8mKJBPPVsDz2HNgM98tBtPOoU47tczdW6kIVTPFU5kTyEx928tBhCOxkL2bwGRAm8QeXsvBq+IDmuVgE9WF9vOkVJBLwpx1I8TZ0VvVz7FD3XoP275gYRvJhVAzzLU6m7KOc4vNgR8jxMkq88J7bjPAhx6Dwaioi8+aUKPMmX/jxh/k48GpZhPO3HRzwmaf48Lk4OulDuprz/B5E6dT6QPKx/JTv7khW9gX+jPPPey7l5gY28MGacPKenursg5vy8GJWQPKzti7s59aI8bPW2POLpNryCLyo89gIkPJ+/trxuvRs576q8vHfE9zt1nGe7sPp6vP0gd7xe/8a8sBPLuw+U7br0qdk7w8OdvA2Yvbvy5Pk7FBysOga0PTwOnjs7aEsuvaTpTr3AoIS8xRlyvIxr9Dx3KAS9+TKrPGpzRrxhW548mXLeO3Q/3rz5KRu8Jy59PFSohTwbWuk7YN4YO86siLy9Igy9pNu8O+Xvr7u9h5669OhhPEbfWjxhuKg8JPsYPPirHbwsT2U8UGCzPBLll7wyKdq8079wvKCaGLziA8I8RGnmu40ahTwpNds8sLVRPPENyru+7Z68a+37PDIq4DxWlO07QQOnvAAgEb0ExK08KQ+2vKnTt7w0cfc7XL+GPE7gPr1pnlQ83CKBvDdj77wM3IG8Eq+luy4X7jwjSgS8nm5qvLFHYjvz8Vo7FhIKvCo/mrwm7Q28smmzvBYePTtrzVa8p8r3OgDjtLu8ang8JmrEPJZyM7uiBEM8fjibPI9EELxg4PC8PTiovI28KjwMBie8ZMG3u/TuODsutUY8nzgTPfQrl7zSRkK6nqw4vEDRADvIkHs8m0SePGwJ77v19tA8wwQLPUVRKrxgZAu9iBMtu86q2jvEB9m8NoQUulDtjTxhs4a8RnJJPTRlqjzWFzG9DYKGOgZQVDyIZ7u8JaOJPHJEWzw+pLC7YgkDve/ZGLycPgC9T0xgO5JpwbyqZNs7RB+gvAHHpjz4OcA76uQUvMtXpjxm2A68NiriPJ3Syzzq0GO8AeL6vD2YE736j528A/CTvPeNDL2ZgM67W6AzPCqyGDzy7ZG8EwcSO2B1wru2lpq8c6oBvLEe1butoME6xZHjOm35WbvAWRy8mLYCPSlT3jqV3lq7kx/gPHCGbDs05YY8ikcjPFIPZbzyM9O81IaCPPhLsDvehXY8dmMdPL6evTnunem819XPO1x5Z7mLYMY8NrHKOwe+nbxGzII5uGJlvGhNu7yU8Bw8Nzuvu0FJWrysEZK8tUmFO0US+zt6Av26pZreu5tsqDy3U528E0FSvKDVcDz04xg7e1x4PPd1lDypPnI6ked3O6ESEDydLig7AQtcPNKmpDwoV7c8uyShO6o9vDypKNC8zCVKPPeoxLsKn7A6+5S/PMbf+bwktC090y/3PHt6G73pH8m8xZwdPDjQ8DvG6Bq8/9vAvMTN47zl4FC8b3DGvMi0Jr0jDEI8rdhXvMA0Wjtlm/W8HMqMOrqYMz2KZPi7+L4YvYFrnrxcVx87P00RPMvyLjwQwVi75dybOzQFar0nQ1I9FQLvvNe+Wbq6OaC7v6sPOzRVFT3R/hc96jE/PDyHqrvuuEk97n0cvNiAgbxzCDg7GMqvPCwIhjsY2ye9xfeEPIw2+bwdtwQ8B+evuumyeDxJePu8GAOOvPA3yDrm0he98lMcvAL4OLxHfjS84r2kvBqn+rxLpow8kw8pvCUFQzzIcZG7CVwXvbD7o7yV35W7xpLpPP4zYTxvAZc817zQvK48wTy9xRm8Bf+0PPO5zzxFpq28OOMUPEf7GjwnJ2G83d6rPKFVIbwzPPA8pNACPdWRoLxfkr67sWg2u034bDpXGXC7L0A2vIOs7rxzpy677JtavHT1wDxtXf677zkhPPWLiLwGFb48shc0vU93mju3su+6xkcbuUBWnDtpFYI8Joc+vHlBmzxSpJC8WhbWu4JGWbs8SWC7PbadPO+uET3MhDg8O8xsO/pzxro3yUO9QczdvBeOPr1OKlo8U8waPFp60rwRJiI7zgp2PEkJaTtkKvS8POCSvGSQBbzdbJq6AbQPu9zJgzyCpZk77NjsO4RVebyf6dK86nE8vP/Z0rvVLXk8l2Q2PDU34LuSqIY7eLi/O1QTKrs8Ls87rjMhuqJeRbvteAA9k1f0vCMu5DvHCVk8Tn8wPPk0/jxA4lC7/7xeO2SAYTw2iP68dZ3iudiShLzQb7M8D/MRvKGq1Dxlzyq8IDTIvIh4r7ztigs84vulO6PE3zz30lQ8s1LDvHPb47wL8cs7Fc1rOwNdcjuTa8S7dWA1vJL+Fjx6jKG8eS0fvP5qoTrVFEa82HG/uzRwCbwFMWC7gSHPvHpTpjyjTTA7Bo+SO8V2ADzGXJi7Ugz6uz59UzylB+u8lnzjPPKmcjzTgYK8Z6KAvNOkfrnTOUK8PzNsPBueFLx2DwO8FAqPPMiChDxpbxI8v5sKvY/sKLwOV868spr7uj4e8rs9D7i7sQZyOwY1XDsJ+7A8loOyPAptRDwI99q86gwlPOxcJbw4DNs7s/vsO3bTjbxLI6O8b5BMPIRJ+Lz5ZnC8BIf2POKUw7wMWwc8l9tfO51p9Twb+5s8RmELvAhJ0jwM0p27G5bWvL8L2jsr1Xy8jqgXPIcmcDzViIE7Ecbou1WKb7yW/AE9S8z+PN9qWbx/u4a7h5LHOiMNwjsXMsk8yDbqu6imEbxhapA8pxIVOqc3sbxpsGa8+g4+u6oaV7x5Ybm7KRJdvK9NbTuFeVw8xcY/vBiTiDuQsRE4wxlEPOHEIz03Umk8imXCvDvP27zV+jm9aqPmPKV+HrtfHMo8/fCEvDSI0rxTxhq8d56MvBL3xLvetJE8NydxvKYlpbteNro8sf+zvBIBuTzeRp28s0g5u9X6lbx7k6a67vxVPLdAlrtABrK7JhQHPHl3grx7ckM7WSQZPA8RFjxOFbQ5kcaIuzC+FD3yeZg7nAjTvEeHJjyI8Oc7dz7susiDcrs1Ruq6RN8svLVkKDwjcyc7tw2LPBtwRjuikNI8m7YHPGSgtTxDZLw8kYsCOUItVryVH9E7uIm/OqroAbtwuDA8W2MDvAN/ZzyHMn28A2JDvMrLsjwyYlU72yYFObQzyLuTxyu7CHcCPNcAwruDIki7Gq1xOytoDrzqkr27uugEvfKFQjxZmqy8GKBoPD+llbsCJYG7DWYpPIO9RTyCroc6WFljvEGd5Lug7pk8FYGWPNKTQTtHrw497hTLu2lszrsjIow7K7Jdu5d4Mjsol1c75JgVPNP8/Thxzhq81m5vO378yDtyk2E8oQjHu+EucTocJku8pAJdu7T2HTzYhRi83jR7PM1MX7sl/087wLaOu0zgoLpCdn+8XZfzuzf6TjzMk4q8+smNu8uGHr18bcG7hhZlvIN/jjsoDiu8Rho/PLYejjwxeZe8U73rO3rhbLtVfJA8M/R0vLofprw01cG7i4r6uybTaby+IYo7R92/uybEbbwXeM081aK5u1pO3ruo9lO816c/vEtRNLzKL1Y8ioTlOzs+Ozy4CVA8TthavA==
-        index: 38
-        object: embedding
-      - embedding: 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
-        index: 39
-        object: embedding
-      - embedding: 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
-        index: 40
-        object: embedding
-      - embedding: 15sguaFDgTtlMMA8vkEgO4J9SLpPaIM9NCWQPdu2kbzWLeE8uBEpPEZMPrzMwNy7ROcfO/GXf70+bFU97R6cu0cR9TyJXl28wbFYvDWJurvzuJS8Gc8FPfpk0TqehZ05KDoeu+hs5jyILue8sh2VvVUoYDxfMz895M4fva3t5bx484w9zGYpPFSDiTt0qz27HfczusgBLryOS6m8TSkkPPW0vjwX0CC9e8D8PMF41juiRKM8H2TBvFnC6TumRkq84iuGu7DS5rzUdg48aCxYPOAKBTzOdYS8Qa6gPJhxILyvcYk90Kp3uydCFL1KJ3K7WdJ+Oph9uTu9EMu882uLu7t1QrsCvNC8vUmxvM2SFr1xvT08r/CzuPRGcLsJnY27mTsnvCQPcbt5sU08LPUEvVhmIryzhU89+po7vL6DmDyLTx67OffOOhaJBTylBP67QWOOPIQq0jwuFHg8tZ2bu1hAOL3Qya67S1VxPEhcuTzhsBq8Y0aMOzcyfjwGDwi8PC8EvOESybyYvWq8ylFRPOl/cbtfhii82HL9uuxInrxSSY28toe+vHsxuLxndxO80q7ZuhY2CTztTg87222YOz0WWbxt5BW9vCbQOj69C7yoCIo7Nhj3PH+Moro+Zrk7CYjSu29ShTwajwk82O1cPPaEljzGzes8jBOlvJrL7zsEJbW85jzpPAvYmjwGOH68Q+otPKo7XLw7STm8KXNgO7CYlrwClIe7liGoOgSAkjyEbwm8TSA6u5mPRTwtTKW8xp+qvLlwN7wukLw7H1I9vHchwDyEJj27HQaMPG19jrwcm0Y7R8IBPT26brwO1fA8ltYAu1cv1TyZ9Fo8n4n3PAICmrnC6Pw74M+iO3gQfj26oji7FLOjPIxWhjtMEdI7Uaq6vH8TI7y54lU76myBvKMVO7xWvC+6Ki3mvOv6lTudTLO87T5kO7d/FDtJMXc8ctcqvE17p7zqGYc8n7m5u7bLVzwNHwQ8kykzO3eJ1Dv2vKE8Y03tOxDK2LwwahS8sv5LO5jiVbzBqbc7lolbvFWWg7y4EU07UhexOdQy5DxuUCu8F1WZu/kyyDtPLpC61DAwPF0F1DsDBam898d4vN9pqDzqoD28r38rPDH7iTwKJNi8L1cfvXi36DsWAFE7Xq/JvFRnh7zSwK08SN3kPNURiLrakcw7AzyfvNb6Bzz/l2G9imfAOzryDzzxoLe7NF+OPH9xlztcSmo7Lji4PNr0n7t45Zy8dzRNu8Yv1jxchTS8Fp10vAj8XDyuLwM727nmuveCIrx7iby7HkqVPMq6YLyE1fS8VAUovB0AnLskiWg5Ie+AvC//1by9fP66MyynPP6gEb0TO8s6SxiFOxHeHrycGIa8/q6yOxNOKDzaqW47ivB3vNlDgLzY5nU8jZVfvEYHXDv0VA48UvrTOhX0WDxG5827ZiEXPdenU7o1y8m8ly23u3mY0rjTLsW8MLlDPA6wKrp/c0Q8xx8IPWlht7zo2mG7oBHtO8gNkDxxlZe6qc2vuYfcJrq1+Tg81uOwvN/f0jpXpzU8vgLjvD7JnzuQfJa7LWY3vBoQT7ytKo48yemWvGhmTTwl0g29NaTFO74Ku7tksQI8DZLmPGY7kTyAeI88g28CuzwQOztqEI68xL/LvMSDJrx8KtW8Ua91uun0GLofCpQ7qIAtvdEAljuEDYs8dmggvVvQrbwfn4A8VBdOvVZ+UbxSN4W8xNOku0fX8Do16AA9RRKUPMt0czy1yx49z4/gvPiBqjymz7m89HcWO8hKPrtDL0o6lF7PvHg3Fj39JNE8miLRPKOMsbvxzys8Hf/ePGvKAbvcaei7W669OuQ3dDx1ypq87MkkvUPMkLwJFTI7CHa4vMBY2TysIgu9EdcSveO/8TxgGxs8acO8PE3daLxyHMK8+eGxvMW6PTz3+QK8MdrwO7mS/jqfAhq8z76dvFjCJT2VsiS8B7XSu2xhJzy3u9i885+pPAQ2m7wKGRk8FtFru8zs0Lk7H868rR1AvMajjzxPtdc8D1CzN0wPcryv0Ss8La8pvOMSsTwKGIi8DJ8YvMVWxbu623U88yWdvFztBjzcEDA859M+vC2eHbyLiyG7GxMHPGMx5zvK3xi8X99ivL48rzvEREu85TgkvefkBL0JqE68E4l0O+ECjrmvyeA8ZthnuwauTrzHBOW7FpeYPEIQp7slYA282EJ7u57rzTza5iE7pnCOvPdlsju5Xiw8/jXEuydGXrzL1166Aw/iPLwhgTyZ9BW7LOOavDY7yDzHWuc7rt2APJVpx7uA/L48hjGDPFuDJz2z0747VtV9PEjbOzu1zJ+8CiEOvN4Robxipse8o/CROOnYFz1lv5w8uTQvvbppSbwLp8g7UgJAOzn/ED2jF127/hfmvNTvkjojJbC7LcoavIpZxbyKKQw9I/+LPAqqhLzmWqK84M8kvON/y73JQLg77+u/PPqkk7waCwS9goJHu/UP9jubkaO8XrAvvGXBsjt1ph+9Y8rivCA6QLxaTXI8YOU7vKkh0Tx4m7Q8klHau6SKVjzbbSU9cvgwPSVTbTzPoOM7U9yIu2XiM7wkd548L5eIPEemCT2HvtY7LGwTvHYpqDwS1MQ80TXAvBnfFLzBTBA8dJpfO5xlzzwsNUe9KCU5PPcV+DzuCZG65I2pPO9a1Doch7U7Cgqqu892Az0EPZA8aWPqvJB+WTnwMSg8Zkn4PFXW37gpfXm7GWpCvXf02DzXRZW8cFQzOgaXz7ygo786zumIvGvfULyYDU687eZaPXeO0rznWIQ7mUiKPPnO1zxtNcy8AL1gPG5y+jzFKKw6Yk57vFFrM7xqaaI87GI+vQoZoLsUo4w8HpCZO9vkgTxtFIq6mjgwvA2fMDp8Tx48VLiEO4V0wDwSvRY6QWDTOrHJV7xi4Xu9RcMIPdbwebsluPU7/CpOPBQPYrw9QzW8gItqPSMdlTzMT6Q8r3hdvCUP4jqaBm08anADvZUIyruc87I8kbUpPWSv3rxo8+u84RPVO04Q8LurPPW8l3XkO0RNZzwLCOa6K80gvEjaEDxWVje94YlTO7Eg2TpkDA+9o8MZvEOJQjxF0og7A4Stu0qcobwYujk8uJ47vNrC5jzY/rY7eiu2PCIR8LlX5Nm8KdDKO36qfDvLV5o7J6IVPI7Tj7x4U2E8U7OdvETAqbydw0U8TF74PInNhrwjgoE7XXUIO+uMJryF/gW8v0kuPJNRLz2WX/C7ctU+PJTr6Lu7kTK8jT+ZOWvMXTxLP988Xh0gvUN9Pbxjsue8ANOGvKe15btxfIi9KnfnvCpi5LypV8W6WfVovD7GPL3Z+728MKM9uxvD/7tG0Be98AYEvIUKiLwhpMk8b9IRvQVIVTycyEC8aRQIvVnANT2V7lc8yFqFvF7pljwzly48TAIwPJxYrjp/v/M8DzMAvUBWBr1P0Bw8lScfvW27jzwzrwQ8m3jPOw6k1bwhuj+8XpbiOxZQi7ySIm08pRFkvKb5BD0yx/e7RMJSvLZyjbtUTzi8AqFTuyH2Y71yqMG6L1gbvXISMrwZuxo9d/mEuSl+QLuO+MW8Wea3O95XjLwttRI6DEu+vBEYmjvLqT+87vpcPI5NULzVeua7rBX8u+rHHD1kv348WiVOuzR9pjzMrei7vOyRO676NDvmjO88aCXvOkcc17wQZoY8pZ+LPHlaZTzAO4s8kZRCu+qaajqn4Em9w7iJvFexMTu4WKo8esvzO3HG/zyHr0W82FkwPGaV5Ty+WeM8TpOCPHp/9DzocfI6ZUzCvLNbD7y9ppS8sbyZvFDgJr0asGy93FLVOnOtILxbxGA8wbyUPODBujxaoTm92kbdu3YbYbym7AI7fPebO2vDirvf7Gq8/kjEPNiEAjyAdTc7zu2Gu13GTj3kse66jlYJPbkBwLz9CHo844PEvBLlZ7zrDHM8LruzOn1zDDuXWdU8Vac4O/LZBj0IoEO9mCwWvdxCUzygbna7+t9WPOc0uznPMTY8aX1kvCM64LvLerI8gSP2OxCljDxE+Ik8fpTlO1m/nTq7NnK8I0bEu/P9LTxHWb68bk7+O052sLsvzxK9cKcdvDL3obvvz4e8IDFGPJdlDLwYenK7VJ3LPO7ImDyIoFC9zq0avcH5mTy8ypC758kEPddEALw0mLG86kynO7ifhrxpihy8QlbgvLnCdrvxd4y86HoivbxombuGhsg8bDIhvYMZobzHmmQ8HN4sOaSrA7rGrwe8MxUJvXPYODzZCSW8CeyPvOC/6Tx9jWo9XJ07u+6MID0G7lA7KbOwPFesAL2/VJo8aWSAu8XXI73XITe8Sgd3PMqX6rzUoiC9QbmOPLOFRTs6na48dMEpPXYIvzwfsrQ89wRlvCQaAzzfvkw8DB6evARSSzzousq8xnRTvGfPF7xX+/07TF87u1tFYTycoQM7zHGcvHU2A7wEMM87Z85hvZxEWL11S3s603/EOiAJpDxb/OI8UzkDvXJmubyrD5Y8iZkqO+ALG7ygMgi8GUPrPFtfszzso009CYeWurQRaTxENwK7UG6LPOc86TyzFkC7g+uEvGHyoLwj8aQ7+swjvX7TCLyY7/y8NiALPef5lLx7wPe6yN/TvEehK7zRexC9ALcIPT+5CT1elv88alk0PGp8ujtbMxE8SY7au27XrbweMPy7hQIUu0KPuDxGZ1G7TaMsPRhPrjw4mMW8J/wKPHzfgbrP54g8412bPClwprykSvy7bDAoPCcgOTzAjko8NRt7vAnsVjzBsXk8XthLvfJ/qTzStIu7TKEkvCLh3zuy3pu85fyjuXv7vTyjbFU5G/jsvPd9+btZTxi80l1FvImmI72QeMK8EhKyPPo42Dmz+S69TokZPAyCNjyhipq8KBnRvAaiJrxbjwg9OitsvBv89rxyr5282aF0PTBig7y1E8C7IUDQO8z+k7zBYV88M90fvS+QZTsX2wk7Mm6kO92fhLvzxB474i0ePFIhnjyWkP08+QYgPFuj4rszhGG8Rm5fPILLRT1FpS+8W+/TPLrSBT3by1C8f0kxPC1jrTwzTWC8GiamvDWigbzKW2C7hzK8vHygTrySDR48F9cZvJq9ELspFyo82QyZvPLI5zsaQCC9E0Mpu6p0Yzt0n8M7aTHIOxjDiLwXr/u7uwmUu73zJrykqRS8X/gRPHUGPrwaX6K6oHT2PAWg0LtWqQ89QoozPXKAAz0rx0W90xmDujBn+zv2Bpq6OGsdvPF+S73HRlI8sdsuvSWOHD1n+h+9x+94PG7/STtQHYO7XVU5PYmPE7uCSiA9teUovHmsdToAEkk8/qsfvOp3T7yKhtg8kR2BPFpzurzjJCO8b3XfvB772jwQnTw8pb0UPGX9sznO+6Y6dUdXOtx0njy6BO+8EAqqu0mKsjrNvGk6A45wuz2UDL0CaiI8nFHiO/a4wrtajq07szCGO31mfzy50hG9OyiTPLP5JDv5eg28bjCUPDMBDrzFsTG8g/29vD4TEDp4a047IpwqvJMDxTv8fgY8itmCPMF2ezy269O8b4seO/LxNrxD34C8jA45PH2qTjweThM9waDhOr3JIr2F45Q8wt9CvAlgoTzTb0G8/fm1OgF/Q7wcJ6a6woiru6sYizuIcAE9JxXNPAdl9DzbDXC8fs2dvE5atbverLW7NkLNvDfuSzx+wLY7PDDEvIZXnTziA0K8yx/7PFetjLwxk/c85t/hO3m5M7xcfqS6vKVUvJAyoLzHSCO99xpvvf61FLzOZqG83nkqve7feDyOGg295QTbPCD5SjsRNEe8OCHvPBLr5zzfpUM8BsGOu55K6rooqRY99YZDu8SjCb1E6Em8erBfPId3zzm05NU8SUv0PG1yJzlVEWY8ipsXvA9b/LujPio8ZBjTPPxNHLvoCAI8EEtgvCu2Cjy0K9C7G7zwPP3pPTrd3vW7iRCavPRdRbwVhcG8fDAcvMHUhzzI7cs8s0wYvC2Qu7zxgpi8zCEBvGt0/zzuZZU83ZGKvKrZzjzCCSY7/K6mPCpC+zrhqSy8bRdePZLKljyLrOK8kkp1u/5zojwzrum7WD6MvIWiOLybwJ28wQE8O5fjhzwUFP88iIwKvUujXDwuA608gPk9u6abOr2EaXY8SjarvI0VxjyJ/RG8c0gDvKt2/jrLjLA8T3gvvOeLlTzLHYs6NmYjPJtfX7xlrtY83c4PvCci1jylX6W8ncy3PFmAI7ycYi46nQYTvCtKFr2RjDI89ZciPBPlqbxtCcQ8FGHwPEXqJTxQ3pS7oi/gvNAUGT0aiS+8SrjePJKV9LwSssm8LOmeu7qM3jzPLn08AQgcvHqbMDz2tBc9TvyhvFMNkDshUPa741oUPIPIB71GnmI8PHq/u45387wUytK8LwDVu/ML5Lya+LY8oSjcuwVUKLxNYPA8lM+xutFZpTwC0608BcTWPODL6jyP6IE7EIzHPEF5JDy6PJ68Lwh2vCpj/zzNna66drAHPGDvA70dIIO8adITvNVdRDyWZam8ctqwvJ+y87ykNUs7Kz0TO+ApnTnYC1+8eDsjPP82PzyOw7Y8TIXxPIPnI72/IEM8WShNvBMJS7xAsNA6/N/pPHwp4Dv9gZc8sy60vP9Xlbsw+Bk9J3qHPCzMu7xqo3A87mlruwPnAr1LUIM6xDNwvPo1vrum5Ee8RiSsvPBodLytEAI9/10JvKKd7Tv51wU9nNMwvQnj3bxreBS9vpLbvBZ6DTz7PGY8CR2eO80/Gbx2p3Q7XZznPD/2JzxDvmU9buoKvNazPT1osiS8jCfZupZQ3Lx+jiy8twXvuaFi2Lz1bFE8vPc0PIxSl7saJ2i8aC61vH/jAr0btwq9rIMUPaN7wbyag+u8i5FTvEuPUTwe4gO9d2HdvO8ZUjys9Au8hy+KPP1ljrxB93E8l4jauyP/Gr0me408a6HIPF1oOr0hZ3s8VJw9vX1PUT201H88PfD/vHUg37oPgkW7xR3hvDq7E723E7Y6dqtpuzsdsLsOCLi8jkvZvPIsL7xXQ3460bxjvEQuyrvpxX87NCAZPeSdujySXfM6rz7SO62mE7tr/447xPghPIG4gbyY59o8Zp1IvIaF3DxdOYu76cOhvGuLyTudH/i7Bfw2PdQxxryL7Iy8fximu9VdCr01ZOE8chGoPEHZDTz0PUm9IX/pPAiLijz1Rpm8BLEwPFNfX7x3ZoY8Z7K+PKexL7xVt3A8ai/fO+C9bbyLo5M7KtgNOt+SjDy9X5C836EuPNj+j7x5EsS8AOUcPG8MMLx47q675L52vExr7DxQhfk8JMkMPS/Egbz3oJo6a681PPnjNbsGyQ+8Bwuwuz5j27qlorW8ZDVtvNdB1rxiox88qcThvPBXGD12o6s8gEabvLgkBj09Tus81i+UOz5/QLyq5l08hjBovH6oHTxHV3c71kMTvbRYgzpEvTi8xF91vPrnaDyWFRk9bLXEOkUCrbxo2uG7vmz3O+IbarxtRoI7HofeO3JGv7yxAU28iHixvGKARj0vaZ28eSAKvRdYhrxrEaa8w49HPFHGObw7uoK7g8+jPPM7/zzWfpk7SX3SuShATztlC1o8Sk2Xu7uZAD2Drqe8yMAjvGK9sLvBu5m7hqOJvMpRubw+RLC898OhOzPEkjwUq9k78v+luyJ8mjyzcBO9+or8vPo/BjvJaiY8k9BwO2Flvrx83zI7pZBXO/6ioLxR7N08OfW6PJtIFrwY54q8kwcJPdv/qbuOgsk8KVWyvK+j7bmWQ5Q8l8gvPTw3wrzNFGk5MOzJPA+hArylUK08+xJjPQ5HTbx4d867oZFavNORFTx9QhC93losvJLmUDwm/6G8PPY7u2MFT7z4cRA8HyOLPAR6x7zTt6k8mZBivDu3cDtux8g8CRrtu46/hDxJAJq8KM2ku78cuDy1ZnK8NRaLPNeMuzyqiVk7mziavHsRITysc7y8UjAHPZrz+DrFtTq8G8qiu/2/QTxX+C28AzM6vBqN2jvIseg8YlAYu6gyfzn/WMC8QXNlPKacWLxRn308p5qBOz18qjqggee8xZ1TPIsMV7sZ8hK8sBKmPFh6kLkNHo+77CcMvA3VpzyfVya7BbWpO+8S7LzW7jq7rZQyu7K3BLwYVhc8ye13PCNVDLyZZ7U8uGL2utiQK7tQ1Os8Q5HIumDgiLx50wM9aACBvNHkmjylAUM9l2KiPLfjYbc31TA8WCTHu7tyEzzRTQg8K8xRvE1LjLvOxuo6iZ8IPeS0CDuFAS87lSWkvHYt0jyyAVs8L3HIvIuvczwOucI8xCtoO9C1CLzJQgY8ZI4SO0zu7Tx5AZC87YbWO/dEELqUYaW8wJJXOy37qDzy7Bo8ULB5PFZGv7x18FY8InOJPC8Q6jsiLpW8xmMQPAAVazuE/kA8QYRwPIQJgbuhcmk7tltbOoPvj7sYzDu8CVWHvNdm07x9oO88Ki28PDLoqbxR4JC840kSusAWizqrk2s7wJQZPBLWILzFuDS8PV6XOi14ijx6mh09nIEDPYq8YjtBjt273L3YO7P0AT2fiKa86K6jvEB2ADyzeB+8NcgYu8nR+Dpv3Ao8yy+GPFu4FTwgD9S7UZGZPLL7mbwkaae8I+1QPaNSkbymOdo7fxCbPNnNtbvBSz68LOUKvS821TyNubc8BsiuO5lFZbv0uLK7krpzvIIdhDzS4z46EDWkuwdIoborlxG947hHvDgbmjtBlxw9qim1vNoPoLz5J7q84yL8vCPjHz03Qqq8t/uIvBYB1TwjT7M7I9XYu0VszDx4Aay8OuQCPX8e2joiwjk8HXbGO5RpO7we6To8BWcXvKHljrwviwa97UE1PA1eYLxF+Ja88W1LPTOq3rtNSwa9j70PPOtIFjzGLXe7TazcvD+vBr3BKDY8I2QQvbd+UrpRPL08BiQuvZkfP7yRGQ87C7EqPFYTzLpCqFO73NKLu7MS0rwITR+8opW8PNIIQjtLNvq83li5u7YQmjzQGQq9ds8qPCaHjbsOrnc8FV/ivPYDaTzVpK28t2SrPG5sAjt195m8e+iCO9in4zzQ38u8X2e3PIdSEzz34Ge8NhPZPCpYYjtcOWa8EtOEvEOl9rubG7m64ZERPUZucDyd6I085OH1OywY7jyyJg68An9VO2CSsTpGvtU5UQIrPIf5QLykb+U8MlyHvOPofDwkOfS7QKWBPFCVYTz9wlA8PbqkPNbLIz0YOtQ86+lVu/HMCT3VgyO9sbbwOuyBAL2lJ4C5mAeJPI+tJzzcRIu8jVJSPCUSqjyQA3C7gWOLvEzQBju/e6o6eBBsOxYmyTzjznW6pdXEuyzZ3rtWnFQ8d1dFPGhplLxRcvS8NaGTPO+qLzy1kaY8KV8hvXSQkbs5Cj08zikavRt9/bzO7ue89xjavOznAryhRZC8Ls+QPBRlR7w/0oY58VKUup6dUT0Owww8UJnpPCU+8zwksKE7MuUpux43Pj1lZlu8SeefuCq2ozxXLoY72Rk7vMFEnLsq/ws8dyl8uuCm/Lsvbpw8ZwGVPK5WhTxcLDC8mQMBPZ+MFzucCtW8QvTROzhG2zwjDck6XKMsvFrQQzoFAww7fb0uO12oRb1RGEI8QFAdPIVJrzyOhBQ8CmVpPHyR0jzwmmU8uaplu2LDIj36nyg8M2l6O95v/bwn+Zg606Ssu6P08LpRqLg7hiurOx22i7yYr1S78/mZvEENGbz+ri28nd2BvMjLmbzl8ak83MWuvPySBr1In4U83CHuu48uzTuD++C8YcHovFK0QTzJk9q863K2PMvtJ72jblU8PEfwO0UwDj0XXZo8FHr8vD8jIj3LAsG8szQVPFUoz7yfFI88K8aovIH9Aj074gk79Va1u9CQD73Iszy704NWvI6kpTkAWsG8gZbrPOKjMj0cS8A6aIeFvH7V1Lw4Y4e8Gat/PIZ4GD04NiE8EGOBvLKbmrybhLO8/g6QOjb35brlc2y87kqVuiyuKLwmg8O70zk/O7G3Xzxmq7k7LSCzOwOoTDxomGg7j9cfPUVOsDqbxgM9mvLPPEqcPLzEulO8yG/kPCLLRDy6TYa8cqiJPGLCCD0oEd4846ZXupKdMDtdHDG7qNvVO+OmqjyOhH+8BIPKOw93TryPBBU8Xu3avH8zjDsfcU48IRMLvDVjNrznoO48bddkvN2CizyuGpi8m6lwvMvgRDz7B0O8uQACvIWpVT1ha5k7hsbIPD+yDT1EEau8vGMGOcLlyzzvnVI8suLWOsOJwTzyNAI9KHBdPIIhgLyHdE47KQsOPN6j47ofqha9sI5RPIqTBLxa1+W7PR9pPFltGTz2jVi8m3kAPWKEwrtJMQ08EahGPLYqU7yZGJU8PSvKO/CngLvvkEW8kQBMvBvjojuQOzk7ZwYDvVKum7y1M+G8gi0nO6NEUDuVBtQ8n/8CvKnP2rs4BYg8eQl2umzDmjxpYfI7B2UGvdO/eb1nmkS8dwt+PNk2CD1A+uu8hKibPPvzILwh0Q46vB+AvHsrOLxZt9m6YgVgPF+TYzxEPiC8kr7kuuSYXbwC2mS8ibu6O/rwTTrpi/a8RqhePJSw3DyfR7U8CrIWPBZKu7sKWI08KR4fPZDqTrwOuPO8aqVzvG38JDth8608dVEmvBU0qjxXpb88y/wkPCo6ubvOVK68fOjDPFFuGj2RMiq88qjmvKYzt7wsZrA80VzgOQfx1LoKy6E7gkMuPDWie73c0Cc8m8ERvVue5by6My84Tl0Hu8Ig7jzkcpC8hBFLvKQKubvY9Fk8rx1tvGp8HLx2CN27qDvQuxvApTufAb282/KmvOEcVbyZjVk8YB8RPNHTobxpxgc9r3lmPPgqRrxvOi69Uh/qvCnwjjyJkV+8dLG8uw+P7bv0xzI8IudCPbXaObxUz3y7A5uPuy1SqruRooc8BPXhPKfb1LuM9t07NJ4qPMTXEbzzmA69H7qHvLEZNTwy5MO8SxrAu0qGjDyifCe8CmkkPYfv2Tz3S1G9lO3eO/lbBjxo6N28MLwePLC4Njz4Iom7ce0Ove5KfbvLSeC80agfPPrv+rzjY5s7kkLDuck97zxynQo7/UvbvMSUJLwXEyY7yfp6PMheAj1+zby8H65evN6gAb034pe8kZ87vCgQCb0334a7LVuKugLCHzyASyW8ss4MPLp91DtiJ0i8ZsGdvKlDHTzDjdq7hegCvJa1ebv2dwy8BEYiPA49JDwzXrs7N6jUOyFVODxf1tE7luwtPFLjnrxqYyq8hBn5O/B3rDzvbTA8Vl5RPOrXH7tKXmm8csd8POUP6jszqRc8MoyfO5rceryIkI87idM5vFE6x7yKJJM8aaJVOsUqBbyCSqa7FaAXPBcgRjwuEkQ7SDCuvGZPwjz9cI+8JZXCO1VCZTvGveC7qDlSPNfFB7t7fk08ctsXPI1vWru2UnA8QiMLvMGVnTw0IN482IFqO1rjHj2ZZ8m7rFXCPPchs7u0q8C7n1P0PBIJaLwhUus8c40mPAzR3bzul++8hdycO2hZsDxmfJ28GM/vvCFBtbxv7Hi8xl+2vDkQ97zFiEw8y9MjvOZISzwUGbS8fGXjOwW2Lz0t1Ty89Rsdvd3037znp+i61cKOPFRgrDsQNQC8G0jLuqPXZ70WzGw9b5lMvDlvm7w8FwC8XJMTuiRnyTzD9hs9pSOZPBjMh7wd1v08Advxuo3OsbyrPJU76VLPPJsfjTw/gUG9Sy0gPF0cSbyczBY8cB/DO9ZpSzzjPwS9Qa28vFITrTsYOOu8ju5UvD9AK7oFWnS82T2FvOV5Cb2uSa88E7aavDp9hrzKgwQ8vKjOvKQd8bxZ5wy8sQgMPc/wqjzs+IY7SaR2vKHhHDy5Ga67xxhwPI1dzTycxg29a8oWPLGPuDu68Je8TuriPIG177yZ6cY82AfrPL+SY7wSfUy1h81MPFuGOjzHBXm8UPpDvKN0yLzi7i28Ilx6vNb2rTxM86+7u+dOvGyFarzZM6s8vUhEvQL3tDup6E+8v+dluxVd5zgkEoQ8Vy9Gu2hEWTxGwBG8olpzvLoZXzyeeYG7gI8wPL624Ty6uOg6o28UuraMkLuSrFG9nlq8vHJaY72MG3c7LPGzuwM1wry2veW7Gp2MPCTY5jtLLvG8yzc0vBW4yLr+wc+6RavEu5o8djzR/Wo6OewJPJAgEbslviS9KvusvJ+HHbvL0ag8Ng6hPMmxQby2okg8/PDZOvPvtDxAZFA8GQb+u6TPGTv2T1M8MuSTvAufZjy9Zcc8KY+BPIaamjztexm7HESuOznruzyjGg69YHgDPBLlb7xC5Jw8W1ofu2833Dw/xfG78VjLvO9X1bw9QIw68Ug8PEle/DzYGcg8QA3UvFedDL2IHKw7GFsQPKR347oli1O84E4svI0UZTxbOLG8pT3Suz8KJTuioAy8oaM5vGDdkDsu7YK82OUhvfRzoTxG8hw8mb8yPNJ2YzzaA4G6SASJvEiHATp3iry8WdfxPP0osjwdzTC6Fe3HvBWmXTwBhBC8XGnUu1lFQrnoXbW7pw9BPFPxMLrNM/07v4TVu+df9bsGFeK8MSY0u99IrLxQqiO8xvCQu/nvEzy9vQY8dPNjPEr20TynYEa8cFlBPJFAIrr/xJI8bWYZu5pZFLx9xeC8QRdFu/TYKb3nnjy8AwW3PLrrNrwkgJg6GuOCPFL9/Tyo1d0809iDvCA/cjzO08I775SgvDv3YzpOLVO7b+S0OwS6/DxAcLc8iTIHuuArwru+6vQ8WNH+PEylZTts3iA7e7mtuzqv57uekFc8uI/uO+8tWLxPUJU7XB/zu+UK+bx22CK8TA8ovIU8YLtX7Vu7r2djvNHq4DqodK08rBfsvFogjzzqbQ68LMStPM5QJz08Yag7YIN2O+PuTjv7Piu9nfWvPKwo2Tr+EAE9FdvavEf/CL3SJTG72koZvXNWrbv0R7O7qgCvu9s8mry2odg8qagavOfTmzxj/aq80XDruv0u3Lzwuiy85ZzWPLEBK7y3jTG8sijYPNO3grzA8H87oDk6PJEcRzwGt+m7/J1iPK0L/TyJj1k86jtcvOPqc7ysswW8GCBcPKtXwryt29s7h1qeO8dIGTxUHNm6ELK5u5ycS7wA7Wg8WItRPLKrAT16uLA8qkg+PPloe7qKXZq7LDHXOpWYhbvR0qQ8dHIEOr2W2zxxZLq8JCsXvCYi7DxvSK87+7iGu3OvarwbpYS7UUjyO3TPmrtewz0859w7OxYDgTuSj547733cvLFfdjxvtNS8emxUPNzOM7yfFKs8L5eEPH5JmjyeJHO7eItEvLO36byg1hM8lLMxPNvuUbwjjxA9k2PBuUGen7s8Qze84pNrvNESzjslPG28J2n+O+i4e7uNzYy7irl4vGZ73Lo0XSw84XQevENPAztmi0k8+uevO8w0CjwddYy89t2cO4RTzbsKVTo8yuAgPAsuLLy4CSS88/Rmu6TtLjwva/e7nBp6O7po5LzpTJQ72G4fvOBWwDtTTDO8NcWsPOspgDxcuF672USCPCcLZzw772k834+ovM1wrLwjx368bp1UvIgJhrw5O7U71J35u1CSmrtTpB89gDPJux6j/Tpbv+G7rbRtvGdawbk/4ck8B8wEPN4lpzy3XTg8Z2YSvA==
-        index: 41
-        object: embedding
-      - embedding: 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
-        index: 42
-        object: embedding
-      - embedding: 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
-        index: 43
-        object: embedding
-      - embedding: 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
-        index: 44
-        object: embedding
-      - embedding: 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
-        index: 45
-        object: embedding
-      - embedding: mhSkuKR6hTuy5Yc8BGwkunsji7kkiXU9qfmcPWCrg7yWUOs8V8y6Okib77z/EJK7NQbxOp4fA73s3089s6PAu47y1Dy1dte8boAHPFNnqLtKQ6O8DkkiPUoQFzy+npE7c71PvJ052Tx4E/m8BNCPvdFkGTwNvTM9Llwfvf0VkbyKgak91H/wOjmFljszQma8RgkguwAfxLs/F+K80+REPH46cjwbHlq9yXcCPbqlCzxOzwY8eLCAvDHK8DtSgyK8VAL1u7fX7LxaT/47R/sYPDT9M7vdk7y8dvJfPGt4a7sGZ389WqqNuz+7H73WaAe6nVusO7T6JTzbHh+9WmHNu9xtU7uMqaq8k8aHvHL4Y712Vhk8U9SEu1KB/7tGE2+7LRmSux9Og7xVeHI8SwfbvHz6/rtFHDo9rTiBvFRnzTwXSEu7nsS5u7C0uDvnt187/tx/PL81BT1odz67BD3su2VgEb29fCy8tVmZPDT3vjwHCQ27w1bAOw1VTjzYIBi7mxqFvDbgdrzH6Gy8Cp5EPFmT37tdFoy7syJcvIzYb7yr6+m72OPXvAE4zrxa5OG7Ei/cu3gOJTyUl8G75FS/O5qitTvcMBC9Wyt7uUewELyUtXU841DfPI/9uDvagEu8ZP+du2nzrjxgsAa8OtBTPFCFOTwzloU8tmvbvIYh8bsJ4MK8xCzFPBl0jjzmqS+8kcrWOSHlxrx6V1q7MSM3POV3x7wlXQ+7QJIBvLzenzyfgBq8QHLou/K+mTy50tS8HSqvvLu5XrrJy/K6A8CDu4cSsTyt8+K7XVckPOlaP7x+2Cs8fEf/PHfXd7xfDMs84GzYu595BT1704M8mnkOPUA1lzsRODM79HNwPNdUGT2QCw06b4yEPBv3MLyt4pE8Vq3hvAM1I7zd4pk79nKKvEdmALwAccg69/r3vGegvjq01P+8AuQdPDGQJDu4ys88i/+BO4+sy7yDbU08Fofwu6LsrTzI4zY8q9IjO1uxtTtKfXM89k8EPAR6I7wXpoC8a/yIOzZbO7yPItY76WCevK66UrskHVa8ITVcPB+N6jxAh+i7Q/HzOz3PoDylye666O95PN1H0ztmupu8TuMDvEEoZTwPPp682pwwPHIwRTyJ6QK9fSvLvMWFyzuAS1u438eqvE53jbzO5dA82Y7EPAf47DsxE3k7Uka/vMw2kjsz/C+9xtUWPAbKZTwOqpq5bJeJPDf2Q7v3CTK7kw6ePDx3OLwRBPW77RsovAF5Wjx+U+O6/fiJvER+kDxeAeS7BbEsPEC/+7v3Z0O69KNWPNTLaLznn/S8uLQDuxcB4LtBVVs7vDsIvPck4rw0Zu27zT6RPJ1sGr3yJR08eGKjO44wMbzd6Tu8W3F8u9RUCjw8Wkq7UIiUvPwBYLzyI6w7jKisOU2saTsbraU795gBOxeJjTyzeRO82BUePRZps7ouRsW8YNgcvB6UA7wOpmi8MqVsPMN3RzxhzlM8hNcVPZGUrrzRXqw4+WXvu+LEHzwuFU67vBinO5TnZLwu7XQ8MWHivP+f9Ttlp4E8KxIFvfgIATwmpu86w9IWvMUKkbxvIRY8BdS+vLwCPjylniW9mLYGO7F8HLwA6YI8klWqPOKchjyMzNs8R+wBu3edAzwc3U+8EU1pvIUE8ruL5Ym8zegIPCtCdruCfB68r/EZvV7jzLtxwkI86foPvWjCh7zgbo48YqAvvZiBNrzvDei7ogVLO4Ve7Lf9qwU9rvEMPTsstDy6OgE9COnOvBkehTyiJ028s85OuzrrgLwClSO7z3qpvC5SBz2sFtQ8qhsGParCSLxozhI8JaSzPJ5p0rytfmm8MNOnOmX8uTzGuAC8oKAPvXRaw7rm5cS7glLGvA9OxTxblQa9TL8IvZr3Bz0hxTM8h4lMPOFlYDkZdcS8gWG/vDW9cTz1JCK8ZvwKvL16JbwXa/W7nEyuvE6Z3zxmkZ27aJhqvK+vJLuiTyq9b5zDPCNAOLunALw6MhgmvAkwXDwBRPO8Q7QTvDOQIjx8WKo8aCQGvJLtj7uZle06iCwKvDOZ5Txe74C8m6NDvNhWyrsDx0c8ehbnvM3ckDyi6xY8EqaFvPFkN7wMnHC6zvTrO8Z9Hjy1bgI8JK4OvIcj6rqeWUq8exLtvDW7Z72+2j+8SyYAu3l9vbp/ywo9MUswvFoRk7vOUHi8w1SWPMBHObzy5Ui7gBE5vLiXrDwfEZS7bEGFvJwha7t15SM8TBr3u+qWp7w56Is7bEQBPVDI+Dtqmqi7pyitvDmb9jz9O7k6iFDMO73Zsbyjau48pVddO9t9Dj3TY0Q8Ox60PMOroTxf/i+8nFcRvDoRzrvyJdC8m6K0O9/END3KXlk6BoYEvU9637taDiE8bFMFO/V2DD17j1A7vkejvMA0L7t7Yoq8DTeWvC8M+LxnxZQ8FYeCPLiSQLy+UX+8BcAwutfOu70bteQ78C0WPWpkibzBoR294gRRvGz4CDvo+oO8PaSRvJy+PzxPX/e8HzrQvMQ3Nrz2fI48NaODvFlIAD0ts5Y80Ma2uzJQMDy2Vy49LUsnPeWz0Dx5U5Y8uj7MO7k2Jrw41ak8M5KNPJ+60zw4gsk7WpAivGCsKjzwMew8HnO1vIi9HrtKrJo7vb0xO9BwzTwOrRm9nLWkOoyxqTwPbVI8zDDbPKl/kzp6Mro7LZTRu6a1+TwEZpQ8OPrCvGckkrrChMw7G9HZPKLFUTuFeKm7uOhJvZB9wDwHvJK7GttNvFYenLya1jQ8Q9x1vGbUPLyeSXm8Ik5LPc6/Db2bCTK80WjlPKIJUDzSnYu8c38EPA6owzzcfvK7lNagu9+qRruR7d08OqJbvao/0LtSWBC8S+WiO5xSKTzSgQ+8wgMqOpGEDLvbuwo8Cp2iuVM75jxdfUg88W4Uu3saLLzRYme9rpcVPbaML7sF4Da5e5GIu63zqbyEvFS8K8lCPQtDzzxSfac8lFxKvItDAjyj7TO794kovebGlruLdpE8cC4OPUFW07zC1se84/hSOw6dtLvfe9e8txHCO0kCmTuuQkC84ltBvLxuibvpAy+9MhkhPJCHD7uKNpO8cErduumM8DyyhYo6842CvCsc2bz/7nY7AmEYvGFOzTzgdKA8hY+ePFl8F7uvUSO9vLjUPLxChzprBDq8rzR5O9VyzLxt6cY8Rk2UvBt+yrz4sqE8RMwsPSOWu7yLbD47KVL9O/HiKbyfcaC8IUBiPF6QGD3nX0W8QKauPAvJMLzzyLe8+fwuvF1aTTyqlRU9XDllvVzpQbyELMu8ZSA1vD4g17sIkoC9ZTYPvcSzeLwol32884pavNHPWL3qB+e8zksLvGFV7jtlbOe8Pj+0u3fw9LwZGNU86VzhvMmjOjyS4wG8PYDOvKWYCj3J2L88obTOvNQ6EDzoz2s7YYiuPI0oaDs6ZLw8EWjzvMMfKr3hAO87cH8svZDfkjx2boY8+kpePJyKr7w2qUi7a+2BPK7EmbxZg9I8cfHMuxdD/TyPkZ47yVmgu+PW6Tof5ue7gqOouzkBir3YlLm7Mx66vJTnRbujMew8qAu/uyTBkjvOW/+7HOBwPGgIlLw18uc5NjoFvWSiEDww+rK727counDTgryVzww8a4B7vNmPBz2eLsE80Y+Cu0yagzzTxA47wDiBO5i2OTwDk6w8HO8aPBdskbxzwuc81e/HPKSdSzzPzNA8GDePujf6Qjx/PSK9I4ZqvA1LtznA48A8g9PpO+JEjDyBSyu88Ku2OzuH/zyJ7Lg8+BB9PHli1DzCt8q7RDWsvJBrfbylTRW9/tGfvBw3Qr0G9mO9L2Uhu+9uTbyEqTY8AbdcPJ469Tw5Ikq93iS8OWlyHbwV55c75vmXOqCUjTsOj+m736E4POYtpTwpzd87fpxZOnYpLj0WG007Q1wTPaFxAL0xZ7o73eGfvChel7wU5Y482QCrO23IPzy3Q6c8CFcsOZhqkTzBKlC9nBKyvCmBcDwyKMO7WcaDu5JsiTpMWJw8g82cvMhEHTl7GGw7gr8LPErk0TyR3J88pzi1PH0nBTzwMSm8F4j4vIpW9zukiwm9OaOPOm4fPLwoI9y8XVySvDY5PLx4MsS7YOvIPPkodrzF4dS7/8TtPDKmtDytpX+99YDkvLk0qzxXKj+8B5PQPCXbKrzH7fS8Lq5MPIMyj7xIVrI64rKevM5lTryYePi7uhHmvGoJ7TtP5fo845IlvdRewLwh0Zw8BvwWvGmqF7yStXG84BaqvLO9MTxGoqm8PhWHvALRuTxWnEM9yTe7O1l6Ej11lb48IUe2PKtikbw9pqQ8AX1DumuTQL14kmc7xGwuOj5/pbziNRG9c25gPK+s7TlI3lw84ficPEBWAT3VOAE9ZtmvvGWdyDq6oXM846q8vCac7TxC00K8DqhrvIaYYbtgYQk8BK7duvas+Tr4pKY7CY2lvPRqrrxJsqU8qmtGvURLIb3ga0U8ZsEfOyRWkzylsvc8CR+5vMs3l7uQXZM8I6fQO7paW7tSz7y7J6cJPSSC4jzHAlQ9XC2wu4u+MzwwxSY83nybPCzFlDyb50Q8iXCOvGTw4LzIipo8IXchvQILgrvUvLK8Y9tHPN0vs7ywPcU7KJsQvdh/MLzrgSW9d/3xPHyxujxbM6U8LCJKPJsW4juVyQC82w4+u6JxALxZSBu8cA/zuVXstTy9Hzu8YHsXPVI6jDzEcCC9aAWVPDTdAbwiNFU8aVBOPPgzSbwI8L26ryWcOumKTzuqmxY8aZqRvECcPbv2Fyc8W2MWvbOq/jwunOW7iZFvvPtWyTxb5/K8Sh7aO6+H6jzEFEw7dELlvP6RmLswW3m8rIFjO5Gu+byuGQO9iJNFPN7SerwNkCi9b4nOugr0ijyYHTq7ZNMIvT1AqrxW3RE90O+GvBiXHL1g0gs6BFNzPW0fKL11Nom8vs0nuk6BvLwKh4U77aIOvbEjkTxAXEY8GH5BPNRGKju8xka72MixO9BLdjyNMbU8DuRzvLR5R7zHcmW8ya2rPBueHD0ByZe7huS1PGj8CT19Mbg5qpiaPAfAoTwuZy28WYF/vB9bprwRr488d2wCvOQjprzK0wQ82kDKu/uk5joYEdE7PcurvMZjvLtBXMq8h38avPDspTxRBcY701ssPCerqLxybpS7xH4EvOyy+rvcuvM6+7T+O5V8DLpaYqQ7ReoaPax05brCP6089Cc3PZqcxTwvdjW99je2u0rot7tFrQO7zHYHvGCqOL0cCLy7cTwSvdvQEj0n1w29WHv+O7/pQbog+Sy8G6UUPUAysbqJ8zQ9mOTivMvQkjx/kQS62TlxvFu5YLzVHpU8Q6IfPLf8fLxh2/W6BH32vOO0oDwh9ty6CBdBPPNGFbx1pBq7PSYFPFyBsDtK8qy8kuLMurcn/ztnS5O67Ou2O1chGL1n2cu6fp2cu8vYWTsxPM87Hremulds5DxCyvm8TLfUPKkzq7uEENy7WA2dPBetx7ugL0e7CphSvLXtJzxoxxg8SqHeuwEeMTzCYvg6VH5hPK97gTxGLy68UEpEumF6aznTZTu8rivIPIalWjzdVAU9QTp+PN9L9LwhxYw8HgCavF3E3TwRY5m8mI1XOwj8CLxb2lS8DJpAuhFEhjsXxho9eU5nPLErGD2G2oa8r7evvGgyCjxQiIq8ZUoNvborkjxr3ew79n7TvH4KPDzeAQW9p9GzPK8MUbyoeAM9ZHJdPBTjFzte8VM7f/UmvNyMJbyvJwO9bkc/vTjxjbv008m7ymwmvVsTsTutvDG93D/ePLkjzDs7+fu8cG0oPdTSAT0CxV48izSSvEQ4E7tsuzM9FTq1OwFMubweMwi8n22SPLcHBLxFgL889lj7PLaxGbvueY07Z0eSvMhRn7vV5ig8YSDpPMQd+btb9hQ87nnZvHRlPjxsZVu8NY11PGZGhLs2Ez47YfqOvAeysLzeDmK8FM8hvKgMnDzHKGg8AFjSuz+/g7xWvB+8v+73u0rsFz1/gEI8jckvvJIX1jxyGqS7hh+OPGehPDz6qbe7EC9VPeCebjyTb/i8x1bXvEhglzxRCK470OrEvOmmgjr7PS25kqE7PF0nIrpfU708eFmjvFecaDxBIFE8lPFKvE32Jb1MBLc7l8rEvEluCz1qnVi8tfKcvEKH5zvmMMU8tdzZu0VTmDwsDaG5FRJauymnxLttxfA8lBLxu+j6ujzBTCy8qiTNPKe4lrzdxcu8QVAHvPhmHL28dUg8EoTbOxpooryAH908NjASPZ94UTtigz+7od21vFhA7TymvYy6T8XAPN1Pvrx7Ggu9QFsCvLqNuzw6qhE8iqNTvL/gCDzz+Tk90Z/PvGm0kzuiqZC5zZVYPE3i2Ly+pnY8q3mWu52lqLx1yQ+93iAOvBMDu7zBnr88oacCvMiDMDu0rrg8/aaBu5tx4TwcFs87RyxoPHhsDj1Bg+g6znW8PC+NPTuK7IG8wYPUu7ToCz1/hgC842CdPGHVv7w8VSa8/0kNPNP/mTwdPuq8GtgdvDJdvrwkIXw68eJzPMJkOrvmyly8pLUxPNvsbzxNEcE8VqCcPHNmML0o2Wg8ScEhubqf9LsMaHC7uhodPULxgzy/+CA8BpmvvPKtJry6bdc87QuyPG6Pc7z5jCy7bGvvuqY12LyaLSy85h+xvMiglrzsjLK8WuKVvCxOyLs4NMI8gXNDvLDcOzz9lv48vUQyvVFFJL0YER29brfqvEAh6zs93XE8Vd8wO4HGcLwC76O7Tw6+PG3GgzyRdjc90WFLvMJf/TwanaO8FXLGu7E+qbyNZbW8VbCau/UW1Lzv/M47UvIkPKT1GzvLPHy80TipvHhMtbzTAvy8/hcKPV3tnLsCgfa8zkgRvDm6nDwfjhG9SBPCvCEeszzEl5+8LbqvPIPozby980e7thFsuzu1GL2+MHs8NcqvPPa4RL2tOjM8KE5AvVQhRz0beaM8yVDMvM4mhjvaRJw7TPLqvAJ7Db21EwE8U1xAPKuf1jpWOJ68hTLHvFlyXrzwCxq8kBtHvJKJXDYbm4o77cwEPTzcCTytpcG77pk3vD5tM7s3LxW8WA81u95oFb0HlFQ8Y1bAvFPY1DwVcne8k8CcvBJaoDxbkPm7EBsRPRfPsry7bGi8epAyvL0m2byX/7k8Wo/PPOX4DjwMFXu94Ea7PLbN+jswTxa7cD+bPCTFubx0atU8ICuyPNLl/7tCjqk8YZsbPNBlwLtcd2W8X+GIO1iFyzy3V468vdQ1PL2Uw7y8uJe81TFxPFz24buPC+26ly28vNnCyzy4ZOI8rUUHPULwqbpwfNg6nBjCPGKSnbrruiy8aZ2vulbCBrxnjQO92MQkuzmU27wL3eU6qVzfvNvADT0ymKc80jlFvCQclTwmseE8t8zbup/bULyxB5A8btmPvLA5Vzs1XgO7N0+1vLbJNDz5U6O83ACyvDXeETxF+C09RdjMO8XZ5bzCfxk8zEiRPEDrhrxAAky7kuXZO1HpBb1cOce8q2+hvCIQPz2xkqq8EPwFveazurz93X+7j6cSO+wiVbtawvm7EAaDPBCDozzmoBO6YFwFPNzOgDuAA3M8AGmVvLDJIT23TGW8wcUwvCzJFTzxMP860bBivFJblbz+t2y8dBT2O1gwkTw5m108o+1Gu6VrzDzpHsi8K+GHvLePgLodiaU60LtQOwjWorwPNMA70u8QPER6e7xCKrE8/qUePH5hVryOArO8Ln7gPI8gkrxu9tU8gdrevE7hGTxCJqw8n54pPTBlAb33boA8J9e+PIzPJ7vOifo8gLw8PQEfD7w9lsK8sRCQvLa8gTw9iCy9bu65u2i9vDzZn4G8AisRPB8gTLyPtT08Uo7GPKrM1bwTbK48R7lFvFp6DzyntfY8fEzsuxcBELr1dre81oBZO3q2AD2gACu8cBhWPHBwEDyB2ua7ZF6gvMYwZDvPNsa8CNsIPbhEkToNISC8qvJtvLi2DjwOo+K8B50KvA4Yrjz85Jc8vpdePIChhDzxJ2q8Af9NPAgx9DrWsIA8TBMdPJqXdryRhwm9kr1gPOP1Gru2KAK83UewPOQUgDqU4yS8At7AvMpqajym58Y7cDAyOwJym7xUYh870Ke5OkxfRbwdzKc7AoqmPFJdr7txkAw9+gwRvBe5aLtMmCU9qtu7uVaw+LxlSMM8Oq7rvJJmmzxtaRo9dWKZPL8iM7vw9bo7WfdTul4p0zv5Fho8kQCQvMR177vaR1S7O08yPLSbHjz2FYk7iMLOvPmb7TwtJus8qMQAvC25qzzLS8I8ap8+PFe7IDxEKCk8q+Gpu6k/Bz3frIC8jJ0TOyyVDDytp0S811oAOan0Az0P7yw8TyH2Oz3/oLwzVYk8Wo0BPHpPFjw/7Pa77ckAvOl/ObydbOc8goBhO6BppDvZwjs7SFU5PGVkUTuwrha83RdvvHylhbugaDk8e7HXPIJKObx97WA7FPNrO5NERLszZDY74hlzupixS7yORXi8xhO4OzXwOzzUcBE9Glb4PEH7HDyleca8UBDgO5yqFT0I1y68hEqnvDvpWjsPrzS8UL6vu7+rITwA39I89g6ZPDzysTwzsYG73SzSPDiVkLvbiMK8EihyPTFMk7zFEUw88SG1PMR/MjytsF+7i7qfvOf7rjzaO608mTyLPHF2EDtVqYQ7UFQ0vCF2MTyLGg48FiAtPJWftbw01f28kZbzumXCLrxyX+c8lp9FvEzirbxbmzy8MkbavBBX0TwjLUq8o3U6O4QejDxrN507loWPOxGJzzx5qSS8KGYEPQzLbjoigbU8UkazO8bujLwHPOQ7xz3mvOM4sLurbOe8PRCru7GKWryoFzm8bi6DPfX6ezuPTOG8sXQyO0eLArwBBG+8WyzFvIxwI730sQQ7ic0avSF8gTyZaMs8QopKvWWIy7xgQhO8r+QjPHfMorx9ZfA64wfUu51q8rxcZDS8X+sAPff9drrfIf68X0BMPG+APjzjubq8YZKNPEpoJLuN/Eo8F8uvvHGsEjxgAh28sqQPPZF4nDsKvhK8sgezuhrfzjyBMPe8to9gO73b4DvHZ8O8BtJJPHdWCTziT+S8l5yrvDAS9rsfsMk7WEpDPdTsyDzm3q48neRKO/b5mDx2vpK54uNSPNRsurrqYfc6NPoMPLsfErx9V6M8mseyu8kX5zxmIai8ywcHPAqEAT0GOqs73mAXPJl9Yj2Mimg8Kzz9uoaRzjyC4VO9CawcPB/F/rz4AIK6/t43PPzOuDtLs4i8MRwvPDRPnjxOYS27LMeBu36u17sFyCq6q+Aaup9grjzK/as7pB8ZO6KwTryhV2I8hSwpO3ij47w/iiG9SSSZPHGxnjwy5Mg6vrQVvXSmgDvnHRM8nUopvS5BKb2KQfC8EaJwvEYBRLwWAuq8j+jCPF0EGryrica7XII1u6CvHD08MgY8B8CyPBmzwDzKXK47z8ZYO1ycDT0vxMK8tSTMOxvEjzxcCNE70LkGvENs7LvoaoI8izKeO4B8Mrx6iXw8+yqsPFnRlzxkT9K7o2HyPKtgSjqruAG9E8u1usRhpTy33EO7kcm9uuH2Art0ztg7XH2DO8kAQ72xanM8DO0UPC1fbDw9fm48csTlO8kKjztHwhM95y0BumIINz39Pzc7OO7SO+GMFbzpZ1W79GjWO+9Narv7lFE8VbBXPCyuTLw8nA28KrSNvOUNKLy8lMm8RrAJvIOwmrxDXs04G0ervC2ZuLwlBSE8RHWru1Cz+jt5Oem81AYSvZiNozzyuQa9mmLaPP0VIL1LN7k8e7cJPMIJnTx44Bs8nfFcvFgTujzvzbq8jvd1PA9gq7wH3To8z+f/vI6M5zwR89c7eW2yuhBlPr3QNfm7et5WvFocNDoc5ua8C5luPP6WLT3iGv67MeGMvEnkw7wW8YS7xJudPGhXGT1evYE7XQeovFlzwrx4b9m82usvPN2tZbzyuaG860WDvFOh37urCyg7+cQaPJfS4zyY/iM7sXYfPOJ/NTzxizA7oPchPQgiMTwX8f48KLehPJOEkLyWtr45OP8MPcOCyzw/G4G8H7kaPCIT7zyZW9k8u9/HOkkWCTy0jdG79il3uP0bPjzyZLC8KOI4u85r17x4x3q8SVXUvAhOATtjuzU8S0yLuumWAzzb1n08T7C2vNXKXjz+Yom8NYZ1vJfLaDwRnzS86F46vB4OLD2ne9c89WpEPNonwjxyKDW8gWObOxzCwTwbyZk8DKL/OzNQBz06R5M8GX6/PIcFWrxN/r06ueeCO9+3C7vVYtK80O2HPCaz6bvatqi7WjgJPfUKuTxmMfi7aXoCPb6sSzsNPXQ8B2JfPOZwNbz6R9Q8PYabuzZLGbwGbhg7dGutu4PedDy6MKa7/qnFvL7vl7xei1S8/qv9tiOsuDu4ReE8r1hnvEOuZrwsrqU7kndXugoWKTxg9hM7xL8JvXZBJb0Jj8a82COLtjvW2zwfP6m8iaT8PGtkwLy7g8M7cMARvCkNGLyizHy5dcQrPFiBwzwOdSi8/6Dyui1hJbxyL628kfmmPNKEejs2Eq68BTS3PDLPozzEoeE8FuE6PPARGLu8dE48VNY7Pedpibs0zwG9QsACvCtEfbv4OL48znLJOk4pgzyAePI823b+Oy9fFbxYjYm8SsZSPN946TxGqXu8kTbAvIl/7bxPogQ8b1+0u5P3+ruQxWy7YOOdPKN1WL0kQ5c7v98+vbqn0rxoIHK7B5cPOlsizTx9HY+8Jp8+u3n9SrxBh0s8loervOOfOjhA9qe8WeqDvCrvKjsWWBK7rvcZu/AV9Lun/Kk8EWAmPCGQoLyK4tc8DATNPGLOjbyF6CC9Mj39vD8wqroK04u8WaAAvFq3ezsrrw48hbwjPVsILjsW4mQ7TToMuyX09Lo+CME8Fb4UPVV8hzukz3c8xOfTu9OlCbyH/Pe8mapevLBPzzylW7G8QtSCO7iybjwmOUG6tvv4PBjsHT0Iyy+92IrWPAsCC7w2uEG8puzDPJGWdjw+SxG8p1gHvR4YNTsUfa28MsoCuqwL4rwo14E7AznXOIKc9TwSNp47VkDyuxZLjzvRfX07i44vPPav9jyAtQK9HbyrvJ0bjrxIPKi8llObvJabqbzF1Vi8jZ2hO8+KZzu290C8OVtfuyzbizwGnvC7teqAvNKFILz/13280OalO3J7VjuvqHy8H9++OyAHgbn61zQ8GImDO4TLYjvCPjM8oG3zO0qZD7xNQDy876AXOyeHuzylz4c88KKKu8YibjucSLS8+ZSiPILItDuQHq48tuQmu4sj1LwqSgA7Iq/wu6XUh7w907Y72JMxO1toQ7wCCQe8C45/PBLcazw4Wbw6CYKrvAEtCj1B9rS8IWerOzGRejxsTOK7SyF/PISaCTzp0KA8nz3+O7bHorvM+lU7p+uOO3UswTw+xi87oPGfPOIPFz3C/oe8nS+rPAJSErx2/ou6gjXQPPLXtbyIDzE9+aiXPJ7iv7y0qdK88wVKPH3J+Tu7f5e8eZKnvELW3rxcCr28rDyHvEa/x7yIvJE84XvZvFo5aTyANqa8h6CDOs97FD3sjhu8m843vaPsBr3Ph/27ogMAPOnyiTxF++W7FG4WPDKVZ70oezI9ppXluwbP/rtYTfO72qdjunBosTwb1yE9N3TVOxzKWLwqrAw9pv3qu0TJqrw4sg88IaWzPK6oxLqgglG9HBCBPLiR5bzof9U6SOsrPDeBwjxWHNa8VTMxvD5lGjwmgvy89hycu61s7Lhio5u8klTTvOtQ8LxuLNk8qS9ovKlTwLkbBwc8JwdovAcs77yhBqy617EaPYGwHj0RL4s8SNyTvOuyFbppvYa5V77sPG2P1Dz1wgu908X+OwpcnTpR0HS8vUh1OxfOybxjFLY8vHrLPEpjpbwgYMi6noUvPE/woTzH0FS8JyxXvLUYvrz7tws8LTnNu8HaAj3rZfU2K/wTvCr0B7z/QAc8X0cPve220br+Yn263L/Ku17YbDl5J7A8hXeHvFWGmTwz7iy8v3MRu4kTTTxQzYO7p45DPMAFozzxNGs88Mw3vEFKtLxriCa97H25vOm2b73O2F44tn0WPAsv+7yh5za8o/T4O6faRjunxdO8BmQ1OxjGPrudaXY85sm2vHHGXzx0zbi6MhPGO5tXRrxrvQ69BiNFvGX1jjz5PrY8oYeUPA/TKrybFoo7Z+noOw4gpTzvE548U/khu2xn4DtX6yo8DW7Gu1PDTzyLhJQ8KwGOOkYYgTxUwji7wT7rO2wxgDxZDQe9NoEWO1FLkbzGPhs8PDA5u5z7ijzmiHa8/qH2vLrFBr1p0eK6ACFmPPv6Az3SyGA8HQGfvDTI0rz1B/m7Ph93O0F0ETs58eK8x33/u/7NNjxlo7O8VB/uO4CRA7s/o7W6e14/vDLvXDykGz+8rdggvTj+dDwMH9W7PlowPDTjhTw146I6Yd8jvLaAGDxkPCq88w0qPUrcHzxz+Uk7w2SYvKwzoDxxUuA68t0UuhUs+TsPR0u8MB+dPGOJOTsUjJ06WPqrvJuxkLxDsQO91xIfu/nGzLsajIW8xey2u+aWIzx0X6o8fPjTPOhlAj1PNgq9bTHlPMmDzrvxDO47ItUOvCMLlbtj0qW8nEhzPHK5Cb1ZGyK8yVWQPG0jr7y3vp48A95rPGIdjzz0b9c86HyfvNIWzTzujIU8pfoUvPlnSbv7H4Y7CRMlPBcSkDw7OpI8m3B/O2RFWbz4FgE99GG+PBJRPTtSo7E7gE7KvOY16zuhSn48XEOePLTKjrx5Q9U7z1n6u0zRD71oWSq6vuozvMt5iLtTeaA7anJnvBNUFDz0+wA8jtWXvOXtMTzhS8S73baFPJLgNT3dvw48lFLbvFaDr7zMsTq9XvrKPE/J6TucPwc903YVva5IFb2eLrO6Qj4XvaeR8ruzaN47O5JZvEvPq7tYrwQ9ESiKvDS1dTzvLJy8CTx7PAkTpbxGu2a81ELnPIR5prsgNfO8YbjPPGfHgbxSRgi81ZZoPCMpyDs6m2S7VEwxPLyfIz2xq1E8mSuJvI5qLbw8VxU8U62CPPjaFbwOhzm8EZ8NvGwCcjzL+hW7SXSVuVqgTLz6bVI8zpCDPOm69TxQXmw8cd0QPDMNCzy4mkY7dUG4O7Xh/TtpMys8JsztO8OvojwFQe+8/2tMu3XP6zynQQi8Q27BO4kwSDpJaQa7+UexO23Kd7s3YYk8p0FmO/VFJTzKuPG7IJXMvHuPjzzGv5q7ZQjPPMakMrunLow8yCnIPGCCijwDMYO7rXCru7snTLxoCCa8AV6OOx9KNbrOFuo8BzEmvJBC7js/h/+7zQvnuw6/jLsxT3O8NgsRPMmZNryTf6+7Yuz/usPKxbtWVUA8LLpWvD9VUTtG7Aa7Vo3WO8v+WTypRbM7uhuju+Y2cLpd+Z46NwoyOmlAADqKNb28oySEO/I3Djx3So87AH86POUGF71U8qQ520WOvDRrnzyIHcy77NEtPFQW0zzK5W68SG/jPLp51jvveps8rdFRvN5X3bwTd527F/hNvJK2JLyagr874VdiO/NWtrvegAA9ve70O7aIYrw/NyO8ff6DvIhTOLq95q48o+6eO99bljwLZ1c8fH9WvA==
-        index: 46
-        object: embedding
-      - embedding: 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
-        index: 47
-        object: embedding
-      - embedding: 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
-        index: 48
-        object: embedding
-      - embedding: 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
-        index: 49
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 190
-        total_tokens: 190
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1727'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>One more question?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '841'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need to propose first question. The user says "One more question?" but context lacks content. Probably
-            ask clarifying question: What is the research question?'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What is the specific research question or topic you would
-                like to investigate?","reasoning":"The user asks for a plan but does not specify what to investigate. We need
-                clarification to start the investigation."}'
-              name: final_result
-            id: call_cc4baycy
-            index: 0
-            type: function
-      created: 1770981683
-      id: chatcmpl-472
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 100
-        prompt_tokens: 365
-        total_tokens: 465
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2868'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the specific research question or topic you would like to investigate?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1118'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'The user asks: "What is the specific research question or topic you would like to investigate?" Probably
-            they expect to answer by providing a research question. We need to search? Actually question is generic. We can
-            propose a research question. But content requires basis from retrieved content. We likely need to provide a research
-            question for them? Could search for "research question examples" but no. Maybe answer: "The specific research
-            question depends on your field..." Could we use general knowledge? But guidelines: use retrieved content. We may
-            need to search something. Let''s search for "research question examples".'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":3,"query":"research question examples"}'
-              name: search_and_answer
-            id: call_bi1ly0a0
-            index: 0
-            type: function
-      created: 1770981687
-      id: chatcmpl-388
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 149
-        prompt_tokens: 629
-        total_tokens: 778
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '96'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - research question examples
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 4
-        total_tokens: 4
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3957'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the specific research question or topic you would like to investigate?
-        role: user
-      - content: |-
-          <think>
-          The user asks: "What is the specific research question or topic you would like to investigate?" Probably they expect to answer by providing a research question. We need to search? Actually question is generic. We can propose a research question. But content requires basis from retrieved content. We likely need to provide a research question for them? Could search for "research question examples" but no. Maybe answer: "The specific research question depends on your field..." Could we use general knowledge? But guidelines: use retrieved content. We may need to search something. Let's search for "research question examples".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"research question examples"}'
-            name: search_and_answer
-          id: call_bi1ly0a0
-          type: function
-      - content: |-
-          [ce470dfb-0214-469f-aa03-064bff607abf] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_bi1ly0a0
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1117'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "**Answer**\n\nThe specific research question or topic you wish to investigate should be clear, focused,
-            and answerable with the data and methods available to you. A good research question typically includes:\n\n1.
-            **The subject area** (e.g., climate change, machine‑learning algorithms, public health outcomes).\n2. **The relationship
-            or effect being examined** (e.g., how, why, to what extent, what impact).\n3. **The context or population of interest**
-            (e.g., in coastal cities, among adolescents, in low‑resource settings).\n\n*Example structure:*  \n“**What is
-            the effect of urban green space on residents’ mental well‑being in city X over a five‑year period?**”\n\nFeel
-            free to adapt this template to your field and specific interests."
-          reasoning: Irrelevant. Probably we shouldn't search. Just provide answer.
-          role: assistant
-      created: 1770981694
-      id: chatcmpl-240
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 183
-        prompt_tokens: 851
-        total_tokens: 1034
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4958'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the specific research question or topic you would like to investigate?
-        role: user
-      - content: |-
-          <think>
-          The user asks: "What is the specific research question or topic you would like to investigate?" Probably they expect to answer by providing a research question. We need to search? Actually question is generic. We can propose a research question. But content requires basis from retrieved content. We likely need to provide a research question for them? Could search for "research question examples" but no. Maybe answer: "The specific research question depends on your field..." Could we use general knowledge? But guidelines: use retrieved content. We may need to search something. Let's search for "research question examples".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":3,"query":"research question examples"}'
-            name: search_and_answer
-          id: call_bi1ly0a0
-          type: function
-      - content: |-
-          [ce470dfb-0214-469f-aa03-064bff607abf] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_bi1ly0a0
-      - content: "<think>\nIrrelevant. Probably we shouldn't search. Just provide answer.\n</think>\n\n**Answer**\n\nThe specific
-          research question or topic you wish to investigate should be clear, focused, and answerable with the data and methods
-          available to you. A good research question typically includes:\n\n1. **The subject area** (e.g., climate change,
-          machine‑learning algorithms, public health outcomes).\n2. **The relationship or effect being examined** (e.g., how,
-          why, to what extent, what impact).\n3. **The context or population of interest** (e.g., in coastal cities, among
-          adolescents, in low‑resource settings).\n\n*Example structure:*  \n“**What is the effect of urban green space on
-          residents’ mental well‑being in city X over a five‑year period?**”\n\nFeel free to adapt this template to your field
-          and specific interests."
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '909'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to provide in tool call format final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"What is the specific research question or topic you would like to investigate?","answer":"A
-                clear, focused research question should specify the subject area, the relationship or effect being examined,
-                and the context or population of interest. For example: \"What is the effect of urban green space on residents’
-                mental well‑being in city X over a five‑year period?\"","cited_chunks":[],"confidence":0.9}'
-              name: final_result
-            id: call_nhhqyptq
-            index: 0
-            type: function
-      created: 1770981701
-      id: chatcmpl-836
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 118
-        prompt_tokens: 1059
-        total_tokens: 1177
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2989'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>One more question?</question>
-            <prior_answers>
-              <item>
-                <question>What is the specific research question or topic you would like to investigate?</question>
-                <answer>A clear, focused research question should specify the subject area, the relationship or effect being examined, and the context or population of interest. For example: "What is the effect of urban green space on residents’ mental well‑being in city X over a five‑year period?"</answer>
-                <confidence>0.9</confidence>
-                <source>null</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '640'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We just answer question: "One more question?" Probably ask to ask another question. So answer: "Sure,
-            what''s your next question?"'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Sure—what’s your next question?","citations":[],"confidence":1}'
-              name: final_result
-            id: call_n1mocx4w
-            index: 0
-            type: function
-      created: 1770981705
-      id: chatcmpl-732
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 69
-        prompt_tokens: 478
-        total_tokens: 547
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_on_qa_complete_callback.yaml b/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_on_qa_complete_callback.yaml
deleted file mode 100644
index 950c349a..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_on_qa_complete_callback.yaml
+++ /dev/null
@@ -1,843 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: fBiAuV0A37x9cpu8MldhvIUktrrMji48KD6OPfOBDj0Lc7Y6WPwBvSlEkT3KlU08+UnWueLDDrxz8nA8U+YsPI0eejyjjmq9X0mavPDKEjk+94a7VK3RvNBIOT1dW248mtCCvKn4/zy5HpG8LXOlvOoWCbvTNGg9N6jMPDAgXDw7Cxi6rN5/PCun6TnyX8e83LsIvG54bro0T3o73z15uyx3UDv5b7Q7SZQ7O2Jl7jzFsCy8aJ9FvY17zjvWNsY8QkWQOVIzAL2hfc66lO7Wu1bdzDyvpK+8zCfhPC/dPD2vEUo8l6abu2ObnjtMWLi8tI25u/LZgbrnnAm8x/DTu4eJzrtSDaO7gnAgvKuvdzrdsRg80tmNO6erlrwDShc9PvXxu47uszxefMk7xAcivKhJKrw5gxU9CLywvN3VdDt6yom8AGiCOftU+TtfT1Q90VpEO4n+wDtEwuQ7imCUumizIruXFBK8IpevuvQQXTydluA69z+zPGLS4jpsDZU7cl9EvJ+2BLxMko+7ck2HOtDlAjyxNU28G23mvPyAYrsUBpu8a3pxPG2TLbwLhnM8/yK8PNaEvDuhGZQ7VSgpu4FUWzszPpU71nyBuoYyJzw9NBm8ykrcPLycwjuBjlI9SaaUu2KNjTze8MU7TlkNvc1R8DuMGwy99YZXPOz1rDwbZ0+8kOUhu0QM7jtP/v+7ho+nOpjYXrwD0Sw8z/JIvGa2EjteJpa8D11Tu2/msTwylCQ8QlL+OzaBEjrJSd08LNUiPCa+fL0st6u8mRMMPQUPVzxjuQA8ivqwPF+LrbxCMo471nrQO4TVCzyf/JI8gjbsu7UTrjwgSba6FCVLu/8n1TuGrOq7xX6rPIKkFj1+oW28tyNbPNVINr3npzi8issFO1GiB7xatkA84cEYu+VCWrxK+x08yENSPFQcFzxsQ527ZPJCOv8Fwzmhtle7mBXcOytakjyZpQm9uyYqO4kBajujsj08cZIcvLMP7jzDjgA8HK/3Om0sjLxbI7e8y6nLutr+PTwffci7cnhMvD962LxWD+O89/iWuwHmszxxVNI8NWYLvKCH57s9Ywm8dbxJO83DOzxgKOO5kASYu/5V8DpyaOA5G0ljPJOp2bvangm76bWqvU2JGLx0Xe27VACKvIeco7w+bjw7rE8qPb6r8TrqnxQ8n4Bvu3MDZjoHCBG8D9eVPAHMWrung3o8warhO4f+F7wfqxs9YzzqusrDVztNiaO8RoeKuw5RbDz8YoE6BJmau+khEzzN+v+7KO4PvIAL5rwslo474TsiPFmDhzzYZfW7mjX9u42KLry8N4E8oxORvHMTLzzTBzQ7rw6XPJWdM7zw/pU7ukcTvBVR6rvwW608MkU2vFkeMDwh/ea5nt9tPBPPtroe5uq6mCmhPHcFKr1y26083J/lurJamjxqzwm8maKsPJKqsDtKQTs7VW4mvCdhAL0GImy8FxqVPKFa4zvA5Oc6aMdAPN5uA7yPsmi7GSTMO0zIVTzl1IO7vLuoO1NjHD1P08g6enp/vBBO1Ty694u6RU56vdeSsDxK6zG7eEPzvPyAGj24E7U80ZSvukA7Dr3LD7K8y/rjOlrTHzyj0Hy8PVjEOy4YWjwMniA8yD+luxhgODx4L2Y8QTFRvJHStbwWNW+8XKr2O+heHLt6H9w7vK0MuiSDuLtj1UG8wYPpuxtlVTqcfpw8lPA+vceTYry4MjG7J/wvvfUwrLzBANY7GWEovJbdbbxsKGS8YiQpPSSrxTxLGti803K0u2+LoDw7Iq+77ToAvJUhCT3YrgC7vTt3vEiGIDyWqfw7f+C2vC3oo7x4XOQ6rYJYvBdkQzxbO5282RxRvNHc5bzz+D+9bzbju73Cab1tqTe87W2dvGdNg7zfN4m8NWTxvPWQhrt0+rC7OcigObgqujyXdcI77NO1PPzAAzzkNgi6/LTovJEwWLyh19I7Emm4uu0DejsAKEu8r+GJPPc2MzxXmQo7hzyZvCxL2LxiGni8euUZPH7ozTuL6G46oeDouwTciLztV7U7cWuKOn9IADzojYW8xAcxPCQfkboGmZY8C2+Hu8o11ruYSBk8l62xPFnkTr0icGc8+fmMO1L3tTx4lBW9QO/DvFtc97yyDA+8WbT4vNfEsTzhNt08efW5vNyNYjzH5zE9yoHUO/sGTbwSgK88Cy6nu+jPXjwR4ag8K+ibPAnHlDzkkuQ8ly1KvAXafL02BkU6MWMIvMuyH7tH4wg83d7jO0UbmTyY5Cg8/FePvMZ7VTxcUWi8Ow54PM3hkTusQAq8aF4XPcbtqjx/TbC8OFp9PP2Lar0qXAw9Iiuou1CCoju8HnK5Z+E8vCRiEz1KpgS7DSYivTslxDvTUFw8KM0VvBy8Zzmtz1a9D+73uJW4U7uj3aY7yGU7vT+/Or1F/t88WTjQO64hVbzWFKS8vLXOOhZUVL2fd648xcSkvAUKLzzUkge8cwmIvDDfA70PzZ08hwRLOjGvpTwTkZe8cqZou0OBubz1Ef472zREPCVPMTsn/Qk7dNelOjh6jrzbOca7NI/buxLwNTyGYX28RVnBu98OIrwEdbi7JabUPLQhIz2k2TI5/XgoPObvKTrlGY+84R4FPBJ4j7yn3U283Z7hOguWhDxyF568foBzO1WS2jpYQrc8iIYbPDqTVzvBdR68cnvkvJONDDywags934lXvPQMtjvV7F081WuCvBD5FDy7QqI8f2T/vBAFlbsytS67TOhcPY9nwzuLNbw7xfTEOxiRwDx9QSm9XQz4PO3iD7wrHKC8dEwDPOKL8TxK44m9K0grvETh1Lw0Axq9YPv3PPfyDr37Id47Or68vMbDNzws9+05k7uRPKOBK7t5D5M8mFNRvMTWeDw4Wp68ZsRpPKNXgLs5/Hm8xWcPPEVpIrs7AYA8a9NpOtJffjzH4FO89ZpJusriCr1C7LG7mLHDPAWYETyClqs8RMAFPbJZAT1eBgE91ynnPALEmbvQtjC8hEeOu8Q/irx5pD68ASQTu2y1T7x0DAs5ujZGvFczTbyBTHg8xuSpOyBlOb03Eow8q7MLvYHHBDvQ/RM86ceTPM7NIrz1pDY8yBBnO+5kybwHqco8bAekOblzFL1H/2g74mdlO8a+D70IFQS9kcOtPNDy67sdjU08oHbVPOMQXbvh0gS9kxpEOZ5AkLw6gkc72JAhPTNNELw+wBY7i5qWPIYp5rsldBM8uqPJvCmKljz/5Qc70wN2uhE1C704Sxy7eRqQvIxO4DyhzTA9fJIzPJAsnzrb4Z68W6XWu8PM47o3xMk6Whl3vNQ9mLxYnqq9B94wPeYYqLyGWEe8+NcPu0yORb3oIna8YjgKvHADtLzZh1I8OwUnu8QmLrylwQA8gzjXvJ/ZILwiaCe9FQ3uu7PbgDwk3c0807SuPMtUdLus9eI84V/xO0NZgLx6zvC8hiQuvYOeADxn//66P2GCvNnSg7wCQxG8zuqeu4wlz7zO8xg9ara1PK7dML3eE+E8+Ft/PPUM0TtrAt48iUVfPE5k6zpJP/M5QRFevewCgryvjmg9ry8WPPoWsjzWdfa8WiFUO9pZa7r20sQ6K/u4u+eOLz1hOx49twawPDXej7v8Mdw8IJTxO6f8ebzbGAO80e/fPHyNEj3Hs5K94xdFPOeL4bsT8865w8oavB1GIz1fOww8QEGzO90hATt52pq8BrPrPHmMyjymuPG76cbju8qNEbwd6EK6xwtrvDe2HbxFdaG8sJSBPbfti7wO6Jo8KHAnOwv5ILtm5ae8OlcAOq8LD73Tens8vxgeunag4DyOHZK8FZthvExk0bzjbDS78e5kPOuDAj23Lcu8N+gFvAxrpTuEzqO8jv3QPEVPkrzLKNa6tEAdPVOAtjyiJxi6IbZAvOA5rDwTQKa8jJuGPK/0ljtjtqI77YtoPPnGTLyGZSA82wK4vAVgqzxyf8Q8KuafPE01/7mXJ9u7tooGvIDC8juHmak8zt2vvNyBuzyen4q8AYcavKWt1DyV2CA8tLaWOj4iDTxQn4039q2zPPZdlbxvNR+8U7zoPNGN8btOPqy8BShVvDD6Oz3JeQQ87X/PvM+yRTw5ye28HlK5PB2EjTtTkya7RHzEuwnLcjxsIEI8sUFPvG0lBz0EElO7SnPAOawQBL184Lm8xqoTvV9JtLsWEa67iyeKvOZwIb1+zOm89o6nvEoc2TxycY08sUggvaVCCr2McCo9hKCcuBGFtztIkGi85hRGvUYHmLuv+4k82z6HvMUWCzzcp8m6V7xvu0SEqjy0Lws8BInEvAWipjp0HAM8NhwEPP+DBLzISoK65Hx3O4nQED0i2s48d/LqPF+l5bxmgYW8ERNyPZuZQTwfPDA8KITRul6cm7vIBj28nZU7vOEG0Dxu/Tu8QNGgPMyjt7uAta074fMhu9zw7zn/a3c5UrB4vJoPobyf6Hq77s1HvWA7KL0Vi1q8vKOtPOtrSDzxjXq85povvErLIzz7sya8VDcOu6W5lTytrKw8afkVuzsMSLwQklw9lPGKuxJY5DpCjYg8B3yaPA+OzTz2B/S750qpPPwy4DwYddU4POY4vTkF5Lx0Xfy6CiF5PFAPJ7w9IR88i4GXu+0B0LxasJk69hiwu4UeHD0RldS8v+JhuhN7CTxrAaI8nyYOPNofh7vgVBc9xPMNvHEiEr0hKq28hPYGPSX8fbwwrZm81VQAPAs8+jwb1BA8b+bFvHxeHz1Y6RK8mce1PB/aOjykBge8Uzt5O1InSzzj/Ma8opuIPOI1wbsGCLi7cwSsPLBlEDzy7DQ88YCQPCREGD2q3Is8n4Klu0E1qLyY1A67TKOXPARj1LsdxMQ8PXKtPHF947yCP268U4iZu7z6ILxTg3E9VOZ/PC7SUb19oxE8U8hWvAkHEbxolyO7EUMYPb3g37s2yy697LGAPLwJXr2cD4q8k2zFOyR2YbvDz5a86rQCvEO9f7wu/m+8ZHynPM2rrTvA6ac8lrMGvXW+rLzc65q8DTZ+PLS4Yjw9q8A81vCWvMEBuLjV+AU9jByJPCWGdz2Jdv87C/dnPL41VjwLh8M8B0UQuzcFjDzDJlu71elEvFdiCjw2IDs8pr3LvNiwury2PxU9S24HussCDrzsqx28uwMRvJMKzTw1SwM8RraTusIv7rtVsJ88PSZGvT5eQ7ysNSu8dD2UPI2SkrtcCNM8ikMxPQAoFjziagK9Ak2fPI8VIbzkvF48VJBjvHN0+bylrpk8oemyvDc3cDyN/9e8AZUlPXovyLwUIgY61D3iu6N0Rzx7lDc8Bgs2vaXJmjy5NaI8ZnVWO+tL9rzCOvk8jY+ePCsJObtSWcA8hmzSvPBCDj0voDk8xa+aPL96ZzyjqbE5cDC+O+hMbLzVIG47wghjuwJD3bxmwla6hIk0vPtcGLuPbdO8y+uJvMlkBLzjIqy8lkhkvJXRGz2raQC9s3ppPM5OszzhyHM8NXvvvH1/irsVlMG8so+rvEgyJ7znydm6ufd3vJN5Bb03NYy8h8bkvF/jmTsEFI68DccCu9ZvGj3CgwC77VS8vAr22LyOo3s65HNqvO6ZAjy4PE49OvofuhhHaz2M5+g6q9GVvAm1iLvGYJU8IC/FPCglDTzD+Lm8lWjXumAjLbpWnG87ERYoPX2I2DstFVe8+iwGvTTZsbu5Lrw8fBJYvFiRHDp9VNE8tP3lvJq+IjsDAL48gWWdPBpv+bxpBi2837ubO1kyAryhH9M7gPU6vfAPI7xFzkm7jG8NPL68HrwK4Fc8qVzzPGwH3rsrAnW83CpfvCKDkjxgpZg8PnNuvMMJcTzxUQQ938AQvZwWkLxPirM8/6RnvJvP/7viWYY7fi9svJAozzz65KY8bkSoOoPRzTzB2SQ7fFU/PMQTx7x+P1i7QQ+Cu9dXlzvfTgC7VhS+O76YWLy6F4g8XjE2u0nwTzzjq/+8KOn1vHXxxDr6RDQ8N3yDvGz8mTw1ZQy8NisevPdtM7x3P0+8+VmUvM/EVTsI5/y6mBzOu6/1jjxvo9i8ERm4PEPQMD0blc079kORPJJc0zs9z708OhJOPGWX0juqxfg76eCcvNJw4Ly8bUA9ZcwDvcGp9jzIRg47ImncPPxpbTvPoZW86dIHPBgHYLwxzCi80H5MPNTeJzz+E9e74df+O2v/PT33XeG8WVSFOv7ujjyCIo488NTkvNVQuDw3m4o7JiNBPMTCyjyKCUq8s/cLvZNYzLzL/1c8G7nWO4lBJLzUJgw8gQC7PM1jbTzrvJ086LQqvCebLTt0iH+8KI1JvPncKjvPlPq8yzHaPO01hTsFfAc8ixrMvFSZ07rZbb06JbNrPEAEkrzuFQ48XVqlPOTmmjxl58c7npZCvUrsHL1m6KK8WkMYvPjgBL0aXiC8fmpyPNmqiDwaNam8hcBHvVROvrv2Ere8+vtgPJ0TQbze19Q8p/YPO1d0yjz9yKW806nfOkCFdDtRJWe8ca7FPOvyLLxCCx89vODKO30YTrxHVgK9FFo1vKmHHzwzXZ68O8+7O3aTh7xINlY8LGmDO+95NbwKwws9BbMuO+9v1rqJP1082p+pvBCbQbwofh08E509PSApLDy1MME607dNu8L2pzw59dk754aDPFCJZ7yBwk48eyhsPCeBN7zE9gC96FPGuxNd8zzorgs98OS0O2p+K7uGg9I7gzNBvIPzfjveExw9H7uevAzbG71ZZMo7mluHO9DVr7z/YK+82vkIvb3SzbpyC9U7A05nOjTq9zzU++c8CUr7PFzK0juQR727ccwMvV+T87z4a4S8x21zO9hGSbt8Zs68kVpXuwv7DTvZE2u8Roc4PDnaLr2Cxgq8eDzOu2UrH7ui0we9d0CvvK8SJzwEH4o8R4EhvLggELw6S8i7TbyTPPAjezyNAji86jyBPJd7j7q88Sq8s4YGPWhs6rsvYTY84ssQvUlCxTwxAhu8hanju0hAUj3rN648L8i0u6jsALwSCkA8TysOPQaSMryzdVi883UVvVQP3rw49hO7BUAvPYJ3o7vTBx09K6c8PEocGTz7Eaq7fh2OvIvd2rslabI8TiUAu9c2X7y1YFE8fx4qvFSirTwgf5o793ITPImTqDujh0y6PX+pOT/9Hr266n27X/MdPZPHWryLuoy6CJnVO4iM1rzkfaA8ToSjPHVmb7weJyo8BMnRvEj+8zyQpMA8JuybPL1bHz1HEKo6F8u6vGWbiTwH6Ra8hDhVvPtxbbw+jxO9jpKNPOS0BzzeA2a8JPPQO6ofJjwdW6I8D9m6vL7qCD0b8K86LXhqPZCPxbx5ini8zTtDPZ4GAD2H9r+7IX67PJewxLssjr+8bvj5O/nMBLwc+P47ufS2uxMwrjxdqiU7xvZLvHhSR7uQs0s8eCk1PDs/G7zbJIU8UxKJPHKvKTxU3LE8XVFXPH1Wq7sCft86kmbpO+1m8jz38Oy87m/jvKdnUDo9phM9YBFdPMA9kjvasEg8+lT3Ox2S/LwM4be8VG+PO9ZaNT0nMR29xqJ0vHI0XDwjRYk8NZ0/PBXr2bwbWG47GEkrvCQC3LqFSz88HVHpuVwh+TuQKGS8ZTqfvEJsCTwpufA8VZR4O0w4mDycfo06fVcCPc81ALyY27u82zZPvArCIDzcbsk8Z4/pPOaQpDzpQ4I8st4DPH1PMrt2spU8NrznO4c12zudfXA71iGqvJ1shzuPJAE9Q708PQZAvjuoOwg7GfkiPHRETbordoY8NZfuPACWZDwNPBo7IWPnvIFmM70z19e8fNS0PH+6RryC4Xs8lQ1mPHQ3nLx5qY67vVAWvJYjsjgVMMq7+d0gvQqftzyVyDo87BULvMOiP73pseo8xiltvP2p1LzfOxk7fsODvJIKM7ore9u6ieefu6diDbz/5ES9KpZ+vH9v1byfJIO8Vi88ODnmezs7LG8853upvDbKmzuYmb88HOOFO/O8WLyMITk8XDwkvWS6Wj20n8O8R/xwuwkFxzz/yVo7L8a0uz40oLz5N6+8vfQ/vGebZ7vtfKE7LKEFvGgQizx9B8s7HAMRPOn8tju7+iS8gbW2vHrUmzxD+Ym7vfXovBZ3hbwEmhE823DYvJYrLLttQR28uUS0PIr5u7uxdVc7ZiZLvCeWeLzO82+7zlUQPWYYL71gjQ280sKrPH99p7zN4jc8LpIgvKf1bbtORj+7TrAAvCb7oDupKc28gmMyPafEvbyGxac8pkWOvL5icTxJ7wQ9dyVPPM6uLruA8p48xb01vV7iZDysLIm7oH3IvGiYxjxeo/E8xgIIufb6YDxpIAM9VZG/vCIRoTzufjK9F6ZMuyS3Or353eO8M5DjOsKyzTyQrhS9zYd0vFZDyLyYV4A8gcZcu9ylwbstZ1W8mcjNO48vkrx63AC9tcZSuWqwPLyjuDg7ttLavDEJJDyy9Jy707TKO0nVOD0rfWO77SA1PN0cDrxLECK9isV4vDSbvDy98Jy8KluUu8p/urzTuiC9K+qNPG5uGD0OUeU8dk+rPObVADxI4tU8xuCevM4mEj3nFjM80jDRvJoGkzvtaaK8ENyrPIdUhzx0JQS8PpLcPOPmNzxFqLY88x6JPP8dLTx6v4E7UKSuPN/lDLu17uO824cmParcTLupjZO8Yb3IvJHLhDsBFio8soMEPC+knTty3ly8+NCEPM8jijyrZmK8qYX2PKIkPbvQ5oK8U8IEOyybF7xwLCA9cqRpvDTWobxbBy48fx+BPJsYlLtxYDq9HqxbPII0IDtPWOU6Bx7Gu8kSRjwp5oE7oEHCOhDlHL3UrU87zj5EvAWfBz0PJ1C8sf4TvNkU8bvl0Jy75w7oupjuIjz36lu9zfiaPO5ec7x8WJO8dE8pvMJtUzluZ5C7mg7dvPrcmjsjSVo8gdBdPBwpwrgEBsu8GlAuuxdcRrqgzkS98uEsvPhIOLyUcS67SjffvC7H5bxOmBA6JG8TPOBeFbxVOVg8nNp4vLl7hDwWSwI9PS7OPGGg0LynRyk8arJ6u6G8XzxVGrY8ZqReu1X/lTxojGa8hYrLu+YkLTy15y28k7ABO/q+eDzAES+8BNlWOyXMujz6B6O8EJIRvXBBUr1+q5M8lMo4u6cLmDygyD09qC8bu0cIKDwN7xY80SNjOzFakDyj4bE71OlwPMsRWLx34sG8S4f+vAM0KLvIJ9S7094pO0AzID3f0kw72K76Nr6iJjzSpyg6YtMJPBpKCz2WK9i7Jud3vHYaRDwMlkS8EWwFvPD0krtDzUU8+EUYPNA+KzxLCVg5rXObu+F40Dsunhk6+MBovMvxLz1gTa48SB5+u+Ix6Dx96GK85TCVvF3197umzw87FC17PFP+H7xB3iU8oP/IvL3PgTskWfm7JIQWvVKP2Lw/hO+7jq2YvDdA5jtEmHE613OZu7TJjrulmt481wH5vFsbo7z2E4k8kW6LvD0najyMRWe85DkhPG2v8DoGjiC8gisyvPmCYjvx6+47zgruPHs5JDw584s8pO90vESGpLumSO88k50cPfHJhbvKkM+8lxuau/LyPzzKI5C8IQmSu3OMhLwsLNm8lwg1Pd+ZkTsyMqQ8htcEvNiopbyA/X88gGoOvWxegzyfjlU6edvVu93FDjw0mqa8CBL8O/BN/rgKG4U5pvN6vEra27yFaxI7NOIFuYrOHbuytQc81NUkvFyj8bpfv+q8ehyLu3eh4DsixEA77QicvHPT7Lxm+F26S2CmvJ1e7rsm7V28inICPD7dHj1qmAo8hlsGPEK9ZTw5SuY8xaozPA8407wT9Oq7s7XTvFbg4juH9Z+4ofRzOxnZuTtuY5E8u4Q7vK7ctbw0Ew+8LQ6kvIiDMjz52Wk8VnXFuyYWnryDuTu82bFxvO7Nrzwbeo68c/UGPJYms7wmRsy6qEJgO92vJr0WP2U8VBX8PGJafDuOUhM8vS0hvDAfNDxg/TA8IkQbvezFU7v4Ake8ue7EPC7+Ibp5B6882qgqvLn3ULwnHBw9zknKOiGEPL2hwsU82qDbO0FJabxMDmi7WaW2PPQVpjy/3Gu6ZIXKPJDaf7weDCW8u+v3PIiIIT2A8ZI6DaLSPBoV4jxN7qE7PEhvPAydErxJgTy7sdCVPP9iRDwIYoE8RmEmPMcqnbyb0hW6LrTtvJlU4zreL6a8ehafvD+XhTv+VcQ8zAN0OhAkgjsTD5g823IwvIR9FT17W7M8OpYsvAE2SDwZMJe8LJOpOhPpc7ucOIM8QYgNvbbB7rtnMiw996fpOqS/DTxBjfI86Q2rPDrRF7sJY5K7+m4RPfWVzTwn6+06W+OHvMpyeTyRaoo8V7vAumT1jzt53dO8coA9PGlgo7yZoQ09uEhmO8mukDvTWhY7m2+nvE26Fj0FYVs8QHfevICwNbuZUok8JuimPKW4FDyNnR09lhZPPDEzkbog0Yc7AzpTO3/bnDzPiDg8V02JPAMb+7wz6Aq9ToqEuwUuAL1Awsm8mGQWPcxDi7zlipG8SzIgvLjQ4bw7fzq8du3IPEWF6bszzJw8RVMquu27nbwsUtG8e3EAvM1rLTwUffO8x1YvPJ/kQjvKj7+7WPe9PI5ChbxFlSG8+zLBO7foID0hG2W8htbEOzorEjqr8Je7B30KvX2MeLz2wUc8753oO4jJ2jvLF4S8TwrIPDqEIz1Akxs7EkfCPBGPj7zV7BG8wMojPO5yPTzzfMu7jNzZPJeF+LyzXJO8hwCpOYLQZbzoGfC7eendvOXugjvbaDk8xGkbvVYNkbrFdz279q+YPLxzD7vHCjs8LfoGvVwPYTw9v2w7AEWPO8Zd0bvZaZI6tvKTvMgtMrxtpLm8KXH5PK6tZjxLegi8FBcfvPJ4Zrwvs/a7FU+qPNdPl7we+D88wpEiPdZllDxLCps8xhmSPE9W7bxF+ka80HCCPJEhrrt8mCA887Rzu3Q10TyoIqq8pC/0O0ApmTyO2cG8Gh92uzhsC7xtnNG8sgEIPX0/Fb1zdPS8J18NvE+zjbwPXGA8KYCZvNiGEDw7a4w8+WLQvJs2GTonY7u8sKsZvfSTnzz9Lt+8fvTTvKfejrm5Tw299VJ1vGicxri8SQE7Z/ayum4XWzzmRJu8XCiUPL/mR7zFNOc8Qld9vJebwzovQDm6JwPLPAduRTw6x2M866W/PAJXjLsBV1i8pwHHPCkkOrxyTXs6EMNEPLKYkzsT0HQ6qCQIubplejz+HCw8bksUPGOJHTw3+9i8jtjKOtD++Lxz3Hg8AdWFu7DQgLxBWp+8yhEwPPBAVLtibuS8sIWNvDJ6G70+5dg8eODDu5/aJ72mh3Q8Q/HVPCni2LybDYU8tEm8u4wK3TxooGc7NfWhvHO+ZLxGeBu9CQmLvPiJwzseeJ66M1VDOySlnjz+KJG6Q36DO82thzsJjKC79tpTPK33eTx4YKQ8wp2yOr2IBzsih+67f56/PMFQwDxdbMY8uzSAO4aiArzxPmU8ga0bvEDyWbxO3jS8EhgGO+JznTwQsXA82Y7Ou90gyDuW7EW8oNOnOlss2bw+oe67J3APPfINDb0AcGS8nQ7dvE+gmLwn0tm8Vc+6POdjoTv5RwY8ktfdvEbC/Dtqwxq9L2O4POh5yTq7mp88wRv/uyDYSbwwcp87VQlAPXyTkDuAoti6QbZNPd0gpjwZdt884aiQPM/eFDwBjpi64DrUOv0HSTxc1ZW8lf6JvC0ybDo+MT+9jss9OzwSQ71jd0w8c01RvDmnBjypyme8jSlku9hwgbwea2C95bc+uxMPALsLqb47CHnhu3s41byPEsY8hBaKuuOlzjw8wi28oRoWvTan6jzrXSi9pqcrPDz7ZLw+tuw7uieqO2ztejp+lwW8RAUVPZLT5rsB3uG8PR4cvONZRDwSPgi606TcPKlay7tQMkQ8i/GrPIBxP7tFAOE8wNxdPKkgfzyGdIu8bKOWPNDa1bvJxdG88FkFvIReobp6LKO8KkamPFiqMDuPjqk7JM/NvKqkebyGoP+7j3+nOXiRmjz7gbQ85/TLvGNDojqBqfC7vvqkvJ09xDvWnii8D8vpOov3/rumz7Y790TUu1CS5bwPMUK9e28gvBQgBDwZYyS7ZZn0ugqD6rwH8oG86C9Uu9CkNbsPKoq8Av0qPN4KorykSiI8OPBWPAROEb0vG0I90OfBvOSWs7yGY768gTQ7PDr7y7tK6mE8szSQPLkrcrwKA40703LNvEB4xDvoiP+7VpKZvDehSjw6uva7iajrPKsbuzzbpFc5u9WROiNZlTxX3K67VuahvMu6YrtloNa7v3NBPKlOjryT22O8LnvVuyEegruN0z88ecjXuwn6cb0DCNo86jMPPXdqIbyhgJs7l46dvPdDoDoGSEY5m2s5u0ato7tg3YE87NsCvbW8abwbHea7Y9qFu+LJxjz7zey7o0pUuwjLObxNfjW8+7hzu1+kIjyetMA8QclPPNbaNztP38Q8/AjbvO5fdbzBY0y6gZFPvDNg7Tr2zLo6ufbXPL3smzxC5eW8ThCEvAgDED19k+Q8XJulvOUwvjtydPa8aDdtvJ0RBTqb4ke8qBiIvLJqSTx0SK87/dTDvM3xwDtyiqU8tvRfOtcE8rzX0hE8LkJDu8X1hDo5vqO8C9mTvJmLEDzCtoW7bjWKO5wA6rwr1/K7RluFuhqYODw2qq08va+fPLd3yjwGA3E8sO/hvCWtyLudP/m7WsnzvGIWqbtZgw69mlYZukAjCL1PeeY60Iv3PBdOl7sniZq7u/0ZPSf0JbwrY6Q8yeAAPJXOCDyMW7u74f6tPG8f7rwXd+i7sjUhPBln3rupsj28puf8umECLLyCH5O8ns7PPFJUxzyHnZW8auE3vUeRKbtRjIW8aCwkOyjolbygUAG8tmFIvKm3CD3ChHw8KoHEuri5pjsP+wE95/4APVz/5byhQss7OQynPOUbnTxkSSC9uwa4POhndLyum/m6jfCvPB2UiTq3Cdm8EuHzOo1c8jtWX1I8HX07vKuFobxpMIq8K9MkPdOMHTxtsk+8xOklvMJoOr3wvk06vMRiPLiXNrxuqOq7PGtKvMuqmjzdQgO7YFU8PaoEArzmvxi8tGM0u9A34TwGrQY8Qu6CvMOSo7v3bUY8/KQ7PeCrvTyjI0s8boibvJWYGb2an0W8Mgu6vOpQKzzsMvo8ocgKvJ4j7TzmTQ68LTt4PE5S/TvB1LS8aTsEvBa1AbyzH+E7mxAEOtvLYLw0ecQ5LdiFuxGzS7sqIYi842ioPDn3mryqWp28UwD/O/ixyLsFlQ89ooeUPP5eorq/Eg88z5Ylu9jwibyjlkA42dnYOzfznbxCMGk8soM4PGOXrryYZmu8wk7YvEgj8juxIQQ8TTPjOhFrnrzpYtW8TzS0PJW5gzyNhoE79MVUuvgMkzyNO587/X/MPL9osjwhK0+6L0XAPBw1ujwNzee5T1L5PJNnjjpu6QK7kNWkPIg+ubzPC8o8fD03vGMryDsp60w8JXwnOYLBW7yBR8A7T2goPZOGCrz+odQ8fdL4uBeShbzAArq8WXdyvLcE+ToW4As8ZX4IvBdNw7wf8tC88mgZvc5Ygry5QJk6kFvHvDXaojwX3gg99Dj/O6kKoLx1BLi8+60qPOr5obwYxGA8RD+Iug==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1016'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We need to propose first question to investigate about "What is Python?" That's simple. Probably need
-            to ask about defining Python, maybe "What is Python?" But to investigate, we can ask "What is Python programming
-            language?" That's the subquestion.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What is Python programming language?","reasoning":"The original
-                question seeks a definition of Python. The first investigative step is to formulate a precise, self-contained
-                question that obtains the core information: a definition and basic description of Python as a programming
-                language."}'
-              name: final_result
-            id: call_uloaz5bi
-            index: 0
-            type: function
-      created: 1770981660
-      id: chatcmpl-407
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 129
-        prompt_tokens: 365
-        total_tokens: 494
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2826'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is Python programming language?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '507'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"Python programming language definition","limit":5}'
-              name: search_and_answer
-            id: call_wusk3506
-            index: 0
-            type: function
-      created: 1770981663
-      id: chatcmpl-960
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 36
-        prompt_tokens: 621
-        total_tokens: 657
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '108'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python programming language definition
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 5
-        total_tokens: 5
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3305'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"Python programming language definition","limit":5}'
-            name: search_and_answer
-          id: call_wusk3506
-          type: function
-      - content: |-
-          [356242d6-4917-4933-b4fb-9813e8172bb2] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_wusk3506
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '539'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- **Question:** What is Python programming language?  \n- **Answer:** Python is a programming language
-            widely used for web development.  \n- **Cited_chunks:** [\"356242d6-4917-4933-b4fb-9813e8172bb2\"]  \n- **Confidence:**
-            0.8"
-          reasoning: Provide answer.
-          role: assistant
-      created: 1770981669
-      id: chatcmpl-40
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 78
-        prompt_tokens: 732
-        total_tokens: 810
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3731'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"Python programming language definition","limit":5}'
-            name: search_and_answer
-          id: call_wusk3506
-          type: function
-      - content: |-
-          [356242d6-4917-4933-b4fb-9813e8172bb2] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_wusk3506
-      - content: "<think>\nProvide answer.\n</think>\n\n- **Question:** What is Python programming language?  \n- **Answer:**
-          Python is a programming language widely used for web development.  \n- **Cited_chunks:** [\"356242d6-4917-4933-b4fb-9813e8172bb2\"]
-          \ \n- **Confidence:** 0.8"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '685'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We must output a tool call to function final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language widely used for web development.","cited_chunks":["356242d6-4917-4933-b4fb-9813e8172bb2"],"confidence":0.8,"query":"What
-                is Python programming language?"}'
-              name: final_result
-            id: call_39cgf1je
-            index: 0
-            type: function
-      created: 1770981672
-      id: chatcmpl-899
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 86
-        prompt_tokens: 835
-        total_tokens: 921
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2734'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>What is Python programming language?</question>
-                <answer>Python is a programming language widely used for web development.</answer>
-                <confidence>0.8</confidence>
-                <source>Python Guide</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '713'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We must give answer directly, using evidence. We have one prior answer that says Python is a programming
-            language widely used for web development. Provide answer accordingly.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language widely used for web development.","citations":[],"confidence":0.8}'
-              name: final_result
-            id: call_x39mseof
-            index: 0
-            type: function
-      created: 1770981675
-      id: chatcmpl-483
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 74
-        prompt_tokens: 427
-        total_tokens: 501
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_populates_citations_history.yaml b/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_populates_citations_history.yaml
deleted file mode 100644
index e080e943..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_populates_citations_history.yaml
+++ /dev/null
@@ -1,844 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: fBiAuV0A37x9cpu8MldhvIUktrrMji48KD6OPfOBDj0Lc7Y6WPwBvSlEkT3KlU08+UnWueLDDrxz8nA8U+YsPI0eejyjjmq9X0mavPDKEjk+94a7VK3RvNBIOT1dW248mtCCvKn4/zy5HpG8LXOlvOoWCbvTNGg9N6jMPDAgXDw7Cxi6rN5/PCun6TnyX8e83LsIvG54bro0T3o73z15uyx3UDv5b7Q7SZQ7O2Jl7jzFsCy8aJ9FvY17zjvWNsY8QkWQOVIzAL2hfc66lO7Wu1bdzDyvpK+8zCfhPC/dPD2vEUo8l6abu2ObnjtMWLi8tI25u/LZgbrnnAm8x/DTu4eJzrtSDaO7gnAgvKuvdzrdsRg80tmNO6erlrwDShc9PvXxu47uszxefMk7xAcivKhJKrw5gxU9CLywvN3VdDt6yom8AGiCOftU+TtfT1Q90VpEO4n+wDtEwuQ7imCUumizIruXFBK8IpevuvQQXTydluA69z+zPGLS4jpsDZU7cl9EvJ+2BLxMko+7ck2HOtDlAjyxNU28G23mvPyAYrsUBpu8a3pxPG2TLbwLhnM8/yK8PNaEvDuhGZQ7VSgpu4FUWzszPpU71nyBuoYyJzw9NBm8ykrcPLycwjuBjlI9SaaUu2KNjTze8MU7TlkNvc1R8DuMGwy99YZXPOz1rDwbZ0+8kOUhu0QM7jtP/v+7ho+nOpjYXrwD0Sw8z/JIvGa2EjteJpa8D11Tu2/msTwylCQ8QlL+OzaBEjrJSd08LNUiPCa+fL0st6u8mRMMPQUPVzxjuQA8ivqwPF+LrbxCMo471nrQO4TVCzyf/JI8gjbsu7UTrjwgSba6FCVLu/8n1TuGrOq7xX6rPIKkFj1+oW28tyNbPNVINr3npzi8issFO1GiB7xatkA84cEYu+VCWrxK+x08yENSPFQcFzxsQ527ZPJCOv8Fwzmhtle7mBXcOytakjyZpQm9uyYqO4kBajujsj08cZIcvLMP7jzDjgA8HK/3Om0sjLxbI7e8y6nLutr+PTwffci7cnhMvD962LxWD+O89/iWuwHmszxxVNI8NWYLvKCH57s9Ywm8dbxJO83DOzxgKOO5kASYu/5V8DpyaOA5G0ljPJOp2bvangm76bWqvU2JGLx0Xe27VACKvIeco7w+bjw7rE8qPb6r8TrqnxQ8n4Bvu3MDZjoHCBG8D9eVPAHMWrung3o8warhO4f+F7wfqxs9YzzqusrDVztNiaO8RoeKuw5RbDz8YoE6BJmau+khEzzN+v+7KO4PvIAL5rwslo474TsiPFmDhzzYZfW7mjX9u42KLry8N4E8oxORvHMTLzzTBzQ7rw6XPJWdM7zw/pU7ukcTvBVR6rvwW608MkU2vFkeMDwh/ea5nt9tPBPPtroe5uq6mCmhPHcFKr1y26083J/lurJamjxqzwm8maKsPJKqsDtKQTs7VW4mvCdhAL0GImy8FxqVPKFa4zvA5Oc6aMdAPN5uA7yPsmi7GSTMO0zIVTzl1IO7vLuoO1NjHD1P08g6enp/vBBO1Ty694u6RU56vdeSsDxK6zG7eEPzvPyAGj24E7U80ZSvukA7Dr3LD7K8y/rjOlrTHzyj0Hy8PVjEOy4YWjwMniA8yD+luxhgODx4L2Y8QTFRvJHStbwWNW+8XKr2O+heHLt6H9w7vK0MuiSDuLtj1UG8wYPpuxtlVTqcfpw8lPA+vceTYry4MjG7J/wvvfUwrLzBANY7GWEovJbdbbxsKGS8YiQpPSSrxTxLGti803K0u2+LoDw7Iq+77ToAvJUhCT3YrgC7vTt3vEiGIDyWqfw7f+C2vC3oo7x4XOQ6rYJYvBdkQzxbO5282RxRvNHc5bzz+D+9bzbju73Cab1tqTe87W2dvGdNg7zfN4m8NWTxvPWQhrt0+rC7OcigObgqujyXdcI77NO1PPzAAzzkNgi6/LTovJEwWLyh19I7Emm4uu0DejsAKEu8r+GJPPc2MzxXmQo7hzyZvCxL2LxiGni8euUZPH7ozTuL6G46oeDouwTciLztV7U7cWuKOn9IADzojYW8xAcxPCQfkboGmZY8C2+Hu8o11ruYSBk8l62xPFnkTr0icGc8+fmMO1L3tTx4lBW9QO/DvFtc97yyDA+8WbT4vNfEsTzhNt08efW5vNyNYjzH5zE9yoHUO/sGTbwSgK88Cy6nu+jPXjwR4ag8K+ibPAnHlDzkkuQ8ly1KvAXafL02BkU6MWMIvMuyH7tH4wg83d7jO0UbmTyY5Cg8/FePvMZ7VTxcUWi8Ow54PM3hkTusQAq8aF4XPcbtqjx/TbC8OFp9PP2Lar0qXAw9Iiuou1CCoju8HnK5Z+E8vCRiEz1KpgS7DSYivTslxDvTUFw8KM0VvBy8Zzmtz1a9D+73uJW4U7uj3aY7yGU7vT+/Or1F/t88WTjQO64hVbzWFKS8vLXOOhZUVL2fd648xcSkvAUKLzzUkge8cwmIvDDfA70PzZ08hwRLOjGvpTwTkZe8cqZou0OBubz1Ef472zREPCVPMTsn/Qk7dNelOjh6jrzbOca7NI/buxLwNTyGYX28RVnBu98OIrwEdbi7JabUPLQhIz2k2TI5/XgoPObvKTrlGY+84R4FPBJ4j7yn3U283Z7hOguWhDxyF568foBzO1WS2jpYQrc8iIYbPDqTVzvBdR68cnvkvJONDDywags934lXvPQMtjvV7F081WuCvBD5FDy7QqI8f2T/vBAFlbsytS67TOhcPY9nwzuLNbw7xfTEOxiRwDx9QSm9XQz4PO3iD7wrHKC8dEwDPOKL8TxK44m9K0grvETh1Lw0Axq9YPv3PPfyDr37Id47Or68vMbDNzws9+05k7uRPKOBK7t5D5M8mFNRvMTWeDw4Wp68ZsRpPKNXgLs5/Hm8xWcPPEVpIrs7AYA8a9NpOtJffjzH4FO89ZpJusriCr1C7LG7mLHDPAWYETyClqs8RMAFPbJZAT1eBgE91ynnPALEmbvQtjC8hEeOu8Q/irx5pD68ASQTu2y1T7x0DAs5ujZGvFczTbyBTHg8xuSpOyBlOb03Eow8q7MLvYHHBDvQ/RM86ceTPM7NIrz1pDY8yBBnO+5kybwHqco8bAekOblzFL1H/2g74mdlO8a+D70IFQS9kcOtPNDy67sdjU08oHbVPOMQXbvh0gS9kxpEOZ5AkLw6gkc72JAhPTNNELw+wBY7i5qWPIYp5rsldBM8uqPJvCmKljz/5Qc70wN2uhE1C704Sxy7eRqQvIxO4DyhzTA9fJIzPJAsnzrb4Z68W6XWu8PM47o3xMk6Whl3vNQ9mLxYnqq9B94wPeYYqLyGWEe8+NcPu0yORb3oIna8YjgKvHADtLzZh1I8OwUnu8QmLrylwQA8gzjXvJ/ZILwiaCe9FQ3uu7PbgDwk3c0807SuPMtUdLus9eI84V/xO0NZgLx6zvC8hiQuvYOeADxn//66P2GCvNnSg7wCQxG8zuqeu4wlz7zO8xg9ara1PK7dML3eE+E8+Ft/PPUM0TtrAt48iUVfPE5k6zpJP/M5QRFevewCgryvjmg9ry8WPPoWsjzWdfa8WiFUO9pZa7r20sQ6K/u4u+eOLz1hOx49twawPDXej7v8Mdw8IJTxO6f8ebzbGAO80e/fPHyNEj3Hs5K94xdFPOeL4bsT8865w8oavB1GIz1fOww8QEGzO90hATt52pq8BrPrPHmMyjymuPG76cbju8qNEbwd6EK6xwtrvDe2HbxFdaG8sJSBPbfti7wO6Jo8KHAnOwv5ILtm5ae8OlcAOq8LD73Tens8vxgeunag4DyOHZK8FZthvExk0bzjbDS78e5kPOuDAj23Lcu8N+gFvAxrpTuEzqO8jv3QPEVPkrzLKNa6tEAdPVOAtjyiJxi6IbZAvOA5rDwTQKa8jJuGPK/0ljtjtqI77YtoPPnGTLyGZSA82wK4vAVgqzxyf8Q8KuafPE01/7mXJ9u7tooGvIDC8juHmak8zt2vvNyBuzyen4q8AYcavKWt1DyV2CA8tLaWOj4iDTxQn4039q2zPPZdlbxvNR+8U7zoPNGN8btOPqy8BShVvDD6Oz3JeQQ87X/PvM+yRTw5ye28HlK5PB2EjTtTkya7RHzEuwnLcjxsIEI8sUFPvG0lBz0EElO7SnPAOawQBL184Lm8xqoTvV9JtLsWEa67iyeKvOZwIb1+zOm89o6nvEoc2TxycY08sUggvaVCCr2McCo9hKCcuBGFtztIkGi85hRGvUYHmLuv+4k82z6HvMUWCzzcp8m6V7xvu0SEqjy0Lws8BInEvAWipjp0HAM8NhwEPP+DBLzISoK65Hx3O4nQED0i2s48d/LqPF+l5bxmgYW8ERNyPZuZQTwfPDA8KITRul6cm7vIBj28nZU7vOEG0Dxu/Tu8QNGgPMyjt7uAta074fMhu9zw7zn/a3c5UrB4vJoPobyf6Hq77s1HvWA7KL0Vi1q8vKOtPOtrSDzxjXq85povvErLIzz7sya8VDcOu6W5lTytrKw8afkVuzsMSLwQklw9lPGKuxJY5DpCjYg8B3yaPA+OzTz2B/S750qpPPwy4DwYddU4POY4vTkF5Lx0Xfy6CiF5PFAPJ7w9IR88i4GXu+0B0LxasJk69hiwu4UeHD0RldS8v+JhuhN7CTxrAaI8nyYOPNofh7vgVBc9xPMNvHEiEr0hKq28hPYGPSX8fbwwrZm81VQAPAs8+jwb1BA8b+bFvHxeHz1Y6RK8mce1PB/aOjykBge8Uzt5O1InSzzj/Ma8opuIPOI1wbsGCLi7cwSsPLBlEDzy7DQ88YCQPCREGD2q3Is8n4Klu0E1qLyY1A67TKOXPARj1LsdxMQ8PXKtPHF947yCP268U4iZu7z6ILxTg3E9VOZ/PC7SUb19oxE8U8hWvAkHEbxolyO7EUMYPb3g37s2yy697LGAPLwJXr2cD4q8k2zFOyR2YbvDz5a86rQCvEO9f7wu/m+8ZHynPM2rrTvA6ac8lrMGvXW+rLzc65q8DTZ+PLS4Yjw9q8A81vCWvMEBuLjV+AU9jByJPCWGdz2Jdv87C/dnPL41VjwLh8M8B0UQuzcFjDzDJlu71elEvFdiCjw2IDs8pr3LvNiwury2PxU9S24HussCDrzsqx28uwMRvJMKzTw1SwM8RraTusIv7rtVsJ88PSZGvT5eQ7ysNSu8dD2UPI2SkrtcCNM8ikMxPQAoFjziagK9Ak2fPI8VIbzkvF48VJBjvHN0+bylrpk8oemyvDc3cDyN/9e8AZUlPXovyLwUIgY61D3iu6N0Rzx7lDc8Bgs2vaXJmjy5NaI8ZnVWO+tL9rzCOvk8jY+ePCsJObtSWcA8hmzSvPBCDj0voDk8xa+aPL96ZzyjqbE5cDC+O+hMbLzVIG47wghjuwJD3bxmwla6hIk0vPtcGLuPbdO8y+uJvMlkBLzjIqy8lkhkvJXRGz2raQC9s3ppPM5OszzhyHM8NXvvvH1/irsVlMG8so+rvEgyJ7znydm6ufd3vJN5Bb03NYy8h8bkvF/jmTsEFI68DccCu9ZvGj3CgwC77VS8vAr22LyOo3s65HNqvO6ZAjy4PE49OvofuhhHaz2M5+g6q9GVvAm1iLvGYJU8IC/FPCglDTzD+Lm8lWjXumAjLbpWnG87ERYoPX2I2DstFVe8+iwGvTTZsbu5Lrw8fBJYvFiRHDp9VNE8tP3lvJq+IjsDAL48gWWdPBpv+bxpBi2837ubO1kyAryhH9M7gPU6vfAPI7xFzkm7jG8NPL68HrwK4Fc8qVzzPGwH3rsrAnW83CpfvCKDkjxgpZg8PnNuvMMJcTzxUQQ938AQvZwWkLxPirM8/6RnvJvP/7viWYY7fi9svJAozzz65KY8bkSoOoPRzTzB2SQ7fFU/PMQTx7x+P1i7QQ+Cu9dXlzvfTgC7VhS+O76YWLy6F4g8XjE2u0nwTzzjq/+8KOn1vHXxxDr6RDQ8N3yDvGz8mTw1ZQy8NisevPdtM7x3P0+8+VmUvM/EVTsI5/y6mBzOu6/1jjxvo9i8ERm4PEPQMD0blc079kORPJJc0zs9z708OhJOPGWX0juqxfg76eCcvNJw4Ly8bUA9ZcwDvcGp9jzIRg47ImncPPxpbTvPoZW86dIHPBgHYLwxzCi80H5MPNTeJzz+E9e74df+O2v/PT33XeG8WVSFOv7ujjyCIo488NTkvNVQuDw3m4o7JiNBPMTCyjyKCUq8s/cLvZNYzLzL/1c8G7nWO4lBJLzUJgw8gQC7PM1jbTzrvJ086LQqvCebLTt0iH+8KI1JvPncKjvPlPq8yzHaPO01hTsFfAc8ixrMvFSZ07rZbb06JbNrPEAEkrzuFQ48XVqlPOTmmjxl58c7npZCvUrsHL1m6KK8WkMYvPjgBL0aXiC8fmpyPNmqiDwaNam8hcBHvVROvrv2Ere8+vtgPJ0TQbze19Q8p/YPO1d0yjz9yKW806nfOkCFdDtRJWe8ca7FPOvyLLxCCx89vODKO30YTrxHVgK9FFo1vKmHHzwzXZ68O8+7O3aTh7xINlY8LGmDO+95NbwKwws9BbMuO+9v1rqJP1082p+pvBCbQbwofh08E509PSApLDy1MME607dNu8L2pzw59dk754aDPFCJZ7yBwk48eyhsPCeBN7zE9gC96FPGuxNd8zzorgs98OS0O2p+K7uGg9I7gzNBvIPzfjveExw9H7uevAzbG71ZZMo7mluHO9DVr7z/YK+82vkIvb3SzbpyC9U7A05nOjTq9zzU++c8CUr7PFzK0juQR727ccwMvV+T87z4a4S8x21zO9hGSbt8Zs68kVpXuwv7DTvZE2u8Roc4PDnaLr2Cxgq8eDzOu2UrH7ui0we9d0CvvK8SJzwEH4o8R4EhvLggELw6S8i7TbyTPPAjezyNAji86jyBPJd7j7q88Sq8s4YGPWhs6rsvYTY84ssQvUlCxTwxAhu8hanju0hAUj3rN648L8i0u6jsALwSCkA8TysOPQaSMryzdVi883UVvVQP3rw49hO7BUAvPYJ3o7vTBx09K6c8PEocGTz7Eaq7fh2OvIvd2rslabI8TiUAu9c2X7y1YFE8fx4qvFSirTwgf5o793ITPImTqDujh0y6PX+pOT/9Hr266n27X/MdPZPHWryLuoy6CJnVO4iM1rzkfaA8ToSjPHVmb7weJyo8BMnRvEj+8zyQpMA8JuybPL1bHz1HEKo6F8u6vGWbiTwH6Ra8hDhVvPtxbbw+jxO9jpKNPOS0BzzeA2a8JPPQO6ofJjwdW6I8D9m6vL7qCD0b8K86LXhqPZCPxbx5ini8zTtDPZ4GAD2H9r+7IX67PJewxLssjr+8bvj5O/nMBLwc+P47ufS2uxMwrjxdqiU7xvZLvHhSR7uQs0s8eCk1PDs/G7zbJIU8UxKJPHKvKTxU3LE8XVFXPH1Wq7sCft86kmbpO+1m8jz38Oy87m/jvKdnUDo9phM9YBFdPMA9kjvasEg8+lT3Ox2S/LwM4be8VG+PO9ZaNT0nMR29xqJ0vHI0XDwjRYk8NZ0/PBXr2bwbWG47GEkrvCQC3LqFSz88HVHpuVwh+TuQKGS8ZTqfvEJsCTwpufA8VZR4O0w4mDycfo06fVcCPc81ALyY27u82zZPvArCIDzcbsk8Z4/pPOaQpDzpQ4I8st4DPH1PMrt2spU8NrznO4c12zudfXA71iGqvJ1shzuPJAE9Q708PQZAvjuoOwg7GfkiPHRETbordoY8NZfuPACWZDwNPBo7IWPnvIFmM70z19e8fNS0PH+6RryC4Xs8lQ1mPHQ3nLx5qY67vVAWvJYjsjgVMMq7+d0gvQqftzyVyDo87BULvMOiP73pseo8xiltvP2p1LzfOxk7fsODvJIKM7ore9u6ieefu6diDbz/5ES9KpZ+vH9v1byfJIO8Vi88ODnmezs7LG8853upvDbKmzuYmb88HOOFO/O8WLyMITk8XDwkvWS6Wj20n8O8R/xwuwkFxzz/yVo7L8a0uz40oLz5N6+8vfQ/vGebZ7vtfKE7LKEFvGgQizx9B8s7HAMRPOn8tju7+iS8gbW2vHrUmzxD+Ym7vfXovBZ3hbwEmhE823DYvJYrLLttQR28uUS0PIr5u7uxdVc7ZiZLvCeWeLzO82+7zlUQPWYYL71gjQ280sKrPH99p7zN4jc8LpIgvKf1bbtORj+7TrAAvCb7oDupKc28gmMyPafEvbyGxac8pkWOvL5icTxJ7wQ9dyVPPM6uLruA8p48xb01vV7iZDysLIm7oH3IvGiYxjxeo/E8xgIIufb6YDxpIAM9VZG/vCIRoTzufjK9F6ZMuyS3Or353eO8M5DjOsKyzTyQrhS9zYd0vFZDyLyYV4A8gcZcu9ylwbstZ1W8mcjNO48vkrx63AC9tcZSuWqwPLyjuDg7ttLavDEJJDyy9Jy707TKO0nVOD0rfWO77SA1PN0cDrxLECK9isV4vDSbvDy98Jy8KluUu8p/urzTuiC9K+qNPG5uGD0OUeU8dk+rPObVADxI4tU8xuCevM4mEj3nFjM80jDRvJoGkzvtaaK8ENyrPIdUhzx0JQS8PpLcPOPmNzxFqLY88x6JPP8dLTx6v4E7UKSuPN/lDLu17uO824cmParcTLupjZO8Yb3IvJHLhDsBFio8soMEPC+knTty3ly8+NCEPM8jijyrZmK8qYX2PKIkPbvQ5oK8U8IEOyybF7xwLCA9cqRpvDTWobxbBy48fx+BPJsYlLtxYDq9HqxbPII0IDtPWOU6Bx7Gu8kSRjwp5oE7oEHCOhDlHL3UrU87zj5EvAWfBz0PJ1C8sf4TvNkU8bvl0Jy75w7oupjuIjz36lu9zfiaPO5ec7x8WJO8dE8pvMJtUzluZ5C7mg7dvPrcmjsjSVo8gdBdPBwpwrgEBsu8GlAuuxdcRrqgzkS98uEsvPhIOLyUcS67SjffvC7H5bxOmBA6JG8TPOBeFbxVOVg8nNp4vLl7hDwWSwI9PS7OPGGg0LynRyk8arJ6u6G8XzxVGrY8ZqReu1X/lTxojGa8hYrLu+YkLTy15y28k7ABO/q+eDzAES+8BNlWOyXMujz6B6O8EJIRvXBBUr1+q5M8lMo4u6cLmDygyD09qC8bu0cIKDwN7xY80SNjOzFakDyj4bE71OlwPMsRWLx34sG8S4f+vAM0KLvIJ9S7094pO0AzID3f0kw72K76Nr6iJjzSpyg6YtMJPBpKCz2WK9i7Jud3vHYaRDwMlkS8EWwFvPD0krtDzUU8+EUYPNA+KzxLCVg5rXObu+F40Dsunhk6+MBovMvxLz1gTa48SB5+u+Ix6Dx96GK85TCVvF3197umzw87FC17PFP+H7xB3iU8oP/IvL3PgTskWfm7JIQWvVKP2Lw/hO+7jq2YvDdA5jtEmHE613OZu7TJjrulmt481wH5vFsbo7z2E4k8kW6LvD0najyMRWe85DkhPG2v8DoGjiC8gisyvPmCYjvx6+47zgruPHs5JDw584s8pO90vESGpLumSO88k50cPfHJhbvKkM+8lxuau/LyPzzKI5C8IQmSu3OMhLwsLNm8lwg1Pd+ZkTsyMqQ8htcEvNiopbyA/X88gGoOvWxegzyfjlU6edvVu93FDjw0mqa8CBL8O/BN/rgKG4U5pvN6vEra27yFaxI7NOIFuYrOHbuytQc81NUkvFyj8bpfv+q8ehyLu3eh4DsixEA77QicvHPT7Lxm+F26S2CmvJ1e7rsm7V28inICPD7dHj1qmAo8hlsGPEK9ZTw5SuY8xaozPA8407wT9Oq7s7XTvFbg4juH9Z+4ofRzOxnZuTtuY5E8u4Q7vK7ctbw0Ew+8LQ6kvIiDMjz52Wk8VnXFuyYWnryDuTu82bFxvO7Nrzwbeo68c/UGPJYms7wmRsy6qEJgO92vJr0WP2U8VBX8PGJafDuOUhM8vS0hvDAfNDxg/TA8IkQbvezFU7v4Ake8ue7EPC7+Ibp5B6882qgqvLn3ULwnHBw9zknKOiGEPL2hwsU82qDbO0FJabxMDmi7WaW2PPQVpjy/3Gu6ZIXKPJDaf7weDCW8u+v3PIiIIT2A8ZI6DaLSPBoV4jxN7qE7PEhvPAydErxJgTy7sdCVPP9iRDwIYoE8RmEmPMcqnbyb0hW6LrTtvJlU4zreL6a8ehafvD+XhTv+VcQ8zAN0OhAkgjsTD5g823IwvIR9FT17W7M8OpYsvAE2SDwZMJe8LJOpOhPpc7ucOIM8QYgNvbbB7rtnMiw996fpOqS/DTxBjfI86Q2rPDrRF7sJY5K7+m4RPfWVzTwn6+06W+OHvMpyeTyRaoo8V7vAumT1jzt53dO8coA9PGlgo7yZoQ09uEhmO8mukDvTWhY7m2+nvE26Fj0FYVs8QHfevICwNbuZUok8JuimPKW4FDyNnR09lhZPPDEzkbog0Yc7AzpTO3/bnDzPiDg8V02JPAMb+7wz6Aq9ToqEuwUuAL1Awsm8mGQWPcxDi7zlipG8SzIgvLjQ4bw7fzq8du3IPEWF6bszzJw8RVMquu27nbwsUtG8e3EAvM1rLTwUffO8x1YvPJ/kQjvKj7+7WPe9PI5ChbxFlSG8+zLBO7foID0hG2W8htbEOzorEjqr8Je7B30KvX2MeLz2wUc8753oO4jJ2jvLF4S8TwrIPDqEIz1Akxs7EkfCPBGPj7zV7BG8wMojPO5yPTzzfMu7jNzZPJeF+LyzXJO8hwCpOYLQZbzoGfC7eendvOXugjvbaDk8xGkbvVYNkbrFdz279q+YPLxzD7vHCjs8LfoGvVwPYTw9v2w7AEWPO8Zd0bvZaZI6tvKTvMgtMrxtpLm8KXH5PK6tZjxLegi8FBcfvPJ4Zrwvs/a7FU+qPNdPl7we+D88wpEiPdZllDxLCps8xhmSPE9W7bxF+ka80HCCPJEhrrt8mCA887Rzu3Q10TyoIqq8pC/0O0ApmTyO2cG8Gh92uzhsC7xtnNG8sgEIPX0/Fb1zdPS8J18NvE+zjbwPXGA8KYCZvNiGEDw7a4w8+WLQvJs2GTonY7u8sKsZvfSTnzz9Lt+8fvTTvKfejrm5Tw299VJ1vGicxri8SQE7Z/ayum4XWzzmRJu8XCiUPL/mR7zFNOc8Qld9vJebwzovQDm6JwPLPAduRTw6x2M866W/PAJXjLsBV1i8pwHHPCkkOrxyTXs6EMNEPLKYkzsT0HQ6qCQIubplejz+HCw8bksUPGOJHTw3+9i8jtjKOtD++Lxz3Hg8AdWFu7DQgLxBWp+8yhEwPPBAVLtibuS8sIWNvDJ6G70+5dg8eODDu5/aJ72mh3Q8Q/HVPCni2LybDYU8tEm8u4wK3TxooGc7NfWhvHO+ZLxGeBu9CQmLvPiJwzseeJ66M1VDOySlnjz+KJG6Q36DO82thzsJjKC79tpTPK33eTx4YKQ8wp2yOr2IBzsih+67f56/PMFQwDxdbMY8uzSAO4aiArzxPmU8ga0bvEDyWbxO3jS8EhgGO+JznTwQsXA82Y7Ou90gyDuW7EW8oNOnOlss2bw+oe67J3APPfINDb0AcGS8nQ7dvE+gmLwn0tm8Vc+6POdjoTv5RwY8ktfdvEbC/Dtqwxq9L2O4POh5yTq7mp88wRv/uyDYSbwwcp87VQlAPXyTkDuAoti6QbZNPd0gpjwZdt884aiQPM/eFDwBjpi64DrUOv0HSTxc1ZW8lf6JvC0ybDo+MT+9jss9OzwSQ71jd0w8c01RvDmnBjypyme8jSlku9hwgbwea2C95bc+uxMPALsLqb47CHnhu3s41byPEsY8hBaKuuOlzjw8wi28oRoWvTan6jzrXSi9pqcrPDz7ZLw+tuw7uieqO2ztejp+lwW8RAUVPZLT5rsB3uG8PR4cvONZRDwSPgi606TcPKlay7tQMkQ8i/GrPIBxP7tFAOE8wNxdPKkgfzyGdIu8bKOWPNDa1bvJxdG88FkFvIReobp6LKO8KkamPFiqMDuPjqk7JM/NvKqkebyGoP+7j3+nOXiRmjz7gbQ85/TLvGNDojqBqfC7vvqkvJ09xDvWnii8D8vpOov3/rumz7Y790TUu1CS5bwPMUK9e28gvBQgBDwZYyS7ZZn0ugqD6rwH8oG86C9Uu9CkNbsPKoq8Av0qPN4KorykSiI8OPBWPAROEb0vG0I90OfBvOSWs7yGY768gTQ7PDr7y7tK6mE8szSQPLkrcrwKA40703LNvEB4xDvoiP+7VpKZvDehSjw6uva7iajrPKsbuzzbpFc5u9WROiNZlTxX3K67VuahvMu6YrtloNa7v3NBPKlOjryT22O8LnvVuyEegruN0z88ecjXuwn6cb0DCNo86jMPPXdqIbyhgJs7l46dvPdDoDoGSEY5m2s5u0ato7tg3YE87NsCvbW8abwbHea7Y9qFu+LJxjz7zey7o0pUuwjLObxNfjW8+7hzu1+kIjyetMA8QclPPNbaNztP38Q8/AjbvO5fdbzBY0y6gZFPvDNg7Tr2zLo6ufbXPL3smzxC5eW8ThCEvAgDED19k+Q8XJulvOUwvjtydPa8aDdtvJ0RBTqb4ke8qBiIvLJqSTx0SK87/dTDvM3xwDtyiqU8tvRfOtcE8rzX0hE8LkJDu8X1hDo5vqO8C9mTvJmLEDzCtoW7bjWKO5wA6rwr1/K7RluFuhqYODw2qq08va+fPLd3yjwGA3E8sO/hvCWtyLudP/m7WsnzvGIWqbtZgw69mlYZukAjCL1PeeY60Iv3PBdOl7sniZq7u/0ZPSf0JbwrY6Q8yeAAPJXOCDyMW7u74f6tPG8f7rwXd+i7sjUhPBln3rupsj28puf8umECLLyCH5O8ns7PPFJUxzyHnZW8auE3vUeRKbtRjIW8aCwkOyjolbygUAG8tmFIvKm3CD3ChHw8KoHEuri5pjsP+wE95/4APVz/5byhQss7OQynPOUbnTxkSSC9uwa4POhndLyum/m6jfCvPB2UiTq3Cdm8EuHzOo1c8jtWX1I8HX07vKuFobxpMIq8K9MkPdOMHTxtsk+8xOklvMJoOr3wvk06vMRiPLiXNrxuqOq7PGtKvMuqmjzdQgO7YFU8PaoEArzmvxi8tGM0u9A34TwGrQY8Qu6CvMOSo7v3bUY8/KQ7PeCrvTyjI0s8boibvJWYGb2an0W8Mgu6vOpQKzzsMvo8ocgKvJ4j7TzmTQ68LTt4PE5S/TvB1LS8aTsEvBa1AbyzH+E7mxAEOtvLYLw0ecQ5LdiFuxGzS7sqIYi842ioPDn3mryqWp28UwD/O/ixyLsFlQ89ooeUPP5eorq/Eg88z5Ylu9jwibyjlkA42dnYOzfznbxCMGk8soM4PGOXrryYZmu8wk7YvEgj8juxIQQ8TTPjOhFrnrzpYtW8TzS0PJW5gzyNhoE79MVUuvgMkzyNO587/X/MPL9osjwhK0+6L0XAPBw1ujwNzee5T1L5PJNnjjpu6QK7kNWkPIg+ubzPC8o8fD03vGMryDsp60w8JXwnOYLBW7yBR8A7T2goPZOGCrz+odQ8fdL4uBeShbzAArq8WXdyvLcE+ToW4As8ZX4IvBdNw7wf8tC88mgZvc5Ygry5QJk6kFvHvDXaojwX3gg99Dj/O6kKoLx1BLi8+60qPOr5obwYxGA8RD+Iug==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '811'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need first question: what is Python? So ask definition.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What does the term \"Python\" refer to in the context of programming
-                languages?","reasoning":"The original query asks \"What is Python?\" The first step is to clarify that the
-                question is about the programming language. We can directly ask about its definition in that context."}'
-              name: final_result
-            id: call_n7vdp89o
-            index: 0
-            type: function
-      created: 1770981593
-      id: chatcmpl-992
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 97
-        prompt_tokens: 365
-        total_tokens: 462
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2869'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '528'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-              name: search_and_answer
-            id: call_taijxk2o
-            index: 0
-            type: function
-      created: 1770981597
-      id: chatcmpl-497
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 40
-        prompt_tokens: 631
-        total_tokens: 671
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '129'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - definition of Python programming language term referring to
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 9
-        total_tokens: 9
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3369'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-            name: search_and_answer
-          id: call_taijxk2o
-          type: function
-      - content: |-
-          [3efaa37c-c1f0-43df-945b-784a9d20bf80] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_taijxk2o
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '663'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- **query**: What does the term \"Python\" refer to in the context of programming languages?  \n- **answer**:
-            \"Python\" refers to a programming language. It is widely used for web development, among other applications.
-            \ \n- **cited_chunks**: [\"3efaa37c-c1f0-43df-945b-784a9d20bf80\"]  \n- **confidence**: 0.92"
-          reasoning: Only one result but states definition. We'll answer.
-          role: assistant
-      created: 1770981603
-      id: chatcmpl-608
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 111
-        prompt_tokens: 748
-        total_tokens: 859
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3917'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-            name: search_and_answer
-          id: call_taijxk2o
-          type: function
-      - content: |-
-          [3efaa37c-c1f0-43df-945b-784a9d20bf80] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_taijxk2o
-      - content: "<think>\nOnly one result but states definition. We'll answer.\n</think>\n\n- **query**: What does the term
-          \"Python\" refer to in the context of programming languages?  \n- **answer**: \"Python\" refers to a programming
-          language. It is widely used for web development, among other applications.  \n- **cited_chunks**: [\"3efaa37c-c1f0-43df-945b-784a9d20bf80\"]
-          \ \n- **confidence**: 0.92"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '764'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use function final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"\"Python\" refers to a programming language. It is widely used for web development, among
-                other applications.","cited_chunks":["3efaa37c-c1f0-43df-945b-784a9d20bf80"],"confidence":0.92,"query":"What
-                does the term \"Python\" refer to in the context of programming languages?"}'
-              name: final_result
-            id: call_d6rfhil0
-            index: 0
-            type: function
-      created: 1770981608
-      id: chatcmpl-832
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 104
-        prompt_tokens: 884
-        total_tokens: 988
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2822'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>What does the term "Python" refer to in the context of programming languages?</question>
-                <answer>"Python" refers to a programming language. It is widely used for web development, among other applications.</answer>
-                <confidence>0.92</confidence>
-                <source>Python Guide</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '894'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need to answer what is Python using evidence: prior answer shows Python refers to programming language
-            widely used for web development. Provide direct answer.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language that is widely used for web development and many other
-                applications. It is known for its readability, flexibility, and extensive libraries that support tasks ranging
-                from simple scripting to complex machine‑learning projects.","citations":[],"confidence":0.95}'
-              name: final_result
-            id: call_xu9bt153
-            index: 0
-            type: function
-      created: 1770981612
-      id: chatcmpl-961
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 102
-        prompt_tokens: 446
-        total_tokens: 548
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_with_session_state.yaml b/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_with_session_state.yaml
deleted file mode 100644
index e080e943..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_with_session_state.yaml
+++ /dev/null
@@ -1,844 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '811'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need first question: what is Python? So ask definition.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What does the term \"Python\" refer to in the context of programming
-                languages?","reasoning":"The original query asks \"What is Python?\" The first step is to clarify that the
-                question is about the programming language. We can directly ask about its definition in that context."}'
-              name: final_result
-            id: call_n7vdp89o
-            index: 0
-            type: function
-      created: 1770981593
-      id: chatcmpl-992
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 97
-        prompt_tokens: 365
-        total_tokens: 462
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2869'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '528'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-              name: search_and_answer
-            id: call_taijxk2o
-            index: 0
-            type: function
-      created: 1770981597
-      id: chatcmpl-497
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 40
-        prompt_tokens: 631
-        total_tokens: 671
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '129'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - definition of Python programming language term referring to
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 9
-        total_tokens: 9
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3369'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-            name: search_and_answer
-          id: call_taijxk2o
-          type: function
-      - content: |-
-          [3efaa37c-c1f0-43df-945b-784a9d20bf80] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_taijxk2o
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '663'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "- **query**: What does the term \"Python\" refer to in the context of programming languages?  \n- **answer**:
-            \"Python\" refers to a programming language. It is widely used for web development, among other applications.
-            \ \n- **cited_chunks**: [\"3efaa37c-c1f0-43df-945b-784a9d20bf80\"]  \n- **confidence**: 0.92"
-          reasoning: Only one result but states definition. We'll answer.
-          role: assistant
-      created: 1770981603
-      id: chatcmpl-608
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 111
-        prompt_tokens: 748
-        total_tokens: 859
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3917'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What does the term "Python" refer to in the context of programming languages?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition of Python programming language term referring to","limit":5}'
-            name: search_and_answer
-          id: call_taijxk2o
-          type: function
-      - content: |-
-          [3efaa37c-c1f0-43df-945b-784a9d20bf80] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_taijxk2o
-      - content: "<think>\nOnly one result but states definition. We'll answer.\n</think>\n\n- **query**: What does the term
-          \"Python\" refer to in the context of programming languages?  \n- **answer**: \"Python\" refers to a programming
-          language. It is widely used for web development, among other applications.  \n- **cited_chunks**: [\"3efaa37c-c1f0-43df-945b-784a9d20bf80\"]
-          \ \n- **confidence**: 0.92"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '764'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to use function final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"\"Python\" refers to a programming language. It is widely used for web development, among
-                other applications.","cited_chunks":["3efaa37c-c1f0-43df-945b-784a9d20bf80"],"confidence":0.92,"query":"What
-                does the term \"Python\" refer to in the context of programming languages?"}'
-              name: final_result
-            id: call_d6rfhil0
-            index: 0
-            type: function
-      created: 1770981608
-      id: chatcmpl-832
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 104
-        prompt_tokens: 884
-        total_tokens: 988
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2822'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>What does the term "Python" refer to in the context of programming languages?</question>
-                <answer>"Python" refers to a programming language. It is widely used for web development, among other applications.</answer>
-                <confidence>0.92</confidence>
-                <source>Python Guide</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '894'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need to answer what is Python using evidence: prior answer shows Python refers to programming language
-            widely used for web development. Provide direct answer.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a programming language that is widely used for web development and many other
-                applications. It is known for its readability, flexibility, and extensive libraries that support tasks ranging
-                from simple scripting to complex machine‑learning projects.","citations":[],"confidence":0.95}'
-              name: final_result
-            id: call_xu9bt153
-            index: 0
-            type: function
-      created: 1770981612
-      id: chatcmpl-961
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 102
-        prompt_tokens: 446
-        total_tokens: 548
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_without_context.yaml b/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_without_context.yaml
deleted file mode 100644
index 4cd2237a..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACore.test_run_qa_core_without_context.yaml
+++ /dev/null
@@ -1,1092 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '1724'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator planning the investigation.
-
-          If a <background> section is provided, use it to understand the conversation context.
-
-          Your task:
-          1. Analyze the original question
-          2. Propose the first question to investigate
-
-          For simple questions, investigate them directly. For composite or complex questions,
-          you may decompose into a focused sub-question. For example:
-          - "What are the benefits and drawbacks of X?" → Start with "What are the benefits of X?"
-          - Ambiguous references should be resolved using background context if available
-
-          Output requirements:
-          - Set is_complete=False (you are just starting the investigation)
-          - Set next_question to the question to investigate
-          - Provide brief reasoning explaining your choice
-
-          The question must be standalone and self-contained:
-          - Include concrete entities, scope, and any qualifiers
-          - Avoid ambiguous pronouns (it/they/this/that)
-        role: system
-      - content: |-
-          Plan the research investigation.
-
-          <context>
-            <question>What is Python?</question>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '974'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: |-
-            We need to propose first question to investigate "What is Python?" Could break into "What is the Python programming language?".
-
-            So next_question: "What is the definition and purpose of the Python programming language?" Provide reasoning.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What is the definition and purpose of the Python programming
-                language?","reasoning":"The original question asks broadly what Python is. To structure the investigation,
-                we first need a precise definition and overview of its purpose and main characteristics."}'
-              name: final_result
-            id: call_wgdgv8qq
-            index: 0
-            type: function
-      created: 1770981617
-      id: chatcmpl-144
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 119
-        prompt_tokens: 365
-        total_tokens: 484
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2860'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and purpose of the Python programming language?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '573'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"query":"definition and purpose of the Python programming language definition purpose Python programming
-                language","limit":5}'
-              name: search_and_answer
-            id: call_wdpqyg2p
-            index: 0
-            type: function
-      created: 1770981622
-      id: chatcmpl-681
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 45
-        prompt_tokens: 627
-        total_tokens: 672
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '174'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - definition and purpose of the Python programming language definition purpose Python programming language
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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BTz25ZE8BOn9vNYPB72ZDw+8EptVu9v0LD09kqc8QjvkuxDP6DvuH508YL+fvAeZBj3njoG8hsmFu4NnCrzIT9K7qrWYul/Ul7o7XC67BjuauygCujxZpms8gs9EOmsl7bx7NYi8WMUcPJGTUDsCltk76JavOnHKkDosakc7xf2hu2oO37siUx46n4qCu7z4Hr3fJou8xuoaPXVVTbwE1ik8FdwSPeiPDTshjFi8oDB7vInRH7xRibO84fPTu3BnAjpk/S090DwQvHsNADwPeOK81RlXO0JTsbucZfQ7wR5sPM3HTLuI3Qg9AkKvvMaxWDw4eOY8cn6rO3yE3LzygLY8pqHAPGaBzbskphE8JJcxO+cuBz3TI5a8n1RlPTcoobwu1d+7Uw+POhgWBjz8qF28yVP1vCrmobvI4F48lHKEPEc2xryuv7c7UDdAuxXTg7u3/nG892oivYYBGz15dwg7+5Z0PPI2CTyAEwK8l1AgvOaGobuqICa7Ga4yvJhZbLxJWFI8v0GDPDBzBrxCtzA8hDzHvBOkrryE7lw7XS3iOY1T4Dy0HqS8KtbRvE7Wd7sUnwU9l70yO/l1rry4FA89+QHsPNGkBD2XwW48OA93PF2CCr1EuqM84+vOPG8UyTx9dLw8M+yDOzYZUztL3Sa8l18GPcxI7zsocAS9ajwkvZmV1rpEza87/PW+vOMzMzze4hi9jbeIPNH7ibxEKtY7XZqBPOercLyJTvU6G89GvOHXb7yP+9W7uqnHvG2sVDvDhDe8qzDquhD0hDujUwq8DrPRPINiFj3sHaO8F3UGPHmX5jy0DrA8L9kIvH7Whbzh8tk89BEFvZljmLwUa4K7lpt1vF9h0Dvxtc476LqKuczrjjz0ZGo88mpdO5GHXLrjpzK8UNysPNR70zrzpbI8IMpTvOIPhjy0T6e8QURFOt8OcLyEWkA7GpzevGAAmLsqxUq9zqAavLUOo7poIbQ799dkvO6jWjzK/GI7uRiOPBh53zs4UqS8pr4AvVKaLDzqAhY8jljfPKlZRjyHgse8C/Y/PfKl5Tx5oJa7ZDNPPMBg3TsoZoc8wsXqOkALZrtRqqG7nkkTvND2gbu9RPM8enz4vFDO5DxBedc8KEGDPKFuhryqHDm6jkb7POyfuzsxNws87ITEu/YjDj2U8nk8wBDDOzYSADwHlw28vPf1OjUv8Tw2pKw8IJtevS189DxhH0g8ZXw1vGKwlTtJcdK6bScKvAZpGb29fyI8cNoJPGN91DyNvNA8HkVIPBysWzwTIOo7q5bZvD1QFT0hxp28u8joPMnmV7wwajA7JumNu0b9YTuwUi28GGSaO1LqiLtax5U8EAdPPFEfF7xLr+K6rCi9O3irwrvVlYG7FkEkvUfVWr1jlQG9Q5IOvFvdM717EY88JK5BPATPNzsfpOo7XBEJvRnYxzzhHRM8/vpHPFwxbrzkvbw8N5QivJ2P9jzqtxC8iGdou4kX7Dvr+fG8ljsMOx/tWjvu0kC7v/icupNnADzFfr27OFE2vGoDGzzT2Le6TLOLPAyjRjxr3lM8O9dmPLjCAzzLExw9W2bNOzjtEDoMloY8yxFqvJEg6Dvjgr88Sew+PTJ5tbxYDo885dvrPLZW7TvoIxM8iI/9OwqXlrvWkRE8W6jeO4Zjs7wSwt68a1F4uh8f17t08Ni7wgmBPNxwWjzL1/08xKpmvMWYGju6++g88v/HvHtU6rz33Q29RDZ8PE+l6bsVpWw6zciqO+cKCDy8kmq8L+l4PGM8QTyeMsA8BXXoO3ML/zwYlo28Vg0mvCqJAb0m6oK8bYENO0s6CTxbWTk6GWCUPALZgzxNru671D9SOjSPy7wT8E696/QFPZOSuDvBgAC9JfecPAZCdTs6Hsa8mMpXvECYFr3TWse8B/I/PAO7MLq06q68uMeCvPZMvrtKVas6vzOYPK9czDszj7q792ZevEWlQz28+PY713rIvEpCmTxpfbE64r+AOZnXlLwumpQ8KrQGPdGrM7zJCKq8PSXXvB++CbydwY+8ADrZPD+D8DoxQL478vrJPGT7tDw4deQ7AD2FvCF0aDusgFs8pzAmO/RYd7yo7I08vzxKvHPmgjz7WDW8cD9mO4b0ljw/cq08JIUFPe81Dbyl1ui8ixUAPQDc5LzWjBY8y7qhObIJEDxxDSa8AYPFulfMlDyCbwg7ACXVu8BNJj1BmiA9ZlwoPeRJ6zzIOPE6o7g5PIvGtTwG+0M7QvMjvHOEcbxuare8T0f/POS+YDy9abu8sjQaPFMsMj2OsAs84qEIvR1UBj04+0E6bStjPQmuFL0kQUc7+5YdPU3bYDzCTHE87tmpPMTcODzI0x+83a72OzsASTswjbo8O5UnvJ/18DxU+SY9b3W0vECKyDy45DQ8r6DePLc6UbwuBSS8p85RvPZHxDzsTfU8EVGcvFWhADzKnwo8Ii01vGgMejy4Cy88LdZKuxo2bjtNi/U7VHZ7vJBIWbwyz3w8RMr0u1ZZjbznJg69wYZIvHEkAj0jAhS9MveQvNWtqzxmbfu6qPZoPHpaK7zj1M27pnEIvX9XhjqE2XK8m5EkPE9hTDuEpsA7jrR3vJM3yjz8v+c8AZzYO3PSJrzS4p08Ngt4vPXDAbw7zWK8cX6DPBmBHT2SW6Y8HRzdPBtsBD1G2II8jbOWO+oQoLz9S8w8nIWgO7HlzTvD1Vk8vYaWPDCJV7xJcSk8daiuPI4AJ7z+ecU77u0MPQ13YDxzf8M8Y0iBO/3qtzxb9uS6jm5YPON447xW5QS9vOz1PGrD97zY4nw8VhHHPAWqQ7wAU7C7qe6qu8HvKDvFp5W817AOvQ7gzjvhb5Q8Q84ZvHKhA735Oco81aDLPOFgH70owp48TCHmvIkHCj29Myq7tBIIPNns8zv271W9UY7LvJ6cu7wy3gi8W/+1PBhtxDxMqZq7XFL9u7xP/TscrJA8WVjaPJ3YzbyWRv66H7jVvEMb4TwSo/28cWLQPLcwT7uRagE8HZZiu2nh27sCpFq8KPy5OntytLyKdkA83tEDPPhcpryoUwK8NfvrO21o+7uhr/e8ozVpvAA8eDyv6Xq8/Bv0vJLesDwcG8y7lmfIvJvGmLw/ofI69NanPJC0HL1OtIc8sT/Au0+NFzxE9WS8IonbOzrYyLznuRs8LQEMPItlzrwUC0I8tbQYPA7GYbtq7wC8vUGbu6PGuTwMd8k7tt4BPOm2GTyqjz+6InA2vO/c4DwHHJk8bfo0u4DgPjt8O368uL4DvWoNuzwlSAK8bBkpvXS8dzzmXhQ9y6OtPDOW7bksmrk84CO8vLliIj0ItrO83vwFPBZoH728apO8msgovCeRzTwziUO81CZjvDmpJL1t4UW8Y2XUPCmgUzuSwza8kShBPO9QkLyeCt28j75FO57cUbzRc5c7KhvHvHBMRbzu1NG75bBoPMGXxDwGi1K8Z9RqOxvfKzwntrO8zsKrvGE4TDw5b9O8fXCRPGHhyzojjAi9RteJPLKVJD29OSs8LU3rPCRWj7qbWa67m7IMPBJBCj1lZpu8c/Z4vJUO7jyITgK9oplXvOG3xjzca1E7N74JuzlvdTyovIo823MbPAlUR7zXF2C7oQ0LPZnNmLuEjHA7ypIWPdtsjTpLee28wz8FvX+VWDw3fiQ9SLQJuwjeaDw1tZe7vbmHO2nTMD0R5Qq8+u8xvPNKBL2Hzu+8awFmPELan7zkf+08FFneO6YR3bvDm4g8wkJFOycYhTxm6uS877OhO4p7QrxUeiU8p11ZPL71XjsgERQ8UapqPHL7gruG4tI7wLYhuv9UhTx0tkM8m/MAveNUCDwUk6e7Zhm6vF7/ertMupq8ZR0TPY5Y0LxaU768hAKLPPwd9zv36qm8fn03vVmS5jvSUnU7CzrRu+jhwTzKJOc7ljPYvLe/UDxhm5i9g+fPu6D1Trz67D+81l+vvEaWALwdMaC72LfiOxZgDbzmBiW8GmJdOt6uhTyTlZk8gG/gPPGoCL32sHa76Mb2ux7zTDuKFgU85jDnu11sED37dNa8PJEhPJwj2Ty5C6K7ALz0ulK9iDvjHI+8Ur3ZORe1ojuUpSi9dcDRvJgVX70mwdw8MYvkPNCDFDnRoaW8CnQmuyiluTxTXLu7hrdEPOl24Dww1gc89zYGPIOV1zskQFw8pvDtvLBjKDzZ9kg8+YGFu2j67TzHzbI6AYmDPMvyjTxBIQw8VD6HPAH/jjzcd3S8wTdzOTeJqbxWGzu6R9BXvIzQQLwAA4U7PnuQvKE40Dy+xx+9qfFEOwwKQ7yCg7s7Z+C/vLegDT3jAt48q9UNvE4qcjzcf8E8Bp4TvLV5ADwTGYK8dWGLPNX+RbxrpJo8t8LGuwWRnjyXf4s8vR08vX1KobyoUoe8QhrNvFHqi7usRBe8fq0IOn6X6bthnZI5VN0cvG1FbTooJOS7Dyn6uwjtoDwdHYu81tS5O5uLlDx1S/W8ZtphvC9L/jtah0s8a5MLPP8/OTy6ibA86Fvuu+nMFDxbZJY8x/AjPR7+CD3l75W8I1HGPIbVILsdtai8L+YPvMjXFjvVhwS92aXkPBsBBL1oxRA9Zuneu5vP6bwRTSm8ibM8vPT1W7qWYas8sWbVurxdoruwXGu8Ve9bvKAYcjy8GGC8xf3JvJPNlbv3Yo08g4lCuyz8QrzbKAg9Lu0AvKXQnrzodnu8lzNdOzdJMzwnppY7xY3vvBBDI73ZV0W7itUEO93PIbxqA308giENvTs+6Dyrpic8Rkz/vDE4fDzlTBe8P7vcOz+VBb03xPq7dQ7UvC4y3jxROIi8dfjYvCAdKTuGpY86CFvUuQkqvby7tf26giMJvfA2CT0Apkw8FOKFu1xMjbspFJE8zySdvHmh5zxJbtS7sR/EuhjfI7w/rZC7kYanuvJgsryAuBw8gHqMOxJb2zoYHzi8oOy0vBgJrrv6z147IpUkPFFIgru2FsK8QBCMvKEIJDzXuRI8hxDbO91iaTuMWcI85VqzO+5WFLy9zWg8sv+vPDBoBry98TY8xIr2PFVRDTxDypk8l6g7PCD6iLshSmC8B3lCvNFPPj2M3W07dLb5OyAiB7w32ry8yw4ePBu77rujT5O8YcZaPHDGmLy14Qm84dvRu9LB37vH+Es7EM+Ju0SiJ7zhPcm8/4rKvLwkNrw2Uho8kJmUPFUXGjxS4xU7g1Svu7AIaT3z2zw9Shr9uV6wBD3OuwC9UdHpu7isaDvI14w8rhkFu4z+D7z1ZU889yVPvCkluryAS4w8O+3iPD2C5DtPsoW713ozPeSgKbxY3XS6PWaBO7O6pDyipDs6weNDvHJeAjrKs4y86/yYPOpB57yx0s88Fa5kPCJzRjpjBLc6zQgrO/cwiTxx6Ta8vurKOwCH7Lz/LN6823DwPCXpGLs5+QU8EAeBPC0kIrwCXGc8TPiePAQGBD1ttJe7Dp2YO2B0Zr1hIja8b/5aO5cFvTuHU1u92F8QPcS767z1wLS8pSoKvUTiFb3th/y8Xs9XO/9fS7sjFKA8GSxOO0hdY7yS8ke9cCiaOnIZBLwm+nk6Ni5AO5gr7zwWBP27WD7VPO+MjLy7Axe8H2k6PcVsmDxYTwe8dfUGPXYhhjxZueo8Vs87veu6U7t+00U9WV0VO4MZszrLxdG8dGt7PAAMZz2wWn88tueZPHqkML2BDc28mhIJPNussLyrZdO8ZV29OtkVDb09iPG7tvlcutX7n7zY0go8wuiFO4LLXDw6b2K8pZYvvFOjiztCAMa8B1/UPFEfkLzowd66GoGZvAy0/zrU1gk8BlgpPEYzB7yY48u8Zwffu/0H7LveTdC6er08PLxK0ruV9xa91g83vHmHHjzcDxm85h7NuROvAbv7I108EIDYPHuzSzzFV9o656ovPIgwhbx5M1k8YbSXPMAVgbwuADE8ERL6vMYafzzTCWi8ALdEuo22AjyMTK+8VXnyvLcrMbv+7O284DD1PLO8iLwNqfC8mRHdOzdCwLwOALi7tBGvvEsjWrytkCQ818QtvS7RejzI5Be90d0EvYIM0jro0n87kf/DvCVV5roWjHW8vAjGvBsWzjtAxOS7oUNsO9vUtDynJ2O8UUC8u9tzGLzgk908kRoPPPfMw7xGoYw71H6qPBe6+Lu2HRQ8LPQ9PRAfET1x45I8DoYIvBpp8DtTsqi85YifPDQIn7teT1q8gkuCO0oF2Tw68pY8cwa3PPp71zzqQp28s/fVPHi+J73oRT28Ps1luyrKmLueNZi63hBsuxWkJrzEEOy8PISHvD+J6TuMyIM8s4NVvPZ9kbybBsA8RvYevBJbYbz+29e7KMlHPKtQ97pNpLw6g9jNvGb5+ry5vJ+8/7pdvHDti7sfapu8L6+VPORSuzyByyg8Lz09PDBlFj3KIz29E7uGPCxGHDyqlHO7KV4VPF+nSTsHFAe80T8kPdWlxTsq8wI9x5+uPEHQFb1qLRu8EaN6O3peh7wIrqq62L+7O9XTf7sJmLO7DT9EPPmtcDtAqfC7bmWxvEVPGTvo/L26qhS5OVqR/Lzbqao8ipySvI0peDt4PRi8yK2AvN09B7sNmMM5iGQmvbJeFDxYAtG8Vv+GPGDAiLyL7BW55E1Mu6j/f7wq4Cw9cRjKPI36nbvHyiC8Qf/UPEKCXj3tDq88jaJAO0rCYzwtw/8896sXvZ04jbw82mM8n4H4u36XVzzDUBG9oarxu2u317zT6RG8bh6svC+WGzzJ1RQ7r2ubvGSPP7xp9oO9hF2+uj82RLtLZoy8T0p5PIGUrbwqVTU90r8+ve4GOD3PiC+7Q2U0vX8vW7yc+728hCYHPStDLTxauoo8WbURPE+aBTuhY8w6zFIVPQdp4Lw0fdu8jE7TvKVu8Ds3tFk86YQxPG+QkLzZU0U9gJ/xPDjxOztKC0g8VeGHu05AL7vRMQ68xAksPGZFrryqYe+8KS8st5INYDjh+KM7zM0SPFcW6jzlRbC88S/fvJt6uToZV9O6wquXvDHGHzwpO7w6hDmru1c7pzuAG0Q8SqqovD9fOTzGTBi7xnFYO+PuCDycjc+77NKjO8qGrDxVEvy8/0Dvuye7zjrQbTG5UeTnPPEzK7ymwTQ8TZYMu9jnCLtoAZu80WDZO3FtyLsIYYE7XOshPHjrYbz4l648oRrkvNzQn7nhoB28k7/1u0Q+VzyxCS882NqJPNVEszubhmG8H90gvAN4hTyVU9A7/qb2vKuQjTwGDyI8wxGRPH4mozxHKAW65IgvPLJQLjxWsII7aWbwu4LuEjw1Tyo8DEIIPHmaz7vGl5M8a62Tu2G2LrzAHI88bmARvAR8Rr3OB+o8TubCPDu3AT1Vb7E85uffvPO89bxSliw8TutaOxt7STwyw0O8M/GvvA0ICbyhQm+8UqSLu6Q1yjw72Ku8O763vEd0EDv61CE8uJStvClphjy7Wjs8EAH/vNeplbtNIeU70CbUu2tdjLyMgui7i/FdvJYqizshLc08auOQPNrVDbw9Sea8YVq7u2wukDw1tM67PXcRvJDaQLuPZaW8wUfSvNF2GjySYSe8QmeDPCRxyru0SD28bDQUvfA1FTuFrn08yD+su/D8e7xiCfA7Is1qvAvVwrtjqL27bFRJvaWCrDtbCtC8wh4QPdd4Q7xaYJo8c4GmvC+Rsrw4lM88mWDKPDSl8Dz9rr48iRQovE06NbuqO1g8n2wOvWdEWrwqyvu8NgySO/idT7xWFM885R/1u7/1mDs3CPI8pspKPEgwxjwpiho9FLFvPLksyjiO+Hi8c53cuoE6l7z5qoS7nZ8WPI7WdbzmlIO6LXmxvGkdsTv0T/U76t/yuwPtHDzLrDy8xMxMvRTZgTz4yzC8ozU1PMoGmjuVhLM82p0kuybGhDw5niU8QpbvOosh67mKVCk9zB6ZuvHbOL0Esz+8uyY4vDE58zuf8ru8qvytPCK8QDygCrg7B3FQOyFQ7TlpkY68RRIEPEOnnzpMUW+76gnSux04Rbtvbsy8Aty0PMKysjwRFLq72XxcPL6LprzX9wG7bzwCvGhtxLuIl4m85Qw+uockljzzKM47HzL9PFkbdTu5Q0s8p0DUvE6wvTxOe006n+7HvBiBCTx7o7A8YK9nPbIunjygPI+7oKnyOzA7rbxY6JG7wzA1PD7xijzesoc8PtdNO2RNFDwJLJi8H8aBPODNvTwdYoi8zgC8unBAbjzKRxQ891P8PGo3Drw/ngE8FD6yuw7D07wajjO8Fha2uvLe5rw4lc+8S+3gPCCQhjsQ2II8T7IwO12lrjuAY6m75sDJu2lGMrzPyW687+GuPGZicLwFoQO5hCyWvK2jGzw8vhm8jSm4O1xNkzyfQQa9dea4O3db/ToNLNS85R9JO4Z62rseCsk6r+UjOzL1Hz3W8vk7gBynvOsKmjvhyGY804pivOJLGTu8bEO7hZHgPEsSL7wC1xw7G7mqPBhvMrx2PpY8U3waO2ipNDsH74U7t/hDPBeWYbwH25g8qlzdO94ydLqUvM88A2IgvA1zT7y3wZk5UQxvvMGYNzyRSpW8cJ+cvA0HeDu+Xom8O2mru4pY8bp9fxI8+OmMvAqmrzsiFi88mxtOOwOW17t/7na8m2KCu5JwCrw6eb88g3ZCPA==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 14
-        total_tokens: 14
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3405'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition and purpose of the Python programming language definition purpose Python programming
-              language","limit":5}'
-            name: search_and_answer
-          id: call_wdpqyg2p
-          type: function
-      - content: |-
-          [7bdfdef5-e9e1-4e67-bced-ae93ece64396] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_wdpqyg2p
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '619'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Only one result; insufficient. Let's search again with more keywords.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"Python programming language definition purpose \"Python\" \"purpose\" \"definition\""}'
-              name: search_and_answer
-            id: call_ljxqxzrl
-            index: 0
-            type: function
-      created: 1770981630
-      id: chatcmpl-699
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 55
-        prompt_tokens: 747
-        total_tokens: 802
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '154'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python programming language definition purpose "Python" "purpose" "definition"
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3996'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition and purpose of the Python programming language definition purpose Python programming
-              language","limit":5}'
-            name: search_and_answer
-          id: call_wdpqyg2p
-          type: function
-      - content: |-
-          [7bdfdef5-e9e1-4e67-bced-ae93ece64396] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_wdpqyg2p
-      - content: |-
-          <think>
-          Only one result; insufficient. Let's search again with more keywords.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python programming language definition purpose \"Python\" \"purpose\" \"definition\""}'
-            name: search_and_answer
-          id: call_ljxqxzrl
-          type: function
-      - content: |-
-          [7bdfdef5-e9e1-4e67-bced-ae93ece64396] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_ljxqxzrl
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '388'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I couldn't find detailed information on the definition and purpose of Python in the retrieved documents.
-          role: assistant
-      created: 1770981643
-      id: chatcmpl-555
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 21
-        prompt_tokens: 877
-        total_tokens: 898
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4262'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What is the definition and purpose of the Python programming language?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"definition and purpose of the Python programming language definition purpose Python programming
-              language","limit":5}'
-            name: search_and_answer
-          id: call_wdpqyg2p
-          type: function
-      - content: |-
-          [7bdfdef5-e9e1-4e67-bced-ae93ece64396] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_wdpqyg2p
-      - content: |-
-          <think>
-          Only one result; insufficient. Let's search again with more keywords.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python programming language definition purpose \"Python\" \"purpose\" \"definition\""}'
-            name: search_and_answer
-          id: call_ljxqxzrl
-          type: function
-      - content: |-
-          [7bdfdef5-e9e1-4e67-bced-ae93ece64396] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_ljxqxzrl
-      - content: I couldn't find detailed information on the definition and purpose of Python in the retrieved documents.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '762'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to output JSON via final_result. Let's do that.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I couldn’t find detailed information on the definition and purpose of Python in the retrieved
-                documents.","cited_chunks":["7bdfdef5-e9e1-4e67-bced-ae93ece64396"],"confidence":0.2,"query":"What is the
-                definition and purpose of the Python programming language?"}'
-              name: final_result
-            id: call_z5lo9ert
-            index: 0
-            type: function
-      created: 1770981645
-      id: chatcmpl-576
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 100
-        prompt_tokens: 923
-        total_tokens: 1023
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2809'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>What is Python?</question>
-            <prior_answers>
-              <item>
-                <question>What is the definition and purpose of the Python programming language?</question>
-                <answer>I couldn’t find detailed information on the definition and purpose of Python in the retrieved documents.</answer>
-                <confidence>0.2</confidence>
-                <source>Python Guide</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1090'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need to answer "What is Python?" Use evidence: prior answer says didn''t find info. So we have limited
-            info. Need to acknowledge limitation.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a high‑level, interpreted programming language known for its readability and
-                versatility. It’s commonly used for web development, data analysis, artificial intelligence, scientific computing,
-                and many other applications. Python’s syntax emphasizes clarity and reduces the cost of program maintenance.
-                However, the evidence I reviewed did not provide a more detailed definition or purpose, so this summary reflects
-                the general consensus about the language.","citations":[],"confidence":0.6}'
-              name: final_result
-            id: call_tjsiarnm
-            index: 0
-            type: function
-      created: 1770981651
-      id: chatcmpl-935
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 142
-        prompt_tokens: 439
-        total_tokens: 581
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_qa_tools/TestRunQACoreWithPriorAnswers.test_run_qa_core_matches_prior_answers.yaml b/tests/cassettes/test_qa_tools/TestRunQACoreWithPriorAnswers.test_run_qa_core_matches_prior_answers.yaml
deleted file mode 100644
index 51d5dea3..00000000
--- a/tests/cassettes/test_qa_tools/TestRunQACoreWithPriorAnswers.test_run_qa_core_matches_prior_answers.yaml
+++ /dev/null
@@ -1,1113 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2012'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are the research orchestrator evaluating gathered evidence.
-
-          You have access to context that may include:
-          - <background>: Domain context for the conversation
-          - <prior_answers>: Previous Q&A pairs with confidence scores
-
-          Your task:
-          1. Review the provided evidence carefully
-          2. Assess whether it sufficiently answers the original question
-          3. Decide whether to continue research or synthesize
-
-          Decision criteria:
-          - Set is_complete=True if the evidence adequately answers the question
-          - Set is_complete=False with a next_question if important gaps remain
-
-          If not complete, propose exactly ONE high-value follow-up question in next_question:
-          - Focus on the most critical gap not covered by prior_answers
-          - The question must be standalone and self-contained
-          - Avoid repeating questions that have already been answered
-          - Include concrete entities, scope, and any qualifiers
-
-          Provide brief reasoning explaining your decision.
-        role: system
-      - content: |-
-          Review the gathered evidence and decide whether to continue or synthesize.
-
-          <context>
-            <question>Tell me about Python</question>
-            <prior_answers>
-              <item>
-                <question>What is Python?</question>
-                <answer>A programming language.</answer>
-                <confidence>0.9</confidence>
-                <source>null</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Output from iterative planning step.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              is_complete:
-                description: Whether research is complete and can be synthesized
-                type: boolean
-              next_question:
-                anyOf:
-                - type: string
-                - type: 'null'
-                default: null
-                description: Next question to investigate, if not complete
-              reasoning:
-                description: Brief explanation of the decision
-                type: string
-            required:
-            - is_complete
-            - reasoning
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '966'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'Need to decide whether evidence is sufficient. Only one answer: "Python is a programming language."
-            Not enough. Need follow-up: e.g., ask about key features, history.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"is_complete":false,"next_question":"What are the main features and typical use cases of Python,
-                and how does its design philosophy influence its popularity among developers?","reasoning":"The existing answer
-                only states that Python is a programming language. It does not cover key features, history, ecosystem, or
-                usage, leaving the original question incomplete."}'
-              name: final_result
-            id: call_4x2qh2o1
-            index: 0
-            type: function
-      created: 1770984591
-      id: chatcmpl-363
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 125
-        prompt_tokens: 423
-        total_tokens: 548
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2927'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the main features and typical use cases of Python, and how does its design philosophy influence
-          its popularity among developers?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '552'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"Python main features typical use cases design philosophy popularity among developers"}'
-              name: search_and_answer
-            id: call_vra95hbf
-            index: 0
-            type: function
-      created: 1770984592
-      id: chatcmpl-70
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 43
-        prompt_tokens: 639
-        total_tokens: 682
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '154'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python main features typical use cases design philosophy popularity among developers
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 12
-        total_tokens: 12
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3452'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the main features and typical use cases of Python, and how does its design philosophy influence
-          its popularity among developers?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python main features typical use cases design philosophy popularity among developers"}'
-            name: search_and_answer
-          id: call_vra95hbf
-          type: function
-      - content: |-
-          [baa219b9-f481-423c-98f5-c4d4de173a9c] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_vra95hbf
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '580'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Only one result, too vague. Need more search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"Python main features use cases design philosophy influences popularity developers"}'
-              name: search_and_answer
-            id: call_fshb8v3o
-            index: 0
-            type: function
-      created: 1770984594
-      id: chatcmpl-392
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 49
-        prompt_tokens: 758
-        total_tokens: 807
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '151'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python main features use cases design philosophy influences popularity developers
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 11
-        total_tokens: 11
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4004'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the main features and typical use cases of Python, and how does its design philosophy influence
-          its popularity among developers?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python main features typical use cases design philosophy popularity among developers"}'
-            name: search_and_answer
-          id: call_vra95hbf
-          type: function
-      - content: |-
-          [baa219b9-f481-423c-98f5-c4d4de173a9c] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_vra95hbf
-      - content: |-
-          <think>
-          Only one result, too vague. Need more search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python main features use cases design philosophy influences popularity developers"}'
-            name: search_and_answer
-          id: call_fshb8v3o
-          type: function
-      - content: |-
-          [baa219b9-f481-423c-98f5-c4d4de173a9c] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_fshb8v3o
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '348'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I couldn't find detailed information in the available documents.
-          role: assistant
-      created: 1770984594
-      id: chatcmpl-472
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 14
-        prompt_tokens: 883
-        total_tokens: 897
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4230'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the main features and typical use cases of Python, and how does its design philosophy influence
-          its popularity among developers?
-        role: user
-      - content: |-
-          <think>
-          Need to search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python main features typical use cases design philosophy popularity among developers"}'
-            name: search_and_answer
-          id: call_vra95hbf
-          type: function
-      - content: |-
-          [baa219b9-f481-423c-98f5-c4d4de173a9c] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_vra95hbf
-      - content: |-
-          <think>
-          Only one result, too vague. Need more search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Python main features use cases design philosophy influences popularity developers"}'
-            name: search_and_answer
-          id: call_fshb8v3o
-          type: function
-      - content: |-
-          [baa219b9-f481-423c-98f5-c4d4de173a9c] [rank 1 of 1]
-          Source: "Python Guide"
-          Type: text
-          Content:
-          Python is a programming language. It is widely used for web development.
-        role: tool
-        tool_call_id: call_fshb8v3o
-      - content: I couldn't find detailed information in the available documents.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '733'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: We must output using final_result tool.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I couldn''t find detailed information in the available documents.","cited_chunks":[],"confidence":0.3,"query":"What
-                are the main features and typical use cases of Python, and how does its design philosophy influence its popularity
-                among developers?"}'
-              name: final_result
-            id: call_xow5c7z0
-            index: 0
-            type: function
-      created: 1770984596
-      id: chatcmpl-866
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 78
-        prompt_tokens: 922
-        total_tokens: 1000
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3013'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          Generate a direct, conversational answer
-          to the question based on the gathered evidence.
-
-          Output:
-          - answer: Direct, comprehensive answer with a natural, helpful tone.
-            Write the actual answer, not a description of what you found.
-            Use as many sentences as needed to fully address the question.
-          - confidence: Score from 0.0 to 1.0 indicating answer quality.
-
-          Guidelines:
-          - Base your answer solely on the evidence provided in the context.
-          - If a <background> section is provided, use it to frame your answer appropriately.
-          - Be thorough - include all relevant information from the evidence.
-          - Use formatting (bullet points, numbered lists) when it improves clarity.
-          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
-            Instead, directly state the information.
-          - If the evidence is incomplete, acknowledge limitations briefly.
-        role: system
-      - content: |-
-          Answer the question based on the gathered evidence.
-
-          <context>
-            <question>Tell me about Python</question>
-            <prior_answers>
-              <item>
-                <question>What is Python?</question>
-                <answer>A programming language.</answer>
-                <confidence>0.9</confidence>
-                <source>null</source>
-              </item>
-              <item>
-                <question>What are the main features and typical use cases of Python, and how does its design philosophy influence its popularity among developers?</question>
-                <answer>I couldn't find detailed information in the available documents.</answer>
-                <confidence>0.3</confidence>
-                <source>null</source>
-              </item>
-            </prior_answers>
-          </context>
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Conversational answer for chat context.
-          name: final_result
-          parameters:
-            $defs:
-              Citation:
-                additionalProperties: false
-                description: |-
-                  Resolved citation with full metadata for display/visual grounding.
-
-                  Used by both research graph and chat agent. The optional index field
-                  supports UI display ordering in chat contexts.
-                properties:
-                  chunk_id:
-                    type: string
-                  content:
-                    type: string
-                  document_id:
-                    type: string
-                  document_title:
-                    anyOf:
-                    - type: string
-                    - type: 'null'
-                    default: null
-                  document_uri:
-                    type: string
-                  headings:
-                    anyOf:
-                    - items:
-                        type: string
-                      type: array
-                    - type: 'null'
-                    default: null
-                  index:
-                    anyOf:
-                    - type: integer
-                    - type: 'null'
-                    default: null
-                  page_numbers:
-                    items:
-                      type: integer
-                    type: array
-                required:
-                - document_id
-                - chunk_id
-                - document_uri
-                - content
-                type: object
-            additionalProperties: false
-            properties:
-              answer:
-                description: Direct answer to the question
-                type: string
-              citations:
-                description: Citations supporting the answer
-                items:
-                  $ref: '#/$defs/Citation'
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-            required:
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1894'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: 'We need to answer "Tell me about Python" based on evidence. Only prior answers give: "A programming
-            language." and "I couldn''t find detailed info". So we have limited evidence. We can provide basic info. Confidence
-            moderate maybe 0.6.'
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"Python is a high‑level, general‑purpose programming language that was created by Guido
-                van Rossum and first released in 1991. It emphasizes readability and a clean, expressive syntax, which makes
-                it popular for beginners and experienced developers alike.  Python’s design philosophy promotes code that
-                is easy to write, read, and maintain—principles such as “There should be one—and preferably only one—obvious
-                way to do it.” This has helped the language build a large, vibrant ecosystem.  Typical use cases span web
-                development (e.g., Django, Flask), data science and machine learning (e.g., pandas, scikit‑learn, TensorFlow),
-                scripting \u0026 automation, scientific computing, and even game development.  Its extensive standard library
-                and third‑party package ecosystem (available through PyPI) mean that most tasks can be accomplished with relatively
-                little custom code.\n\nBecause its syntax is concise yet powerful, Python is widely taught in introductory
-                programming courses and is a common choice for rapid prototyping. The community’s focus on open source and
-                the language’s clear design philosophy continue to drive its popularity among developers worldwide.","confidence":0.6}'
-              name: final_result
-            id: call_hz71naz6
-            index: 0
-            type: function
-      created: 1770984603
-      id: chatcmpl-124
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 308
-        prompt_tokens: 486
-        total_tokens: 794
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_multiple_accumulates.yaml b/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_on_results_accumulates_across_calls.yaml
similarity index 100%
rename from tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_multiple_accumulates.yaml
rename to tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_on_results_accumulates_across_calls.yaml
diff --git a/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_accumulates_in_state.yaml b/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_on_results_callback.yaml
similarity index 100%
rename from tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_accumulates_in_state.yaml
rename to tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_on_results_callback.yaml
diff --git a/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_without_context.yaml b/tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_without_on_results.yaml
similarity index 100%
rename from tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_without_context.yaml
rename to tests/cassettes/test_search_tools/TestSearchToolExecution.test_search_without_on_results.yaml
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_citations_accumulate_across_calls.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_citations_accumulate_across_calls.yaml
deleted file mode 100644
index 20321446..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_citations_accumulate_across_calls.yaml
+++ /dev/null
@@ -1,162 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: tQhruW85IjwZwts6qP3LvKol2bpy00c9FtedPaDporxCNWo8yd/HvIBoSD3ZItg8Uh5VO6nPDL0uZWU7C7a2O532H7x4qHi9v6UkPMvzzrp5XkK8MDNjPA4d8TtunO68Dv6RvJNKILx3Q+O8AQ5LvQEEC73KvJk89uLiPFfllbobYi467myXPLftnzubPI68MT4PvAq3DbswCcu7i16aPMq/LTybth88NNi1PNFcIDwJ6qQ8LDSBvVHVODyCziI9eeuLOrnDFL0ZG4U7G4TDusuOyzyQfMK88EaIvBrIyzyYjie9oBNsu/hwOr3k7CW954YFPAixxzxZ1lW8EcvjulUtx7swnCy8LK5Zu8gjLbxXxgQ8KUyXua72SruDbY09gQehuyjBczzUbNe8k5yFvCJ5I7ytcrk8nkf3PAIodzwdkQO8hlCFOyClEzsTNGY9uyS6O0hUaLyYPrU5CEW7u3l3hrvowem7HUUbO89uLjxc4au7jJ9JvNwVADrulBw8rjZWuwjZr7zodFS8kQk0O04HJzspkQq8mGHlu7fHVzzowbu8snCFuUEcPrxuFIK5v4tyPNEcOjxgU9K71vJKO1mzkTxhRgw7sKIpvLFCS7xrovW8Q8y/PEEeVjySSTg9F2Q3vJzVrTxLRfy7akcwvR//5bszTuS8pGAdPPvnnDvWhYa8W7W4O/7FNzytDqg64Ehku7Ynlzu9KGK7V5UOPK+YqDoACoW82LSCus4kZDv0yMI7g/ogO9sLFzwJt4s8aXFdvHtRDjtBRiS9RRc3PKqktzyuDtE7Wm7ROxzS67t+BnG8A66CPIrEMTwLSwU9xTLUOzxeXzzmbYA89Ij+u2nViDzoTYC7BmmHPLnDcD0dM7c7TZs5PCZJEr0TTmu8Grw+u9AvrLxs6Qk6aO3PvK7wQLz9XCw8hDHmu7spRDz8FY+8963Su3vZZrvsmai8t0hPO7DIzDx8P5G81E3VOxNmbDz96sg8wGopvNbGmDw7IGc8gD5kPNsGULyLOFo6vPZtO4AZJDx71Mk743SGu8vQ1ruHFqg7AcmmO1OJgzz3dtU8tTMMvE79qTsnuJG8/eBDu+RIgjwxLWo8HZbPu7StELnPPPm7qLh1PBlW7Tsf4ye8a538vHEXjLp2NHE8zpaKvDaRbLyvah08+7wgPfamw7wiue081JL9uhIOkToxGoW8YK3JO5XhjTxWNog7iR4SvL3juLuuNyw8hZIiPI4rUbxfTMG8TackvB6agDzbcgA7vbxlu8sEIzz9m028MKbCPEeGzLySZIE8U2N4PIPhTjyGgcO8iAc2OiZXiLxoewM5hprvvN0KjDrWY9+6KR8kPD7Ok7xBix47S3ZEuxfZt7y63s48bCaHO4V4w7sP0Yk654hdPVapHTsVGBo8ACGHPMxb97w30+i7rGCIPA5TkjsyUgA7C871PCMZLLyQck88A2UFvN4wm7ysnz+9x+HmPAQOqzx83588AiTMuuEaV7wsxiI89GeRPH9Zkbvq8TC8vREBPezZAby9QMY66kpwvO+2gDwLcac7sbdWvSkhhzxFzSi7QE85u09Vgz2i5748CMmqvBQtD7zANVC7Vf9Yu0u+ET0M5bG83yY0vG66uDylpLw8h7t0vIvHezwRNSG7kRC6vIpI/rtuzq+7NDJbPBaBgjwua5U8/XgEvcbRZzuMNec5t4IfvPDoOr3a/Gc8e45VvcevhDyFYzO8sN1qvddhrrsZCuw6mqlqur3nar3LSXW7OWilO/eODz0d2IC9rtTzu73K2Tz8UH+8pcP5O2bILTwgGRo9lOxoOy8WnDsAgKy7WaDlOzcEbzs5PIi8B85kPG0kuDxXBE+6qVDLuxrC4bxLp3W8pGVevD9GGr0fx4m8148tvHH5jDulAwK9soLDvK6YvjpVAR69oGIOuxZgaDxk8S086LVyPM1OGLymDxw8JhTmvB/X3roSUyu917puvArv5bvUCSK8fKZ/PFeeHrzT1x08D1MMvMQYLLwM8bK83xKvO3nsgTwCSYs84JaTvMkXwLxeyN+76VXBOsWAnTsEBfa7PqggvNzRrTxQ/Eg883gpvNnqJL0++Jo7LowcvFNwGb2g/lw9GJOEugIDQzzoh1O7Bj3fvHnr9ryFW9q86MD/vPPGxzwcsAw97EZZvPaJtLo+GDE9Bnc1PB0CFjtqOyo7urigOIA3tbtDLt27t8BMPHuvC7ooMuQ8en2jPCLogr1PyEy8/UAfvIaIMLznNAA7pVeMvGxXyTznSUE8/O4pvFVy0jy5E4q8i6jvui0QlbzLP6K7QnkRPWtSjTwHX9m7LHJkPKz+AL1z7Tm8RDXQu/PeTDtdrpO63hOmvJqxkDyT9/47OcfOvDRaYbxoxGU8tDsMu+cb5zxgm4S9LdP5vFVVY7wSG8a7uGnvvBDEPLzJcig9p/4nu39Q1jyXiPu8Flonu6D3y72G5sQ8rL6FPCg48DqXaG+8PjVdvMSpAr36Vbe7kBGYPOshLTyp7KC8e6+QPIPfqTw3W4y7jAy4vDbYzrtenzo8YByMOwBdjzw0YH+8gqG+urkia7xKI/u7ErcjPHRAcznVAhI8v5XCPO/X7DyrYCs84YGDvGRsTzyEabC8SF26PAwp47xWor06BmOcO4rVKjzJtme8voiGPA2zSTyiNEk8yUKmPMstm7vn0/q7Jf4Avey4ILxc6Ak9cs/dvFfvUDt6sao88Z//u2YZmzxL0eo6W3Q7vcWJwjzC+tC79qmTOZ3zmzxLyS06UBsdvDIaXbtKnCm8xQ8DPUGefbzqfC+9lFmPO5y9CD0v1wO8wFMFPCuJTrya0QC9tWKmPHt+mrwuQtE7+1wgvfcZ7TvpiXW8Xh7pPFqoGjzIHyw8eqv6O6PxpjyVcpi6R3MsPER0oDsdCn+83qqvPKGZn7zbwfU7iL+Ouo0wGjwx11S74DnIvFJNf7zT1sw7BOeLPAsPjzyduSI97M+BPHsODT1vIKG7IMGqPHBnxjuwCYm8tWOzPI2VxryPDp267tokvPHkBDsyTAK8B4NYvKjFq7q79tQ7rLMRPKxPx7xdhTq89XsYvU7v8jsCgyq85glPOrI6yTsYWwM8tUYNvAxAjr1+7Kw8U1ICvfJeo7xMDcw85vcLvJqcvDuDrg69e5vlu6k4OjycpR08/0W3PEFUlLtNxpK8TwN7PLvcXbwrCdA7pxmQPPC6NzqDNrs7l0tGPCz7sjq+MMu71L6MPH+jyTxl0ri8eMGhu2HOn7xQCxm9wL+iu6HK1TzczJw8k4CVO80EkzugkBW7R2IhvIDgPTxC8Py761aCvMBasjoQgJi8S+p/ujYMKTvM6Be8DSOIvP11krxsz1U6MfXqO+jQDjyPH7Y7n2MGvAauWTv+lD+8dHE7ve8RJrmDAtK8AT3JOztJ1Twr7pc8E8owPY+XxTl6yhs9+F22u1dQ0bwKHYK8rhIrveFEmDvBJ/a7JlQBvGQ6kLwnUne88SJjOx1Sar3DJcE82QwrvU+Y87uK0Eg8/x6QPJPQIzpu0Xs7ot45PDWMpDwFlFu9sEgGvRXf6jy/REU9WGTePAzo87z7o2Q88mQAu8ABlbwdPTg7A94CvfOQQ7l3R3w7LmSvPPrrRr2CKOY8LcZnu3ig2jvHm7q7UKELPfIiCT06Oee8nIedPFSOaTu7AJM8Nx8rvMOuyzybr4I7u2Alu+Gz0TzoF807X3wUPCqKqDzD2/u8fYLIvKikALs3oi888QJUO5zfSLyG3Aa9rpoCPY44nrzjS508dMvVu0prUDzGhgq8zLqSvEL0ZL1ErXK8hEGxPK20hTwiNni8/CM5vL3PyrwutZo8dd2PPDW3ajxjRbi8AbS/uxKHgrzDXHU8x4CPPAeIxLq6CQa7g+8GPdGGqzxJ9DC8e8ccO3Oe7jzd7Q47YAcCvG1roDt35I48Oj1ovN+noTy/Gei88EjDvKhi9TvFm+S7Iaz/vLzOljxICKy7jTpcPLD4Nz3oMxA74VYKvWBP4jys9WG7/0ETvAYKXzwrDcw81PzoPPf9mjzkz5E8gQ4tPX+8xrw38668tB4MPIw7d7w12528LPMbusZPMj19JAE8bc3LvNNJqjwaxI28DZ8hPeuIDjw1qIa8MynyPAgPNDwBVuc7olaovK8dADxYf5I8jz0HvMNNHL2vhLe8zkjMvK6UQL1NQ1o7udSPOe/+Qr0BmyC9Ywy3PGLgkTwBOi09/HkGvET7Ar2Nrig9cK2rus+K5zxIRIq8CrPLvGq1Yjz+TE67qm9evGv8zrvaZJW5PpuAupopCjy8OWg8gmrnvOD+fTxH89Q8JWD3O4nUkLzGzZe6DsyCvC8n7zk8U966JvASPJI4wrzivCY7lW8cPdg2qDyVvzw7HVHtu2BHMTwr4t87nUa2vK0zuztdEfW7Gp96u5Q6gTzViPS6XVchuzMuYzzuIAw7Tp6+vIv2irx1XBU8G6G/vCIuIb3Xifc7Sm+BPHfamjwLlxK828ZjvPz/OjwYw6484cfAu0NxGLtS6EO7OBUXvAJ2GjzsR/084ueYvLleHbxINSc8y1y6PA7jCD0t39i8TLkVPYWiITzo2Ds8/ytNveGgEL1ChwU84GYAPeE4IryXUi896DGZvBbeh7zOVYq7tjt0OiTQqTwCYw885ImKPJMAFDv28mw77Bi+PF9xFLwV/bc7yLzau4cTUjpkchG9og3SPL3sejzvhcy8N8gHvFKhbTz1CCu7WkgwveufTLrQd6m6JJeQPNkPp7s0pAM8KDDYPEKGHjtohKC7vBvKOjEiHD2Da9O7QBMrvBFn9DxRj4k8MLd9PH5+/zwI/kk8mOU8u8ljLzurT367xRq7PILhhbx8HPy7Iq0WPWdX1bxcJhe97OeRPMZSQTy8r5w9ww2AOsHpCL23tbU8Th7fu6eLfjvbSbQ828U+PWakQjxHDCC9K6n2O2RHSL21Frm7m13IvOKzAb2LsYG8067FvC9Od7uQwFa8zypxPDivqDxEDcE8gEXVvEwFibz+kY+8ISe/PAGkBj3zzY47SqFZuxpK0zwsAwk87/HuPAIDJD0qBSi8n6gLvCa3o7zfo0I8AeIRuq73jjtUCR88YNFkvM9UmDuvCLk8DaMCvT05E7wq5wQ9O8wtPMflW7zNsfQ7UB5xOjOsaTzL1T06yrfxuhLLvrw43aM8bLWSvD+6Bzu5avq86jm5PAK4fTyOW3M8eEo1PY8jyjzelbm8xClmPJSpbLx8hI66BFyBvGnBMr03odq7ygyxvB2a7DxzGcK7YpXLO8Y3nDu4Qa+5ds4kvLNY3zy+Mxk9GC0pvQIjjjwEW7M8zyZePBtGC72z6/Q7AM5XPB6OBLz8YpS7n/FIvGrINzzennO59x2OPZVBDLxmcb88xhKAu4Xk47wO9do7d+ehvBn4rLvIZV08897UvJqb4ruWMmK9ywxZvRbRTrz4Eqi8MeykvHZQHD1Sds686P0+PSSTlTzImaY8KP+GusrXzjqPnsi829qMvKionbqIM6O7fJPQuy6gLrw/wyS61LgevMdAlbu7sS27YcaZu8Px4jzbguE6hqgwvPRIJ7zx7uo7hXtpvME98zyA97c8HHX+O8sjPz0r1M08cp4LvG5XrLo1uUG7Bb+xPLImsLrLeKQ8yu/kPD+2yjwkaYE7EZoLPckUp7zA0wi9xj8yvZMn8ztO9Q08Oy2SvK/GyDzLDKG6TwtlvLPPkLqgwJw8fyCWPFEK7rpjQdm8o3jNvJ0qtzoxlZ+821l1vU3nGTzwXxC7uuUovHX3gzuFPue8AT3WPMtj3Dvkjzu95GaIPNfQy7tf/Bm7/8ujvA8CTTwHMYY873ETva+VFr03AhE8LxIIu85oiLs8vFI6t9xAPPF0+rtuPoY8kbEkO4DW7DxsNJ676YW+u5kKV7wPOso7k0tuOj5ue7q8C4O82spoPG6ddLzVloi7S5wbPfdTuDxK5iq9QcYLvcHUqLzFNoc7BBpZvNNeYjzk2oA84rOfvJJa1zswzR48Mxkuu5SpU7uuhdC8FdA3PNPlvDz9aZy8mC/1PIiv+DwXKvG3PLgHvF0eTzs7Lx06Ho8gvJu7Rj2TQd+6aOqsvEdJprsa9nU9UgbkvIwfIj2LH888gLLJPPLBmbueeig6ebE1vG+ug7ynhQU83OyyO5YuUT0aFR87NS3TujObJD2Scuq71757PBlamjzuQpw8aZNOvf4ZJDz4ViK6n1VGPK8cyLuSMXm8dsbRvPOIl7xDRbw6wsonPA9vyDthhxY96HtpPd3wMTz89Jg77asevN9vhjy9+Z28R/aSvGa1j7w03Si9ifyoPIl65jtETCe7RfMkPLW9CjzEeLK6JyFyOsvwqDsezaM8ebkhPG8yPLwcUqE7WU4avcckyrwafE+8ENYUvGw9Cb0cL9W8z5bCPCOTzjvm01i8nhJavXtb0bxLVRY8NcktPW0rNzz0PyM8ZEyhPOH5yDxMNsG6nLUmvHU4bTy1na68/83rO8FdY7zrRhQ994KsO632mrzi++y89HEbvQN7JDwXIbG8LQQvPNZ1Bb1xZ/I7jzKiPIU5QzrRiiU9U4DCPM82D7z8API7CPdqvD+JVb1fn7s8KBQIPKdcTDzB8F28k0j5vMBCxzvb8ow8w+wlPNn9C7uRh5K6BPrUO3Ud8rwXEeS84ZePvHGrgDklwae8tK6Ru8K0XzyMLRQ87Y+KvICjuLwfaeQ8gHQ/PC3lu7xZh1w8s6AqvFqHvrzIkqW6OsKxOgENPDxi6Yk8q2CnOlIAWTxH9CE9RHPkO6cesTtfsua7pxWmvAlkjbxjHMS8goRQvHu76juMZjK8peKxPPGFq7sj2pa85sgSOvvKEL1OFtK7tOXhuJW7mLs4Z8C805qFvJeoRj1de8S76BKNO75prLrwhEY6szCAPAQO1zy+2zq89Kudu/pc/bulUZm7Evb6O59LFjtWrPk8wJIAvTtKpzwBjLa7AhRnvEJlTDx+v+c7yfyqvMN26LxuYSo8DYemPPEV9bwCEoa8Oi3GvBd0Gb1jsWq86WifPIKrAzuJ2JM8zMRSvKj8ary5pQm8vWEFvTU9+busb9O8RcSXvLkiBr3e+S08WJ78Onh7dDt5Hoo6F6QavM2mgLzcOZs8LOkRPVvozbztI548ke6zPNG1rbwCPO27Sb/EvIF/nLuSKVm8smwvPfoWDDxqnYM7vhm9vCtqkLoRrQA9UIfDPMnVUjtVMAG8f+INvY+pCjyxQTw7qRUJPCRu27v9fra8XM4WPY9shLyDmkI8yovNuUzwDD2CraA6ViC+vJ+mDz1cQR07AdMGPSwu8rtneDy7KUV0PamIOD0UXnC8s/rpPAK0lDzAAfG8hO64vDxXTrzy8Ng61yCGPDC7Aj24GwI9/VZ8vE3skzrJefe769h/POvTXDyErS09pfhNusdHqjulU0c90M6+u5FM7zx/uYO8iCruuYZNwjxe8rC6FXzjvKreArz061c8VPDKO+xFAjt3KQc8/T2KPI+cAr1xhw28laI/Owoi5DzyD4q8RIkhvSchlDxm+5m8wTIRPV82ZLyJfYs7VryEvNvW4Ts1MtK7NFQLO6zeCrwi9GC8NPGcvJJlHj0TrOQ7KQP7OzRoiTtOoIK8gXIvPEHYdTqqLai8bqgYvf+0lzwfGqY8p84NPbYBOT0/SBI8FAbdOitJlzzdHLs6MaaPvB2rMDyy3NE7YPrPuvnbB73gc3g8XXD+O/hGIbw4+Ck7+lRrPIk0g7yT7gk8GWwJPRsIBT10FJC8XkQAvfK36rzWaEC7Gb28PBkZbbyokwI96P4FPWEKqztBs7283ceGO2xPOrw5TG28uovtvK6igDzPtjy7jNCHO+4VlLxZh/08JWr0Oy9rD715b7I7aD2Wu7xo7Dv1R5C8oIL5O13qjzsaWnW9Q4AkOk6spLxMM/O8WwI+uzZ7+Tt3XKM8lEAxOvu6njwHmnA8QmQIPW2YdbyZUwM9pGWru1MAnDsXrBC9YHLgu4cBrDwo65462D55POIUEzsiXgG8Ru7oudRCsrthFLc8MzCPPPfWQTwHROs6DV0pPGFI8zxWkAk879mlPLgDdTw9ZHY8F67MvLGP6byHm3Q7OZxJvNG4lTtjX7k8d18FPLo+T7wD+co8MFEOOz2m4zuSwEa7aMIJPSKasrwHNG+7yj3iPKTntDsVocg8hjSeu6nywbo383G7Rzb1O/QgITyEBeK8HY/VPBELOrrnSlw89+MKPCxk+Tx50ok8fJbYvMdGIDyWDSS7dZh8vE5Crju2Pam7uDyOvJbn+DwqATs95mJGumHShbztNjQ8HoGIvEr4lTwK5A29Zl0YvFPmnbvrRtq7BmqfuQtEcTsurwK9Gw8CO9Ydy7wUoZi8VGeDvEKB2jyYV/K5+fIXvQtrFbzA2Oo7QkNbus6g5TsXFaW5vtyavByVxjwThqK7EMIcOycGnzx/qnE800eEPNBobjwDn5K88IaZOwkDzTuezNg7F7OJPDSoWbyxWta8XJcEPKzXET2PrLE899OdPOExizz6RQk8+wO+vGUrgDwYaiY8TyNVvG6Ttby83SG9++sBPWGZ8btpW9g5V1iUPFDvMjwcQ4e7hzqjO1ctmTz3LTG8ub2PPAYMNbwrkP68jhwaPWJDjbxfsSY8hC+RvGHkzDt25xw8NaKOvFxADrypwru6Qq0NvExChzxlZXe8jBn6PGtLGDs08F283tZSPPYVwLxePDM99cDRvPScm7wnWJA75hqwPHaEEjzIoEm9dN0XPG9v1brRzT86FjMhvPQVJLxwyw+7kYKAOUCJ4Lz6opc8hczNO53pAz1KVUk8ypiKPCSZ4rlgxZG7ovBlO4V0ETytwYK85NOKPIf4i7zyoLS8lQNhuywlzTvguEW8dV9svBGXpbvQ0Ys8xgMwvBYqzDyfqr07Wtz8uzqA5jiQSYq7EZjAPDxnkLy96+G59bIpvFuQw7wGzWQ7yJOmPCXJ1btncLm8Uf6RvCr0Ezthl4k8x7NpO1bMCLzJF3i7eeNwPFoYALylaJ88BqOdPECcTbtW7gY72pcGPBistTwwFRG8qpU9O02nPT0YaAO8yGgLOsEiDDxrl1G8GPEevQcyAr0H/D48Uf5cOrjaErtSbcQ87B+8vBax4Dt7dVE87k21O5p0Jzsq/x28CcS3vGdkHTwMWPc8EGJMvI/zhjzTIDM84CmpPJL36TzbiSU8VcqWPLKIpTxnQk+8CiuFu24cGT0MJtG8YcjBvGHBgrrFq0a6WGAmvI5SgDp6cgg8yHALPAe9Bj2HYIY8Q5tZvMcRNTxGTNC8SlDPu89zPD2fsIg8eug0OtzUhjwpjzE8R+qcO604irx/Erg7h2J9OxHfQTyhR4E8uKFGupzynLu9QEO8QquCvON3iLxg6NC8rTEivN2x+7uOH9O8PdKWPH0KSjydH4Y8Pi/GOn6sYDtv8KY6R+PwPJ5gwTz3VA+6UEKMPH9bqzx5vYu8BMihOFDYUzxy+3276/SSPKSUy7v4aaw82Yq9O2iVgryNnAs8uwwCPPcavbz+aBO8W5qLuufwBzy17uO89fCDO36Z9bkMSAe9B5oePcb5vLsFxEo8LJZmvH4fh7wFq508ZoEJvXMDfTwgsxk96Dk+vO7UFzwbCXs8SogovO8LAT3dxLe7HsvavN/GmrwymOA8vCFjOsLOibv84Ai5pdGrO4AZTLyfDbq89nYQvDiVnLw086Y82U1lvI5RQTy20N670G8jvCjtGjoZeYA8kcwjObU3zTyzsQy9bTEEPFN3aTyum308D7e1POW3wLz8hyu7tyjUvLgilbplkTk8+YybvISeEjzdpoo8DrTBvITlBTvYRhK9YV8TvTGjWDp0QlY8P/orvAufGL2MR5g8JJHMu97SGTyz8wi9y32GPPbzpjvGhKO8O3oQPa8GOb3/Ab27Wc+pPEs6mjwIJQQ8jqSZuYdqubud4ts7hInvvLWKOjwroiI8ic9XvH3Kqbu/uiA8495LvKpyqLw4Ly890n65PFsrOr3FQFQ86hxCPFkcI7zlk+Q7ggScPG5LizxAKyW7IRWuPIWR87vaQ4y8jzF9vOPadTxfxEg841wpPM3Kqbt7AYK8VJBoO/P447t3zF07/+AJvBmqELuEUig8U7cNPOlXgbtQZqE7evV3vLWE+7sfMN2530RFvVvbzbwIiHM8rc5WvFSYITyL4Ko8pp7hOhwaAD3smro7EBwUPE6CVjvJ5mO7xVM+vMWMZjyiSKs88ENRPKOEtTrvP6o6/qyKu0UXBzz4Rqs8iRgvO6xJODwU6dS8/4R4PIK23zym/d+7vgZfvGfn/jszF7e8gVmtOzgGfDuve/g70nuHPJpmlLwBXLA8LjQ1O9JVVbsEfEs73WJouz/+3jxm+9I7t8IDvSCsSryfmAS8h+Vhu90GNDsuRJU8spYsvOrXRbwZnrG7KH0OPXeFyTmdY207rQI1u+Bn3LxiEhK89O8Gu8m9JDynu8C8Cs4MPQqgybkO/y68tK2qPPSR17ywbsG7vcGPPDL6gzzxhZq7adiMvCuZE7y2wiC9/L/8uppGbTxcnZO82BuAPL3aijuOgRS87SrWPFVEKL22KpG7T0UBPXqbjjx68JS82j2UvAiBTDo2XHc81ThQvAUhq7pVib88YgRRuyWUbDyi9O+5413IPHP+JD0C6DK8oxWhPOvly7ys5ha84tCnufjQRrwuPkO81mcAvNP0Hb3gjww5+i2DvG8GFb1byAG8tRHDu4A8Cbus1Ou85cz9vJT5GbuO23W8nA9KvC77qjsYlyS8x6N9vKtDjzoYNme8mhVFPOsEdLxNVB04lt2wvJY1Ib2C2w49AJkHPc9sVjyC34i8wQVJvKyydjylpmG8FstIvGb6OLzYGC28bBn1PJToFTwGqSe8Ms4pvF9ek7x/pja8TOibPMO0LDs7xtY8QG29O6ojvjz5Q868IN0ZPBP6SDxU8rS8bZaAvGUpgLxZ6V69KM22PHj90DtLGu+8ET9Au5L0h7xjpCU87S0WO93Tjjz8MQS8s/tFvM1rFLyVVB29rv45PESq9LtirTA7p1bOvK2Nwjx/1wq9YYMmvOLldrzK87q8bqaWvCbokDz029C87dITu7JTDL0MvwW7JL0TvAxeeLxsh1C7M/nEPB/BDjza+BM99QHCPBNWzDtqg7C8SKAkPEDMubqz6TO8FI1mu/ZjCDqU8NM82jBDOkfoGrzwTb48tOXrPI1MXrwL/em8ZjpePGQjR7yRiJg7XktSPL28gzxO9lg7mwnWvG3ddzvnIsO8RbdDvNo0M7xOIZa6a90IPHGlEL0F19E8mqK4PKcOubsJgUc8X/70u9U53TtmbMS4q0zHvD9IibyXsFa73f7FugY0OjwHrx+8Nv2PvETssjySIwW9vuOHOwAtrjz9ikc6lL7WPK0u8zwgsEk5U3dRu2Nr+zscAii8qGKjPIye8DyTxyC7KKhWvE5RwDsDNrs8G4l0vJNAiDraeNw88PS0OxfP0zya3qg8i738O6DTlDxCefm663RJPNa+5rsJuRO8mX0CPGhcZL0vxHq7ukQivQd//rzF1Ym8Ka6BvKkfoDs79hq8yR82vfZDCrxWKSK81BOgvFpivLp2gT06rHcLPPw8h7x63lg7FISwPFp6rLxRYI885AkHPSs9rjxnI8876ZTVPKADDrqFVSQ8RlRAPN+Knzq6J2i8llJzvFKvPjxY3hW9+dSyu1SBZ72/gjc86FKMvCNmgDwTxvS7K4kHvGtzJr0LaCC9EVMLPKtsPLu/GO27M/rEu73cd7wO4gQ9NoZdPBz43TtAj/k8mm1KvA23WjzRnTm70Z5DPdCOsjzSpes8j2ylvBVHfDzlPC+8PJtNPeX91TzUxQ28b1ySPA+VA7wFTjY8rr2oPPn4jbyr8xM8UV+AOw7TMzuSISk9fj/wPOrKBD0Gb6u7XMomPGTyurxgR7+8R8rHvHe24Tx9CVK8JmOhOnA+mTyqP828Z9nQvPYIxbuDvJs8OZiFOlIhBjzgPYo8nemEvHfYF7wcR1Y8JnAevcmBDrx5eZW8UhrZO98NPLsWwaW68F1rvB5ohL16WRy9mx/Cu+8zaTt+xny8q0wCvB0W6bzlt0E70r2PuscgMjyDg5y7jnh3PC2cQzy9q7651rsIvG5Kz7zUcYE8d+2bOzWo1bwE+k+9UaOXu/QGgDzujKW7riGQPH5JjLsvS3086OiquhS7Ers2bHK8PZDSvD73q7vX/WQ8bPegO2gnSzwM6gO7WqwOO/3krTwmNmo8GKW8OlO8PTwpIhW92K2evF9N2bzZmxI8ZmcCvNm01TwKy5u5GmCYvK3wDr3Q+y27wRZLO4hlgLs/AI48Xx02vEWbJDx2bpW7wA/zO2JYrDzFBli8mWikvFPZITwkZoq8XhQ7vPjWHDx56q+7vIQ2vDsuMrwc0Ve87sLAOxsFpDzageU8cokgPKPRaTzzdgE92QJFPMFO8jpXkfO8jy7KO/lnEjyH4RC9OZY3PC8CHjx8GJm8Ow1XOxfWDz0seiE8J52FPBiFAL31eou8QCmLvHWERrx+bW+7XrChvDTxsTxuvvm8lab5ufuuVjy5ojM9gOnKOyZjm7wKA+a8AnE4uRvgarzSV068+xMru+PtN7xo1jI63CwFPO7e2rxmqDS788YJvMgXb7uWHye8UpjmPKg1TTwf6Y08qO35vMrR2zqyVl45cj7mvCo+gDwaEuG7t/yiPA0AqrzGs1o8b7TOPOJWqbwD3ZG68rgtPf9ZUTx4kKk8/eA5OYqu1btQ0ne8xLaWOf2EIr0S7pe7Ztaru/4b47tk8mA87NmSPGd0cLw+Rt68J6QZPV5XlTxbEpE73mPdvLfCYbw5KTO8RostvNk4njz0wci7XTtCvLTD7jqyw4c8sNqdugVGHzz99xE9Bu95u6uNAb37Fys8+S18umh8HzyY5uy8EVh6PDF/dDzMvyc8+IT8O1lAAj2eBRq9opu5OtumtLxL+u070O7FOphQH7x7RbG8l56DPLtQRrvo/CC9JsBwu8Nu0rx9pFA86X3KuNMQdbuMgvk6ZnRfPNRJzzxgqBI86jTXPDnPfbyoOdu8hZCVul2bAzzXJ2M8iAyrO4QCTrwNTN08Vqy8PE7hmTxZ9tI8WE+YvGKghrycC6C8F77OutGKEzyp7cQ8fhIhPGFWxDz10M27tAwPvPvo5DryiNG8VzemPHvmprwhZXw8+TmuvKra17td2oA8/MeevII3gLwGnWM8EDe1u8orOLziKO26yBs2PNAIcTyJJTo9AbuoPOTRJjx3d0e7gjDzvKLkE73cYg87JDrsO3U61rp1Ng497bm3POUS8Dv0LDO8e7GCvOUKvDzinxs9p5iQPMwZTrxMHW28dRKZPEQNiDxZR5079gNWvGIMobvBIGe8IQdTPBDuFTzf4JW85HwsPaRY8zvZtJq6HJboPHm7DTqiOc27QQotO4hpdjshX7s6nYC0O5BNlDwZ6pO8wexdO2HNKTzBBss7mhpfPOrxuzoAhuc8D6kdvP9NDDyPXDY8FMifOakT3jzWlmQ8Ka9TPOw7OLwihpC8cToavXlyEbph1eG8VxqwvPGgGbzsQN+7UYeHufNqkbvlNyM77VxcPKpfRbxt8nw8zj+bOw==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '80'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: jzMOOYbOjTwJJes8Z7nMuu1chzkfJR09g+apPTJ5xLxV+eQ8Ey+xuwumCz0gFgw9tUvHuMdy5bwkmyQ8NrrtvFK3fjw0bla9IZksvECtNry+BJK8ZGM9PI012Tu9FF88nv5lvQPPSDw43wa9fKDjvRHdPbwGIp+7hYPMPBuXQ7xKvqo9S57OPKA/uDsOMtS8dzUcvJFqwLv6vyK7AW2ru+FQNLtEUIK7N7GtPFpOVzzKV7M82KOqvPM1BTyRZbc8oH+nu2PLhbxLsZg7i1yoOztDhTxdG/C8o7rvO6U3qDyAgvq8zsj0uzY0Y724xUe97xgBO6aDpjyxkvW8JWEXvMcb6bu+U/G70laTO63fXLzvNjA8/AwQPEkXebz8jzg9R9X+u6zS4jx6gpa8wrCfvLC7t7sVyNU8Wcf1PGX1xjzRVoA7WZaDvF+q8TvpWcM8Z2a0PMnWprxoO8M8QRWyuzLtGbyu/co7BfcZO2jPl7o8hdQ7HzlYvI+slzufoMk7qM6juzP4RLxojpe8XchFPD9fZrwOt9e8CpwzPB99czqq6cq8HYGxvM2taLykMpE88sX5O1glvTp2STO8JppuvO9LGj0cBo08YKZ8uyF8i7zsi1+8vv9FPSwz0zsI6yQ9NZMhvOM0sjysK1y7mTwqO8MjFrumcZu7gC0zuy9tIby7iYS9QOHOPEuxlTwe+Qi78JcmvIKrn7yJ6j88NmwoO/t7JrxqlzG8j1NkO02QNDs7Fzm8gc/mO/0ECTxEpsk8TJVJvMsoPTykBo68NmHBO3ePAzwhIgg8MvlpPC+E8DufHou7ik8RPPGNSTySxvc8ZrzIO2pQdzsnm1c7TYt0PDnCEDuWIP47i8sJvFV/HTw9naQ7FxQsPLmiHbyIsH08yTWHvITt1bwDzks8qcwSvYBuB7wnCo08NrbDvL9FcTtnKo28nB/Wu+c/mbvJiyE8lzkOvF5W0zzImLm7/7EYO4UkWTx1D+s7zqsqvAp38TqWado8H49Hu09KDLs5MY285QDluuSH77mih6s7H+EgvITUM7y5tpE6/QQ7PAKI2DxsVhg8DChIOkR92TyBlsa71bGBPBXZUzyGNt07sTypuxSqPDzV16S8LZYmOrSYsLzq2ry8OLUMvci+mjsGWlE85re4vENhD7wrywk8fDAJPUWBGLx8M9w8gfBbvHWyYTx75M28h8POOa7+AzwAnvg6fOcguxqQ8LgUp4E8DqX9PEgNU7z/tR688PEyvDKVazxvIrw8X0stvERvWrzWN7C8jZugPJej2bu2C5U8FAAnPPa/8DvI6AS9mWB5u5MMs7y75hu8A3jhvE+Ws7zaRJe7RothPLGezby+Qq28JG3bu+CgArswje26Wn7GO4F/v7rMaRc7cK2MPV0gM7xg1vc7rcM7vIbGx7wcMY48BCgDPYEaZDw6HqM7ricWPYI5CbypmUc7vlsROhMbrbvezqS8ouGbPNNGTTzdmwQ9v+TOPKUDHryrrhs8dIzeOaRQZjtvWIQ8fEyMPEz1XLpsIII5jnCIvLD7QTyyzEw7bLkAvYO0bjxdof277LtVPELNvjwvvpU8uGosvB7KkrzhskO8ozdEu28uiTzWqWO8lODLvFaxwzwffK48vd8pO6AL+TtRXCK9PLczvKT8+LsPD9W8pPKCPJQFSTzrkGw7mR2Avah9eDz+KiG7FRnTvAfOPLtkfhk8jT1JvXPDbTvGg/y8teEuvIdXIbzsvLE8mkGqOv+YFr0cPMO8gn20vG6Y6Dybnqy8oa8wvEuJljwvycK8ZzilO0YCYDyXz7k8vC/DPETwWzvcck67lEofPVBzyTupAwu964TZuxU+gzzKASA7gMkzvK2spjvJeoG8iqHevAiTDr3rhwO8nHQNvFw8gLvijLC8wNSMvIdzeTyh38+8imNSvPn43LqzYCI84ciKPF88C72/KyM8/q+zvF8GSTtIt8e8+fnCvJoxWru9zWS7KamcPG5rQzxkusW8UBV7vAcnFrwrZDU7eKOou00A8TuOllQ8frwrOyEXjrxlEAc8fOzcuaILqTr9Vew5CW8tPAhHtjwqogG8sVXCu3Iss7wP2Hw8gHumPJbDKb33UDk9DrKLuwFjGjyNgnw8TrppvPhJTLxGjiq8ECcRvbpn2Lyri2Y8P6qRvD2EQTwE9Us9b/NCPDLehbsLrb28ztadPOluEjxoGsK7HFOGO28RRTxALO483eANveeSQrxAaQ68Xvmeu7lmo7ykv+M7JbFpvGBKYDwfjDG8uIf1uhJQDj0Z9GC893G+u+P+nbxvMZ+86eK1PLjsZTxrVya8EYOHPL9+lLxjCcw7J8xHvIwKvjsP7LI8Bn5hvBHozDzRU7g8Rl9bui7D1rzwXl07/IVDPCC67jyy5+28Me11vAM7KrwSMaw79GJ4vJJEa714gQM9aMtpvFdXVzw5gvu8rK+HvJBexL0/eIU8G9kNPVikl7zpFz88WV9vvKj/fbtyuye8we0/OzNXDT1X1/W8T0RMvLcWHLxZM5C7ThcvvBkKezvKlPW7rW1GPEbg/TtlZ5O75qrpPCLuYLwJGPs8SXHrPCcY+To7Tng8gPToPDr/jDzfJTc71dmHO44pczxEQUY8Eow2vPeZsLyEPW68DsVWPFhyqjnnDpm8AphWO8IO7DxXrh28XCU7PHT+0Tw78Zo74jmsOV9R17xSFEc9Ywz6vEYNkTzuhBU6Yn1ivOR9VTxcGjg8dhNovW3GGDy1OWM7mDXROzaHTDyvUo07aArmO3jr4rw1gom7eoZLPcZVirwR9ta8pGamOz4EDD1Z+2m8k3OTOaO7qbuykIm89AGBO5+b17wWIjM8txMrvV7YXzug+bG7X5YVPL5yqjxC7Ug74kJ6PJHZpDshqku8SNpMu2mhDTygphA7PDIhPB4km7wLRI+83wwpvGDdcDxcr268X49Ju/KftLzS43A8slPOPPVBwDyeH2k9CRWiu4LFizzA99e80AMnO5/ZqzwG5F67SXElPPPFzzsMiq46eIKXO9qKBLzkQb685ub8ui8yAjxQg0g8KzwRPRBF/Tu5Pwy9RbDbvLaPZLzpXTu831iIunu8sjw/YuA8x8N8vMUdY71oW0I5JuWPvNJJ4Donj7082fYaO8Wnlzt1OS+9raN8OszPX7ouZQs7gIDnPFbNNLxP8LS8N6iyO6uSqruDMue6pwpGPNaBJ7wLfZ667W37PKNjv7xCQCw7wa/FO7ON7zwl7+K8dGbavI1t/7t31uq69229vGAPCz0Ci+U8MNINvVW4szrt2BE80Sc4vO/RyjxGY5+7WMOUvPqjtTuN16o7J4IFOwOOUb0U2si83uofvE3upLsIu5G8AI1QuxsoObxXrrI6xc1AOqZbj7pZHeO8fyRfvSWMxzzFgKS8wrkZPNPNzLvVmUc86MkNPUl1abxuy6Y8FzQ7vIaIOL0GNdW8TQW0vLSeq7slDAW8s3WoPDoAK7wZuYI8r3r+O1iZyLvMqMQ8F5AEvdMDRTxrsfw7o+lMvN1KvbxXofO6KkxwPErurbx4+SG9HSNfvKbIqTz3W1M9MyHBO4jnnbtdnU88uTsfvLkMEbvy7Y48AfIUvUpz2DwpjMe7bHOPPEHSgbyqFOA8oHmRvLzopzylrus7L0IKukRNVj3U9ma8ukjwOugN6rsfg2087fOSvDDimDnsahk83MzUPFmtqzypJQU8KVl/vJVjnzwbx9m7YTvqvLfelrt76mg8xtcfvGE8Mrw6/ke8b84MPRDuRrzbfyu7ESEvvPlIozxIpsK8U4QmvIBsBL3FwC68MCuYO5hbjjxCuJi8uOEou3cJlbxFAb87TfwIuyNTwTyVbhK9BMA4PO4fNrwi5k480UDWPOah6zyfnpK85VUxPUHe1zyoJqy8AXADuaOhIz2IMvO6a4IXPLquGzw1zZk7AcCVvPc3ujxVsr28SZGtuyxfqjxLTZO6fPVpvBUCkDy1lk654uTUO5N/Mz0jkmo86jeKvGIQUTwXwZY8PA2du/8ygjznqbI8U/+TPC3e8jpsnys915UbPfAM9LxPrS68IklCPGfekrw65we9PDcjPHZX/zx/jIY8cPaqu2Sz0DwBqqW8D5cjPRGTCLx2rqi8FZixPMEnQjyAWpu8OQoTvJfllDxQbPs8EWnCO3lHy7z1TPq8jxDuvBeZ8bwGUNi6k3owvCmnP7074bW8jErEvHQp5TwP5UQ9pvZDvNkqEbxO0ec6MmmBvOfNljwzZwY8fpTuvPpYvDxUbSE7jaEKvD6rHburgtM8D6+Eur9f6zxzJfI7jEEpvPdpMTuN0Og8EcpjOkA4irx2OhA8tDyQOkFG97uH4V+8lBliPFd8N7ycGiq83ifjPI21ajzpn3U8kqhlvAf70zs7dIQ7+/pfvO6Y6joGOQs8WO0Vu6JiVjwlzAA9qzcRPMKoZzw8x0M8G0MrvNDG6rs/cTG7QToqvTQzZLzEAYa7J26UPIIjxjyW9A89Us82Owd/p7kB7va71mgVvEuNhLwn0ac8ollcPMbOkLxVzlQ9S546uxcGT7wnAog7db7GPJkoYD1qPwe935/nPKhZfTzT+ei7Xe8TvRvXGr0AXwy8Q5p8vOy0szgkgpU8X9MxvZQINLz1DLi8ZOQ+vF3kiDyVTD484bMtPFYW/DrFekS7xNhWPIuF/Toq4s86iI0XvPWGhDwrLsG8iYgPPWEyLTwpFCO9q9wdvOnyvbs8Nok7zg29vAgWgzwmNI880strPAdF7DwE3o48jX6xO65bJrzQHpa7UcoTPL4Q/jyalLG8YXcVPP5pED1ZB6s83JmrOwKARj3J7rI8WItqPANJmLvHEYc8Czz8PDddZjtqw428hJl5PJ7oMrueOT29RMrRuzKegztfqzQ9xCQVvBezqzomEHM8JjjSui9OGLyz4wU8VoxIPadbfTs/AyC9Pj01uyrFX7130DU7z7/UvHd0rjvyy0+7NIqqu4VPgryVKny8mUmCOOKooTz2dLI8sEfUu4NS8rptwpy8JaY4O2YEOT0CG3A8uR0ou+x9j7uz+G86TQsIPdv74zzW1eS8BwWHvGIOGruKvPs7VyUPPNSe6rwpb7w8D46bvKmSwLs8D4e60/OTvKTHo7r0g587tQrIPFoxrTzySuA6CHVYvDRpMry8sqE5sUbluxDCxryTzxY9QP4gvHVggrzzCQG5/DniPNAwUjxl5rU8T6caPahOIj1unYW86elDO8Jhkbyts7O8sqGgvFvTQb3Z3wY8Wmp2vMhqGT0MfgG80FLDO38hErzhl4M8YQ3buzlHAj15VGo9m3cCvSEpW7yjFlK7AHBhvB6pu7wPkW07MdGjOxuWl7sxbRo8QX+1OrXl9TxruX05ScTgPMyyQzxZhz+8KPfOvJ2eAb3EF+m7JTTLvARjbLsUJ748UXbKvCQQ7bu2ti28U4hXvHU/OzsNaFI63DVovN77JD3ZV0W8Os1rPQHERTww6jE8k5G7OiEXizxpIvu7T4JdvEpxDjzggTu8GwMlO72IXLy82gm7/BIKvD/vJ7ygqSe8VXG5vKYOUjzH3KO7RJ8cvDmhf7vMA4M8g2OMuz6iyzvgYXQ8Jo0VPMBvMT2WRYY8tJF/vIutn7wihIq8TotdO9cClzuJal88OM3qPFi/ND2/Ma284I8NPYMorrxpOMc7MEZBvZtPuTxT9Bc853JMvNRH1Twwioq8/BXFPPTyjDk4v0O83pLMusGtlbwZOvq8aALGvE/RmLzvUym9H2MFvXQgojxx5Eg6U0gtvWqbl7yDaRm9ySWuPI39sTwVcQi9lIlfPNSWoTwfdbm8Z6jxvHoclTtApgU9ZHfBvOlQLL3tdlK7OL6nO+iKtLx3eBW70BN/PLVL0bwaLbe6t9w+O8xr4bvV8o67xvaYPFHicLywuP07kUHfvN+0QzsHBMi7fQUpPcAugrwsQgc8tvKyOmZZ6Tvp+fq8z6XRvIgmwrzS8Ao9rH72u8kZJzxeIAk8rWomvDxygzzNBuk7TclLvHef5jvspNy7tXKuPK1jxjzq8gq79wsfPUKtYTsLro+8+xcovHBIz7tsS8Q7je2AvKSykDw0Dai3almVvKgdQrzRqIs9+RMOvVg/RD04jgY7DTrsuyvzNbxVi3Y8dblZu/NuOLy78lA76obgOsuxkDzaswY9WzUGvbbnFz2GUpk5RNUgPFoRyrzWTTU8+5AqvTpvYDyy/xE70YM7O2T8uLzRBKy7vegyvDIFZbx0cQo8nxXiumcz8zslheU81NDcPG20gjz9F3g71DMAu4o0bDziwOA7Go3IuojkH7vkS2C93ziTvF097rusSly8nkldvOCnvrsLytc7iFaTu3R1zrxc/B08FLHaPLiGtrtuT/07IUkMvR2UlLxCJrm8LnJ1vLa1NL254rC8llSavMWsoby13cU7DvkKvYjPzbm5Awy86hMwPeNlLbxSaLk8stwGvPJsDz0Nfdu6D2Lku4kk8jv/S5a8DyizOw3aJrxCjYo7f/ilOrPbfLvJHxC9/x3fvOhOXLthSL27VoGkO1FLAL3KAhG8OShvOqZ8Yzwn4xc8EAAaPGpZDDwjjJs8w5movGWF/Lwgo7Q8TQnCudh2bbzg7rg7Bj4GvLa7OrsKZFM9fy2FO+u9e7zqfvU7d9ODu9o3U7wzY9W8+X/XvJ9/uLyTko28gr7sOTpWCzxjsTU8CSdmu+OStbucY9Y8fOEOPXE497zbTKK5pV7qvNB7sryYDvo7PHDgOv1ugTtFuyM7H8FtvFz0yzyI43o81rEkvHbnHTwQrvK7pUy2u9PTKr2SIrq8N0z+O50l3rsRz9W8n4+1PL5W6Ds3z667CotPvC5S7LyDtgG934zlPDth97t6T8O8ZEDBO46q8jy+fLO8TpyoOsTHiDsp2L479jOnPCLG0DwEi9e7BS2muxJz6bvcuOa76bSnu+Lo4byJlYw8edQQvWNIsTwmpfK7I7uvvK5/6jx66gY9M6BNu9bcarxgtis8t0fNPPX/Rbwa16K8fQ7LvKlHyrxlqtS8YZMWO99aLLtOJg272gI7vHSr7zw7P7a7CoVdumDudDyZooG8GcoBvE/p0byakIY8bgbHvFEERDxRUKc7WAcHvFOJqDyOhaE8dtdAPQqbprzsWxe8rLmQO5WUsbyj6UA8yCUsPCCUfLx0NbG81tMSPQDeBTydnxS8zL5AvFXdGLsbPu88GQDTPBO9QLwZ3Nk8JsCvvLJa5rmNUYk7e+Y0PHb1yTw/zYa80YAOPekbNrwmXxc8rRohvL8POD2PynQ7mMPvvH6DQD3ziRi8e6ltPFYTnrxRd6i8xZg+PchmAT3mlj+7EWuoPJtuDzyMNrK7hBnSurxBEr2HQYc8/mmLPB85Kj1R69U8tiwdvMZ4jjxHzgS82KDgPGCqFTzby2E70pq5vMvwUDw6ZNo8jtmIvNX36jxYbzw7y2shvGoeCTxBULM71JZpvFAxBb2nMaY75woBPRVixbv6FnA8QByLu7ugB735vZC7ZU8OPPowJz2jmfW69PoIvaSKEDwgr3y8xmaQPH/3qrsR2kY8jiPWu31E47qRZ6o8HCk/Oz7QXLx8iVk71rsMvFG+BD0Dgpw8N5VVOwyS1bxaGkI83AWVvDu5+bu+CMK8ibawvBArhTy7g6s81GYKvOOH7jzmb5U7tUWLO/08qTsLRsu7Vx3DvKsVIzxyvAe86S7PuxtbmryDuTc879CiPNBnmbyTXIc8D9kJPINDUjq8bbO8T12TPDUdZDxUkno8kjcovDT1HL1znvg7/exwPeMBzbzgVcQ8M+coPY9Fpjv++l68o57uvNCf2Lsd+Se99saqvLV13DzQpBS8lHjbOshLh7wrASA9dGBGPNpchryMhQi8ffySvC2Dnzwf+6C8PZ+BO2aAJDzRYya9loKGOvqKgbvLYd287zASPC+HpTxintk8bXc+vPQPfDxI++i7s0ZfPeLpmbuuO1g8BKifvMkL+TvVB7O8uh8SPBSUhbvD9I65vUtOvP81EDxk5ci8mRUfO8d4obw44jw85h+2urdsgzzzz+w8zJAXPM+uEDx2yu+89rmhPEqR/jv7uJ07gMDfO2MJk7wPv+G6FGmlvD21PrzS8yu8fYhvPECE2ryoZ9E8Jb1kPD0YCT2ZU7G5cKNmvH8cwbwtfQM81Mk4PFm+ADwCyZs8ov3IO+eo7TojLxQ8SnCaO7JmC7xQvm861cGJO1XbGTxSxXc8i9sWPK1vqTx2/XY8cDcsuuINyzutbNG8/7GqvFY8Azv+OZA86KSmOrHn5jxcdsE8Hw4gPJouSbzkPQa8Lxjou2rNpTye/AW8o1KvPD/wTjvMfnW8ZJFZvJ43hDvz7OW798yaO0olh7ssPOk7nl0FO6l9aTzE89282ziPvFUXF7z2s6M7V9Z7u+OsUbpgIhm8A+kbvfozozvLgf07APh1PPYfGTwNHpS6wI/aPJ9XgjwcMMy8kgmvvMDHdDw5dVa7ite0vGImGbpWPFe8P0lBujZ4Cz0Witw8Yn2UPLXzQTwHXMi8vGuyOtvW9jz0UFW6yYc4Ov1YFjwS4R685i86vL6KgLzAiS48TLvEPKFt3zvzY6o7Kl2DPJqH4LmuDUC8qODKPBDGdLsBDgg5wAgnPGzNL7xvrAa7MfzRvKPsBDxpphE9/wjKu6GxcjsV5gg8kPv1O/LwzTylmrG8Jrf2O8gOgbv0xVu82F2HPIymzbzkrE09awEIvGYMYby3zg28KxRIucPHwTwfhM68o/VQvOto2bmww8Y8sNTZuzJ0wbtVY/47N13pPPDuSbygQpg7Rk4TPBSFdTzfe6q7jiiVPEszl7qNwBQ8W4lBvN2KbTvivQ48kuIfPcpZtLuatBG9XdsIO4XrQ7xkEIW8fGmiuyUyYrxLvIg8Tee+vMrzFzuZueU8vySPu8E/EzysmAy9YzBCPLjZFjw44zC8t9kHvM2B47x8JyG87KhOPGxoSjyMNaq8VGD+u0GXSjpoj1y81EQUvOX7V7s4I0u8Kh6Wus4dbTw0tig7QAsGu2HmjzsTq9S83VQLvHnLwDzZAqC8Mlaju6rxgDyICJO8OHFpO/Hn5Tw519a8CaESvUw07rzADsc80CaDvHRZq7waooq72TQKvV5eObvTbYg8eF6YO+JhojxZRsO7YLeoPH+/Ezx+Dmw9HtNpvNFRUDw8RCs7JvaAPKcPTT2u3C48tUokPBKr9jw+p0M6B8r0vBv0Bz1RQRW9HzaRvGB5Eb3mW1G7F7Ptuwnwr7xucvS8CjXMu07QLT08sCw8d9ODu63RZTwQLCC83L7uvDf/Nj2XoPs6weXKPFtGBjwa0hU8ojinvH+8CL1M44q8fisKu7Ebmjwn/B08wYC8u4xEcTsNWYA7JwQWPNrJC731i7u8McvFvLg6HDr4mF28IXwxO6apuzyo1CM8p0i2vJjwkzwG01I8epTJPAAhwzwx8xi8npaLPDCw1jyBRwu9JxJ2vC220ro/DhY8Cd6Sui/RxjxPXI88A0nmPAMj/Lzicbc8eMucPJwVjLzIbVW84kYpPPi2iLtK2/W8rCjBOgfBTDwqcyC9Vn5+O/VpNzr5G6A8rEGmvKX1rbzIXoE8/PFeuqL5Uz0Rhi09owIWvKlpMLz1qqc8zEeyvOLIID2ai2i82CIvu7ts+rrGzwC863G+OngHK7uO1Gw8un6YO/ecwrxDtPa73LH2O5Y4ArzPK1s79PkPvKSnwbuy3ui8X5fCO65JsbxCz7E8CZwKvVusHj353C+9y64EvAUu3zsk2RI8HwUIPBpCDbw7hBu6W9KVvGMpbzyRadA8iQSGvdHKzztB45a8B90YvKuXC7xzspa8BbTovHEXjzyzbD07kvbQvKMH/bwHhYE7ozalu218sLzhkrO8aIkmPEPvszz7IW28dneuPF5FBL3fArQ6HUPMPIv7ijzCg4o8SkpgvKtdjryCAMe8TGqduixGiDvpq7a7+7PAvDnUi7vl4Km8PS0FvA12jLvUWYg8ZIrBOk8eprxt8r67zpkqPBvYk7x+gxc6h9HaPPiydDw2RIO6y+kBPYj6EDyYi7g6e0dFvA31ST3TB6Q8ksmQPKVdDryOEYE6yuZ5POR+9zu2S/S81nvbu51eDLtFuvW8ZbGhvMre0bvTBzQ8CPt3utOUIjxQPRm9E0pCvGhxRrvyIB26KpuNPKSS2TwFUO+7BZCZuyu2LD1e0Hc83z2FPMIcVTxeoRy8kVjTux4N0bt/XpE8Yp8hPX0PcDznT4k8Qu5puZBuwzs49T48TJlRPATCSDvOcOa8pgC/O9XSvjzByps8ntuSPGGJADynw847GOfFO1OIlzwQlZe8Zlzau8PCqrw8tNI7V/hCu9wfHLsbyUc8Cie7O4zz/js9z5+5TlChvLfldLwN45A6lg9cvKuP6bslpIe7Fl4rvIuDWrxQzyw89rr7PAPaqTwzfMy73s45vJWABb3F5gi9NWXIuWy7vDyw95q854rDPDG6G7zDVKC8CdWlPCT8hDrtY4m8csxxO+2cozzkRZO8UDszvA7TH7qhk0a9hfaaO2SKWrkS9L28sKU/PLmIJzy1v5Y62UYGPWhFJ70nHbG8BrWAPOyLsjuK1l48eiYuvLgXuDrwPN48y5XRO8/amjyA4aE8LRmPPNhnrLj/Ur48tOBIPU8Y/jtBZF28Om8BumloY7ybcZK80dZSvILEtboUrei8A4Z0u9AuM73YJdQ8p4uzvMYkLL2kudC8dVwHOsg+k7x4F1O8QcMZvTH2ODw+v3i8Rg/MvGXPpbulIL+8PgnXvHVO7LnCmjo7SzLGOzJ1cjtJaVo8iAk9vN5aGb1v9hY91tB+PJE5QLyOlCK9xSShuhWBdzx3ogS9yek5vKbx7jvD1b260umzPLln27yga5O8HGvcPEN4QLzwqj08c6YlPOFnk7xgoQw9lx0pPKytdzv+UKG8MWZwupSACT18qQK9ZApbujqPkTvEQMa8czR9PBRrMDuvPgG90DtrPF6XFb2gL9M6ln0CvN1RcjxMWDS8Kh3EvL2pOrx1mLu7A8HzO3E117w032i7EaKTvDPsZDxo8OW7yF3zvDw/h7semdq8wN/KO95Q7TwkFeC8+QsAvSCqELyGA5C7SOXmPMXE1rw6kSy8RMjBPH4bRTsQwhA91tJRPW1jsjz5VO476PcCvAA3gjzyM4e8oMW4PICqjTstS9Y7mGhkOwQwB7xbFXs72B2ePEOjw7u3dfm7GIiCO1Aprrz6u/G7z6ZPvFNSiDyZFOI7ISUKvFA0ZbzUi+O7+/t0u2ZEUTwOgca6YfoZu2u/cLwxM488086FOzeh0byPEUK7Zx5LvJ84nbwGU4W80ZT6OyINhzzRYRE8nNh0vKx9oDwZfdW8k2cjPKQdezxaVQm9hUKtPMauFj1nBPM6GZq8PEKlcDtjbpi7mjXpukd3hzxmCp08f30EPS9n1zxo3zO8OmHKPNo6Fr3sq9o7Kix1u6TIFDrx+dY8TP3nuDgtgzxmcSc8h0v0O6fogzsBORG8Rw2OPFdwE7wsmYO8UrNMu45qMr0P5MU7y9TavAh0Bb0o8P+8+M/XOb6NJjz2WC+86eW2vFUmFL3fq4y8XQmEvHBw3rtuKTA7ofnpO+DOdLt5o108FwSdvGsuW7xzCwq8kOOEOqL0AD2U5Be8b08vvBQHtbofa2q7j1/puxC64Dz8Sx683rhlu+DfmzrRuQ69KSMFPI0eHb3zgi+5+IYJveqMDjyBmy+82nHjvGOBjTwELhi9Zy+duzFBgbt16Bi8brrfO9AStrzq8vc8Vj8nvCrjAT2zYes7XuOcvI5Zpzxwzim8UvY+PZRhozw2q8k8kRjgvIW1ATzB/FE67VLRPFr+nDttWW48UKG6PJ914rpy01y7/G9yPC37E71DNnQ8UdEgPVlV47yclWY9x0lsvLBrgzzZ07K8pS4kvAPX37znX5q8PIsIvQ8QZTxHNSw8zvg7PBdhbTwoHum8sVjtvEhYTburggO8fSJuvJ0ICj3DCYw8QEvbvA5PATo8ShA9gp8bvdeM1ryl8ue7/UCyPNRxkDzrnbc7muA0OTc0DL2qT2y9/1C2vL2pXbyaYoK8TqPOOnMOXr1CDl880zVnO/HmczvoMFi8b0KauqL5VDydQuG8MZD8OOwaDLz27Vq8VXqJvDGMprvVOxG9dGDDvEHMYjwEtdg8itULPabXZbuPdpY4xFeWu3v1qjzI2zC8mH4mvfDNzDzoLuW7tRs5PBOlGzywO2k50N/BvHZ3ejys+k+85rScu/DfgTxQ76S8vSmAvCoFAL2QRYK7Rl6PPJQA2Tx2oiO8se27vB0dwbylWW27AKLaPC7YuTwIb6g8ZYmSvBdRtjztiRe9y6OPPHJ/ozy0kyC8m1kvutp1tbpYjxC9NqXXvFYhl7tdo7S7m+NkvNwG07vyLbu7ALw8vC6Z0jxfqrs8e8BHvGAtkzxxY+C7oWmqOpWhFjwyw2C9l1axPECDFDzL7SK9F5uZvHILAjxjXFi8CnKquUtBzzxNHss7P4nVPLVAZbzFuD+8xyiqu0dTSLwo0pC7lS9EO3HwkbvXGhC8nBqIPBo0krxr36g80VgUPKRWAb31hQO8CW7SPHxAMLyYOmG8Rl4DPYKaoruF6Ge7ekZXvD8mrrwufa84pfAgPHElhbv+4UU73m40PRNGaDz5Hr88q3uGOywDyTyf9PG6qMBivSkCFTy66+O8pOaDuhR2NLw8KIA8y2yPPC55grwfM7Y8zuFNPUaJITxpDpk8gc8BvSnqxbyS/q87SJqOPBP68LyvJ9U7CFPpOv4uobrKh1k7iPqSuyjOrTv5KAW9gCYpPPZqDDwT0PU7WbusvIl3c7s5tu+7fTzHu64Bwjw3o0g8D6uAvLT+TrzcgIq6lQyGOghZRzni9SM9SxA0vEOsQL3HA3U8LBnbu28a/zqz5QS9akuFtzRUdDzhCLY87562u6rrFT1dLsa8OkAau46Q/byeFbY7DRX0PFspwLxqufq75Cu7PHanSzyKGy+84lGfOyWV87v0rQA9hHGYPN8HOburgkg8f/ZVPOOjUzyne0w8xftmuioa97sZ08C64dulvFIsQDxS5I87zeOuPKguOrvCMZQ8ocR8PBRcubndaH88zzmuu2ITDb3br6K88SAMOkB32jxr/+M77hzqO3TosTwSf4K8l8K/PIsVm7vhASW9OciDO9y6DLoRFfI88dVQvMrsiLwxsfk7l7GJPAqaeryNN6W87GUzvDxFjrwXgBi96bmmO3PcMbuiNgs99yrlPK9clzqdl948ufp6ulONEb2LTwy8fmGXPGFVGDyaqNY8p4u4PP6fHzvea8K8RQ4xu9uTMzpqVnc76FKqPDWJy7vWp9y8F4oxPNVE8jsuK6U8/6a+u6kDDzxLZxE8ZNowPENjkDscUg28fEroPBD77robm4I81sCluZX/ojzwm/W7BsTkPFJdqjz9Zd67Ye/KPASHgTyE5RO8/CiIPIzVxTzBWk68JmIAvFeQezw3CLg8NsIJOcx2P7wZdOM7+PL5PJxOgTsXmJc8MmacO7eb2DvzZe68L4bbvO5yLjw4LHq8OV6Xu2iW77sY9Yi8Edu9PAy0krwJE0C7BYcgPC69lrzMMoU8zd0HvA==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_multiple_appends_separate_entries.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_multiple_appends_separate_entries.yaml
deleted file mode 100644
index 20321446..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_multiple_appends_separate_entries.yaml
+++ /dev/null
@@ -1,162 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: fBiAuV0A37x9cpu8MldhvIUktrrMji48KD6OPfOBDj0Lc7Y6WPwBvSlEkT3KlU08+UnWueLDDrxz8nA8U+YsPI0eejyjjmq9X0mavPDKEjk+94a7VK3RvNBIOT1dW248mtCCvKn4/zy5HpG8LXOlvOoWCbvTNGg9N6jMPDAgXDw7Cxi6rN5/PCun6TnyX8e83LsIvG54bro0T3o73z15uyx3UDv5b7Q7SZQ7O2Jl7jzFsCy8aJ9FvY17zjvWNsY8QkWQOVIzAL2hfc66lO7Wu1bdzDyvpK+8zCfhPC/dPD2vEUo8l6abu2ObnjtMWLi8tI25u/LZgbrnnAm8x/DTu4eJzrtSDaO7gnAgvKuvdzrdsRg80tmNO6erlrwDShc9PvXxu47uszxefMk7xAcivKhJKrw5gxU9CLywvN3VdDt6yom8AGiCOftU+TtfT1Q90VpEO4n+wDtEwuQ7imCUumizIruXFBK8IpevuvQQXTydluA69z+zPGLS4jpsDZU7cl9EvJ+2BLxMko+7ck2HOtDlAjyxNU28G23mvPyAYrsUBpu8a3pxPG2TLbwLhnM8/yK8PNaEvDuhGZQ7VSgpu4FUWzszPpU71nyBuoYyJzw9NBm8ykrcPLycwjuBjlI9SaaUu2KNjTze8MU7TlkNvc1R8DuMGwy99YZXPOz1rDwbZ0+8kOUhu0QM7jtP/v+7ho+nOpjYXrwD0Sw8z/JIvGa2EjteJpa8D11Tu2/msTwylCQ8QlL+OzaBEjrJSd08LNUiPCa+fL0st6u8mRMMPQUPVzxjuQA8ivqwPF+LrbxCMo471nrQO4TVCzyf/JI8gjbsu7UTrjwgSba6FCVLu/8n1TuGrOq7xX6rPIKkFj1+oW28tyNbPNVINr3npzi8issFO1GiB7xatkA84cEYu+VCWrxK+x08yENSPFQcFzxsQ527ZPJCOv8Fwzmhtle7mBXcOytakjyZpQm9uyYqO4kBajujsj08cZIcvLMP7jzDjgA8HK/3Om0sjLxbI7e8y6nLutr+PTwffci7cnhMvD962LxWD+O89/iWuwHmszxxVNI8NWYLvKCH57s9Ywm8dbxJO83DOzxgKOO5kASYu/5V8DpyaOA5G0ljPJOp2bvangm76bWqvU2JGLx0Xe27VACKvIeco7w+bjw7rE8qPb6r8TrqnxQ8n4Bvu3MDZjoHCBG8D9eVPAHMWrung3o8warhO4f+F7wfqxs9YzzqusrDVztNiaO8RoeKuw5RbDz8YoE6BJmau+khEzzN+v+7KO4PvIAL5rwslo474TsiPFmDhzzYZfW7mjX9u42KLry8N4E8oxORvHMTLzzTBzQ7rw6XPJWdM7zw/pU7ukcTvBVR6rvwW608MkU2vFkeMDwh/ea5nt9tPBPPtroe5uq6mCmhPHcFKr1y26083J/lurJamjxqzwm8maKsPJKqsDtKQTs7VW4mvCdhAL0GImy8FxqVPKFa4zvA5Oc6aMdAPN5uA7yPsmi7GSTMO0zIVTzl1IO7vLuoO1NjHD1P08g6enp/vBBO1Ty694u6RU56vdeSsDxK6zG7eEPzvPyAGj24E7U80ZSvukA7Dr3LD7K8y/rjOlrTHzyj0Hy8PVjEOy4YWjwMniA8yD+luxhgODx4L2Y8QTFRvJHStbwWNW+8XKr2O+heHLt6H9w7vK0MuiSDuLtj1UG8wYPpuxtlVTqcfpw8lPA+vceTYry4MjG7J/wvvfUwrLzBANY7GWEovJbdbbxsKGS8YiQpPSSrxTxLGti803K0u2+LoDw7Iq+77ToAvJUhCT3YrgC7vTt3vEiGIDyWqfw7f+C2vC3oo7x4XOQ6rYJYvBdkQzxbO5282RxRvNHc5bzz+D+9bzbju73Cab1tqTe87W2dvGdNg7zfN4m8NWTxvPWQhrt0+rC7OcigObgqujyXdcI77NO1PPzAAzzkNgi6/LTovJEwWLyh19I7Emm4uu0DejsAKEu8r+GJPPc2MzxXmQo7hzyZvCxL2LxiGni8euUZPH7ozTuL6G46oeDouwTciLztV7U7cWuKOn9IADzojYW8xAcxPCQfkboGmZY8C2+Hu8o11ruYSBk8l62xPFnkTr0icGc8+fmMO1L3tTx4lBW9QO/DvFtc97yyDA+8WbT4vNfEsTzhNt08efW5vNyNYjzH5zE9yoHUO/sGTbwSgK88Cy6nu+jPXjwR4ag8K+ibPAnHlDzkkuQ8ly1KvAXafL02BkU6MWMIvMuyH7tH4wg83d7jO0UbmTyY5Cg8/FePvMZ7VTxcUWi8Ow54PM3hkTusQAq8aF4XPcbtqjx/TbC8OFp9PP2Lar0qXAw9Iiuou1CCoju8HnK5Z+E8vCRiEz1KpgS7DSYivTslxDvTUFw8KM0VvBy8Zzmtz1a9D+73uJW4U7uj3aY7yGU7vT+/Or1F/t88WTjQO64hVbzWFKS8vLXOOhZUVL2fd648xcSkvAUKLzzUkge8cwmIvDDfA70PzZ08hwRLOjGvpTwTkZe8cqZou0OBubz1Ef472zREPCVPMTsn/Qk7dNelOjh6jrzbOca7NI/buxLwNTyGYX28RVnBu98OIrwEdbi7JabUPLQhIz2k2TI5/XgoPObvKTrlGY+84R4FPBJ4j7yn3U283Z7hOguWhDxyF568foBzO1WS2jpYQrc8iIYbPDqTVzvBdR68cnvkvJONDDywags934lXvPQMtjvV7F081WuCvBD5FDy7QqI8f2T/vBAFlbsytS67TOhcPY9nwzuLNbw7xfTEOxiRwDx9QSm9XQz4PO3iD7wrHKC8dEwDPOKL8TxK44m9K0grvETh1Lw0Axq9YPv3PPfyDr37Id47Or68vMbDNzws9+05k7uRPKOBK7t5D5M8mFNRvMTWeDw4Wp68ZsRpPKNXgLs5/Hm8xWcPPEVpIrs7AYA8a9NpOtJffjzH4FO89ZpJusriCr1C7LG7mLHDPAWYETyClqs8RMAFPbJZAT1eBgE91ynnPALEmbvQtjC8hEeOu8Q/irx5pD68ASQTu2y1T7x0DAs5ujZGvFczTbyBTHg8xuSpOyBlOb03Eow8q7MLvYHHBDvQ/RM86ceTPM7NIrz1pDY8yBBnO+5kybwHqco8bAekOblzFL1H/2g74mdlO8a+D70IFQS9kcOtPNDy67sdjU08oHbVPOMQXbvh0gS9kxpEOZ5AkLw6gkc72JAhPTNNELw+wBY7i5qWPIYp5rsldBM8uqPJvCmKljz/5Qc70wN2uhE1C704Sxy7eRqQvIxO4DyhzTA9fJIzPJAsnzrb4Z68W6XWu8PM47o3xMk6Whl3vNQ9mLxYnqq9B94wPeYYqLyGWEe8+NcPu0yORb3oIna8YjgKvHADtLzZh1I8OwUnu8QmLrylwQA8gzjXvJ/ZILwiaCe9FQ3uu7PbgDwk3c0807SuPMtUdLus9eI84V/xO0NZgLx6zvC8hiQuvYOeADxn//66P2GCvNnSg7wCQxG8zuqeu4wlz7zO8xg9ara1PK7dML3eE+E8+Ft/PPUM0TtrAt48iUVfPE5k6zpJP/M5QRFevewCgryvjmg9ry8WPPoWsjzWdfa8WiFUO9pZa7r20sQ6K/u4u+eOLz1hOx49twawPDXej7v8Mdw8IJTxO6f8ebzbGAO80e/fPHyNEj3Hs5K94xdFPOeL4bsT8865w8oavB1GIz1fOww8QEGzO90hATt52pq8BrPrPHmMyjymuPG76cbju8qNEbwd6EK6xwtrvDe2HbxFdaG8sJSBPbfti7wO6Jo8KHAnOwv5ILtm5ae8OlcAOq8LD73Tens8vxgeunag4DyOHZK8FZthvExk0bzjbDS78e5kPOuDAj23Lcu8N+gFvAxrpTuEzqO8jv3QPEVPkrzLKNa6tEAdPVOAtjyiJxi6IbZAvOA5rDwTQKa8jJuGPK/0ljtjtqI77YtoPPnGTLyGZSA82wK4vAVgqzxyf8Q8KuafPE01/7mXJ9u7tooGvIDC8juHmak8zt2vvNyBuzyen4q8AYcavKWt1DyV2CA8tLaWOj4iDTxQn4039q2zPPZdlbxvNR+8U7zoPNGN8btOPqy8BShVvDD6Oz3JeQQ87X/PvM+yRTw5ye28HlK5PB2EjTtTkya7RHzEuwnLcjxsIEI8sUFPvG0lBz0EElO7SnPAOawQBL184Lm8xqoTvV9JtLsWEa67iyeKvOZwIb1+zOm89o6nvEoc2TxycY08sUggvaVCCr2McCo9hKCcuBGFtztIkGi85hRGvUYHmLuv+4k82z6HvMUWCzzcp8m6V7xvu0SEqjy0Lws8BInEvAWipjp0HAM8NhwEPP+DBLzISoK65Hx3O4nQED0i2s48d/LqPF+l5bxmgYW8ERNyPZuZQTwfPDA8KITRul6cm7vIBj28nZU7vOEG0Dxu/Tu8QNGgPMyjt7uAta074fMhu9zw7zn/a3c5UrB4vJoPobyf6Hq77s1HvWA7KL0Vi1q8vKOtPOtrSDzxjXq85povvErLIzz7sya8VDcOu6W5lTytrKw8afkVuzsMSLwQklw9lPGKuxJY5DpCjYg8B3yaPA+OzTz2B/S750qpPPwy4DwYddU4POY4vTkF5Lx0Xfy6CiF5PFAPJ7w9IR88i4GXu+0B0LxasJk69hiwu4UeHD0RldS8v+JhuhN7CTxrAaI8nyYOPNofh7vgVBc9xPMNvHEiEr0hKq28hPYGPSX8fbwwrZm81VQAPAs8+jwb1BA8b+bFvHxeHz1Y6RK8mce1PB/aOjykBge8Uzt5O1InSzzj/Ma8opuIPOI1wbsGCLi7cwSsPLBlEDzy7DQ88YCQPCREGD2q3Is8n4Klu0E1qLyY1A67TKOXPARj1LsdxMQ8PXKtPHF947yCP268U4iZu7z6ILxTg3E9VOZ/PC7SUb19oxE8U8hWvAkHEbxolyO7EUMYPb3g37s2yy697LGAPLwJXr2cD4q8k2zFOyR2YbvDz5a86rQCvEO9f7wu/m+8ZHynPM2rrTvA6ac8lrMGvXW+rLzc65q8DTZ+PLS4Yjw9q8A81vCWvMEBuLjV+AU9jByJPCWGdz2Jdv87C/dnPL41VjwLh8M8B0UQuzcFjDzDJlu71elEvFdiCjw2IDs8pr3LvNiwury2PxU9S24HussCDrzsqx28uwMRvJMKzTw1SwM8RraTusIv7rtVsJ88PSZGvT5eQ7ysNSu8dD2UPI2SkrtcCNM8ikMxPQAoFjziagK9Ak2fPI8VIbzkvF48VJBjvHN0+bylrpk8oemyvDc3cDyN/9e8AZUlPXovyLwUIgY61D3iu6N0Rzx7lDc8Bgs2vaXJmjy5NaI8ZnVWO+tL9rzCOvk8jY+ePCsJObtSWcA8hmzSvPBCDj0voDk8xa+aPL96ZzyjqbE5cDC+O+hMbLzVIG47wghjuwJD3bxmwla6hIk0vPtcGLuPbdO8y+uJvMlkBLzjIqy8lkhkvJXRGz2raQC9s3ppPM5OszzhyHM8NXvvvH1/irsVlMG8so+rvEgyJ7znydm6ufd3vJN5Bb03NYy8h8bkvF/jmTsEFI68DccCu9ZvGj3CgwC77VS8vAr22LyOo3s65HNqvO6ZAjy4PE49OvofuhhHaz2M5+g6q9GVvAm1iLvGYJU8IC/FPCglDTzD+Lm8lWjXumAjLbpWnG87ERYoPX2I2DstFVe8+iwGvTTZsbu5Lrw8fBJYvFiRHDp9VNE8tP3lvJq+IjsDAL48gWWdPBpv+bxpBi2837ubO1kyAryhH9M7gPU6vfAPI7xFzkm7jG8NPL68HrwK4Fc8qVzzPGwH3rsrAnW83CpfvCKDkjxgpZg8PnNuvMMJcTzxUQQ938AQvZwWkLxPirM8/6RnvJvP/7viWYY7fi9svJAozzz65KY8bkSoOoPRzTzB2SQ7fFU/PMQTx7x+P1i7QQ+Cu9dXlzvfTgC7VhS+O76YWLy6F4g8XjE2u0nwTzzjq/+8KOn1vHXxxDr6RDQ8N3yDvGz8mTw1ZQy8NisevPdtM7x3P0+8+VmUvM/EVTsI5/y6mBzOu6/1jjxvo9i8ERm4PEPQMD0blc079kORPJJc0zs9z708OhJOPGWX0juqxfg76eCcvNJw4Ly8bUA9ZcwDvcGp9jzIRg47ImncPPxpbTvPoZW86dIHPBgHYLwxzCi80H5MPNTeJzz+E9e74df+O2v/PT33XeG8WVSFOv7ujjyCIo488NTkvNVQuDw3m4o7JiNBPMTCyjyKCUq8s/cLvZNYzLzL/1c8G7nWO4lBJLzUJgw8gQC7PM1jbTzrvJ086LQqvCebLTt0iH+8KI1JvPncKjvPlPq8yzHaPO01hTsFfAc8ixrMvFSZ07rZbb06JbNrPEAEkrzuFQ48XVqlPOTmmjxl58c7npZCvUrsHL1m6KK8WkMYvPjgBL0aXiC8fmpyPNmqiDwaNam8hcBHvVROvrv2Ere8+vtgPJ0TQbze19Q8p/YPO1d0yjz9yKW806nfOkCFdDtRJWe8ca7FPOvyLLxCCx89vODKO30YTrxHVgK9FFo1vKmHHzwzXZ68O8+7O3aTh7xINlY8LGmDO+95NbwKwws9BbMuO+9v1rqJP1082p+pvBCbQbwofh08E509PSApLDy1MME607dNu8L2pzw59dk754aDPFCJZ7yBwk48eyhsPCeBN7zE9gC96FPGuxNd8zzorgs98OS0O2p+K7uGg9I7gzNBvIPzfjveExw9H7uevAzbG71ZZMo7mluHO9DVr7z/YK+82vkIvb3SzbpyC9U7A05nOjTq9zzU++c8CUr7PFzK0juQR727ccwMvV+T87z4a4S8x21zO9hGSbt8Zs68kVpXuwv7DTvZE2u8Roc4PDnaLr2Cxgq8eDzOu2UrH7ui0we9d0CvvK8SJzwEH4o8R4EhvLggELw6S8i7TbyTPPAjezyNAji86jyBPJd7j7q88Sq8s4YGPWhs6rsvYTY84ssQvUlCxTwxAhu8hanju0hAUj3rN648L8i0u6jsALwSCkA8TysOPQaSMryzdVi883UVvVQP3rw49hO7BUAvPYJ3o7vTBx09K6c8PEocGTz7Eaq7fh2OvIvd2rslabI8TiUAu9c2X7y1YFE8fx4qvFSirTwgf5o793ITPImTqDujh0y6PX+pOT/9Hr266n27X/MdPZPHWryLuoy6CJnVO4iM1rzkfaA8ToSjPHVmb7weJyo8BMnRvEj+8zyQpMA8JuybPL1bHz1HEKo6F8u6vGWbiTwH6Ra8hDhVvPtxbbw+jxO9jpKNPOS0BzzeA2a8JPPQO6ofJjwdW6I8D9m6vL7qCD0b8K86LXhqPZCPxbx5ini8zTtDPZ4GAD2H9r+7IX67PJewxLssjr+8bvj5O/nMBLwc+P47ufS2uxMwrjxdqiU7xvZLvHhSR7uQs0s8eCk1PDs/G7zbJIU8UxKJPHKvKTxU3LE8XVFXPH1Wq7sCft86kmbpO+1m8jz38Oy87m/jvKdnUDo9phM9YBFdPMA9kjvasEg8+lT3Ox2S/LwM4be8VG+PO9ZaNT0nMR29xqJ0vHI0XDwjRYk8NZ0/PBXr2bwbWG47GEkrvCQC3LqFSz88HVHpuVwh+TuQKGS8ZTqfvEJsCTwpufA8VZR4O0w4mDycfo06fVcCPc81ALyY27u82zZPvArCIDzcbsk8Z4/pPOaQpDzpQ4I8st4DPH1PMrt2spU8NrznO4c12zudfXA71iGqvJ1shzuPJAE9Q708PQZAvjuoOwg7GfkiPHRETbordoY8NZfuPACWZDwNPBo7IWPnvIFmM70z19e8fNS0PH+6RryC4Xs8lQ1mPHQ3nLx5qY67vVAWvJYjsjgVMMq7+d0gvQqftzyVyDo87BULvMOiP73pseo8xiltvP2p1LzfOxk7fsODvJIKM7ore9u6ieefu6diDbz/5ES9KpZ+vH9v1byfJIO8Vi88ODnmezs7LG8853upvDbKmzuYmb88HOOFO/O8WLyMITk8XDwkvWS6Wj20n8O8R/xwuwkFxzz/yVo7L8a0uz40oLz5N6+8vfQ/vGebZ7vtfKE7LKEFvGgQizx9B8s7HAMRPOn8tju7+iS8gbW2vHrUmzxD+Ym7vfXovBZ3hbwEmhE823DYvJYrLLttQR28uUS0PIr5u7uxdVc7ZiZLvCeWeLzO82+7zlUQPWYYL71gjQ280sKrPH99p7zN4jc8LpIgvKf1bbtORj+7TrAAvCb7oDupKc28gmMyPafEvbyGxac8pkWOvL5icTxJ7wQ9dyVPPM6uLruA8p48xb01vV7iZDysLIm7oH3IvGiYxjxeo/E8xgIIufb6YDxpIAM9VZG/vCIRoTzufjK9F6ZMuyS3Or353eO8M5DjOsKyzTyQrhS9zYd0vFZDyLyYV4A8gcZcu9ylwbstZ1W8mcjNO48vkrx63AC9tcZSuWqwPLyjuDg7ttLavDEJJDyy9Jy707TKO0nVOD0rfWO77SA1PN0cDrxLECK9isV4vDSbvDy98Jy8KluUu8p/urzTuiC9K+qNPG5uGD0OUeU8dk+rPObVADxI4tU8xuCevM4mEj3nFjM80jDRvJoGkzvtaaK8ENyrPIdUhzx0JQS8PpLcPOPmNzxFqLY88x6JPP8dLTx6v4E7UKSuPN/lDLu17uO824cmParcTLupjZO8Yb3IvJHLhDsBFio8soMEPC+knTty3ly8+NCEPM8jijyrZmK8qYX2PKIkPbvQ5oK8U8IEOyybF7xwLCA9cqRpvDTWobxbBy48fx+BPJsYlLtxYDq9HqxbPII0IDtPWOU6Bx7Gu8kSRjwp5oE7oEHCOhDlHL3UrU87zj5EvAWfBz0PJ1C8sf4TvNkU8bvl0Jy75w7oupjuIjz36lu9zfiaPO5ec7x8WJO8dE8pvMJtUzluZ5C7mg7dvPrcmjsjSVo8gdBdPBwpwrgEBsu8GlAuuxdcRrqgzkS98uEsvPhIOLyUcS67SjffvC7H5bxOmBA6JG8TPOBeFbxVOVg8nNp4vLl7hDwWSwI9PS7OPGGg0LynRyk8arJ6u6G8XzxVGrY8ZqReu1X/lTxojGa8hYrLu+YkLTy15y28k7ABO/q+eDzAES+8BNlWOyXMujz6B6O8EJIRvXBBUr1+q5M8lMo4u6cLmDygyD09qC8bu0cIKDwN7xY80SNjOzFakDyj4bE71OlwPMsRWLx34sG8S4f+vAM0KLvIJ9S7094pO0AzID3f0kw72K76Nr6iJjzSpyg6YtMJPBpKCz2WK9i7Jud3vHYaRDwMlkS8EWwFvPD0krtDzUU8+EUYPNA+KzxLCVg5rXObu+F40Dsunhk6+MBovMvxLz1gTa48SB5+u+Ix6Dx96GK85TCVvF3197umzw87FC17PFP+H7xB3iU8oP/IvL3PgTskWfm7JIQWvVKP2Lw/hO+7jq2YvDdA5jtEmHE613OZu7TJjrulmt481wH5vFsbo7z2E4k8kW6LvD0najyMRWe85DkhPG2v8DoGjiC8gisyvPmCYjvx6+47zgruPHs5JDw584s8pO90vESGpLumSO88k50cPfHJhbvKkM+8lxuau/LyPzzKI5C8IQmSu3OMhLwsLNm8lwg1Pd+ZkTsyMqQ8htcEvNiopbyA/X88gGoOvWxegzyfjlU6edvVu93FDjw0mqa8CBL8O/BN/rgKG4U5pvN6vEra27yFaxI7NOIFuYrOHbuytQc81NUkvFyj8bpfv+q8ehyLu3eh4DsixEA77QicvHPT7Lxm+F26S2CmvJ1e7rsm7V28inICPD7dHj1qmAo8hlsGPEK9ZTw5SuY8xaozPA8407wT9Oq7s7XTvFbg4juH9Z+4ofRzOxnZuTtuY5E8u4Q7vK7ctbw0Ew+8LQ6kvIiDMjz52Wk8VnXFuyYWnryDuTu82bFxvO7Nrzwbeo68c/UGPJYms7wmRsy6qEJgO92vJr0WP2U8VBX8PGJafDuOUhM8vS0hvDAfNDxg/TA8IkQbvezFU7v4Ake8ue7EPC7+Ibp5B6882qgqvLn3ULwnHBw9zknKOiGEPL2hwsU82qDbO0FJabxMDmi7WaW2PPQVpjy/3Gu6ZIXKPJDaf7weDCW8u+v3PIiIIT2A8ZI6DaLSPBoV4jxN7qE7PEhvPAydErxJgTy7sdCVPP9iRDwIYoE8RmEmPMcqnbyb0hW6LrTtvJlU4zreL6a8ehafvD+XhTv+VcQ8zAN0OhAkgjsTD5g823IwvIR9FT17W7M8OpYsvAE2SDwZMJe8LJOpOhPpc7ucOIM8QYgNvbbB7rtnMiw996fpOqS/DTxBjfI86Q2rPDrRF7sJY5K7+m4RPfWVzTwn6+06W+OHvMpyeTyRaoo8V7vAumT1jzt53dO8coA9PGlgo7yZoQ09uEhmO8mukDvTWhY7m2+nvE26Fj0FYVs8QHfevICwNbuZUok8JuimPKW4FDyNnR09lhZPPDEzkbog0Yc7AzpTO3/bnDzPiDg8V02JPAMb+7wz6Aq9ToqEuwUuAL1Awsm8mGQWPcxDi7zlipG8SzIgvLjQ4bw7fzq8du3IPEWF6bszzJw8RVMquu27nbwsUtG8e3EAvM1rLTwUffO8x1YvPJ/kQjvKj7+7WPe9PI5ChbxFlSG8+zLBO7foID0hG2W8htbEOzorEjqr8Je7B30KvX2MeLz2wUc8753oO4jJ2jvLF4S8TwrIPDqEIz1Akxs7EkfCPBGPj7zV7BG8wMojPO5yPTzzfMu7jNzZPJeF+LyzXJO8hwCpOYLQZbzoGfC7eendvOXugjvbaDk8xGkbvVYNkbrFdz279q+YPLxzD7vHCjs8LfoGvVwPYTw9v2w7AEWPO8Zd0bvZaZI6tvKTvMgtMrxtpLm8KXH5PK6tZjxLegi8FBcfvPJ4Zrwvs/a7FU+qPNdPl7we+D88wpEiPdZllDxLCps8xhmSPE9W7bxF+ka80HCCPJEhrrt8mCA887Rzu3Q10TyoIqq8pC/0O0ApmTyO2cG8Gh92uzhsC7xtnNG8sgEIPX0/Fb1zdPS8J18NvE+zjbwPXGA8KYCZvNiGEDw7a4w8+WLQvJs2GTonY7u8sKsZvfSTnzz9Lt+8fvTTvKfejrm5Tw299VJ1vGicxri8SQE7Z/ayum4XWzzmRJu8XCiUPL/mR7zFNOc8Qld9vJebwzovQDm6JwPLPAduRTw6x2M866W/PAJXjLsBV1i8pwHHPCkkOrxyTXs6EMNEPLKYkzsT0HQ6qCQIubplejz+HCw8bksUPGOJHTw3+9i8jtjKOtD++Lxz3Hg8AdWFu7DQgLxBWp+8yhEwPPBAVLtibuS8sIWNvDJ6G70+5dg8eODDu5/aJ72mh3Q8Q/HVPCni2LybDYU8tEm8u4wK3TxooGc7NfWhvHO+ZLxGeBu9CQmLvPiJwzseeJ66M1VDOySlnjz+KJG6Q36DO82thzsJjKC79tpTPK33eTx4YKQ8wp2yOr2IBzsih+67f56/PMFQwDxdbMY8uzSAO4aiArzxPmU8ga0bvEDyWbxO3jS8EhgGO+JznTwQsXA82Y7Ou90gyDuW7EW8oNOnOlss2bw+oe67J3APPfINDb0AcGS8nQ7dvE+gmLwn0tm8Vc+6POdjoTv5RwY8ktfdvEbC/Dtqwxq9L2O4POh5yTq7mp88wRv/uyDYSbwwcp87VQlAPXyTkDuAoti6QbZNPd0gpjwZdt884aiQPM/eFDwBjpi64DrUOv0HSTxc1ZW8lf6JvC0ybDo+MT+9jss9OzwSQ71jd0w8c01RvDmnBjypyme8jSlku9hwgbwea2C95bc+uxMPALsLqb47CHnhu3s41byPEsY8hBaKuuOlzjw8wi28oRoWvTan6jzrXSi9pqcrPDz7ZLw+tuw7uieqO2ztejp+lwW8RAUVPZLT5rsB3uG8PR4cvONZRDwSPgi606TcPKlay7tQMkQ8i/GrPIBxP7tFAOE8wNxdPKkgfzyGdIu8bKOWPNDa1bvJxdG88FkFvIReobp6LKO8KkamPFiqMDuPjqk7JM/NvKqkebyGoP+7j3+nOXiRmjz7gbQ85/TLvGNDojqBqfC7vvqkvJ09xDvWnii8D8vpOov3/rumz7Y790TUu1CS5bwPMUK9e28gvBQgBDwZYyS7ZZn0ugqD6rwH8oG86C9Uu9CkNbsPKoq8Av0qPN4KorykSiI8OPBWPAROEb0vG0I90OfBvOSWs7yGY768gTQ7PDr7y7tK6mE8szSQPLkrcrwKA40703LNvEB4xDvoiP+7VpKZvDehSjw6uva7iajrPKsbuzzbpFc5u9WROiNZlTxX3K67VuahvMu6YrtloNa7v3NBPKlOjryT22O8LnvVuyEegruN0z88ecjXuwn6cb0DCNo86jMPPXdqIbyhgJs7l46dvPdDoDoGSEY5m2s5u0ato7tg3YE87NsCvbW8abwbHea7Y9qFu+LJxjz7zey7o0pUuwjLObxNfjW8+7hzu1+kIjyetMA8QclPPNbaNztP38Q8/AjbvO5fdbzBY0y6gZFPvDNg7Tr2zLo6ufbXPL3smzxC5eW8ThCEvAgDED19k+Q8XJulvOUwvjtydPa8aDdtvJ0RBTqb4ke8qBiIvLJqSTx0SK87/dTDvM3xwDtyiqU8tvRfOtcE8rzX0hE8LkJDu8X1hDo5vqO8C9mTvJmLEDzCtoW7bjWKO5wA6rwr1/K7RluFuhqYODw2qq08va+fPLd3yjwGA3E8sO/hvCWtyLudP/m7WsnzvGIWqbtZgw69mlYZukAjCL1PeeY60Iv3PBdOl7sniZq7u/0ZPSf0JbwrY6Q8yeAAPJXOCDyMW7u74f6tPG8f7rwXd+i7sjUhPBln3rupsj28puf8umECLLyCH5O8ns7PPFJUxzyHnZW8auE3vUeRKbtRjIW8aCwkOyjolbygUAG8tmFIvKm3CD3ChHw8KoHEuri5pjsP+wE95/4APVz/5byhQss7OQynPOUbnTxkSSC9uwa4POhndLyum/m6jfCvPB2UiTq3Cdm8EuHzOo1c8jtWX1I8HX07vKuFobxpMIq8K9MkPdOMHTxtsk+8xOklvMJoOr3wvk06vMRiPLiXNrxuqOq7PGtKvMuqmjzdQgO7YFU8PaoEArzmvxi8tGM0u9A34TwGrQY8Qu6CvMOSo7v3bUY8/KQ7PeCrvTyjI0s8boibvJWYGb2an0W8Mgu6vOpQKzzsMvo8ocgKvJ4j7TzmTQ68LTt4PE5S/TvB1LS8aTsEvBa1AbyzH+E7mxAEOtvLYLw0ecQ5LdiFuxGzS7sqIYi842ioPDn3mryqWp28UwD/O/ixyLsFlQ89ooeUPP5eorq/Eg88z5Ylu9jwibyjlkA42dnYOzfznbxCMGk8soM4PGOXrryYZmu8wk7YvEgj8juxIQQ8TTPjOhFrnrzpYtW8TzS0PJW5gzyNhoE79MVUuvgMkzyNO587/X/MPL9osjwhK0+6L0XAPBw1ujwNzee5T1L5PJNnjjpu6QK7kNWkPIg+ubzPC8o8fD03vGMryDsp60w8JXwnOYLBW7yBR8A7T2goPZOGCrz+odQ8fdL4uBeShbzAArq8WXdyvLcE+ToW4As8ZX4IvBdNw7wf8tC88mgZvc5Ygry5QJk6kFvHvDXaojwX3gg99Dj/O6kKoLx1BLi8+60qPOr5obwYxGA8RD+Iug==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '80'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_populates_citations_history.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_populates_citations_history.yaml
deleted file mode 100644
index fc529b6c..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_populates_citations_history.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_formatted_output.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_formatted_output.yaml
deleted file mode 100644
index fc529b6c..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_formatted_output.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_populates_citations.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_populates_citations.yaml
deleted file mode 100644
index fc529b6c..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_populates_citations.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: fBiAuV0A37x9cpu8MldhvIUktrrMji48KD6OPfOBDj0Lc7Y6WPwBvSlEkT3KlU08+UnWueLDDrxz8nA8U+YsPI0eejyjjmq9X0mavPDKEjk+94a7VK3RvNBIOT1dW248mtCCvKn4/zy5HpG8LXOlvOoWCbvTNGg9N6jMPDAgXDw7Cxi6rN5/PCun6TnyX8e83LsIvG54bro0T3o73z15uyx3UDv5b7Q7SZQ7O2Jl7jzFsCy8aJ9FvY17zjvWNsY8QkWQOVIzAL2hfc66lO7Wu1bdzDyvpK+8zCfhPC/dPD2vEUo8l6abu2ObnjtMWLi8tI25u/LZgbrnnAm8x/DTu4eJzrtSDaO7gnAgvKuvdzrdsRg80tmNO6erlrwDShc9PvXxu47uszxefMk7xAcivKhJKrw5gxU9CLywvN3VdDt6yom8AGiCOftU+TtfT1Q90VpEO4n+wDtEwuQ7imCUumizIruXFBK8IpevuvQQXTydluA69z+zPGLS4jpsDZU7cl9EvJ+2BLxMko+7ck2HOtDlAjyxNU28G23mvPyAYrsUBpu8a3pxPG2TLbwLhnM8/yK8PNaEvDuhGZQ7VSgpu4FUWzszPpU71nyBuoYyJzw9NBm8ykrcPLycwjuBjlI9SaaUu2KNjTze8MU7TlkNvc1R8DuMGwy99YZXPOz1rDwbZ0+8kOUhu0QM7jtP/v+7ho+nOpjYXrwD0Sw8z/JIvGa2EjteJpa8D11Tu2/msTwylCQ8QlL+OzaBEjrJSd08LNUiPCa+fL0st6u8mRMMPQUPVzxjuQA8ivqwPF+LrbxCMo471nrQO4TVCzyf/JI8gjbsu7UTrjwgSba6FCVLu/8n1TuGrOq7xX6rPIKkFj1+oW28tyNbPNVINr3npzi8issFO1GiB7xatkA84cEYu+VCWrxK+x08yENSPFQcFzxsQ527ZPJCOv8Fwzmhtle7mBXcOytakjyZpQm9uyYqO4kBajujsj08cZIcvLMP7jzDjgA8HK/3Om0sjLxbI7e8y6nLutr+PTwffci7cnhMvD962LxWD+O89/iWuwHmszxxVNI8NWYLvKCH57s9Ywm8dbxJO83DOzxgKOO5kASYu/5V8DpyaOA5G0ljPJOp2bvangm76bWqvU2JGLx0Xe27VACKvIeco7w+bjw7rE8qPb6r8TrqnxQ8n4Bvu3MDZjoHCBG8D9eVPAHMWrung3o8warhO4f+F7wfqxs9YzzqusrDVztNiaO8RoeKuw5RbDz8YoE6BJmau+khEzzN+v+7KO4PvIAL5rwslo474TsiPFmDhzzYZfW7mjX9u42KLry8N4E8oxORvHMTLzzTBzQ7rw6XPJWdM7zw/pU7ukcTvBVR6rvwW608MkU2vFkeMDwh/ea5nt9tPBPPtroe5uq6mCmhPHcFKr1y26083J/lurJamjxqzwm8maKsPJKqsDtKQTs7VW4mvCdhAL0GImy8FxqVPKFa4zvA5Oc6aMdAPN5uA7yPsmi7GSTMO0zIVTzl1IO7vLuoO1NjHD1P08g6enp/vBBO1Ty694u6RU56vdeSsDxK6zG7eEPzvPyAGj24E7U80ZSvukA7Dr3LD7K8y/rjOlrTHzyj0Hy8PVjEOy4YWjwMniA8yD+luxhgODx4L2Y8QTFRvJHStbwWNW+8XKr2O+heHLt6H9w7vK0MuiSDuLtj1UG8wYPpuxtlVTqcfpw8lPA+vceTYry4MjG7J/wvvfUwrLzBANY7GWEovJbdbbxsKGS8YiQpPSSrxTxLGti803K0u2+LoDw7Iq+77ToAvJUhCT3YrgC7vTt3vEiGIDyWqfw7f+C2vC3oo7x4XOQ6rYJYvBdkQzxbO5282RxRvNHc5bzz+D+9bzbju73Cab1tqTe87W2dvGdNg7zfN4m8NWTxvPWQhrt0+rC7OcigObgqujyXdcI77NO1PPzAAzzkNgi6/LTovJEwWLyh19I7Emm4uu0DejsAKEu8r+GJPPc2MzxXmQo7hzyZvCxL2LxiGni8euUZPH7ozTuL6G46oeDouwTciLztV7U7cWuKOn9IADzojYW8xAcxPCQfkboGmZY8C2+Hu8o11ruYSBk8l62xPFnkTr0icGc8+fmMO1L3tTx4lBW9QO/DvFtc97yyDA+8WbT4vNfEsTzhNt08efW5vNyNYjzH5zE9yoHUO/sGTbwSgK88Cy6nu+jPXjwR4ag8K+ibPAnHlDzkkuQ8ly1KvAXafL02BkU6MWMIvMuyH7tH4wg83d7jO0UbmTyY5Cg8/FePvMZ7VTxcUWi8Ow54PM3hkTusQAq8aF4XPcbtqjx/TbC8OFp9PP2Lar0qXAw9Iiuou1CCoju8HnK5Z+E8vCRiEz1KpgS7DSYivTslxDvTUFw8KM0VvBy8Zzmtz1a9D+73uJW4U7uj3aY7yGU7vT+/Or1F/t88WTjQO64hVbzWFKS8vLXOOhZUVL2fd648xcSkvAUKLzzUkge8cwmIvDDfA70PzZ08hwRLOjGvpTwTkZe8cqZou0OBubz1Ef472zREPCVPMTsn/Qk7dNelOjh6jrzbOca7NI/buxLwNTyGYX28RVnBu98OIrwEdbi7JabUPLQhIz2k2TI5/XgoPObvKTrlGY+84R4FPBJ4j7yn3U283Z7hOguWhDxyF568foBzO1WS2jpYQrc8iIYbPDqTVzvBdR68cnvkvJONDDywags934lXvPQMtjvV7F081WuCvBD5FDy7QqI8f2T/vBAFlbsytS67TOhcPY9nwzuLNbw7xfTEOxiRwDx9QSm9XQz4PO3iD7wrHKC8dEwDPOKL8TxK44m9K0grvETh1Lw0Axq9YPv3PPfyDr37Id47Or68vMbDNzws9+05k7uRPKOBK7t5D5M8mFNRvMTWeDw4Wp68ZsRpPKNXgLs5/Hm8xWcPPEVpIrs7AYA8a9NpOtJffjzH4FO89ZpJusriCr1C7LG7mLHDPAWYETyClqs8RMAFPbJZAT1eBgE91ynnPALEmbvQtjC8hEeOu8Q/irx5pD68ASQTu2y1T7x0DAs5ujZGvFczTbyBTHg8xuSpOyBlOb03Eow8q7MLvYHHBDvQ/RM86ceTPM7NIrz1pDY8yBBnO+5kybwHqco8bAekOblzFL1H/2g74mdlO8a+D70IFQS9kcOtPNDy67sdjU08oHbVPOMQXbvh0gS9kxpEOZ5AkLw6gkc72JAhPTNNELw+wBY7i5qWPIYp5rsldBM8uqPJvCmKljz/5Qc70wN2uhE1C704Sxy7eRqQvIxO4DyhzTA9fJIzPJAsnzrb4Z68W6XWu8PM47o3xMk6Whl3vNQ9mLxYnqq9B94wPeYYqLyGWEe8+NcPu0yORb3oIna8YjgKvHADtLzZh1I8OwUnu8QmLrylwQA8gzjXvJ/ZILwiaCe9FQ3uu7PbgDwk3c0807SuPMtUdLus9eI84V/xO0NZgLx6zvC8hiQuvYOeADxn//66P2GCvNnSg7wCQxG8zuqeu4wlz7zO8xg9ara1PK7dML3eE+E8+Ft/PPUM0TtrAt48iUVfPE5k6zpJP/M5QRFevewCgryvjmg9ry8WPPoWsjzWdfa8WiFUO9pZa7r20sQ6K/u4u+eOLz1hOx49twawPDXej7v8Mdw8IJTxO6f8ebzbGAO80e/fPHyNEj3Hs5K94xdFPOeL4bsT8865w8oavB1GIz1fOww8QEGzO90hATt52pq8BrPrPHmMyjymuPG76cbju8qNEbwd6EK6xwtrvDe2HbxFdaG8sJSBPbfti7wO6Jo8KHAnOwv5ILtm5ae8OlcAOq8LD73Tens8vxgeunag4DyOHZK8FZthvExk0bzjbDS78e5kPOuDAj23Lcu8N+gFvAxrpTuEzqO8jv3QPEVPkrzLKNa6tEAdPVOAtjyiJxi6IbZAvOA5rDwTQKa8jJuGPK/0ljtjtqI77YtoPPnGTLyGZSA82wK4vAVgqzxyf8Q8KuafPE01/7mXJ9u7tooGvIDC8juHmak8zt2vvNyBuzyen4q8AYcavKWt1DyV2CA8tLaWOj4iDTxQn4039q2zPPZdlbxvNR+8U7zoPNGN8btOPqy8BShVvDD6Oz3JeQQ87X/PvM+yRTw5ye28HlK5PB2EjTtTkya7RHzEuwnLcjxsIEI8sUFPvG0lBz0EElO7SnPAOawQBL184Lm8xqoTvV9JtLsWEa67iyeKvOZwIb1+zOm89o6nvEoc2TxycY08sUggvaVCCr2McCo9hKCcuBGFtztIkGi85hRGvUYHmLuv+4k82z6HvMUWCzzcp8m6V7xvu0SEqjy0Lws8BInEvAWipjp0HAM8NhwEPP+DBLzISoK65Hx3O4nQED0i2s48d/LqPF+l5bxmgYW8ERNyPZuZQTwfPDA8KITRul6cm7vIBj28nZU7vOEG0Dxu/Tu8QNGgPMyjt7uAta074fMhu9zw7zn/a3c5UrB4vJoPobyf6Hq77s1HvWA7KL0Vi1q8vKOtPOtrSDzxjXq85povvErLIzz7sya8VDcOu6W5lTytrKw8afkVuzsMSLwQklw9lPGKuxJY5DpCjYg8B3yaPA+OzTz2B/S750qpPPwy4DwYddU4POY4vTkF5Lx0Xfy6CiF5PFAPJ7w9IR88i4GXu+0B0LxasJk69hiwu4UeHD0RldS8v+JhuhN7CTxrAaI8nyYOPNofh7vgVBc9xPMNvHEiEr0hKq28hPYGPSX8fbwwrZm81VQAPAs8+jwb1BA8b+bFvHxeHz1Y6RK8mce1PB/aOjykBge8Uzt5O1InSzzj/Ma8opuIPOI1wbsGCLi7cwSsPLBlEDzy7DQ88YCQPCREGD2q3Is8n4Klu0E1qLyY1A67TKOXPARj1LsdxMQ8PXKtPHF947yCP268U4iZu7z6ILxTg3E9VOZ/PC7SUb19oxE8U8hWvAkHEbxolyO7EUMYPb3g37s2yy697LGAPLwJXr2cD4q8k2zFOyR2YbvDz5a86rQCvEO9f7wu/m+8ZHynPM2rrTvA6ac8lrMGvXW+rLzc65q8DTZ+PLS4Yjw9q8A81vCWvMEBuLjV+AU9jByJPCWGdz2Jdv87C/dnPL41VjwLh8M8B0UQuzcFjDzDJlu71elEvFdiCjw2IDs8pr3LvNiwury2PxU9S24HussCDrzsqx28uwMRvJMKzTw1SwM8RraTusIv7rtVsJ88PSZGvT5eQ7ysNSu8dD2UPI2SkrtcCNM8ikMxPQAoFjziagK9Ak2fPI8VIbzkvF48VJBjvHN0+bylrpk8oemyvDc3cDyN/9e8AZUlPXovyLwUIgY61D3iu6N0Rzx7lDc8Bgs2vaXJmjy5NaI8ZnVWO+tL9rzCOvk8jY+ePCsJObtSWcA8hmzSvPBCDj0voDk8xa+aPL96ZzyjqbE5cDC+O+hMbLzVIG47wghjuwJD3bxmwla6hIk0vPtcGLuPbdO8y+uJvMlkBLzjIqy8lkhkvJXRGz2raQC9s3ppPM5OszzhyHM8NXvvvH1/irsVlMG8so+rvEgyJ7znydm6ufd3vJN5Bb03NYy8h8bkvF/jmTsEFI68DccCu9ZvGj3CgwC77VS8vAr22LyOo3s65HNqvO6ZAjy4PE49OvofuhhHaz2M5+g6q9GVvAm1iLvGYJU8IC/FPCglDTzD+Lm8lWjXumAjLbpWnG87ERYoPX2I2DstFVe8+iwGvTTZsbu5Lrw8fBJYvFiRHDp9VNE8tP3lvJq+IjsDAL48gWWdPBpv+bxpBi2837ubO1kyAryhH9M7gPU6vfAPI7xFzkm7jG8NPL68HrwK4Fc8qVzzPGwH3rsrAnW83CpfvCKDkjxgpZg8PnNuvMMJcTzxUQQ938AQvZwWkLxPirM8/6RnvJvP/7viWYY7fi9svJAozzz65KY8bkSoOoPRzTzB2SQ7fFU/PMQTx7x+P1i7QQ+Cu9dXlzvfTgC7VhS+O76YWLy6F4g8XjE2u0nwTzzjq/+8KOn1vHXxxDr6RDQ8N3yDvGz8mTw1ZQy8NisevPdtM7x3P0+8+VmUvM/EVTsI5/y6mBzOu6/1jjxvo9i8ERm4PEPQMD0blc079kORPJJc0zs9z708OhJOPGWX0juqxfg76eCcvNJw4Ly8bUA9ZcwDvcGp9jzIRg47ImncPPxpbTvPoZW86dIHPBgHYLwxzCi80H5MPNTeJzz+E9e74df+O2v/PT33XeG8WVSFOv7ujjyCIo488NTkvNVQuDw3m4o7JiNBPMTCyjyKCUq8s/cLvZNYzLzL/1c8G7nWO4lBJLzUJgw8gQC7PM1jbTzrvJ086LQqvCebLTt0iH+8KI1JvPncKjvPlPq8yzHaPO01hTsFfAc8ixrMvFSZ07rZbb06JbNrPEAEkrzuFQ48XVqlPOTmmjxl58c7npZCvUrsHL1m6KK8WkMYvPjgBL0aXiC8fmpyPNmqiDwaNam8hcBHvVROvrv2Ere8+vtgPJ0TQbze19Q8p/YPO1d0yjz9yKW806nfOkCFdDtRJWe8ca7FPOvyLLxCCx89vODKO30YTrxHVgK9FFo1vKmHHzwzXZ68O8+7O3aTh7xINlY8LGmDO+95NbwKwws9BbMuO+9v1rqJP1082p+pvBCbQbwofh08E509PSApLDy1MME607dNu8L2pzw59dk754aDPFCJZ7yBwk48eyhsPCeBN7zE9gC96FPGuxNd8zzorgs98OS0O2p+K7uGg9I7gzNBvIPzfjveExw9H7uevAzbG71ZZMo7mluHO9DVr7z/YK+82vkIvb3SzbpyC9U7A05nOjTq9zzU++c8CUr7PFzK0juQR727ccwMvV+T87z4a4S8x21zO9hGSbt8Zs68kVpXuwv7DTvZE2u8Roc4PDnaLr2Cxgq8eDzOu2UrH7ui0we9d0CvvK8SJzwEH4o8R4EhvLggELw6S8i7TbyTPPAjezyNAji86jyBPJd7j7q88Sq8s4YGPWhs6rsvYTY84ssQvUlCxTwxAhu8hanju0hAUj3rN648L8i0u6jsALwSCkA8TysOPQaSMryzdVi883UVvVQP3rw49hO7BUAvPYJ3o7vTBx09K6c8PEocGTz7Eaq7fh2OvIvd2rslabI8TiUAu9c2X7y1YFE8fx4qvFSirTwgf5o793ITPImTqDujh0y6PX+pOT/9Hr266n27X/MdPZPHWryLuoy6CJnVO4iM1rzkfaA8ToSjPHVmb7weJyo8BMnRvEj+8zyQpMA8JuybPL1bHz1HEKo6F8u6vGWbiTwH6Ra8hDhVvPtxbbw+jxO9jpKNPOS0BzzeA2a8JPPQO6ofJjwdW6I8D9m6vL7qCD0b8K86LXhqPZCPxbx5ini8zTtDPZ4GAD2H9r+7IX67PJewxLssjr+8bvj5O/nMBLwc+P47ufS2uxMwrjxdqiU7xvZLvHhSR7uQs0s8eCk1PDs/G7zbJIU8UxKJPHKvKTxU3LE8XVFXPH1Wq7sCft86kmbpO+1m8jz38Oy87m/jvKdnUDo9phM9YBFdPMA9kjvasEg8+lT3Ox2S/LwM4be8VG+PO9ZaNT0nMR29xqJ0vHI0XDwjRYk8NZ0/PBXr2bwbWG47GEkrvCQC3LqFSz88HVHpuVwh+TuQKGS8ZTqfvEJsCTwpufA8VZR4O0w4mDycfo06fVcCPc81ALyY27u82zZPvArCIDzcbsk8Z4/pPOaQpDzpQ4I8st4DPH1PMrt2spU8NrznO4c12zudfXA71iGqvJ1shzuPJAE9Q708PQZAvjuoOwg7GfkiPHRETbordoY8NZfuPACWZDwNPBo7IWPnvIFmM70z19e8fNS0PH+6RryC4Xs8lQ1mPHQ3nLx5qY67vVAWvJYjsjgVMMq7+d0gvQqftzyVyDo87BULvMOiP73pseo8xiltvP2p1LzfOxk7fsODvJIKM7ore9u6ieefu6diDbz/5ES9KpZ+vH9v1byfJIO8Vi88ODnmezs7LG8853upvDbKmzuYmb88HOOFO/O8WLyMITk8XDwkvWS6Wj20n8O8R/xwuwkFxzz/yVo7L8a0uz40oLz5N6+8vfQ/vGebZ7vtfKE7LKEFvGgQizx9B8s7HAMRPOn8tju7+iS8gbW2vHrUmzxD+Ym7vfXovBZ3hbwEmhE823DYvJYrLLttQR28uUS0PIr5u7uxdVc7ZiZLvCeWeLzO82+7zlUQPWYYL71gjQ280sKrPH99p7zN4jc8LpIgvKf1bbtORj+7TrAAvCb7oDupKc28gmMyPafEvbyGxac8pkWOvL5icTxJ7wQ9dyVPPM6uLruA8p48xb01vV7iZDysLIm7oH3IvGiYxjxeo/E8xgIIufb6YDxpIAM9VZG/vCIRoTzufjK9F6ZMuyS3Or353eO8M5DjOsKyzTyQrhS9zYd0vFZDyLyYV4A8gcZcu9ylwbstZ1W8mcjNO48vkrx63AC9tcZSuWqwPLyjuDg7ttLavDEJJDyy9Jy707TKO0nVOD0rfWO77SA1PN0cDrxLECK9isV4vDSbvDy98Jy8KluUu8p/urzTuiC9K+qNPG5uGD0OUeU8dk+rPObVADxI4tU8xuCevM4mEj3nFjM80jDRvJoGkzvtaaK8ENyrPIdUhzx0JQS8PpLcPOPmNzxFqLY88x6JPP8dLTx6v4E7UKSuPN/lDLu17uO824cmParcTLupjZO8Yb3IvJHLhDsBFio8soMEPC+knTty3ly8+NCEPM8jijyrZmK8qYX2PKIkPbvQ5oK8U8IEOyybF7xwLCA9cqRpvDTWobxbBy48fx+BPJsYlLtxYDq9HqxbPII0IDtPWOU6Bx7Gu8kSRjwp5oE7oEHCOhDlHL3UrU87zj5EvAWfBz0PJ1C8sf4TvNkU8bvl0Jy75w7oupjuIjz36lu9zfiaPO5ec7x8WJO8dE8pvMJtUzluZ5C7mg7dvPrcmjsjSVo8gdBdPBwpwrgEBsu8GlAuuxdcRrqgzkS98uEsvPhIOLyUcS67SjffvC7H5bxOmBA6JG8TPOBeFbxVOVg8nNp4vLl7hDwWSwI9PS7OPGGg0LynRyk8arJ6u6G8XzxVGrY8ZqReu1X/lTxojGa8hYrLu+YkLTy15y28k7ABO/q+eDzAES+8BNlWOyXMujz6B6O8EJIRvXBBUr1+q5M8lMo4u6cLmDygyD09qC8bu0cIKDwN7xY80SNjOzFakDyj4bE71OlwPMsRWLx34sG8S4f+vAM0KLvIJ9S7094pO0AzID3f0kw72K76Nr6iJjzSpyg6YtMJPBpKCz2WK9i7Jud3vHYaRDwMlkS8EWwFvPD0krtDzUU8+EUYPNA+KzxLCVg5rXObu+F40Dsunhk6+MBovMvxLz1gTa48SB5+u+Ix6Dx96GK85TCVvF3197umzw87FC17PFP+H7xB3iU8oP/IvL3PgTskWfm7JIQWvVKP2Lw/hO+7jq2YvDdA5jtEmHE613OZu7TJjrulmt481wH5vFsbo7z2E4k8kW6LvD0najyMRWe85DkhPG2v8DoGjiC8gisyvPmCYjvx6+47zgruPHs5JDw584s8pO90vESGpLumSO88k50cPfHJhbvKkM+8lxuau/LyPzzKI5C8IQmSu3OMhLwsLNm8lwg1Pd+ZkTsyMqQ8htcEvNiopbyA/X88gGoOvWxegzyfjlU6edvVu93FDjw0mqa8CBL8O/BN/rgKG4U5pvN6vEra27yFaxI7NOIFuYrOHbuytQc81NUkvFyj8bpfv+q8ehyLu3eh4DsixEA77QicvHPT7Lxm+F26S2CmvJ1e7rsm7V28inICPD7dHj1qmAo8hlsGPEK9ZTw5SuY8xaozPA8407wT9Oq7s7XTvFbg4juH9Z+4ofRzOxnZuTtuY5E8u4Q7vK7ctbw0Ew+8LQ6kvIiDMjz52Wk8VnXFuyYWnryDuTu82bFxvO7Nrzwbeo68c/UGPJYms7wmRsy6qEJgO92vJr0WP2U8VBX8PGJafDuOUhM8vS0hvDAfNDxg/TA8IkQbvezFU7v4Ake8ue7EPC7+Ibp5B6882qgqvLn3ULwnHBw9zknKOiGEPL2hwsU82qDbO0FJabxMDmi7WaW2PPQVpjy/3Gu6ZIXKPJDaf7weDCW8u+v3PIiIIT2A8ZI6DaLSPBoV4jxN7qE7PEhvPAydErxJgTy7sdCVPP9iRDwIYoE8RmEmPMcqnbyb0hW6LrTtvJlU4zreL6a8ehafvD+XhTv+VcQ8zAN0OhAkgjsTD5g823IwvIR9FT17W7M8OpYsvAE2SDwZMJe8LJOpOhPpc7ucOIM8QYgNvbbB7rtnMiw996fpOqS/DTxBjfI86Q2rPDrRF7sJY5K7+m4RPfWVzTwn6+06W+OHvMpyeTyRaoo8V7vAumT1jzt53dO8coA9PGlgo7yZoQ09uEhmO8mukDvTWhY7m2+nvE26Fj0FYVs8QHfevICwNbuZUok8JuimPKW4FDyNnR09lhZPPDEzkbog0Yc7AzpTO3/bnDzPiDg8V02JPAMb+7wz6Aq9ToqEuwUuAL1Awsm8mGQWPcxDi7zlipG8SzIgvLjQ4bw7fzq8du3IPEWF6bszzJw8RVMquu27nbwsUtG8e3EAvM1rLTwUffO8x1YvPJ/kQjvKj7+7WPe9PI5ChbxFlSG8+zLBO7foID0hG2W8htbEOzorEjqr8Je7B30KvX2MeLz2wUc8753oO4jJ2jvLF4S8TwrIPDqEIz1Akxs7EkfCPBGPj7zV7BG8wMojPO5yPTzzfMu7jNzZPJeF+LyzXJO8hwCpOYLQZbzoGfC7eendvOXugjvbaDk8xGkbvVYNkbrFdz279q+YPLxzD7vHCjs8LfoGvVwPYTw9v2w7AEWPO8Zd0bvZaZI6tvKTvMgtMrxtpLm8KXH5PK6tZjxLegi8FBcfvPJ4Zrwvs/a7FU+qPNdPl7we+D88wpEiPdZllDxLCps8xhmSPE9W7bxF+ka80HCCPJEhrrt8mCA887Rzu3Q10TyoIqq8pC/0O0ApmTyO2cG8Gh92uzhsC7xtnNG8sgEIPX0/Fb1zdPS8J18NvE+zjbwPXGA8KYCZvNiGEDw7a4w8+WLQvJs2GTonY7u8sKsZvfSTnzz9Lt+8fvTTvKfejrm5Tw299VJ1vGicxri8SQE7Z/ayum4XWzzmRJu8XCiUPL/mR7zFNOc8Qld9vJebwzovQDm6JwPLPAduRTw6x2M866W/PAJXjLsBV1i8pwHHPCkkOrxyTXs6EMNEPLKYkzsT0HQ6qCQIubplejz+HCw8bksUPGOJHTw3+9i8jtjKOtD++Lxz3Hg8AdWFu7DQgLxBWp+8yhEwPPBAVLtibuS8sIWNvDJ6G70+5dg8eODDu5/aJ72mh3Q8Q/HVPCni2LybDYU8tEm8u4wK3TxooGc7NfWhvHO+ZLxGeBu9CQmLvPiJwzseeJ66M1VDOySlnjz+KJG6Q36DO82thzsJjKC79tpTPK33eTx4YKQ8wp2yOr2IBzsih+67f56/PMFQwDxdbMY8uzSAO4aiArzxPmU8ga0bvEDyWbxO3jS8EhgGO+JznTwQsXA82Y7Ou90gyDuW7EW8oNOnOlss2bw+oe67J3APPfINDb0AcGS8nQ7dvE+gmLwn0tm8Vc+6POdjoTv5RwY8ktfdvEbC/Dtqwxq9L2O4POh5yTq7mp88wRv/uyDYSbwwcp87VQlAPXyTkDuAoti6QbZNPd0gpjwZdt884aiQPM/eFDwBjpi64DrUOv0HSTxc1ZW8lf6JvC0ybDo+MT+9jss9OzwSQ71jd0w8c01RvDmnBjypyme8jSlku9hwgbwea2C95bc+uxMPALsLqb47CHnhu3s41byPEsY8hBaKuuOlzjw8wi28oRoWvTan6jzrXSi9pqcrPDz7ZLw+tuw7uieqO2ztejp+lwW8RAUVPZLT5rsB3uG8PR4cvONZRDwSPgi606TcPKlay7tQMkQ8i/GrPIBxP7tFAOE8wNxdPKkgfzyGdIu8bKOWPNDa1bvJxdG88FkFvIReobp6LKO8KkamPFiqMDuPjqk7JM/NvKqkebyGoP+7j3+nOXiRmjz7gbQ85/TLvGNDojqBqfC7vvqkvJ09xDvWnii8D8vpOov3/rumz7Y790TUu1CS5bwPMUK9e28gvBQgBDwZYyS7ZZn0ugqD6rwH8oG86C9Uu9CkNbsPKoq8Av0qPN4KorykSiI8OPBWPAROEb0vG0I90OfBvOSWs7yGY768gTQ7PDr7y7tK6mE8szSQPLkrcrwKA40703LNvEB4xDvoiP+7VpKZvDehSjw6uva7iajrPKsbuzzbpFc5u9WROiNZlTxX3K67VuahvMu6YrtloNa7v3NBPKlOjryT22O8LnvVuyEegruN0z88ecjXuwn6cb0DCNo86jMPPXdqIbyhgJs7l46dvPdDoDoGSEY5m2s5u0ato7tg3YE87NsCvbW8abwbHea7Y9qFu+LJxjz7zey7o0pUuwjLObxNfjW8+7hzu1+kIjyetMA8QclPPNbaNztP38Q8/AjbvO5fdbzBY0y6gZFPvDNg7Tr2zLo6ufbXPL3smzxC5eW8ThCEvAgDED19k+Q8XJulvOUwvjtydPa8aDdtvJ0RBTqb4ke8qBiIvLJqSTx0SK87/dTDvM3xwDtyiqU8tvRfOtcE8rzX0hE8LkJDu8X1hDo5vqO8C9mTvJmLEDzCtoW7bjWKO5wA6rwr1/K7RluFuhqYODw2qq08va+fPLd3yjwGA3E8sO/hvCWtyLudP/m7WsnzvGIWqbtZgw69mlYZukAjCL1PeeY60Iv3PBdOl7sniZq7u/0ZPSf0JbwrY6Q8yeAAPJXOCDyMW7u74f6tPG8f7rwXd+i7sjUhPBln3rupsj28puf8umECLLyCH5O8ns7PPFJUxzyHnZW8auE3vUeRKbtRjIW8aCwkOyjolbygUAG8tmFIvKm3CD3ChHw8KoHEuri5pjsP+wE95/4APVz/5byhQss7OQynPOUbnTxkSSC9uwa4POhndLyum/m6jfCvPB2UiTq3Cdm8EuHzOo1c8jtWX1I8HX07vKuFobxpMIq8K9MkPdOMHTxtsk+8xOklvMJoOr3wvk06vMRiPLiXNrxuqOq7PGtKvMuqmjzdQgO7YFU8PaoEArzmvxi8tGM0u9A34TwGrQY8Qu6CvMOSo7v3bUY8/KQ7PeCrvTyjI0s8boibvJWYGb2an0W8Mgu6vOpQKzzsMvo8ocgKvJ4j7TzmTQ68LTt4PE5S/TvB1LS8aTsEvBa1AbyzH+E7mxAEOtvLYLw0ecQ5LdiFuxGzS7sqIYi842ioPDn3mryqWp28UwD/O/ixyLsFlQ89ooeUPP5eorq/Eg88z5Ylu9jwibyjlkA42dnYOzfznbxCMGk8soM4PGOXrryYZmu8wk7YvEgj8juxIQQ8TTPjOhFrnrzpYtW8TzS0PJW5gzyNhoE79MVUuvgMkzyNO587/X/MPL9osjwhK0+6L0XAPBw1ujwNzee5T1L5PJNnjjpu6QK7kNWkPIg+ubzPC8o8fD03vGMryDsp60w8JXwnOYLBW7yBR8A7T2goPZOGCrz+odQ8fdL4uBeShbzAArq8WXdyvLcE+ToW4As8ZX4IvBdNw7wf8tC88mgZvc5Ygry5QJk6kFvHvDXaojwX3gg99Dj/O6kKoLx1BLi8+60qPOr5obwYxGA8RD+Iug==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_returns_tool_return.yaml b/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_returns_tool_return.yaml
deleted file mode 100644
index fc529b6c..00000000
--- a/tests/cassettes/test_search_tools/TestSearchWithSessionState.test_search_with_session_state_returns_tool_return.yaml
+++ /dev/null
@@ -1,122 +0,0 @@
-interactions:
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '142'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python is a programming language. It is widely used for web development.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: fBiAuV0A37x9cpu8MldhvIUktrrMji48KD6OPfOBDj0Lc7Y6WPwBvSlEkT3KlU08+UnWueLDDrxz8nA8U+YsPI0eejyjjmq9X0mavPDKEjk+94a7VK3RvNBIOT1dW248mtCCvKn4/zy5HpG8LXOlvOoWCbvTNGg9N6jMPDAgXDw7Cxi6rN5/PCun6TnyX8e83LsIvG54bro0T3o73z15uyx3UDv5b7Q7SZQ7O2Jl7jzFsCy8aJ9FvY17zjvWNsY8QkWQOVIzAL2hfc66lO7Wu1bdzDyvpK+8zCfhPC/dPD2vEUo8l6abu2ObnjtMWLi8tI25u/LZgbrnnAm8x/DTu4eJzrtSDaO7gnAgvKuvdzrdsRg80tmNO6erlrwDShc9PvXxu47uszxefMk7xAcivKhJKrw5gxU9CLywvN3VdDt6yom8AGiCOftU+TtfT1Q90VpEO4n+wDtEwuQ7imCUumizIruXFBK8IpevuvQQXTydluA69z+zPGLS4jpsDZU7cl9EvJ+2BLxMko+7ck2HOtDlAjyxNU28G23mvPyAYrsUBpu8a3pxPG2TLbwLhnM8/yK8PNaEvDuhGZQ7VSgpu4FUWzszPpU71nyBuoYyJzw9NBm8ykrcPLycwjuBjlI9SaaUu2KNjTze8MU7TlkNvc1R8DuMGwy99YZXPOz1rDwbZ0+8kOUhu0QM7jtP/v+7ho+nOpjYXrwD0Sw8z/JIvGa2EjteJpa8D11Tu2/msTwylCQ8QlL+OzaBEjrJSd08LNUiPCa+fL0st6u8mRMMPQUPVzxjuQA8ivqwPF+LrbxCMo471nrQO4TVCzyf/JI8gjbsu7UTrjwgSba6FCVLu/8n1TuGrOq7xX6rPIKkFj1+oW28tyNbPNVINr3npzi8issFO1GiB7xatkA84cEYu+VCWrxK+x08yENSPFQcFzxsQ527ZPJCOv8Fwzmhtle7mBXcOytakjyZpQm9uyYqO4kBajujsj08cZIcvLMP7jzDjgA8HK/3Om0sjLxbI7e8y6nLutr+PTwffci7cnhMvD962LxWD+O89/iWuwHmszxxVNI8NWYLvKCH57s9Ywm8dbxJO83DOzxgKOO5kASYu/5V8DpyaOA5G0ljPJOp2bvangm76bWqvU2JGLx0Xe27VACKvIeco7w+bjw7rE8qPb6r8TrqnxQ8n4Bvu3MDZjoHCBG8D9eVPAHMWrung3o8warhO4f+F7wfqxs9YzzqusrDVztNiaO8RoeKuw5RbDz8YoE6BJmau+khEzzN+v+7KO4PvIAL5rwslo474TsiPFmDhzzYZfW7mjX9u42KLry8N4E8oxORvHMTLzzTBzQ7rw6XPJWdM7zw/pU7ukcTvBVR6rvwW608MkU2vFkeMDwh/ea5nt9tPBPPtroe5uq6mCmhPHcFKr1y26083J/lurJamjxqzwm8maKsPJKqsDtKQTs7VW4mvCdhAL0GImy8FxqVPKFa4zvA5Oc6aMdAPN5uA7yPsmi7GSTMO0zIVTzl1IO7vLuoO1NjHD1P08g6enp/vBBO1Ty694u6RU56vdeSsDxK6zG7eEPzvPyAGj24E7U80ZSvukA7Dr3LD7K8y/rjOlrTHzyj0Hy8PVjEOy4YWjwMniA8yD+luxhgODx4L2Y8QTFRvJHStbwWNW+8XKr2O+heHLt6H9w7vK0MuiSDuLtj1UG8wYPpuxtlVTqcfpw8lPA+vceTYry4MjG7J/wvvfUwrLzBANY7GWEovJbdbbxsKGS8YiQpPSSrxTxLGti803K0u2+LoDw7Iq+77ToAvJUhCT3YrgC7vTt3vEiGIDyWqfw7f+C2vC3oo7x4XOQ6rYJYvBdkQzxbO5282RxRvNHc5bzz+D+9bzbju73Cab1tqTe87W2dvGdNg7zfN4m8NWTxvPWQhrt0+rC7OcigObgqujyXdcI77NO1PPzAAzzkNgi6/LTovJEwWLyh19I7Emm4uu0DejsAKEu8r+GJPPc2MzxXmQo7hzyZvCxL2LxiGni8euUZPH7ozTuL6G46oeDouwTciLztV7U7cWuKOn9IADzojYW8xAcxPCQfkboGmZY8C2+Hu8o11ruYSBk8l62xPFnkTr0icGc8+fmMO1L3tTx4lBW9QO/DvFtc97yyDA+8WbT4vNfEsTzhNt08efW5vNyNYjzH5zE9yoHUO/sGTbwSgK88Cy6nu+jPXjwR4ag8K+ibPAnHlDzkkuQ8ly1KvAXafL02BkU6MWMIvMuyH7tH4wg83d7jO0UbmTyY5Cg8/FePvMZ7VTxcUWi8Ow54PM3hkTusQAq8aF4XPcbtqjx/TbC8OFp9PP2Lar0qXAw9Iiuou1CCoju8HnK5Z+E8vCRiEz1KpgS7DSYivTslxDvTUFw8KM0VvBy8Zzmtz1a9D+73uJW4U7uj3aY7yGU7vT+/Or1F/t88WTjQO64hVbzWFKS8vLXOOhZUVL2fd648xcSkvAUKLzzUkge8cwmIvDDfA70PzZ08hwRLOjGvpTwTkZe8cqZou0OBubz1Ef472zREPCVPMTsn/Qk7dNelOjh6jrzbOca7NI/buxLwNTyGYX28RVnBu98OIrwEdbi7JabUPLQhIz2k2TI5/XgoPObvKTrlGY+84R4FPBJ4j7yn3U283Z7hOguWhDxyF568foBzO1WS2jpYQrc8iIYbPDqTVzvBdR68cnvkvJONDDywags934lXvPQMtjvV7F081WuCvBD5FDy7QqI8f2T/vBAFlbsytS67TOhcPY9nwzuLNbw7xfTEOxiRwDx9QSm9XQz4PO3iD7wrHKC8dEwDPOKL8TxK44m9K0grvETh1Lw0Axq9YPv3PPfyDr37Id47Or68vMbDNzws9+05k7uRPKOBK7t5D5M8mFNRvMTWeDw4Wp68ZsRpPKNXgLs5/Hm8xWcPPEVpIrs7AYA8a9NpOtJffjzH4FO89ZpJusriCr1C7LG7mLHDPAWYETyClqs8RMAFPbJZAT1eBgE91ynnPALEmbvQtjC8hEeOu8Q/irx5pD68ASQTu2y1T7x0DAs5ujZGvFczTbyBTHg8xuSpOyBlOb03Eow8q7MLvYHHBDvQ/RM86ceTPM7NIrz1pDY8yBBnO+5kybwHqco8bAekOblzFL1H/2g74mdlO8a+D70IFQS9kcOtPNDy67sdjU08oHbVPOMQXbvh0gS9kxpEOZ5AkLw6gkc72JAhPTNNELw+wBY7i5qWPIYp5rsldBM8uqPJvCmKljz/5Qc70wN2uhE1C704Sxy7eRqQvIxO4DyhzTA9fJIzPJAsnzrb4Z68W6XWu8PM47o3xMk6Whl3vNQ9mLxYnqq9B94wPeYYqLyGWEe8+NcPu0yORb3oIna8YjgKvHADtLzZh1I8OwUnu8QmLrylwQA8gzjXvJ/ZILwiaCe9FQ3uu7PbgDwk3c0807SuPMtUdLus9eI84V/xO0NZgLx6zvC8hiQuvYOeADxn//66P2GCvNnSg7wCQxG8zuqeu4wlz7zO8xg9ara1PK7dML3eE+E8+Ft/PPUM0TtrAt48iUVfPE5k6zpJP/M5QRFevewCgryvjmg9ry8WPPoWsjzWdfa8WiFUO9pZa7r20sQ6K/u4u+eOLz1hOx49twawPDXej7v8Mdw8IJTxO6f8ebzbGAO80e/fPHyNEj3Hs5K94xdFPOeL4bsT8865w8oavB1GIz1fOww8QEGzO90hATt52pq8BrPrPHmMyjymuPG76cbju8qNEbwd6EK6xwtrvDe2HbxFdaG8sJSBPbfti7wO6Jo8KHAnOwv5ILtm5ae8OlcAOq8LD73Tens8vxgeunag4DyOHZK8FZthvExk0bzjbDS78e5kPOuDAj23Lcu8N+gFvAxrpTuEzqO8jv3QPEVPkrzLKNa6tEAdPVOAtjyiJxi6IbZAvOA5rDwTQKa8jJuGPK/0ljtjtqI77YtoPPnGTLyGZSA82wK4vAVgqzxyf8Q8KuafPE01/7mXJ9u7tooGvIDC8juHmak8zt2vvNyBuzyen4q8AYcavKWt1DyV2CA8tLaWOj4iDTxQn4039q2zPPZdlbxvNR+8U7zoPNGN8btOPqy8BShVvDD6Oz3JeQQ87X/PvM+yRTw5ye28HlK5PB2EjTtTkya7RHzEuwnLcjxsIEI8sUFPvG0lBz0EElO7SnPAOawQBL184Lm8xqoTvV9JtLsWEa67iyeKvOZwIb1+zOm89o6nvEoc2TxycY08sUggvaVCCr2McCo9hKCcuBGFtztIkGi85hRGvUYHmLuv+4k82z6HvMUWCzzcp8m6V7xvu0SEqjy0Lws8BInEvAWipjp0HAM8NhwEPP+DBLzISoK65Hx3O4nQED0i2s48d/LqPF+l5bxmgYW8ERNyPZuZQTwfPDA8KITRul6cm7vIBj28nZU7vOEG0Dxu/Tu8QNGgPMyjt7uAta074fMhu9zw7zn/a3c5UrB4vJoPobyf6Hq77s1HvWA7KL0Vi1q8vKOtPOtrSDzxjXq85povvErLIzz7sya8VDcOu6W5lTytrKw8afkVuzsMSLwQklw9lPGKuxJY5DpCjYg8B3yaPA+OzTz2B/S750qpPPwy4DwYddU4POY4vTkF5Lx0Xfy6CiF5PFAPJ7w9IR88i4GXu+0B0LxasJk69hiwu4UeHD0RldS8v+JhuhN7CTxrAaI8nyYOPNofh7vgVBc9xPMNvHEiEr0hKq28hPYGPSX8fbwwrZm81VQAPAs8+jwb1BA8b+bFvHxeHz1Y6RK8mce1PB/aOjykBge8Uzt5O1InSzzj/Ma8opuIPOI1wbsGCLi7cwSsPLBlEDzy7DQ88YCQPCREGD2q3Is8n4Klu0E1qLyY1A67TKOXPARj1LsdxMQ8PXKtPHF947yCP268U4iZu7z6ILxTg3E9VOZ/PC7SUb19oxE8U8hWvAkHEbxolyO7EUMYPb3g37s2yy697LGAPLwJXr2cD4q8k2zFOyR2YbvDz5a86rQCvEO9f7wu/m+8ZHynPM2rrTvA6ac8lrMGvXW+rLzc65q8DTZ+PLS4Yjw9q8A81vCWvMEBuLjV+AU9jByJPCWGdz2Jdv87C/dnPL41VjwLh8M8B0UQuzcFjDzDJlu71elEvFdiCjw2IDs8pr3LvNiwury2PxU9S24HussCDrzsqx28uwMRvJMKzTw1SwM8RraTusIv7rtVsJ88PSZGvT5eQ7ysNSu8dD2UPI2SkrtcCNM8ikMxPQAoFjziagK9Ak2fPI8VIbzkvF48VJBjvHN0+bylrpk8oemyvDc3cDyN/9e8AZUlPXovyLwUIgY61D3iu6N0Rzx7lDc8Bgs2vaXJmjy5NaI8ZnVWO+tL9rzCOvk8jY+ePCsJObtSWcA8hmzSvPBCDj0voDk8xa+aPL96ZzyjqbE5cDC+O+hMbLzVIG47wghjuwJD3bxmwla6hIk0vPtcGLuPbdO8y+uJvMlkBLzjIqy8lkhkvJXRGz2raQC9s3ppPM5OszzhyHM8NXvvvH1/irsVlMG8so+rvEgyJ7znydm6ufd3vJN5Bb03NYy8h8bkvF/jmTsEFI68DccCu9ZvGj3CgwC77VS8vAr22LyOo3s65HNqvO6ZAjy4PE49OvofuhhHaz2M5+g6q9GVvAm1iLvGYJU8IC/FPCglDTzD+Lm8lWjXumAjLbpWnG87ERYoPX2I2DstFVe8+iwGvTTZsbu5Lrw8fBJYvFiRHDp9VNE8tP3lvJq+IjsDAL48gWWdPBpv+bxpBi2837ubO1kyAryhH9M7gPU6vfAPI7xFzkm7jG8NPL68HrwK4Fc8qVzzPGwH3rsrAnW83CpfvCKDkjxgpZg8PnNuvMMJcTzxUQQ938AQvZwWkLxPirM8/6RnvJvP/7viWYY7fi9svJAozzz65KY8bkSoOoPRzTzB2SQ7fFU/PMQTx7x+P1i7QQ+Cu9dXlzvfTgC7VhS+O76YWLy6F4g8XjE2u0nwTzzjq/+8KOn1vHXxxDr6RDQ8N3yDvGz8mTw1ZQy8NisevPdtM7x3P0+8+VmUvM/EVTsI5/y6mBzOu6/1jjxvo9i8ERm4PEPQMD0blc079kORPJJc0zs9z708OhJOPGWX0juqxfg76eCcvNJw4Ly8bUA9ZcwDvcGp9jzIRg47ImncPPxpbTvPoZW86dIHPBgHYLwxzCi80H5MPNTeJzz+E9e74df+O2v/PT33XeG8WVSFOv7ujjyCIo488NTkvNVQuDw3m4o7JiNBPMTCyjyKCUq8s/cLvZNYzLzL/1c8G7nWO4lBJLzUJgw8gQC7PM1jbTzrvJ086LQqvCebLTt0iH+8KI1JvPncKjvPlPq8yzHaPO01hTsFfAc8ixrMvFSZ07rZbb06JbNrPEAEkrzuFQ48XVqlPOTmmjxl58c7npZCvUrsHL1m6KK8WkMYvPjgBL0aXiC8fmpyPNmqiDwaNam8hcBHvVROvrv2Ere8+vtgPJ0TQbze19Q8p/YPO1d0yjz9yKW806nfOkCFdDtRJWe8ca7FPOvyLLxCCx89vODKO30YTrxHVgK9FFo1vKmHHzwzXZ68O8+7O3aTh7xINlY8LGmDO+95NbwKwws9BbMuO+9v1rqJP1082p+pvBCbQbwofh08E509PSApLDy1MME607dNu8L2pzw59dk754aDPFCJZ7yBwk48eyhsPCeBN7zE9gC96FPGuxNd8zzorgs98OS0O2p+K7uGg9I7gzNBvIPzfjveExw9H7uevAzbG71ZZMo7mluHO9DVr7z/YK+82vkIvb3SzbpyC9U7A05nOjTq9zzU++c8CUr7PFzK0juQR727ccwMvV+T87z4a4S8x21zO9hGSbt8Zs68kVpXuwv7DTvZE2u8Roc4PDnaLr2Cxgq8eDzOu2UrH7ui0we9d0CvvK8SJzwEH4o8R4EhvLggELw6S8i7TbyTPPAjezyNAji86jyBPJd7j7q88Sq8s4YGPWhs6rsvYTY84ssQvUlCxTwxAhu8hanju0hAUj3rN648L8i0u6jsALwSCkA8TysOPQaSMryzdVi883UVvVQP3rw49hO7BUAvPYJ3o7vTBx09K6c8PEocGTz7Eaq7fh2OvIvd2rslabI8TiUAu9c2X7y1YFE8fx4qvFSirTwgf5o793ITPImTqDujh0y6PX+pOT/9Hr266n27X/MdPZPHWryLuoy6CJnVO4iM1rzkfaA8ToSjPHVmb7weJyo8BMnRvEj+8zyQpMA8JuybPL1bHz1HEKo6F8u6vGWbiTwH6Ra8hDhVvPtxbbw+jxO9jpKNPOS0BzzeA2a8JPPQO6ofJjwdW6I8D9m6vL7qCD0b8K86LXhqPZCPxbx5ini8zTtDPZ4GAD2H9r+7IX67PJewxLssjr+8bvj5O/nMBLwc+P47ufS2uxMwrjxdqiU7xvZLvHhSR7uQs0s8eCk1PDs/G7zbJIU8UxKJPHKvKTxU3LE8XVFXPH1Wq7sCft86kmbpO+1m8jz38Oy87m/jvKdnUDo9phM9YBFdPMA9kjvasEg8+lT3Ox2S/LwM4be8VG+PO9ZaNT0nMR29xqJ0vHI0XDwjRYk8NZ0/PBXr2bwbWG47GEkrvCQC3LqFSz88HVHpuVwh+TuQKGS8ZTqfvEJsCTwpufA8VZR4O0w4mDycfo06fVcCPc81ALyY27u82zZPvArCIDzcbsk8Z4/pPOaQpDzpQ4I8st4DPH1PMrt2spU8NrznO4c12zudfXA71iGqvJ1shzuPJAE9Q708PQZAvjuoOwg7GfkiPHRETbordoY8NZfuPACWZDwNPBo7IWPnvIFmM70z19e8fNS0PH+6RryC4Xs8lQ1mPHQ3nLx5qY67vVAWvJYjsjgVMMq7+d0gvQqftzyVyDo87BULvMOiP73pseo8xiltvP2p1LzfOxk7fsODvJIKM7ore9u6ieefu6diDbz/5ES9KpZ+vH9v1byfJIO8Vi88ODnmezs7LG8853upvDbKmzuYmb88HOOFO/O8WLyMITk8XDwkvWS6Wj20n8O8R/xwuwkFxzz/yVo7L8a0uz40oLz5N6+8vfQ/vGebZ7vtfKE7LKEFvGgQizx9B8s7HAMRPOn8tju7+iS8gbW2vHrUmzxD+Ym7vfXovBZ3hbwEmhE823DYvJYrLLttQR28uUS0PIr5u7uxdVc7ZiZLvCeWeLzO82+7zlUQPWYYL71gjQ280sKrPH99p7zN4jc8LpIgvKf1bbtORj+7TrAAvCb7oDupKc28gmMyPafEvbyGxac8pkWOvL5icTxJ7wQ9dyVPPM6uLruA8p48xb01vV7iZDysLIm7oH3IvGiYxjxeo/E8xgIIufb6YDxpIAM9VZG/vCIRoTzufjK9F6ZMuyS3Or353eO8M5DjOsKyzTyQrhS9zYd0vFZDyLyYV4A8gcZcu9ylwbstZ1W8mcjNO48vkrx63AC9tcZSuWqwPLyjuDg7ttLavDEJJDyy9Jy707TKO0nVOD0rfWO77SA1PN0cDrxLECK9isV4vDSbvDy98Jy8KluUu8p/urzTuiC9K+qNPG5uGD0OUeU8dk+rPObVADxI4tU8xuCevM4mEj3nFjM80jDRvJoGkzvtaaK8ENyrPIdUhzx0JQS8PpLcPOPmNzxFqLY88x6JPP8dLTx6v4E7UKSuPN/lDLu17uO824cmParcTLupjZO8Yb3IvJHLhDsBFio8soMEPC+knTty3ly8+NCEPM8jijyrZmK8qYX2PKIkPbvQ5oK8U8IEOyybF7xwLCA9cqRpvDTWobxbBy48fx+BPJsYlLtxYDq9HqxbPII0IDtPWOU6Bx7Gu8kSRjwp5oE7oEHCOhDlHL3UrU87zj5EvAWfBz0PJ1C8sf4TvNkU8bvl0Jy75w7oupjuIjz36lu9zfiaPO5ec7x8WJO8dE8pvMJtUzluZ5C7mg7dvPrcmjsjSVo8gdBdPBwpwrgEBsu8GlAuuxdcRrqgzkS98uEsvPhIOLyUcS67SjffvC7H5bxOmBA6JG8TPOBeFbxVOVg8nNp4vLl7hDwWSwI9PS7OPGGg0LynRyk8arJ6u6G8XzxVGrY8ZqReu1X/lTxojGa8hYrLu+YkLTy15y28k7ABO/q+eDzAES+8BNlWOyXMujz6B6O8EJIRvXBBUr1+q5M8lMo4u6cLmDygyD09qC8bu0cIKDwN7xY80SNjOzFakDyj4bE71OlwPMsRWLx34sG8S4f+vAM0KLvIJ9S7094pO0AzID3f0kw72K76Nr6iJjzSpyg6YtMJPBpKCz2WK9i7Jud3vHYaRDwMlkS8EWwFvPD0krtDzUU8+EUYPNA+KzxLCVg5rXObu+F40Dsunhk6+MBovMvxLz1gTa48SB5+u+Ix6Dx96GK85TCVvF3197umzw87FC17PFP+H7xB3iU8oP/IvL3PgTskWfm7JIQWvVKP2Lw/hO+7jq2YvDdA5jtEmHE613OZu7TJjrulmt481wH5vFsbo7z2E4k8kW6LvD0najyMRWe85DkhPG2v8DoGjiC8gisyvPmCYjvx6+47zgruPHs5JDw584s8pO90vESGpLumSO88k50cPfHJhbvKkM+8lxuau/LyPzzKI5C8IQmSu3OMhLwsLNm8lwg1Pd+ZkTsyMqQ8htcEvNiopbyA/X88gGoOvWxegzyfjlU6edvVu93FDjw0mqa8CBL8O/BN/rgKG4U5pvN6vEra27yFaxI7NOIFuYrOHbuytQc81NUkvFyj8bpfv+q8ehyLu3eh4DsixEA77QicvHPT7Lxm+F26S2CmvJ1e7rsm7V28inICPD7dHj1qmAo8hlsGPEK9ZTw5SuY8xaozPA8407wT9Oq7s7XTvFbg4juH9Z+4ofRzOxnZuTtuY5E8u4Q7vK7ctbw0Ew+8LQ6kvIiDMjz52Wk8VnXFuyYWnryDuTu82bFxvO7Nrzwbeo68c/UGPJYms7wmRsy6qEJgO92vJr0WP2U8VBX8PGJafDuOUhM8vS0hvDAfNDxg/TA8IkQbvezFU7v4Ake8ue7EPC7+Ibp5B6882qgqvLn3ULwnHBw9zknKOiGEPL2hwsU82qDbO0FJabxMDmi7WaW2PPQVpjy/3Gu6ZIXKPJDaf7weDCW8u+v3PIiIIT2A8ZI6DaLSPBoV4jxN7qE7PEhvPAydErxJgTy7sdCVPP9iRDwIYoE8RmEmPMcqnbyb0hW6LrTtvJlU4zreL6a8ehafvD+XhTv+VcQ8zAN0OhAkgjsTD5g823IwvIR9FT17W7M8OpYsvAE2SDwZMJe8LJOpOhPpc7ucOIM8QYgNvbbB7rtnMiw996fpOqS/DTxBjfI86Q2rPDrRF7sJY5K7+m4RPfWVzTwn6+06W+OHvMpyeTyRaoo8V7vAumT1jzt53dO8coA9PGlgo7yZoQ09uEhmO8mukDvTWhY7m2+nvE26Fj0FYVs8QHfevICwNbuZUok8JuimPKW4FDyNnR09lhZPPDEzkbog0Yc7AzpTO3/bnDzPiDg8V02JPAMb+7wz6Aq9ToqEuwUuAL1Awsm8mGQWPcxDi7zlipG8SzIgvLjQ4bw7fzq8du3IPEWF6bszzJw8RVMquu27nbwsUtG8e3EAvM1rLTwUffO8x1YvPJ/kQjvKj7+7WPe9PI5ChbxFlSG8+zLBO7foID0hG2W8htbEOzorEjqr8Je7B30KvX2MeLz2wUc8753oO4jJ2jvLF4S8TwrIPDqEIz1Akxs7EkfCPBGPj7zV7BG8wMojPO5yPTzzfMu7jNzZPJeF+LyzXJO8hwCpOYLQZbzoGfC7eendvOXugjvbaDk8xGkbvVYNkbrFdz279q+YPLxzD7vHCjs8LfoGvVwPYTw9v2w7AEWPO8Zd0bvZaZI6tvKTvMgtMrxtpLm8KXH5PK6tZjxLegi8FBcfvPJ4Zrwvs/a7FU+qPNdPl7we+D88wpEiPdZllDxLCps8xhmSPE9W7bxF+ka80HCCPJEhrrt8mCA887Rzu3Q10TyoIqq8pC/0O0ApmTyO2cG8Gh92uzhsC7xtnNG8sgEIPX0/Fb1zdPS8J18NvE+zjbwPXGA8KYCZvNiGEDw7a4w8+WLQvJs2GTonY7u8sKsZvfSTnzz9Lt+8fvTTvKfejrm5Tw299VJ1vGicxri8SQE7Z/ayum4XWzzmRJu8XCiUPL/mR7zFNOc8Qld9vJebwzovQDm6JwPLPAduRTw6x2M866W/PAJXjLsBV1i8pwHHPCkkOrxyTXs6EMNEPLKYkzsT0HQ6qCQIubplejz+HCw8bksUPGOJHTw3+9i8jtjKOtD++Lxz3Hg8AdWFu7DQgLxBWp+8yhEwPPBAVLtibuS8sIWNvDJ6G70+5dg8eODDu5/aJ72mh3Q8Q/HVPCni2LybDYU8tEm8u4wK3TxooGc7NfWhvHO+ZLxGeBu9CQmLvPiJwzseeJ66M1VDOySlnjz+KJG6Q36DO82thzsJjKC79tpTPK33eTx4YKQ8wp2yOr2IBzsih+67f56/PMFQwDxdbMY8uzSAO4aiArzxPmU8ga0bvEDyWbxO3jS8EhgGO+JznTwQsXA82Y7Ou90gyDuW7EW8oNOnOlss2bw+oe67J3APPfINDb0AcGS8nQ7dvE+gmLwn0tm8Vc+6POdjoTv5RwY8ktfdvEbC/Dtqwxq9L2O4POh5yTq7mp88wRv/uyDYSbwwcp87VQlAPXyTkDuAoti6QbZNPd0gpjwZdt884aiQPM/eFDwBjpi64DrUOv0HSTxc1ZW8lf6JvC0ybDo+MT+9jss9OzwSQ71jd0w8c01RvDmnBjypyme8jSlku9hwgbwea2C95bc+uxMPALsLqb47CHnhu3s41byPEsY8hBaKuuOlzjw8wi28oRoWvTan6jzrXSi9pqcrPDz7ZLw+tuw7uieqO2ztejp+lwW8RAUVPZLT5rsB3uG8PR4cvONZRDwSPgi606TcPKlay7tQMkQ8i/GrPIBxP7tFAOE8wNxdPKkgfzyGdIu8bKOWPNDa1bvJxdG88FkFvIReobp6LKO8KkamPFiqMDuPjqk7JM/NvKqkebyGoP+7j3+nOXiRmjz7gbQ85/TLvGNDojqBqfC7vvqkvJ09xDvWnii8D8vpOov3/rumz7Y790TUu1CS5bwPMUK9e28gvBQgBDwZYyS7ZZn0ugqD6rwH8oG86C9Uu9CkNbsPKoq8Av0qPN4KorykSiI8OPBWPAROEb0vG0I90OfBvOSWs7yGY768gTQ7PDr7y7tK6mE8szSQPLkrcrwKA40703LNvEB4xDvoiP+7VpKZvDehSjw6uva7iajrPKsbuzzbpFc5u9WROiNZlTxX3K67VuahvMu6YrtloNa7v3NBPKlOjryT22O8LnvVuyEegruN0z88ecjXuwn6cb0DCNo86jMPPXdqIbyhgJs7l46dvPdDoDoGSEY5m2s5u0ato7tg3YE87NsCvbW8abwbHea7Y9qFu+LJxjz7zey7o0pUuwjLObxNfjW8+7hzu1+kIjyetMA8QclPPNbaNztP38Q8/AjbvO5fdbzBY0y6gZFPvDNg7Tr2zLo6ufbXPL3smzxC5eW8ThCEvAgDED19k+Q8XJulvOUwvjtydPa8aDdtvJ0RBTqb4ke8qBiIvLJqSTx0SK87/dTDvM3xwDtyiqU8tvRfOtcE8rzX0hE8LkJDu8X1hDo5vqO8C9mTvJmLEDzCtoW7bjWKO5wA6rwr1/K7RluFuhqYODw2qq08va+fPLd3yjwGA3E8sO/hvCWtyLudP/m7WsnzvGIWqbtZgw69mlYZukAjCL1PeeY60Iv3PBdOl7sniZq7u/0ZPSf0JbwrY6Q8yeAAPJXOCDyMW7u74f6tPG8f7rwXd+i7sjUhPBln3rupsj28puf8umECLLyCH5O8ns7PPFJUxzyHnZW8auE3vUeRKbtRjIW8aCwkOyjolbygUAG8tmFIvKm3CD3ChHw8KoHEuri5pjsP+wE95/4APVz/5byhQss7OQynPOUbnTxkSSC9uwa4POhndLyum/m6jfCvPB2UiTq3Cdm8EuHzOo1c8jtWX1I8HX07vKuFobxpMIq8K9MkPdOMHTxtsk+8xOklvMJoOr3wvk06vMRiPLiXNrxuqOq7PGtKvMuqmjzdQgO7YFU8PaoEArzmvxi8tGM0u9A34TwGrQY8Qu6CvMOSo7v3bUY8/KQ7PeCrvTyjI0s8boibvJWYGb2an0W8Mgu6vOpQKzzsMvo8ocgKvJ4j7TzmTQ68LTt4PE5S/TvB1LS8aTsEvBa1AbyzH+E7mxAEOtvLYLw0ecQ5LdiFuxGzS7sqIYi842ioPDn3mryqWp28UwD/O/ixyLsFlQ89ooeUPP5eorq/Eg88z5Ylu9jwibyjlkA42dnYOzfznbxCMGk8soM4PGOXrryYZmu8wk7YvEgj8juxIQQ8TTPjOhFrnrzpYtW8TzS0PJW5gzyNhoE79MVUuvgMkzyNO587/X/MPL9osjwhK0+6L0XAPBw1ujwNzee5T1L5PJNnjjpu6QK7kNWkPIg+ubzPC8o8fD03vGMryDsp60w8JXwnOYLBW7yBR8A7T2goPZOGCrz+odQ8fdL4uBeShbzAArq8WXdyvLcE+ToW4As8ZX4IvBdNw7wf8tC88mgZvc5Ygry5QJk6kFvHvDXaojwX3gg99Dj/O6kKoLx1BLi8+60qPOr5obwYxGA8RD+Iug==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '134'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - JavaScript runs in the browser. It powers interactive web pages.
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 13
-        total_tokens: 13
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '76'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Python
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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wTtYXhw8ExYTvbS0NbzFkti8s8GHPLcVSj1Qi8c84kPDvP3107lHySM8DqLUO65XCz1ZxZu8IyRMu058krxK3oA7YyIDPS3JqDrUJmg8Qe22vPNzXjvvw4o7aIxMvE6G+bzTBpO8fRuJPFVCHD3mVey6okz5Os5s57vSu688MyiAvKgqHLzZOak89ciqvNQWLL1LY4i5FajvPJVWrTtfpJw8gXFBPZrtAz1rM7S8KcOWu/esejcflnm8FOpNvPGE/LtB/gs8jZWAvFWzFD3qrUi8wAwwPPIBqbxpQzA7JyqUvBQTmTszqAE9VaRIvQP+tDlkGZg8hWMEvPYdOb1rT+M8djqNPEisQTzRynE8NSY3vLkGID2hNRy86y/lPB41abxNsBe9QDsUPHhTlLwqNsw7c4gKvcSe4LsXokk8/lE0vIOd3LwUjJg7snNSPJNyC7yY2oA7ZnyqvOf0fjwnvKm8DpPWPBISFbyml5s7l036u4BLHDzCfpa6c3COuVDBwbvBm9U8oKyDvP9AC7uMSp68cD3outPP5rttKgW8t1Q8PHOrwzyN9dO8N8QWvMYCkzsBjTM8mXRxvNhlgry3tfw8xIWqu3n8ET2NEVE7SDZuvDhA7bwzsPa6QxS8PLG0FT1BlI88ZLu1OxhxPTx9TvS7OUX3PDuKNLxxISO7b80WvYv2ujyhYx08/OYXvVmig7shzJ87kFrHPGIzwjyDAs28pdrQOSVVdrx2XLi8QKnZuzZxtbvEG8y7KRdbvFy8GjyuZUC8bq40vKTyMruUns26pUwpPbNajTvbqp68lNZ0PH5wXTzHCGM8V5u/vPSYkbysjUQ9BJS7vDeKJLzMuIS85+V1vBOycrynRvO6d9lMPCY0njylIIE8U1SeO4vNZLwM0sW7zg27O1zSurvZ2M08G3usvI+OZjxR9vy7jf1BPAmCirwbwgK8oOWpvB0iI7z72wu9H62cvHj0wzySKFg8GLZSu0h0NjzmcRo7CycCO32BRLoepdc7gQ0dvKS3hjxfwtA89A+bOwKIgzyoPZ68krZyPVWYrTyuP3Y8Cr3ouIXisrrHiX08kRsFO9XXeLt5pLc7hBMiPO0zy7yacCU9+DMMvUMUhjxjTxE83bQBOwP05LwecdW77iwcuocJrjxT3qs8mjaxvPJDiDze+oA8PCL1O7XlHD0AU2u8KILAukSmd7ucX7A8mU7hvJ7oVD2yKNE8g2ESPB5bwTlw0ZK7wGA9vFJqF73+gt86YsmGPBgWHzoPk5U8+/0zPLRznjwTsiY8yhqdvP7ilDyHYLM6qJwLPYZMrLw5RrC8Oq5avFfH5TtnMVa4zmCdvE5mhrxfHd88a7rZuaTqP7x4quU7GbdFPF1srruAVQ+8URIAvbeMDr2MkOi7XfMSvBjuFr1XPXE61wTLvCjmgLy8+Ie8DjvDvDO2RTzUO0+87em5PMLuYrxiR048Uw2XvKmnVDx05AU8gbqDu3P+tTtqoA29ULxOPET1pbyxBfI7mB0MvPZj4Dw1cRi8WdqRuvYDNjz3RUG8IK1ePOPJn7sI/MS7Axc3OyM2+Tv9t7A8o1MdPCkKFzykxko89UWRuzLHmjwXpxE9HsUrPdwZpLwRhJA8+feBPOt6HTtKuhk7pTBKPDjMnbwMcI88DIYYvN1kXLz22E29FUHZvFjFTjwnRoI7t5WTPNCuVjyjOrU86N7PuyubYzzf4rU7uwakvCdqab2yTpE2cgdevLhRiLtX9Wi8hTjBvImWyDw2sUi8TOABPNbjpDzF0B08Y5PLum/EoTzlhBO81UKjvIwm4ryuYQW9Zv8JPfbj3bwuy968ItAvuyeYwjxPSp+8Wl/KutgrDb3l+fm8j2m9PBulT7vrHxm9YfKePJs3A7wslUy7cDmnO//rFL1toa87yb4APY6lnbt1HR+8z9GMvODyNTu+TCY6Wgq3O3fb47zKsyA8mZaAvJopHz3XOXW7N80PvcMo8zxuWcg8+yOauw38lzuy8mg8rA3FPLRtqbykspK8ANcHveWaEbz0WWw8i+YqPFIwy7rsUc48VT0VPcTtkjwAGx+9fusEu1/pQzzAAA08Ys+zu9KUrDuLgVc8aQItvKDLrTxgGY68ksI8POEPmzxoOSQ9Y6StPIEaUrx1yCy95EUxPGQgkbxJRLk8ppDgPMQjkbstLtq8tHDWPPVPuTscExc8666JvFXRIDz71CM9uZ+PPMhpCD1qg8c8iLTzO21khbpWmqw7zi12PMWef7zY81m8iKq4OpxtoTy5Acy8T3ecu+weDz3mf648E9GkvHQixzznRr866HZNPRsbf7xJriG8493uPHsatDy1KvG7sOqiPJzwVLw7ZKy8DX2SPBpMp7wotY+7EQWIvLgSUzwkrE89+OK1u23L7zqV+gE81WEnPcYokbs6ciM7qvx/vAU1mTyMOME8zjspvC/oBbsQDHA80rtOvNgu6TtYUUe7ARw0Oubib7xwzsc87hxMPE+JhjosNr48D9jsuxNMmrzKpQi9smefPJfQOT0E3I28viMlvNcPtTygVY88M6D8PH2pAL1T9v0797GTunKavLv3VkI8WCaDu753KzzmPJ4792VAvNUcjzw5zIE8xfQGPQPGwbvQ67A8NLBVPJvJcDsDdQq93T8Lu8vIyDxHAwk9GbBuu3/h5zxrtXM8Z5UpPH/Vg7tKC2Y8MhuiOn3LUzrZyDM8GnQfvITHhzySZLk6RssYPaZoB7wTWQ68ejg6PCPtyDzDXiI8mSKBOw71gDq/xp88dsIRvDDidL1NrZC8k+6sPHqegrwsVpM8qTvPPBK+HL335ni7VBYWvSyDWjwruKy8Qe55vCaFETxEzQu8Unr0uwDNqbwPajY86JWkO4lIC70QcKI8333evIfhEj2Th0a8KljDPBwR27lA/Eq9TK4+vN/Fk7xP4KS8UV6xPLS09DyDdlc8b6TjvC2lmzzLNXc76O8lPUE9I7wZaDE83T4svfVKXjqbj/G8hhO1PIT7rby5/H07w9p4vBu3hryeye28YO/9u51vmbx/tAU8xIEqPLnzjTvwBzs8fnOVuxb/orzCHxe9H6Nku+iehzyGLMi8DG63vCBJ5DthXiA3IKkYvV1saLz/4r274p14PHVJ8bxzbS48nGCgO9sgmztIAQi8z773PNGSZrs+wsU6y/0KPJmstLyTWx28ic0rvN1yITwio6M6f2igO5/E0jwj8d47YyBBPBuAKDx4xXA8UDZQvCLWuTo3Iws97bU5OfPhmTtTRze88H3wvOTolzsz0BK5OV5CvCvCsjzNJYM8KBKQPDcfdTyLFkU87HNqvI22PD2NwNe8YGfFPKxHprseSnO8NQXbvK9TGTxFHBe825n0O9Q9wrxHAZ87cLQLPA8jPDwwfdW8xEEGPTzo2btR5h+8usFHu+zEHbwm1SO6lxvGvO5VVzwgFE48jhDUPGoStTswZxO8UXmdPPGvSbvY2qq8FKgnO7bIyDwrSHu74asqPFKCtDwWqha9RkMcPABEJD0pUBY8QAGvPAQAP7sWVAe8cMW8PGBM0zzU/6Q6jp9vOiDDYTxVOwu9RALruzwWHjxMSTc7aj+OO9N5nDtCdNI8GFqDPCTwg7zqsbe83QozPVWiYTwuaw08WgAGO/Q6Pjw3gUW8j81bvWsVNrtg+TQ9nSL+PAGe+jtef7s87A4jPGl8Nz3Kibq8pu78uRofMbwgefY7o9kqPKZfLrybo8o8VgYTPBpEjbsLAJo8Hn8dvIfKnTxEEKy8ROAyvB77Obr8JxY8fYg7OzcFkzw5asS7I83JOn1JobydexA6ebBEPIH+hzwPw3U889ZUO6Vg3by0XhY8NMEFvWeqj7yLwh688j9qPckiuruN24u89jvWOsHTBDnmx2O88JlHvBbEkLxZ/cS764GIu5p01TkTc6Q6fxb2u4pEYTyBonK97bdLvOf8KTyCIQg7GqOpvKBSLrzs3si8k3OJPKcLTryMnZI8C+xvO6rg7Tt+1Ng6lZ4yPJ18h7yti387w8VPvBmDhTwo/+K81lbKvEqNjTwK5ke8Q2MWPNWa3Lswc/M7p3BivCYUfzykL7Y7Xxq6OwXbwzxChA293vHjvDaMcb1/yQY8TjQkPJEVC7tTNni7UgARvTVbgjvknX48+fWePOlWvzuIKgE9uD5IPWCduruTXWQ7obK5vLHi3Tx20xW7Lm+ZuxDW3jyspj879BFxPP6x8zyOmYY8IxQfveUZJD3D0JG81Lt3vDE0YLteorE7lxZSvCv3qbviyBs8ghivvLFjhDw9y4e8qRaJuzNwATkHfEU8gh5OvPOY1jzxYwI8Xx4zPFgqlLtP0vk8VZRwvH5yCL3V+WO83yjCPIGb+jpFaFi5y4Y5vO2Cjjo27oo8eMepvD3zHb1nAtG8yei+vEl0xDs936c5usdCPINVNjsvFVY8eO3uvGkfsDwQ6OW7ibaeOwK/izx9VCG9aP9uu4RI1TvVAiG9yxOGuUyMLLydKkm6fBRsvJ89OTwSdwU8l37GPBVLxbzKPx89K5LyPFRvcDoylx+94lWqPCX30DwZeLe8NZQxvGjwDDzjTIW8+vXYPLBOCL0+yO88Tn47vHfENr3OPD08Zvt9vGaAUz3VHnQ8/A5ovOk1STxwv706nnrzvCgtjzw7dlo56N1VvDD9arvVg/673vj8OutPMjy0vOU8qhNBO+NDjrxQ5b68CSKju1s5gjzMhxw7dz4ovfMKKr2Ca726Ol3IuYooZrwUluo7kub/vKuLBT3JgUk6bgyYvMo+ODsU1ow7VVEiuxvqy7zbiOI6DYYIvM/CQjx8iim8v9YGvcCi1js5Cq+7LuYgu1LAt7y9rU87r6KbvNKT5jzkP3E8vL2UO9ehp7y0tC+87WeKvN5uNbyqkM28ZZ+/PBdnWzwvKk08QX26O3oNxrxOBxk8/xdIPGxeFzxidP87Ug1AvJ93T7znDXK8Fpc6u6Rkwbvwzf+8QUa6u+//4jtRrt27I9E5PH0LlrveuwI9goycunUO2bxV+E+66HRgPGyPy7v9lRE6uyCuPLOxiTr76167m7YlPRGUUjzyNgw7asCDvN9hUz30P3e7l4nBPK/M0ztEsIe727g0PLQEhztUaJa8nGWAPFyXSzsqr/K8N8lCvHBWRjuGiB+8kIUzu60ZFbwG0au8Ix60O3xKbzzvUI66M0EyPbGlOD2UJdU7apGcO+eh3DxPE9A8J1zVuk5hVT3GR5W80QJEPHeItrxCbB49rJ1XPMWF/zs61fQ8x3kXvAGgHLxV+NA7DkhHPdFzvDuewoA7RyAxPbynvzw9lY48GDNZPOuu7ztgG+Q8evMCPBedhDw+xz68B11qvGE+rrzC/bk8XyiYO0pYb7a8gFU8H6ZruiEVtjwenQi8pJw7vFlGvrweqpq8FalePGPPBTweeHS8XZXyOgkKrruZrnU8PoD0PFm93Dz1/qW8x1PSO1w4M73D5828VSKEu7nkqzecLd68B594PGM3Zbwnu4I7N82zvAAtB72jMgq9F5qDvC8MdbvwyW662eNJvETdK7wAsCu9A8yPPC3/ILu4/qy8U5+mPEQQhjwYuQo8SC+MO301l7xuZSi8A2KmPC6l6TzI3Fk6w4MOPRl5M7ye0NU7NjekvKwVijwzzvI8MelavD+ivruES1u8k1Q4PVSKZzzXTpw8LUktvH9nHL1gYXS8CdUvPGvhNbzb6Im86XbrOpZ6d72y2y85VPmlvNgjGr3Z8/e8CXY7vL4XVbsUohq7QjEavJpsTDxvTI+7S6KpO2y93rysvXu8NGhFvHisCzwnqAw9bk8QPJHnHbwTBda8r5oAusgt97so9ga86TKuO53Uybsq2SO9esesvNumljzTLM28gzsBu0KgZzzum5M89I7/PFZv+Tu0Xre7ZzU0PUkFlrtLP7I8x3ILPS21Gb2CaaA8KnKRvNHUDzxbaBm8Vx+2uysf6zyw9TC93KzDvN7lE7zoclq80CUTPa2/Z7zsrDm9ElK6O3jRybyAESo8O+xWOLOZubqxy3o8kLcYvbUg4TviPGi856IYvbEeGrwyx/Q7noAhveabqToM2tW6y1OSvG1xDj2HzFm89a6XPFMEyTwSbIG8qIqQvG1jxbzuZGg83/N3PLoHB70nEMG8rx8qPVLLwbq0XAY9yFQQPciC6zycaJy8YoWWvDrubTyKnBG8jupQPHZViTsjkYe8kQUnOw+NDj1gUhi7eO2kPC9bszqkQni7v/69PCWnTr1FKNu8mCnFvEaIq7wISJE7FUjUPJkc5jkDiCG9K7nWO0oKRjzKEkY80nBEuxn9nrw8/tC7LC9YPN0sxrwolo466cuzu/NZwTryMYC8jBjEOyqoprqjv0G8jX4PvDL5WjwSFJK8Nij6PDNCnjsVwvA7Zw2qPGQD+jxQQW+8SrdJPHQPTDuF4SE89zz5u51b+ztd3Ok8NssVPfOQsDw8mK45PWofPfkjcL2+qyC8gf2CPDqXEbyHvZW7Eu4GPA2sCbtktLm8M0wPvNGQzLts3YS7Dc8SufhYc7xQcoi82Qv4O2BluLyvGFk7wafTu3fhsbxdKNm86tbOOzzi2TygfIG85vItvMQcobzySGe88XkzPGxPKzybTTI8l5KxvF0/iLxUOck8bJViOxxUA7wNO/E6JSN0O2bD1zzyZw099N8SPFChmTsTSuG7v4NevDCEGTzatxO7XAoKPA/DG7s+atW8XA2IOzaK3rzBdjo8ed2yvOHEHjuTKES7TD5CvBcqjTwH/wy94eVIvFENjroPREe6fpHvO5UA+rzUcJc8jcu8vC72Lj14jZi8GELivN9XGLyCUIy8VngkPZwMyzsnZ7k8S0KmvD/hTbzvFiu8fZSFPMOWsbuvMTo7MM8MvGpgezxHTxU7pGZyPGBZ7bxSeds8KDytPHcbHjzwBu08HNXlvMd3mLzFB1W8KYHfO6bt/rxXi6m8jxE4u/bMszpmiIo7z+CLPN/yujzJNIm8A73YvF4WLDv1CT66mdS1vORXoTy72Lw8dUbfvO/pDT3XLLs8oH/6vIn0rrtAw3m8vGcWPLhKjLp8G4G8JHUuvKrP4TsXKw+9NKOeu0bRsrmSQJQ7Xsq1PAix9LwbJai70EanO+vmg7xN8CC8tnABPENDrTq6Kda6KiPHO1fcSbwMtW48rukFvd+N37xvhmA7mNxVu+F+QDxPDwI9vgLRPG27srzdAp28UHYOvB7VNTzYlxy8zFUovde1Aj2c9IW8miMEPT2lXjvw00A7wA7IOYpSlDxz1wS8w5S4OYzLkDtRTGm6nN8nvIxmFL0sQF08+ayBPJMZgrwX5528GVntuzkSVr2Maxw9VuhUPV1JEj2X7Kk8d/KivHXWYLwlc1m8NO+4Oh4PhjuWqDM7gcCeu8lhWbvmDjy9BugmvPPNqDwtNoC83pPsvGBjU7vyqLQ3O/tjvDG4+TvLBeQ7mgYfPKaDP7oSYnO8SHULvWsYiDvqTQ+9AIx+PAkx1TtQZQK8EzQxPGACuTwlZ7W8IcMGOwxMYzxvUVY7kYcgvBdZcLt1jQa93TgdPDGnbbx4hWG82mJcO2EdlbzqnAc8yxlDvHfuCrxxCPm67p1wO3q2wbyooV+8dDmJu8Grejw72U28jZs7vHMgCjyaklq8SbmIPB+8Bb04lK87Lzg2PMUGXbyUztO7cYf+PCnQDD2+Aj48pFJ8O5hUnTvsbfQ7gApUvd8AELibO/O882lTPMS/8rxXUaI8/PVDPDGZITwy6LA8MpbyPCiFjzy5KC49tuRkvBhyWbyEF8k8J9LqO9lm8rx6ptQ7oTldPL3nALy6Okg8/zK7vLjLfTvBTD+7pghqvD2w2Ltkkt27I2wjvUTdbzueRcc7meuoPOriujtqW6Y8btcCvETAmTtZpVC6RKVbO9FaJ7xJa888hGf6u6ctF70x6EW8mTJ/OxPqYjwQZhq9En16vOeI+juIDrI8fMKtvIQgEDwO3bW8iqQDOxXQkrxDfgg8pVPEui5jO7y/vqi8bYmtPAi4hTxplua7ZrzoO8X+LL107ZU8gJIpPP5BlLy/NTk8Bg88O+vogLv4zQi8EYfgPOsEbLrv7nK89IFEvGeLnDzXrKW7bnOFO4wMFDunTZ88/5YOPYhpVDz3Oow85SKVPLU+BL2rQNC8+xmgvEs1mDxcKm08Ho16PNWnSzsQ5WK7YmNGPIG0gTy3Tpq8CS46PLRy1jxeVlw87Un3u1ea0Lwak+851fFbPNUvA738bce8HaGxO9RhEbw0RWW9l7cvPGT687sJTGE8eJWePHabDzxeg+I7/PPdO87IJbsLBMa6dzgLPSX5vrt8Ovk8vJnjvPCukrnoSn68+wcDPO7W/bq2mzu8vkirOyVXB70H19q8PlGVPN8vkrvgBPE7yFsSvAytpjxo+aM6nShZPN5UcjywTLc8hCLMPKpTpLqcWok7B00bvH4NFbzdmN273cYXPVowlzq1FZg8fseqPHKevbz4Pcg7NFIjPUtAOLxR0Ys3rTiqPGomjrq2Y+E7bFcXvJZIp7zGnVQ61w4Du2rCMjw73X48f4k0vfynt7nVixe8W4AuO2cdOzoSuNS6Eo06OtH6Fzyk7806kh0HOysmg7tY8ii9qVV7PAyPdzx1tbQ87ji0Ow==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 2
-        total_tokens: 2
-    status:
-      code: 200
-      message: OK
-version: 1
diff --git a/tests/chat/test_chat_app.py b/tests/chat/test_chat_app.py
index 48f78d44..dcfd572a 100644
--- a/tests/chat/test_chat_app.py
+++ b/tests/chat/test_chat_app.py
@@ -5,7 +5,7 @@ import pytest
 from typer.testing import CliRunner
 
 from haiku.rag.cli import _cli as cli
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
+from haiku.rag.skills.rag import RAGState
 
 runner = CliRunner()
 
@@ -21,19 +21,78 @@ def test_chat_command():
         mock_chat.assert_called_once()
 
 
-@pytest.mark.asyncio
-async def test_chat_app_has_required_widgets(temp_db_path: Path):
-    """Test that ChatApp has the required widgets: ChatHistory, Input."""
-    from haiku.rag.chat.app import ChatApp
-    from haiku.rag.chat.widgets.chat_history import ChatHistory
+def test_run_chat_creates_app_and_runs(temp_db_path: Path):
+    """Test run_chat() creates a ChatApp and calls run()."""
+    with patch("haiku.rag.chat.app.ChatApp.run") as mock_run:
+        from haiku.rag.chat import run_chat
 
+        run_chat(db_path=temp_db_path)
+        mock_run.assert_called_once()
+
+
+def _make_mock_client():
+    """Create a mock HaikuRAG client."""
     mock_client = AsyncMock()
     mock_client.__aenter__ = AsyncMock(return_value=mock_client)
     mock_client.__aexit__ = AsyncMock(return_value=None)
+    return mock_client
+
+
+def _make_app(db_path: Path, mock_client: AsyncMock | None = None):
+    """Create a ChatApp with mocked HaikuRAG."""
+    from haiku.rag.chat.app import ChatApp
+
+    if mock_client is None:
+        mock_client = _make_mock_client()
+
+    skill = MagicMock()
+    skill.state_type = None
+    skill.state_namespace = None
+    skill.tools = []
+    skill.toolsets = []
+    skill.resources = []
+    skill.metadata = MagicMock()
+    skill.metadata.name = "rag"
+    skill.metadata.description = "RAG skill"
+
+    return ChatApp(
+        db_path=db_path,
+        skill=skill,
+        read_only=True,
+    ), mock_client
+
+
+def _make_app_with_state(db_path: Path, mock_client: AsyncMock | None = None):
+    """Create a ChatApp with a skill that has RAGState."""
+    from haiku.rag.chat.app import ChatApp
+    from haiku.skills.models import Skill, SkillMetadata, SkillSource
+
+    if mock_client is None:
+        mock_client = _make_mock_client()
+
+    skill = Skill(
+        metadata=SkillMetadata(name="rag", description="RAG skill"),
+        source=SkillSource.ENTRYPOINT,
+        tools=[],
+        state_type=RAGState,
+        state_namespace="rag",
+    )
+
+    return ChatApp(
+        db_path=db_path,
+        skill=skill,
+        read_only=True,
+    ), mock_client
+
+
+@pytest.mark.asyncio
+async def test_chat_app_has_required_widgets(temp_db_path: Path):
+    """Test that ChatApp has the required widgets: ChatHistory, Input."""
+    from haiku.rag.chat.widgets.chat_history import ChatHistory
+
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test():
             chat_history = app.query_one(ChatHistory)
             assert chat_history is not None
@@ -47,144 +106,64 @@ async def test_chat_app_has_required_widgets(temp_db_path: Path):
 @pytest.mark.asyncio
 async def test_chat_app_quit_binding(temp_db_path: Path):
     """Test that pressing ctrl+q quits the app."""
-    from haiku.rag.chat.app import ChatApp
-
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test() as pilot:
-            # App should be running
             assert app.is_running
-
-            # Press ctrl+q to quit
             await pilot.press("ctrl+q")
-
-            # App should have exited
             assert not app.is_running
 
 
 @pytest.mark.asyncio
 async def test_chat_history_can_add_message(temp_db_path: Path):
     """Test that ChatHistory can display messages."""
-    from haiku.rag.chat.app import ChatApp
     from haiku.rag.chat.widgets.chat_history import ChatHistory
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test():
             chat_history = app.query_one(ChatHistory)
 
-            # Add a user message
             await chat_history.add_message("user", "Hello, how are you?")
             assert len(chat_history.messages) == 1
             assert chat_history.messages[0] == ("user", "Hello, how are you?")
 
-            # Add an assistant message
             await chat_history.add_message("assistant", "I'm doing well, thank you!")
             assert len(chat_history.messages) == 2
 
 
-@pytest.mark.asyncio
-async def test_chat_app_calls_agent_on_submit(temp_db_path: Path):
-    """Test that submitting a message triggers agent invocation."""
-    from contextlib import asynccontextmanager
-
-    from haiku.rag.chat.app import ChatApp
-    from haiku.rag.chat.widgets.chat_history import ChatHistory
-
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
-
-    # Create a mock stream result
-    mock_result = MagicMock()
-    mock_result.output = "This is the agent's response."
-
-    # Mock stream that returns the result
-    mock_stream = MagicMock()
-    mock_stream.get_result = AsyncMock(return_value=mock_result)
-
-    async def mock_stream_text():
-        yield "This is the agent's response."
-
-    mock_stream.stream_text = mock_stream_text
-
-    @asynccontextmanager
-    async def mock_run_stream(*args, **kwargs):
-        yield mock_stream
-
-    # Create a mock agent that returns a fixed response
-    mock_agent = MagicMock()
-    mock_agent.run_stream = mock_run_stream
-
-    with (
-        patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client),
-        patch("haiku.rag.chat.app.create_chat_agent", return_value=mock_agent),
-    ):
-        app = ChatApp(temp_db_path, read_only=True)
-
-        async with app.run_test() as pilot:
-            # Type a message in the input
-            await pilot.press("H", "e", "l", "l", "o")
-            await pilot.press("enter")
-
-            # Give the app time to process
-            await pilot.pause()
-
-            # Verify the response was added to chat history
-            chat_history = app.query_one(ChatHistory)
-            assert len(chat_history.messages) >= 2  # User message + agent response
-
-
 @pytest.mark.asyncio
 async def test_chat_history_can_add_tool_calls(temp_db_path: Path):
     """Test that ChatHistory can display inline tool calls."""
-    from haiku.rag.chat.app import ChatApp
     from haiku.rag.chat.widgets.chat_history import ChatHistory, ToolCallWidget
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test():
             chat_history = app.query_one(ChatHistory)
 
-            # Add a tool call
-            tool_widget = await chat_history.add_tool_call("search", {"query": "test"})
+            tool_widget = await chat_history.add_tool_call(
+                "tool-1", "search", {"query": "test"}
+            )
             assert isinstance(tool_widget, ToolCallWidget)
-            assert tool_widget._complete is False
+            assert tool_widget._completed is False
 
-            # Mark it complete
-            chat_history.mark_tool_complete(tool_widget)
-            assert tool_widget._complete is True
+            chat_history.mark_tool_complete("tool-1")
+            assert tool_widget._completed is True
 
 
 @pytest.mark.asyncio
 async def test_chat_history_can_add_citations(temp_db_path: Path):
     """Test that ChatHistory can display inline citations."""
     from haiku.rag.agents.research.models import Citation
-    from haiku.rag.chat.app import ChatApp
     from haiku.rag.chat.widgets.chat_history import ChatHistory, CitationWidget
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test():
             chat_history = app.query_one(ChatHistory)
 
@@ -213,7 +192,6 @@ async def test_chat_history_can_add_citations(temp_db_path: Path):
 
             await chat_history.add_citations(test_citations)
 
-            # Verify citation widgets were added
             citation_widgets = chat_history.query(CitationWidget)
             assert len(list(citation_widgets)) == 2
 
@@ -221,25 +199,18 @@ async def test_chat_history_can_add_citations(temp_db_path: Path):
 @pytest.mark.asyncio
 async def test_chat_history_thinking_indicator(temp_db_path: Path):
     """Test that ChatHistory can show and hide thinking indicator."""
-    from haiku.rag.chat.app import ChatApp
     from haiku.rag.chat.widgets.chat_history import ChatHistory, ThinkingWidget
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test() as pilot:
             chat_history = app.query_one(ChatHistory)
 
-            # Show thinking indicator
             await chat_history.show_thinking()
             thinking = chat_history.query(ThinkingWidget)
             assert len(list(thinking)) == 1
 
-            # Hide thinking indicator (remove is deferred, so pause)
             chat_history.hide_thinking()
             await pilot.pause()
             thinking = chat_history.query(ThinkingWidget)
@@ -247,67 +218,38 @@ async def test_chat_history_thinking_indicator(temp_db_path: Path):
 
 
 @pytest.mark.asyncio
-async def test_clear_chat_resets_session(temp_db_path: Path):
-    """Test that clearing chat resets the session state."""
-    from haiku.rag.chat.app import ChatApp
+async def test_clear_chat_resets_state(temp_db_path: Path):
+    """Test that clearing chat resets state and messages."""
     from haiku.rag.chat.widgets.chat_history import ChatHistory
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test() as pilot:
             chat_history = app.query_one(ChatHistory)
 
-            # Add some messages
             await chat_history.add_message("user", "Hello")
             await chat_history.add_message("assistant", "Hi there")
             assert len(chat_history.messages) == 2
 
-            # Clear chat via action (available through command palette)
             await app.action_clear_chat()
             await pilot.pause()
 
-            # Verify messages cleared
             assert len(chat_history.messages) == 0
 
-            # Verify ToolContext states are reset
-            from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-
-            session_state = app.tool_context.get(SESSION_NAMESPACE, SessionState)
-            assert session_state is not None
-            assert session_state.citations == []
-            assert session_state.citation_registry == {}
-
-            qa_session_state = app.tool_context.get(
-                QA_SESSION_NAMESPACE, QASessionState
-            )
-            assert qa_session_state is not None
-            assert qa_session_state.qa_history == []
-            assert qa_session_state.session_context is None
-
 
 @pytest.mark.asyncio
 async def test_citation_expand_collapse_with_enter(temp_db_path: Path):
     """Test that pressing Enter on a focused citation toggles expand/collapse."""
     from haiku.rag.agents.research.models import Citation
-    from haiku.rag.chat.app import ChatApp
     from haiku.rag.chat.widgets.chat_history import ChatHistory, CitationWidget
 
-    mock_client = AsyncMock()
-    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_client.__aexit__ = AsyncMock(return_value=None)
+    app, mock_client = _make_app(temp_db_path)
 
     with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
-        app = ChatApp(temp_db_path, read_only=True)
-
         async with app.run_test() as pilot:
             chat_history = app.query_one(ChatHistory)
 
-            # Add a citation
             test_citation = Citation(
                 index=1,
                 document_id="doc1",
@@ -319,20 +261,68 @@ async def test_citation_expand_collapse_with_enter(temp_db_path: Path):
             )
             await chat_history.add_citations([test_citation])
 
-            # Get the citation widget
             citation_widget = chat_history.query_one(CitationWidget)
             assert citation_widget.collapsed is True
 
-            # Focus the citation
             citation_widget.focus()
             await pilot.pause()
 
-            # Press Enter to expand
             await pilot.press("enter")
             await pilot.pause()
             assert citation_widget.collapsed is False
 
-            # Press Enter again to collapse
             await pilot.press("enter")
             await pilot.pause()
             assert citation_widget.collapsed is True
+
+
+@pytest.mark.asyncio
+async def test_document_filter_updates_rag_state(temp_db_path: Path):
+    """Test that selecting document filters updates RAGState.document_filter."""
+    from haiku.rag.chat.app import RAG_STATE_NAMESPACE
+    from haiku.rag.chat.widgets.document_filter_modal import DocumentFilterModal
+    from haiku.rag.tools.filters import build_multi_document_filter
+
+    app, mock_client = _make_app_with_state(temp_db_path)
+
+    with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
+        async with app.run_test():
+            # Simulate the FilterChanged message
+            selected = ["AI Overview", "ML Basics"]
+            app.on_document_filter_modal_filter_changed(
+                DocumentFilterModal.FilterChanged(selected)
+            )
+
+            # RAGState.document_filter should be set
+            rag_state = app._toolset.get_namespace(RAG_STATE_NAMESPACE)
+            assert rag_state is not None
+            expected_filter = build_multi_document_filter(selected)
+            assert rag_state.document_filter == expected_filter
+
+            # The state snapshot should also reflect the change
+            assert app._state["rag"]["document_filter"] == expected_filter
+
+
+@pytest.mark.asyncio
+async def test_document_filter_cleared_when_empty(temp_db_path: Path):
+    """Test that clearing all document filters sets document_filter to None."""
+    from haiku.rag.chat.app import RAG_STATE_NAMESPACE
+    from haiku.rag.chat.widgets.document_filter_modal import DocumentFilterModal
+
+    app, mock_client = _make_app_with_state(temp_db_path)
+
+    with patch("haiku.rag.chat.app.HaikuRAG", return_value=mock_client):
+        async with app.run_test():
+            # First set a filter
+            app.on_document_filter_modal_filter_changed(
+                DocumentFilterModal.FilterChanged(["AI Overview"])
+            )
+            rag_state = app._toolset.get_namespace(RAG_STATE_NAMESPACE)
+            assert rag_state.document_filter is not None
+
+            # Then clear it
+            app.on_document_filter_modal_filter_changed(
+                DocumentFilterModal.FilterChanged([])
+            )
+            assert rag_state.document_filter is None
+            assert app._state["rag"]["document_filter"] is None
diff --git a/tests/skills/__init__.py b/tests/skills/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/skills/conftest.py b/tests/skills/conftest.py
new file mode 100644
index 00000000..d6def468
--- /dev/null
+++ b/tests/skills/conftest.py
@@ -0,0 +1,64 @@
+import random
+from unittest.mock import MagicMock
+
+import pytest
+from pydantic_ai import RunContext
+
+from haiku.rag.client import HaikuRAG
+from haiku.rag.embeddings import EmbedderWrapper
+from haiku.skills.state import SkillRunDeps
+
+VECTOR_DIM = 2560
+
+
+def _make_ctx(state=None):
+    """Create a mock RunContext with SkillRunDeps."""
+    ctx = MagicMock(spec=RunContext)
+    ctx.deps = SkillRunDeps(state=state)
+    return ctx
+
+
+def _get_tool(skill, name):
+    """Get a tool function from a skill by name."""
+    for tool in skill.tools:
+        if callable(tool) and tool.__name__ == name:
+            return tool
+    raise ValueError(f"Tool {name!r} not found in skill")
+
+
+@pytest.fixture(autouse=True)
+def mock_embedder(monkeypatch):
+    """Monkeypatch the embedder to return deterministic vectors."""
+
+    async def fake_embed_query(self, text):
+        random.seed(hash(text) % (2**32))
+        return [random.random() for _ in range(VECTOR_DIM)]
+
+    async def fake_embed_documents(self, texts):
+        result = []
+        for t in texts:
+            random.seed(hash(t) % (2**32))
+            result.append([random.random() for _ in range(VECTOR_DIM)])
+        return result
+
+    monkeypatch.setattr(EmbedderWrapper, "embed_query", fake_embed_query)
+    monkeypatch.setattr(EmbedderWrapper, "embed_documents", fake_embed_documents)
+
+
+@pytest.fixture
+async def rag_db(temp_db_path):
+    """Create a test database with sample documents."""
+    async with HaikuRAG(temp_db_path, create=True) as rag:
+        await rag.create_document(
+            "Artificial intelligence is transforming industries worldwide. "
+            "Deep learning models are used in healthcare, finance, and transportation.",
+            title="AI Overview",
+            uri="test://ai-overview",
+        )
+        await rag.create_document(
+            "Machine learning is a subset of artificial intelligence. "
+            "It includes supervised learning, unsupervised learning, and reinforcement learning.",
+            title="ML Basics",
+            uri="test://ml-basics",
+        )
+    return temp_db_path
diff --git a/tests/skills/test_rag.py b/tests/skills/test_rag.py
new file mode 100644
index 00000000..a53f9da5
--- /dev/null
+++ b/tests/skills/test_rag.py
@@ -0,0 +1,501 @@
+from unittest.mock import AsyncMock
+
+from haiku.rag.agents.research.models import Citation, ResearchReport
+from haiku.rag.client import HaikuRAG
+from haiku.rag.store.models.chunk import SearchResult
+from haiku.rag.tools.document import DocumentInfo
+from haiku.rag.tools.qa import QAHistoryEntry
+
+from .conftest import _get_tool, _make_ctx
+
+
+class TestRAGSkillCreation:
+    def test_create_skill_returns_valid_skill(self, temp_db_path):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        assert skill.metadata.name == "rag"
+        assert skill.metadata.description
+        assert skill.instructions
+
+    def test_create_skill_has_expected_tools(self, temp_db_path):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        tool_names = {getattr(t, "__name__") for t in skill.tools if callable(t)}
+        assert tool_names == {
+            "search",
+            "list_documents",
+            "get_document",
+            "ask",
+            "research",
+        }
+
+    def test_create_skill_has_state(self, temp_db_path):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        assert skill._state_type is RAGState
+        assert skill._state_namespace == "rag"
+
+    def test_create_skill_from_env(self, monkeypatch, temp_db_path):
+        monkeypatch.setenv("HAIKU_RAG_DB", str(temp_db_path))
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill()
+        assert skill.metadata.name == "rag"
+
+
+class TestSearchTool:
+    async def test_search_returns_formatted_string(self, rag_db):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        search = _get_tool(skill, "search")
+        ctx = _make_ctx()
+        result = await search(ctx, query="artificial intelligence")
+        assert isinstance(result, str)
+        assert len(result) > 0
+
+    async def test_search_updates_state(self, rag_db):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=rag_db)
+        search = _get_tool(skill, "search")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await search(ctx, query="artificial intelligence")
+        assert "artificial intelligence" in state.searches
+        results = state.searches["artificial intelligence"]
+        assert len(results) > 0
+        assert isinstance(results[0], SearchResult)
+
+    async def test_search_applies_document_filter_from_state(self, rag_db):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=rag_db)
+        search = _get_tool(skill, "search")
+        state = RAGState(document_filter="title = 'AI Overview'")
+        ctx = _make_ctx(state)
+        result = await search(ctx, query="artificial intelligence")
+        assert "AI Overview" in result
+        assert "ML Basics" not in result
+
+    async def test_search_without_state(self, rag_db):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        search = _get_tool(skill, "search")
+        ctx = _make_ctx(state=None)
+        result = await search(ctx, query="artificial intelligence")
+        assert isinstance(result, str)
+
+
+class TestListDocumentsTool:
+    async def test_list_documents_returns_results(self, rag_db):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        list_docs = _get_tool(skill, "list_documents")
+        ctx = _make_ctx()
+        results = await list_docs(ctx)
+        assert isinstance(results, list)
+        assert len(results) == 2
+
+    async def test_list_documents_updates_state(self, rag_db):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=rag_db)
+        list_docs = _get_tool(skill, "list_documents")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await list_docs(ctx)
+        assert len(state.documents) == 2
+        assert isinstance(state.documents[0], DocumentInfo)
+        assert state.documents[0].id is not None
+
+    async def test_list_documents_no_duplicates_in_state(self, rag_db):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=rag_db)
+        list_docs = _get_tool(skill, "list_documents")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await list_docs(ctx)
+        await list_docs(ctx)
+        assert len(state.documents) == 2
+
+
+class TestGetDocumentTool:
+    async def test_get_document_by_title(self, rag_db):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        get_doc = _get_tool(skill, "get_document")
+        ctx = _make_ctx()
+        result = await get_doc(ctx, query="AI Overview")
+        assert result is not None
+        assert result["title"] == "AI Overview"
+
+    async def test_get_document_updates_state(self, rag_db):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        skill = create_skill(db_path=rag_db)
+        get_doc = _get_tool(skill, "get_document")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await get_doc(ctx, query="AI Overview")
+        assert len(state.documents) == 1
+        assert isinstance(state.documents[0], DocumentInfo)
+        assert state.documents[0].title == "AI Overview"
+
+    async def test_get_document_not_found(self, rag_db):
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        get_doc = _get_tool(skill, "get_document")
+        ctx = _make_ctx()
+        result = await get_doc(ctx, query="nonexistent document xyz")
+        assert result is None
+
+
+class TestAskTool:
+    async def test_ask_returns_answer_with_citations(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import create_skill
+
+        citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="test://ai-overview",
+                document_title="AI Overview",
+                content="AI is transforming industries.",
+            )
+        ]
+        monkeypatch.setattr(
+            HaikuRAG,
+            "ask",
+            AsyncMock(return_value=("AI transforms industries worldwide.", citations)),
+        )
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+        ctx = _make_ctx()
+        result = await ask(ctx, question="What is AI?")
+        assert isinstance(result, str)
+        assert "AI transforms industries" in result
+
+    async def test_ask_updates_state(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="test://ai-overview",
+                content="AI content",
+            )
+        ]
+        monkeypatch.setattr(
+            HaikuRAG,
+            "ask",
+            AsyncMock(return_value=("AI transforms industries.", citations)),
+        )
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await ask(ctx, question="What is AI?")
+        assert len(state.citations) == 1
+        assert len(state.qa_history) == 1
+        assert isinstance(state.qa_history[0], QAHistoryEntry)
+        assert state.qa_history[0].question == "What is AI?"
+
+    async def test_ask_assigns_citation_indices(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        first_citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="test://doc1",
+                content="First.",
+            ),
+            Citation(
+                document_id="d2",
+                chunk_id="c2",
+                document_uri="test://doc2",
+                content="Second.",
+            ),
+        ]
+        second_citations = [
+            Citation(
+                document_id="d3",
+                chunk_id="c3",
+                document_uri="test://doc3",
+                content="Third.",
+            ),
+        ]
+
+        call_count = 0
+
+        async def mock_ask(self, question, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return ("Answer 1", first_citations)
+            return ("Answer 2", second_citations)
+
+        monkeypatch.setattr(HaikuRAG, "ask", mock_ask)
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+        state = RAGState()
+        ctx = _make_ctx(state)
+
+        await ask(ctx, question="First question")
+        assert state.citations[0].index == 1
+        assert state.citations[1].index == 2
+
+        await ask(ctx, question="Second question")
+        assert state.citations[2].index == 3
+
+    async def test_ask_applies_document_filter_from_state(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        captured_kwargs = {}
+
+        async def mock_ask(self, question, **kwargs):
+            captured_kwargs.update(kwargs)
+            return ("Answer.", [])
+
+        monkeypatch.setattr(HaikuRAG, "ask", mock_ask)
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+        state = RAGState(document_filter="title = 'AI Overview'")
+        ctx = _make_ctx(state)
+        await ask(ctx, question="What is AI?")
+        assert captured_kwargs.get("filter") == "title = 'AI Overview'"
+
+    async def test_ask_includes_prior_qa_context(self, rag_db, monkeypatch):
+        import random
+
+        from haiku.rag.skills.rag import RAGState, create_skill
+        from tests.skills.conftest import VECTOR_DIM
+
+        captured_questions = []
+
+        async def mock_ask(self, question, **kwargs):
+            captured_questions.append(question)
+            return ("Answer about AI.", [])
+
+        monkeypatch.setattr(HaikuRAG, "ask", mock_ask)
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+
+        # Pre-compute the embedding the fake embedder will produce for "Tell me about AI"
+        query_text = "Tell me about AI"
+        random.seed(hash(query_text) % (2**32))
+        query_embedding = [random.random() for _ in range(VECTOR_DIM)]
+
+        prior_citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="test://ai-overview",
+                document_title="AI Overview",
+                content="AI content from source.",
+            )
+        ]
+        state = RAGState(
+            qa_history=[
+                QAHistoryEntry(
+                    question="What is artificial intelligence?",
+                    answer="AI is the simulation of human intelligence by machines.",
+                    question_embedding=query_embedding,
+                    citations=prior_citations,
+                ),
+            ]
+        )
+        ctx = _make_ctx(state)
+        await ask(ctx, question=query_text)
+
+        # rag.ask() should receive augmented question with prior context
+        assert len(captured_questions) == 1
+        augmented = captured_questions[0]
+        assert "Context from prior questions" in augmented
+        assert "What is artificial intelligence?" in augmented
+        assert "AI is the simulation" in augmented
+        assert "AI Overview" in augmented
+        assert query_text in augmented
+
+        # State should store the original question, not the augmented one
+        assert state.qa_history[-1].question == query_text
+
+    async def test_ask_embeds_prior_qa_on_demand(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+        from tests.skills.conftest import VECTOR_DIM
+
+        captured_questions = []
+
+        async def mock_ask(self, question, **kwargs):
+            captured_questions.append(question)
+            return ("Answer about AI.", [])
+
+        monkeypatch.setattr(HaikuRAG, "ask", mock_ask)
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+
+        # Use the same question text for the prior QA entry and query so
+        # their fake embeddings are identical (cosine similarity = 1.0).
+        prior_question = "Tell me about AI"
+        query_text = prior_question
+
+        # Leave question_embedding=None to exercise the lazy embedding path
+        state = RAGState(
+            qa_history=[
+                QAHistoryEntry(
+                    question=prior_question,
+                    answer="AI is the simulation of human intelligence by machines.",
+                    question_embedding=None,
+                ),
+            ]
+        )
+        ctx = _make_ctx(state)
+        await ask(ctx, question=query_text)
+
+        # The lazy embedding should have populated question_embedding
+        assert state.qa_history[0].question_embedding is not None
+        assert len(state.qa_history[0].question_embedding) == VECTOR_DIM
+
+        # The augmented question should include prior context
+        assert len(captured_questions) == 1
+        assert "Context from prior questions" in captured_questions[0]
+        assert prior_question in captured_questions[0]
+
+    async def test_ask_no_prior_qa_context_when_irrelevant(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+        from tests.skills.conftest import VECTOR_DIM
+
+        captured_questions = []
+
+        async def mock_ask(self, question, **kwargs):
+            captured_questions.append(question)
+            return ("Answer.", [])
+
+        monkeypatch.setattr(HaikuRAG, "ask", mock_ask)
+
+        skill = create_skill(db_path=rag_db)
+        ask = _get_tool(skill, "ask")
+
+        # Use orthogonal embedding — won't match the fake embedder's output
+        orthogonal = [1.0 if i % 2 == 0 else -1.0 for i in range(VECTOR_DIM)]
+        state = RAGState(
+            qa_history=[
+                QAHistoryEntry(
+                    question="What is the weather?",
+                    answer="It is sunny today.",
+                    question_embedding=orthogonal,
+                ),
+            ]
+        )
+        ctx = _make_ctx(state)
+        await ask(ctx, question="Explain quantum computing")
+
+        # rag.ask() should receive the original question unchanged
+        assert len(captured_questions) == 1
+        assert captured_questions[0] == "Explain quantum computing"
+
+
+class TestResearchTool:
+    async def test_research_returns_report(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import create_skill
+
+        report = ResearchReport(
+            title="AI Research",
+            executive_summary="AI is transforming industries.",
+            main_findings=["Finding 1"],
+            conclusions=["Conclusion 1"],
+            sources_summary="Multiple sources consulted.",
+        )
+        monkeypatch.setattr(HaikuRAG, "research", AsyncMock(return_value=report))
+
+        skill = create_skill(db_path=rag_db)
+        research = _get_tool(skill, "research")
+        ctx = _make_ctx()
+        result = await research(ctx, question="What is AI?")
+        assert isinstance(result, str)
+        assert "AI Research" in result
+
+    async def test_research_updates_state(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        report = ResearchReport(
+            title="AI Research",
+            executive_summary="AI is transforming industries.",
+            main_findings=["Finding 1"],
+            conclusions=["Conclusion 1"],
+            sources_summary="Multiple sources consulted.",
+        )
+        monkeypatch.setattr(HaikuRAG, "research", AsyncMock(return_value=report))
+
+        skill = create_skill(db_path=rag_db)
+        research = _get_tool(skill, "research")
+        state = RAGState()
+        ctx = _make_ctx(state)
+        await research(ctx, question="What is AI?")
+        assert len(state.reports) == 1
+        assert state.reports[0].question == "What is AI?"
+        assert len(state.qa_history) == 1
+        assert state.qa_history[0].question == "What is AI?"
+        assert state.qa_history[0].answer == "AI is transforming industries."
+
+    async def test_research_applies_document_filter_from_state(
+        self, rag_db, monkeypatch
+    ):
+        from haiku.rag.skills.rag import RAGState, create_skill
+
+        captured_kwargs = {}
+
+        report = ResearchReport(
+            title="AI Research",
+            executive_summary="Summary.",
+            main_findings=["Finding"],
+            conclusions=["Conclusion"],
+            sources_summary="Sources.",
+        )
+
+        async def mock_research(self, question, **kwargs):
+            captured_kwargs.update(kwargs)
+            return report
+
+        monkeypatch.setattr(HaikuRAG, "research", mock_research)
+
+        skill = create_skill(db_path=rag_db)
+        research = _get_tool(skill, "research")
+        state = RAGState(document_filter="title = 'AI Overview'")
+        ctx = _make_ctx(state)
+        await research(ctx, question="What is AI?")
+        assert captured_kwargs.get("filter") == "title = 'AI Overview'"
+
+    async def test_research_without_state(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rag import create_skill
+
+        report = ResearchReport(
+            title="AI Research",
+            executive_summary="Summary.",
+            main_findings=["Finding"],
+            conclusions=["Conclusion"],
+            sources_summary="Sources.",
+        )
+        monkeypatch.setattr(HaikuRAG, "research", AsyncMock(return_value=report))
+
+        skill = create_skill(db_path=rag_db)
+        research = _get_tool(skill, "research")
+        ctx = _make_ctx(state=None)
+        result = await research(ctx, question="What is AI?")
+        assert isinstance(result, str)
diff --git a/tests/skills/test_rlm.py b/tests/skills/test_rlm.py
new file mode 100644
index 00000000..03b60e13
--- /dev/null
+++ b/tests/skills/test_rlm.py
@@ -0,0 +1,98 @@
+from unittest.mock import AsyncMock
+
+from haiku.rag.agents.rlm.models import RLMResult
+from haiku.rag.client import HaikuRAG
+
+from .conftest import _get_tool, _make_ctx
+
+
+class TestRLMSkillCreation:
+    def test_create_skill_returns_valid_skill(self, temp_db_path):
+        from haiku.rag.skills.rlm import create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        assert skill.metadata.name == "rag-rlm"
+        assert skill.metadata.description
+        assert skill.instructions
+
+    def test_create_skill_has_expected_tools(self, temp_db_path):
+        from haiku.rag.skills.rlm import create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        tool_names = {getattr(t, "__name__") for t in skill.tools if callable(t)}
+        assert tool_names == {"analyze"}
+
+    def test_create_skill_has_state(self, temp_db_path):
+        from haiku.rag.skills.rlm import RLMState, create_skill
+
+        skill = create_skill(db_path=temp_db_path)
+        assert skill._state_type is RLMState
+        assert skill._state_namespace == "rlm"
+
+    def test_create_skill_from_env(self, monkeypatch, temp_db_path):
+        monkeypatch.setenv("HAIKU_RAG_DB", str(temp_db_path))
+        from haiku.rag.skills.rlm import create_skill
+
+        skill = create_skill()
+        assert skill.metadata.name == "rag-rlm"
+
+
+class TestAnalyzeTool:
+    async def test_analyze_returns_result(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rlm import create_skill
+
+        monkeypatch.setattr(
+            HaikuRAG,
+            "rlm",
+            AsyncMock(return_value=RLMResult(answer="42", program="print(42)")),
+        )
+
+        skill = create_skill(db_path=rag_db)
+        analyze = _get_tool(skill, "analyze")
+        ctx = _make_ctx()
+        result = await analyze(ctx, question="How many documents?")
+        assert isinstance(result, str)
+        assert "42" in result
+        assert "print(42)" in result
+
+    async def test_analyze_updates_state(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rlm import RLMState, create_skill
+
+        monkeypatch.setattr(
+            HaikuRAG,
+            "rlm",
+            AsyncMock(return_value=RLMResult(answer="42", program="print(42)")),
+        )
+
+        skill = create_skill(db_path=rag_db)
+        analyze = _get_tool(skill, "analyze")
+        state = RLMState()
+        ctx = _make_ctx(state)
+        await analyze(ctx, question="How many documents?")
+        assert len(state.analyses) == 1
+        assert state.analyses[0].question == "How many documents?"
+        assert state.analyses[0].answer == "42"
+        assert state.analyses[0].program == "print(42)"
+
+    async def test_analyze_with_document_and_filter(self, rag_db, monkeypatch):
+        from haiku.rag.skills.rlm import create_skill
+
+        captured_kwargs = {}
+
+        async def mock_rlm(self, question, **kwargs):
+            captured_kwargs.update(kwargs)
+            return RLMResult(answer="Result", program="code()")
+
+        monkeypatch.setattr(HaikuRAG, "rlm", mock_rlm)
+
+        skill = create_skill(db_path=rag_db)
+        analyze = _get_tool(skill, "analyze")
+        ctx = _make_ctx()
+        await analyze(
+            ctx,
+            question="Count pages",
+            document="AI Overview",
+            filter="title = 'AI Overview'",
+        )
+        assert captured_kwargs.get("documents") == ["AI Overview"]
+        assert captured_kwargs.get("filter") == "title = 'AI Overview'"
diff --git a/tests/test_client_research.py b/tests/test_client_research.py
new file mode 100644
index 00000000..95656d18
--- /dev/null
+++ b/tests/test_client_research.py
@@ -0,0 +1,92 @@
+from pathlib import Path
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from haiku.rag.agents.research.models import ResearchReport
+from haiku.rag.client import HaikuRAG
+
+
+@pytest.fixture(scope="module")
+def vcr_cassette_dir():
+    return str(Path(__file__).parent / "cassettes" / "test_client_research")
+
+
+async def test_client_research_report(temp_db_path):
+    """Test client.research() delegates to research graph in report mode."""
+    mock_report = ResearchReport(
+        title="Test Report",
+        executive_summary="Summary",
+        main_findings=["Finding 1"],
+        conclusions=["Conclusion 1"],
+        sources_summary="Sources",
+    )
+
+    with patch("haiku.rag.agents.research.graph.build_research_graph") as mock_build:
+        mock_graph = AsyncMock()
+        mock_graph.run = AsyncMock(return_value=mock_report)
+        mock_build.return_value = mock_graph
+
+        async with HaikuRAG(temp_db_path, create=True) as client:
+            result = await client.research(question="What is X?")
+
+        assert result is mock_report
+        mock_build.assert_called_once()
+
+        # Verify graph.run was called with correct state/deps
+        mock_graph.run.assert_called_once()
+        call_kwargs = mock_graph.run.call_args[1]
+        assert call_kwargs["state"].context.original_question == "What is X?"
+        assert isinstance(call_kwargs["deps"].client, HaikuRAG)
+
+
+async def test_client_research_passes_filter(temp_db_path):
+    """Test client.research() passes filter to state."""
+    mock_report = ResearchReport(
+        title="Test",
+        executive_summary="Summary",
+        main_findings=[],
+        conclusions=[],
+        sources_summary="",
+    )
+
+    with patch("haiku.rag.agents.research.graph.build_research_graph") as mock_build:
+        mock_graph = AsyncMock()
+        mock_graph.run = AsyncMock(return_value=mock_report)
+        mock_build.return_value = mock_graph
+
+        async with HaikuRAG(temp_db_path, create=True) as client:
+            await client.research(
+                question="What is X?",
+                filter="uri LIKE '%test%'",
+            )
+
+        call_kwargs = mock_graph.run.call_args[1]
+        assert call_kwargs["state"].search_filter == "uri LIKE '%test%'"
+
+
+async def test_client_research_uses_config(temp_db_path):
+    """Test client.research() passes config to graph builder and state."""
+    mock_report = ResearchReport(
+        title="Test",
+        executive_summary="Summary",
+        main_findings=[],
+        conclusions=[],
+        sources_summary="",
+    )
+
+    with patch("haiku.rag.agents.research.graph.build_research_graph") as mock_build:
+        mock_graph = AsyncMock()
+        mock_graph.run = AsyncMock(return_value=mock_report)
+        mock_build.return_value = mock_graph
+
+        async with HaikuRAG(temp_db_path, create=True) as client:
+            await client.research(question="What is X?")
+
+        _, kwargs = mock_build.call_args
+        assert kwargs["config"] is client._config
+
+        call_kwargs = mock_graph.run.call_args[1]
+        state = call_kwargs["state"]
+        assert state.max_iterations == client._config.research.max_iterations
+        assert state.max_concurrency == client._config.research.max_concurrency
diff --git a/tests/test_mcp.py b/tests/test_mcp.py
new file mode 100644
index 00000000..a3743168
--- /dev/null
+++ b/tests/test_mcp.py
@@ -0,0 +1,195 @@
+import pytest
+
+from haiku.rag.client import HaikuRAG
+from haiku.rag.mcp import create_mcp_server
+from haiku.rag.store.models import Document, SearchResult
+from haiku.rag.tools.document import DocumentInfo
+
+
+@pytest.fixture(autouse=True)
+def mock_embedder(monkeypatch):
+    """Monkeypatch the embedder to return deterministic vectors."""
+    import random
+
+    from haiku.rag.embeddings import EmbedderWrapper
+
+    async def fake_embed_query(self, text):
+        random.seed(hash(text) % (2**32))
+        return [random.random() for _ in range(2560)]
+
+    async def fake_embed_documents(self, texts):
+        result = []
+        for t in texts:
+            random.seed(hash(t) % (2**32))
+            result.append([random.random() for _ in range(2560)])
+        return result
+
+    monkeypatch.setattr(EmbedderWrapper, "embed_query", fake_embed_query)
+    monkeypatch.setattr(EmbedderWrapper, "embed_documents", fake_embed_documents)
+
+
+@pytest.fixture
+async def mcp_db(temp_db_path):
+    """Create a test database with sample documents."""
+    async with HaikuRAG(temp_db_path, create=True) as rag:
+        await rag.create_document(
+            "Artificial intelligence is transforming industries worldwide.",
+            title="AI Overview",
+            uri="test://ai-overview",
+        )
+        await rag.create_document(
+            "Machine learning is a subset of artificial intelligence.",
+            title="ML Basics",
+            uri="test://ml-basics",
+        )
+    return temp_db_path
+
+
+def _get_tool(mcp, name):
+    """Get a tool function from an MCP server by name."""
+    for tool in mcp._tool_manager._tools.values():
+        if tool.fn.__name__ == name:
+            return tool.fn
+    raise ValueError(f"Tool {name!r} not found in MCP server")
+
+
+class TestMCPReadTools:
+    @pytest.mark.asyncio
+    async def test_search_documents(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        search = _get_tool(mcp, "search_documents")
+
+        results = await search(query="artificial intelligence")
+        assert len(results) > 0
+        assert all(isinstance(r, SearchResult) for r in results)
+
+    @pytest.mark.asyncio
+    async def test_search_documents_with_limit(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        search = _get_tool(mcp, "search_documents")
+
+        results = await search(query="artificial intelligence", limit=1)
+        assert len(results) == 1
+
+    @pytest.mark.asyncio
+    async def test_get_document(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        get_doc = _get_tool(mcp, "get_document")
+
+        # First get the ID via list
+        list_docs = _get_tool(mcp, "list_documents")
+        docs = await list_docs()
+        doc_id = docs[0].id
+
+        result = await get_doc(document_id=doc_id)
+        assert isinstance(result, Document)
+        assert result.content != ""
+        assert result.title is not None
+
+    @pytest.mark.asyncio
+    async def test_get_document_excludes_docling_fields(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        get_doc = _get_tool(mcp, "get_document")
+
+        list_docs = _get_tool(mcp, "list_documents")
+        docs = await list_docs()
+        doc_id = docs[0].id
+
+        result = await get_doc(document_id=doc_id)
+        serialized = result.model_dump(mode="json")
+        assert "docling_document" not in serialized
+        assert "docling_version" not in serialized
+
+    @pytest.mark.asyncio
+    async def test_get_document_not_found(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        get_doc = _get_tool(mcp, "get_document")
+
+        result = await get_doc(document_id="nonexistent-id")
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_list_documents(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        list_docs = _get_tool(mcp, "list_documents")
+
+        results = await list_docs()
+        assert len(results) == 2
+        assert all(isinstance(r, DocumentInfo) for r in results)
+
+    @pytest.mark.asyncio
+    async def test_list_documents_with_limit(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        list_docs = _get_tool(mcp, "list_documents")
+
+        results = await list_docs(limit=1)
+        assert len(results) == 1
+
+    @pytest.mark.asyncio
+    async def test_list_documents_with_filter(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=True)
+        list_docs = _get_tool(mcp, "list_documents")
+
+        results = await list_docs(filter="title = 'AI Overview'")
+        assert len(results) == 1
+        assert results[0].title == "AI Overview"
+
+
+class TestMCPWriteTools:
+    @pytest.mark.asyncio
+    async def test_write_tools_registered_when_not_read_only(self, temp_db_path):
+        async with HaikuRAG(temp_db_path, create=True):
+            pass
+        mcp = create_mcp_server(temp_db_path, read_only=False)
+        tool_names = list(mcp._tool_manager._tools.keys())
+        assert "add_document_from_text" in tool_names
+        assert "add_document_from_file" in tool_names
+        assert "add_document_from_url" in tool_names
+        assert "delete_document" in tool_names
+
+    @pytest.mark.asyncio
+    async def test_write_tools_not_registered_when_read_only(self, temp_db_path):
+        async with HaikuRAG(temp_db_path, create=True):
+            pass
+        mcp = create_mcp_server(temp_db_path, read_only=True)
+        tool_names = list(mcp._tool_manager._tools.keys())
+        assert "add_document_from_text" not in tool_names
+        assert "delete_document" not in tool_names
+
+    @pytest.mark.asyncio
+    async def test_add_document_from_text(self, temp_db_path):
+        async with HaikuRAG(temp_db_path, create=True):
+            pass
+        mcp = create_mcp_server(temp_db_path, read_only=False)
+        add_text = _get_tool(mcp, "add_document_from_text")
+
+        doc_id = await add_text(content="Test content for MCP", title="MCP Test Doc")
+        assert doc_id is not None
+
+        get_doc = _get_tool(mcp, "get_document")
+        doc = await get_doc(document_id=doc_id)
+        assert doc.title == "MCP Test Doc"
+        assert doc.content == "Test content for MCP"
+
+    @pytest.mark.asyncio
+    async def test_delete_document(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=False)
+        list_docs = _get_tool(mcp, "list_documents")
+        delete_doc = _get_tool(mcp, "delete_document")
+
+        docs = await list_docs()
+        assert len(docs) == 2
+
+        result = await delete_doc(document_id=docs[0].id)
+        assert result is True
+
+        docs_after = await list_docs()
+        assert len(docs_after) == 1
+
+    @pytest.mark.asyncio
+    async def test_delete_document_not_found(self, mcp_db):
+        mcp = create_mcp_server(mcp_db, read_only=False)
+        delete_doc = _get_tool(mcp, "delete_document")
+
+        result = await delete_doc(document_id="nonexistent-id")
+        assert result is False
diff --git a/tests/test_utils.py b/tests/test_utils.py
index f486d120..fd3ad83d 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -579,6 +579,14 @@ def test_build_prompt_with_preamble():
 # --- is_up_to_date tests ---
 
 
+def test_cosine_similarity_zero_norm():
+    from haiku.rag.utils import cosine_similarity
+
+    assert cosine_similarity([0, 0, 0], [1, 2, 3]) == 0.0
+    assert cosine_similarity([1, 2, 3], [0, 0, 0]) == 0.0
+    assert cosine_similarity([0, 0], [0, 0]) == 0.0
+
+
 @pytest.mark.asyncio
 async def test_is_up_to_date(monkeypatch):
     from unittest.mock import AsyncMock, MagicMock
diff --git a/tests/tools/test_context.py b/tests/tools/test_context.py
index 28aa827a..44cf4c34 100644
--- a/tests/tools/test_context.py
+++ b/tests/tools/test_context.py
@@ -1,512 +1,26 @@
-from pydantic import BaseModel
+from dataclasses import dataclass
+from unittest.mock import MagicMock
 
-from haiku.rag.tools.context import ToolContext, prepare_context
-from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
+from haiku.rag.tools.context import RAGDeps
 
 
-class TestState(BaseModel):
-    value: int = 0
+def test_ragdeps_protocol_satisfied_by_dataclass():
+    """A dataclass with a client attribute satisfies RAGDeps."""
 
+    @dataclass
+    class MyDeps:
+        client: MagicMock
 
-class TestStateWithList(BaseModel):
-    items: list[str] = []
+    deps = MyDeps(client=MagicMock())
+    assert isinstance(deps, RAGDeps)
 
 
-def test_tool_context_defaults():
-    """Test ToolContext has sensible defaults."""
-    ctx = ToolContext()
-    assert ctx._namespaces == {}
+def test_ragdeps_protocol_not_satisfied_without_client():
+    """An object without client does not satisfy RAGDeps."""
 
+    @dataclass
+    class NoDeps:
+        other: str
 
-def test_register_and_get():
-    """Test register and get state for a namespace."""
-    ctx = ToolContext()
-    state = TestState(value=42)
-
-    ctx.register("test.namespace", state)
-
-    retrieved = ctx.get("test.namespace")
-    assert retrieved is state
-    assert retrieved.value == 42
-
-
-def test_get_nonexistent_namespace():
-    """Test get returns None for unregistered namespace."""
-    ctx = ToolContext()
-    assert ctx.get("nonexistent") is None
-
-
-def test_get_or_create_creates_new():
-    """Test get_or_create creates state when namespace doesn't exist."""
-    ctx = ToolContext()
-
-    state = ctx.get_or_create("test.namespace", TestStateWithList)
-    assert isinstance(state, TestStateWithList)
-    assert state.items == []
-
-
-def test_get_or_create_returns_existing():
-    """Test get_or_create returns existing state."""
-    ctx = ToolContext()
-
-    state1 = ctx.get_or_create("test.namespace", TestStateWithList)
-    state1.items.append("item1")
-
-    state2 = ctx.get_or_create("test.namespace", TestStateWithList)
-    assert state2 is state1
-    assert state2.items == ["item1"]
-
-
-def test_clear_namespace():
-    """Test clear_namespace removes only the specified namespace."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=1))
-    ctx.register("ns2", TestState(value=2))
-
-    ctx.clear_namespace("ns1")
-
-    assert ctx.get("ns1") is None
-    ns2 = ctx.get("ns2")
-    assert isinstance(ns2, TestState)
-    assert ns2.value == 2
-
-
-def test_clear_namespace_nonexistent():
-    """Test clear_namespace handles nonexistent namespace gracefully."""
-    ctx = ToolContext()
-    ctx.clear_namespace("nonexistent")  # Should not raise
-
-
-def test_clear_all():
-    """Test clear_all clears all namespaces."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=1))
-    ctx.register("ns2", TestState(value=2))
-
-    ctx.clear_all()
-
-    assert ctx.get("ns1") is None
-    assert ctx.get("ns2") is None
-
-
-def test_namespaces_property():
-    """Test namespaces property lists all registered namespaces."""
-    ctx = ToolContext()
-    assert ctx.namespaces == []
-
-    ctx.register("ns1", TestState())
-    ctx.register("ns2", TestState())
-
-    assert set(ctx.namespaces) == {"ns1", "ns2"}
-
-
-def test_shared_namespace_between_toolsets():
-    """Test that toolsets can share state via the same namespace."""
-
-    class SharedState(BaseModel):
-        citations: dict[str, int] = {}
-
-    SHARED_NAMESPACE = "haiku.rag.citations"
-
-    ctx = ToolContext()
-
-    # First toolset registers the shared state
-    state1 = ctx.get_or_create(SHARED_NAMESPACE, SharedState)
-    state1.citations["chunk-a"] = 1
-
-    # Second toolset gets the same state
-    state2 = ctx.get_or_create(SHARED_NAMESPACE, SharedState)
-    assert state2 is state1
-    assert state2.citations == {"chunk-a": 1}
-
-    # Both see updates
-    state2.citations["chunk-b"] = 2
-    assert state1.citations == {"chunk-a": 1, "chunk-b": 2}
-
-
-def test_dump_namespaces():
-    """Test dump_namespaces serializes all registered states."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=10))
-    ctx.register("ns2", TestStateWithList(items=["a", "b"]))
-
-    data = ctx.dump_namespaces()
-
-    assert data == {
-        "ns1": {"value": 10},
-        "ns2": {"items": ["a", "b"]},
-    }
-
-
-def test_load_namespace():
-    """Test load_namespace deserializes and registers state."""
-    ctx = ToolContext()
-
-    state = ctx.load_namespace("ns1", TestState, {"value": 42})
-
-    assert isinstance(state, TestState)
-    assert state.value == 42
-    assert ctx.get("ns1") is state
-
-
-def test_serialization_roundtrip():
-    """Test full serialization/deserialization roundtrip."""
-    # Create and populate context
-    original = ToolContext()
-    original.register("search", TestStateWithList(items=["result1", "result2"]))
-    original.register("qa", TestState(value=99))
-
-    # Serialize
-    ns_data = original.dump_namespaces()
-
-    # Deserialize
-    restored = ToolContext()
-    restored.load_namespace("search", TestStateWithList, ns_data["search"])
-    restored.load_namespace("qa", TestState, ns_data["qa"])
-
-    # Verify
-    search_state = restored.get("search")
-    assert isinstance(search_state, TestStateWithList)
-    assert search_state.items == ["result1", "result2"]
-
-    qa_state = restored.get("qa")
-    assert isinstance(qa_state, TestState)
-    assert qa_state.value == 99
-
-
-def test_get_with_type_match():
-    """Test get with state_type returns typed state when type matches."""
-    ctx = ToolContext()
-    state = TestState(value=42)
-    ctx.register("ns", state)
-
-    result = ctx.get("ns", TestState)
-    assert result is state
-    assert result.value == 42
-
-
-def test_get_with_type_mismatch():
-    """Test get with state_type returns None when type doesn't match."""
-    ctx = ToolContext()
-    ctx.register("ns", TestState(value=42))
-
-    result = ctx.get("ns", TestStateWithList)
-    assert result is None
-
-
-def test_get_without_type():
-    """Test get without state_type returns BaseModel (unchanged behavior)."""
-    ctx = ToolContext()
-    state = TestState(value=42)
-    ctx.register("ns", state)
-
-    result = ctx.get("ns")
-    assert result is state
-
-
-def test_tool_context_state_key_default_none():
-    """Test ToolContext state_key defaults to None."""
-    ctx = ToolContext()
-    assert ctx.state_key is None
-
-
-def test_tool_context_state_key_set():
-    """Test ToolContext state_key can be set."""
-    ctx = ToolContext()
-    ctx.state_key = "haiku.rag.chat"
-    assert ctx.state_key == "haiku.rag.chat"
-
-
-# =============================================================================
-# ToolContextCache Tests
-# =============================================================================
-
-
-def test_tool_context_cache_get_or_create_new():
-    """Test get_or_create returns a new context with is_new=True."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    context, is_new = cache.get_or_create("thread-1")
-
-    assert isinstance(context, ToolContext)
-    assert is_new is True
-
-
-def test_tool_context_cache_get_or_create_existing():
-    """Test get_or_create returns existing context with is_new=False."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    ctx1, is_new1 = cache.get_or_create("thread-1")
-    ctx2, is_new2 = cache.get_or_create("thread-1")
-
-    assert ctx2 is ctx1
-    assert is_new1 is True
-    assert is_new2 is False
-
-
-def test_tool_context_cache_different_keys():
-    """Test get_or_create returns different contexts for different keys."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    ctx1, _ = cache.get_or_create("thread-1")
-    ctx2, _ = cache.get_or_create("thread-2")
-
-    assert ctx1 is not ctx2
-
-
-def test_tool_context_cache_ttl_expiry():
-    """Test that contexts are evicted after TTL expires."""
-    from datetime import timedelta
-
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache(ttl=timedelta(seconds=0))
-    ctx1, _ = cache.get_or_create("thread-1")
-
-    # With zero TTL, next access should create a new context
-    ctx2, is_new = cache.get_or_create("thread-1")
-
-    assert ctx2 is not ctx1
-    assert is_new is True
-
-
-def test_tool_context_cache_remove():
-    """Test remove deletes a specific key."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    cache.get_or_create("thread-1")
-    cache.get_or_create("thread-2")
-
-    cache.remove("thread-1")
-
-    ctx, is_new = cache.get_or_create("thread-1")
-    assert is_new is True
-
-    # thread-2 should still exist
-    ctx2, is_new2 = cache.get_or_create("thread-2")
-    assert is_new2 is False
-
-
-def test_tool_context_cache_remove_nonexistent():
-    """Test remove handles nonexistent key gracefully."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    cache.remove("nonexistent")  # Should not raise
-
-
-def test_tool_context_cache_clear():
-    """Test clear removes all entries."""
-    from haiku.rag.tools.context import ToolContextCache
-
-    cache = ToolContextCache()
-    cache.get_or_create("thread-1")
-    cache.get_or_create("thread-2")
-
-    cache.clear()
-
-    ctx1, is_new1 = cache.get_or_create("thread-1")
-    ctx2, is_new2 = cache.get_or_create("thread-2")
-    assert is_new1 is True
-    assert is_new2 is True
-
-
-# =============================================================================
-# build_state_snapshot / restore_state_snapshot Tests
-# =============================================================================
-
-
-class NestedModel(BaseModel):
-    name: str = ""
-    count: int = 0
-
-
-class StateWithNested(BaseModel):
-    nested: NestedModel | None = None
-    tags: list[str] = []
-
-
-def test_build_state_snapshot_empty():
-    """build_state_snapshot on empty context returns empty dict."""
-    ctx = ToolContext()
-    assert ctx.build_state_snapshot() == {}
-
-
-def test_build_state_snapshot_single_namespace():
-    """build_state_snapshot with one namespace returns its fields."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=42))
-    snapshot = ctx.build_state_snapshot()
-    assert snapshot == {"value": 42}
-
-
-def test_build_state_snapshot_multiple_namespaces():
-    """build_state_snapshot merges fields from all namespaces."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=42))
-    ctx.register("ns2", TestStateWithList(items=["a", "b"]))
-    snapshot = ctx.build_state_snapshot()
-    assert snapshot == {"value": 42, "items": ["a", "b"]}
-
-
-def test_build_state_snapshot_nested_model():
-    """build_state_snapshot serializes nested models with mode='json'."""
-    from datetime import datetime
-
-    class TimestampState(BaseModel):
-        ts: datetime | None = None
-
-    ctx = ToolContext()
-    ctx.register("ns", TimestampState(ts=datetime(2025, 1, 27, 12, 0, 0)))
-    snapshot = ctx.build_state_snapshot()
-    assert isinstance(snapshot["ts"], str)
-    assert snapshot["ts"] == "2025-01-27T12:00:00"
-
-
-def test_restore_state_snapshot_empty_context():
-    """restore_state_snapshot on empty context is a no-op."""
-    ctx = ToolContext()
-    ctx.restore_state_snapshot({"value": 42})
-    assert ctx.namespaces == []
-
-
-def test_restore_state_snapshot_single_namespace():
-    """restore_state_snapshot updates matching fields in registered namespaces."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=0))
-    ctx.restore_state_snapshot({"value": 99})
-    state = ctx.get("ns1", TestState)
-    assert state is not None
-    assert state.value == 99
-
-
-def test_restore_state_snapshot_partial_update():
-    """restore_state_snapshot only touches fields present in data."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=42))
-    ctx.register("ns2", TestStateWithList(items=["original"]))
-    # Only update ns2's items, not ns1's value
-    ctx.restore_state_snapshot({"items": ["updated"]})
-    ns1 = ctx.get("ns1", TestState)
-    ns2 = ctx.get("ns2", TestStateWithList)
-    assert ns1 is not None
-    assert ns2 is not None
-    assert ns1.value == 42
-    assert ns2.items == ["updated"]
-
-
-def test_restore_state_snapshot_nested_model():
-    """restore_state_snapshot deserializes nested models from dicts."""
-    ctx = ToolContext()
-    ctx.register("ns", StateWithNested())
-    ctx.restore_state_snapshot({"nested": {"name": "foo", "count": 5}, "tags": ["x"]})
-    state = ctx.get("ns", StateWithNested)
-    assert state is not None
-    assert state.nested is not None
-    assert state.nested.name == "foo"
-    assert state.nested.count == 5
-    assert state.tags == ["x"]
-
-
-def test_state_snapshot_roundtrip():
-    """build then restore produces equivalent state."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=42))
-    ctx.register("ns2", TestStateWithList(items=["a", "b"]))
-
-    snapshot = ctx.build_state_snapshot()
-
-    ctx2 = ToolContext()
-    ctx2.register("ns1", TestState())
-    ctx2.register("ns2", TestStateWithList())
-    ctx2.restore_state_snapshot(snapshot)
-
-    ns1 = ctx2.get("ns1", TestState)
-    ns2 = ctx2.get("ns2", TestStateWithList)
-    assert ns1 is not None
-    assert ns2 is not None
-    assert ns1.value == 42
-    assert ns2.items == ["a", "b"]
-
-
-def test_restore_state_snapshot_ignores_unknown_fields():
-    """restore_state_snapshot ignores fields not in any registered namespace."""
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=0))
-    ctx.restore_state_snapshot({"value": 10, "unknown_field": "ignored"})
-    ns1 = ctx.get("ns1", TestState)
-    assert ns1 is not None
-    assert ns1.value == 10
-
-
-def test_restore_state_snapshot_captures_client_snapshot():
-    """restore_state_snapshot stores the restored state as client_snapshot.
-
-    This baseline is used by tools to compute deltas against what the
-    client actually has, so server-side changes (e.g. background
-    summarization) appear in the delta.
-    """
-    ctx = ToolContext()
-    ctx.register("ns1", TestState(value=0))
-    ctx.register("ns2", TestStateWithList(items=[]))
-
-    assert ctx.client_snapshot is None
-
-    ctx.restore_state_snapshot({"value": 10, "items": ["a"]})
-
-    assert ctx.client_snapshot == {"value": 10, "items": ["a"]}
-
-    # Mutating state after restore doesn't affect the captured snapshot
-    ns1 = ctx.get("ns1", TestState)
-    assert ns1 is not None
-    ns1.value = 99
-    assert ctx.client_snapshot == {"value": 10, "items": ["a"]}
-
-
-# --- prepare_context tests ---
-
-
-def test_prepare_context_default_features():
-    """Default features register SessionState only."""
-    ctx = ToolContext()
-    prepare_context(ctx)
-    assert ctx.get(SESSION_NAMESPACE, SessionState) is not None
-    assert ctx.get(QA_SESSION_NAMESPACE, QASessionState) is None
-
-
-def test_prepare_context_with_qa():
-    """QA feature registers both SessionState and QASessionState."""
-    ctx = ToolContext()
-    prepare_context(ctx, features=["search", "qa"])
-    assert ctx.get(SESSION_NAMESPACE, SessionState) is not None
-    assert ctx.get(QA_SESSION_NAMESPACE, QASessionState) is not None
-
-
-def test_prepare_context_sets_state_key():
-    """state_key is set on context when provided."""
-    ctx = ToolContext()
-    prepare_context(ctx, state_key="my_app")
-    assert ctx.state_key == "my_app"
-
-
-def test_prepare_context_no_state_key_by_default():
-    """state_key is not set when not provided."""
-    ctx = ToolContext()
-    prepare_context(ctx)
-    assert ctx.state_key is None
-
-
-def test_prepare_context_idempotent():
-    """Calling prepare_context twice doesn't create duplicate state."""
-    ctx = ToolContext()
-    prepare_context(ctx, features=["search", "qa"])
-    session1 = ctx.get(SESSION_NAMESPACE, SessionState)
-    qa1 = ctx.get(QA_SESSION_NAMESPACE, QASessionState)
-    prepare_context(ctx, features=["search", "qa"])
-    assert ctx.get(SESSION_NAMESPACE, SessionState) is session1
-    assert ctx.get(QA_SESSION_NAMESPACE, QASessionState) is qa1
+    deps = NoDeps(other="x")
+    assert not isinstance(deps, RAGDeps)
diff --git a/tests/tools/test_deps.py b/tests/tools/test_deps.py
deleted file mode 100644
index 61a9a580..00000000
--- a/tests/tools/test_deps.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from unittest.mock import MagicMock
-
-import pytest
-
-from haiku.rag.tools.context import ToolContext
-from haiku.rag.tools.deps import AgentDeps
-from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-
-@pytest.fixture
-def mock_client():
-    return MagicMock()
-
-
-def test_agent_deps_state_getter_empty(mock_client):
-    """state returns empty dict when no namespaces are registered."""
-    ctx = ToolContext()
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    assert deps.state == {}
-
-
-def test_agent_deps_state_getter_with_session(mock_client):
-    """state returns flat snapshot of registered namespaces."""
-    ctx = ToolContext()
-    ctx.register(SESSION_NAMESPACE, SessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    snapshot = deps.state
-    assert "citations" in snapshot
-    assert "citation_registry" in snapshot
-
-
-def test_agent_deps_state_getter_with_state_key(mock_client):
-    """state wraps snapshot under state_key when set on context."""
-    ctx = ToolContext()
-    ctx.state_key = "my_app"
-    ctx.register(SESSION_NAMESPACE, SessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    snapshot = deps.state
-    assert "my_app" in snapshot
-    assert "citations" in snapshot["my_app"]
-
-
-def test_agent_deps_state_setter_restores(mock_client):
-    """state setter restores namespace fields from flat dict."""
-    ctx = ToolContext()
-    ctx.register(SESSION_NAMESPACE, SessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    deps.state = {"document_filter": ["doc1", "doc2"]}
-    session = ctx.get(SESSION_NAMESPACE, SessionState)
-    assert session is not None
-    assert session.document_filter == ["doc1", "doc2"]
-
-
-def test_agent_deps_state_setter_with_state_key(mock_client):
-    """state setter extracts data from namespaced key."""
-    ctx = ToolContext()
-    ctx.state_key = "my_app"
-    ctx.register(SESSION_NAMESPACE, SessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    deps.state = {"my_app": {"document_filter": ["doc1"]}}
-    session = ctx.get(SESSION_NAMESPACE, SessionState)
-    assert session is not None
-    assert session.document_filter == ["doc1"]
-
-
-def test_agent_deps_state_setter_ignores_none(mock_client):
-    """state setter is a no-op when value is None."""
-    ctx = ToolContext()
-    ctx.register(SESSION_NAMESPACE, SessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    deps.state = None
-    session = ctx.get(SESSION_NAMESPACE, SessionState)
-    assert session is not None
-    assert session.document_filter == []
-
-
-def test_agent_deps_state_roundtrip(mock_client):
-    """Build snapshot then restore produces equivalent state."""
-    ctx = ToolContext()
-    ctx.state_key = "app"
-    ctx.register(SESSION_NAMESPACE, SessionState(document_filter=["doc1"]))
-    ctx.register(QA_SESSION_NAMESPACE, QASessionState())
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-
-    snapshot = deps.state
-
-    ctx2 = ToolContext()
-    ctx2.state_key = "app"
-    ctx2.register(SESSION_NAMESPACE, SessionState())
-    ctx2.register(QA_SESSION_NAMESPACE, QASessionState())
-    deps2 = AgentDeps(client=mock_client, tool_context=ctx2)
-    deps2.state = snapshot
-
-    session = ctx2.get(SESSION_NAMESPACE, SessionState)
-    assert session is not None
-    assert session.document_filter == ["doc1"]
-
-
-def test_agent_deps_satisfies_rag_deps_protocol(mock_client):
-    """AgentDeps satisfies the RAGDeps protocol."""
-    from haiku.rag.tools.context import RAGDeps
-
-    ctx = ToolContext()
-    deps = AgentDeps(client=mock_client, tool_context=ctx)
-    assert isinstance(deps, RAGDeps)
diff --git a/tests/tools/test_document.py b/tests/tools/test_document.py
index c04b3c28..432dc86b 100644
--- a/tests/tools/test_document.py
+++ b/tests/tools/test_document.py
@@ -15,9 +15,9 @@ def vcr_cassette_dir():
     return str(Path(__file__).parent.parent / "cassettes" / "test_document_tools")
 
 
-def make_ctx(client, context=None):
+def make_ctx(client):
     """Create a lightweight RunContext-like object for direct tool function calls."""
-    return SimpleNamespace(deps=SimpleNamespace(client=client, tool_context=context))
+    return SimpleNamespace(deps=SimpleNamespace(client=client))
 
 
 class TestDocumentModels:
diff --git a/tests/tools/test_filters.py b/tests/tools/test_filters.py
index e524e702..de449dd8 100644
--- a/tests/tools/test_filters.py
+++ b/tests/tools/test_filters.py
@@ -1,11 +1,8 @@
-from haiku.rag.tools.context import ToolContext
 from haiku.rag.tools.filters import (
     build_document_filter,
     build_multi_document_filter,
     combine_filters,
-    get_session_filter,
 )
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
 
 
 def test_build_document_filter_simple():
@@ -80,37 +77,3 @@ def test_combine_filters_both():
     """Test combine_filters combines with AND."""
     result = combine_filters("uri = 'test'", "title = 'doc'")
     assert result == "(uri = 'test') AND (title = 'doc')"
-
-
-def test_get_session_filter_no_context():
-    """Returns base_filter as-is when context is None."""
-    assert get_session_filter(None) is None
-    assert get_session_filter(None, "uri = 'test'") == "uri = 'test'"
-
-
-def test_get_session_filter_no_document_filter():
-    """Returns base_filter when SessionState has no document_filter."""
-    context = ToolContext()
-    context.register(SESSION_NAMESPACE, SessionState())
-    assert get_session_filter(context) is None
-    assert get_session_filter(context, "uri = 'test'") == "uri = 'test'"
-
-
-def test_get_session_filter_with_document_filter():
-    """Builds filter from SessionState.document_filter."""
-    context = ToolContext()
-    context.register(SESSION_NAMESPACE, SessionState(document_filter=["mytest"]))
-    result = get_session_filter(context)
-    assert result is not None
-    assert "mytest" in result
-
-
-def test_get_session_filter_combines_with_base_filter():
-    """Combines session filter with base_filter using AND."""
-    context = ToolContext()
-    context.register(SESSION_NAMESPACE, SessionState(document_filter=["mytest"]))
-    result = get_session_filter(context, "uri = 'base'")
-    assert result is not None
-    assert "uri = 'base'" in result
-    assert "mytest" in result
-    assert "AND" in result
diff --git a/tests/tools/test_models.py b/tests/tools/test_models.py
index c01c8990..9d055dcc 100644
--- a/tests/tools/test_models.py
+++ b/tests/tools/test_models.py
@@ -1,83 +1,4 @@
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.tools.models import AnalysisResult, QAResult
-
-
-def test_qa_result_defaults():
-    """Test QAResult has sensible defaults."""
-    result = QAResult(question="What is X?", answer="X is Y.")
-    assert result.confidence == 1.0
-    assert result.citations == []
-
-
-def test_qa_result_with_citations():
-    """Test QAResult with citations."""
-    citation = Citation(
-        index=1,
-        document_id="doc-1",
-        chunk_id="chunk-1",
-        document_uri="test.md",
-        document_title="Test Doc",
-        content="Citation content",
-    )
-    result = QAResult(
-        question="What is X?",
-        answer="X is Y.",
-        confidence=0.9,
-        citations=[citation],
-    )
-    assert result.confidence == 0.9
-    assert len(result.citations) == 1
-    assert result.citations[0].document_title == "Test Doc"
-
-
-def test_qa_result_sources_property():
-    """Test QAResult.sources returns unique source names."""
-    citations = [
-        Citation(
-            document_id="doc-1",
-            chunk_id="chunk-1",
-            document_uri="doc1.md",
-            document_title="Document One",
-            content="Content 1",
-        ),
-        Citation(
-            document_id="doc-1",
-            chunk_id="chunk-2",
-            document_uri="doc1.md",
-            document_title="Document One",
-            content="Content 2",
-        ),
-        Citation(
-            document_id="doc-2",
-            chunk_id="chunk-3",
-            document_uri="doc2.md",
-            document_title="Document Two",
-            content="Content 3",
-        ),
-    ]
-    result = QAResult(question="Q", answer="A", citations=citations)
-
-    sources = result.sources
-    assert len(sources) == 2
-    assert "Document One" in sources
-    assert "Document Two" in sources
-
-
-def test_qa_result_sources_uses_uri_as_fallback():
-    """Test QAResult.sources uses uri when title is None."""
-    citations = [
-        Citation(
-            document_id="doc-1",
-            chunk_id="chunk-1",
-            document_uri="test.md",
-            document_title=None,
-            content="Content",
-        ),
-    ]
-    result = QAResult(question="Q", answer="A", citations=citations)
-
-    sources = result.sources
-    assert sources == ["test.md"]
+from haiku.rag.tools.analysis import AnalysisResult
 
 
 def test_analysis_result_defaults():
diff --git a/tests/tools/test_prompts.py b/tests/tools/test_prompts.py
deleted file mode 100644
index ac82c28d..00000000
--- a/tests/tools/test_prompts.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from haiku.rag.tools.prompts import build_tools_prompt
-
-
-def test_empty_features():
-    result = build_tools_prompt([])
-    assert result == ""
-
-
-def test_single_feature_search():
-    result = build_tools_prompt(["search"])
-    assert "search" in result
-    assert "document_name" in result.lower()
-
-
-def test_single_feature_documents():
-    result = build_tools_prompt(["documents"])
-    assert "list_documents" in result
-    assert "summarize_document" in result
-    assert "get_document" in result
-
-
-def test_single_feature_qa():
-    result = build_tools_prompt(["qa"])
-    assert "ask" in result
-    assert "document_name" in result.lower()
-
-
-def test_single_feature_analysis():
-    result = build_tools_prompt(["analysis"])
-    assert "analyze" in result
-
-
-def test_multiple_features():
-    result = build_tools_prompt(["search", "qa", "documents"])
-    assert "search" in result
-    assert "ask" in result
-    assert "list_documents" in result
-
-
-def test_search_and_qa_both_add_document_name_examples():
-    result = build_tools_prompt(["search", "qa"])
-    assert "search for embeddings" in result.lower() or "embeddings" in result
-    assert "what does the ML paper say" in result or "ML paper" in result
-
-
-def test_unknown_features_ignored():
-    result = build_tools_prompt(["nonexistent", "also_fake"])
-    assert result == ""
-
-
-def test_unknown_mixed_with_known():
-    result = build_tools_prompt(["nonexistent", "search"])
-    assert "search" in result
diff --git a/tests/tools/test_qa.py b/tests/tools/test_qa.py
index 3c2dcfef..f21aee3a 100644
--- a/tests/tools/test_qa.py
+++ b/tests/tools/test_qa.py
@@ -1,274 +1,95 @@
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-from pydantic_ai import ToolReturn
-
-from haiku.rag.tools import ToolContext, prepare_context
-from haiku.rag.tools.models import QAResult
-from haiku.rag.tools.qa import (
-    MAX_QA_HISTORY,
-    QA_SESSION_NAMESPACE,
-    QASessionState,
-    create_qa_toolset,
-    run_qa_core,
-)
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
+from haiku.rag.agents.research.models import Citation
+from haiku.rag.tools.qa import PRIOR_ANSWER_RELEVANCE_THRESHOLD, QAHistoryEntry
 
 
-@pytest.fixture(scope="module")
-def vcr_cassette_dir():
-    return str(Path(__file__).parent.parent / "cassettes" / "test_qa_tools")
+class TestQAHistoryEntry:
+    """Tests for QAHistoryEntry model."""
 
+    def test_defaults(self):
+        """QAHistoryEntry has sensible defaults."""
+        entry = QAHistoryEntry(question="What is X?", answer="X is Y.")
+        assert entry.confidence == 0.9
+        assert entry.citations == []
+        assert entry.question_embedding is None
 
-def make_ctx(client, context=None):
-    """Create a lightweight RunContext-like object for direct tool function calls."""
-    return SimpleNamespace(deps=SimpleNamespace(client=client, tool_context=context))
-
-
-class TestQAToolset:
-    """Tests for create_qa_toolset."""
-
-    def test_create_qa_toolset_returns_function_toolset(self, qa_config):
-        """create_qa_toolset returns a FunctionToolset."""
-        from pydantic_ai import FunctionToolset
-
-        toolset = create_qa_toolset(qa_config)
-        assert isinstance(toolset, FunctionToolset)
-
-    def test_qa_toolset_has_ask_tool(self, qa_config):
-        """The toolset includes an 'ask' tool."""
-        toolset = create_qa_toolset(qa_config)
-        assert "ask" in toolset.tools
-
-    def test_qa_toolset_custom_tool_name(self, qa_config):
-        """Toolset supports custom tool name."""
-        toolset = create_qa_toolset(qa_config, tool_name="answer_question")
-        assert "answer_question" in toolset.tools
-        assert "ask" not in toolset.tools
-
-
-@pytest.mark.vcr()
-class TestRunQACore:
-    """Tests for run_qa_core."""
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_with_session_state(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core with SessionState assigns citation indices via registry."""
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        result = await run_qa_core(
-            client=qa_client,
-            config=qa_config,
-            question="What is Python?",
-            context=context,
-        )
-
-        assert isinstance(result, QAResult)
-        assert result.answer
-
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        # If citations were returned, they should use registry indices
-        if result.citations:
-            assert len(session_state.citation_registry) > 0
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_populates_citations_history(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core appends to SessionState.citations_history."""
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        await run_qa_core(
-            client=qa_client,
-            config=qa_config,
-            question="What is Python?",
-            context=context,
-        )
-
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        assert len(session_state.citations_history) == 1
-        assert session_state.citations_history[0] == session_state.citations
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_without_context(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core without context uses sequential fallback indices."""
-        result = await run_qa_core(
-            client=qa_client,
-            config=qa_config,
-            question="What is Python?",
-            context=None,
-        )
-
-        assert isinstance(result, QAResult)
-        assert result.answer
-        # Without context, citation indices are i+1
-        for i, c in enumerate(result.citations):
-            assert c.index == i + 1
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_on_qa_complete_callback(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core invokes on_qa_complete callback when context is provided."""
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        callback_calls: list[tuple] = []
-
-        def on_complete(qa_session_state, config):
-            callback_calls.append((qa_session_state, config))
-
-        await run_qa_core(
-            client=qa_client,
-            config=qa_config,
-            question="What is Python?",
-            context=context,
-            on_qa_complete=on_complete,
-        )
-
-        assert len(callback_calls) == 1
-        assert isinstance(callback_calls[0][0], QASessionState)
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_fifo_limit(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core trims qa_history beyond MAX_QA_HISTORY."""
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session_state is not None
-        # Pre-fill with MAX_QA_HISTORY entries
-        from haiku.rag.tools.qa import QAHistoryEntry
-
-        qa_session_state.qa_history = [
-            QAHistoryEntry(question=f"Q{i}", answer=f"A{i}", confidence=0.9)
-            for i in range(MAX_QA_HISTORY)
+    def test_sources_property(self):
+        """sources returns unique document titles."""
+        citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="doc1.md",
+                document_title="Document One",
+                content="Content 1",
+            ),
+            Citation(
+                document_id="d1",
+                chunk_id="c2",
+                document_uri="doc1.md",
+                document_title="Document One",
+                content="Content 2",
+            ),
+            Citation(
+                document_id="d2",
+                chunk_id="c3",
+                document_uri="doc2.md",
+                document_title="Document Two",
+                content="Content 3",
+            ),
         ]
+        entry = QAHistoryEntry(question="Q", answer="A", citations=citations)
+        sources = entry.sources
+        assert len(sources) == 2
+        assert "Document One" in sources
+        assert "Document Two" in sources
 
-        await run_qa_core(
-            client=qa_client,
-            config=qa_config,
-            question="One more question?",
-            context=context,
-        )
-
-        # After adding one more, FIFO should trim to MAX_QA_HISTORY
-        assert len(qa_session_state.qa_history) == MAX_QA_HISTORY
-        # The oldest entry (Q0) should have been trimmed
-        assert qa_session_state.qa_history[0].question != "Q0"
-
-
-@pytest.mark.vcr()
-class TestRunQACoreWithPriorAnswers:
-    """Tests for run_qa_core prior answer matching."""
-
-    @pytest.mark.asyncio
-    async def test_run_qa_core_matches_prior_answers(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """run_qa_core matches prior answers when embedding similarity is high."""
-        from unittest.mock import AsyncMock, patch
-
-        from haiku.rag.tools.qa import QAHistoryEntry
-
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
-        assert qa_session_state is not None
-
-        # Pre-populate with a prior answer that has a known embedding
-        prior_embedding = [0.5] * 2560
-        qa_session_state.qa_history = [
-            QAHistoryEntry(
-                question="What is Python?",
-                answer="A programming language.",
-                confidence=0.9,
-                question_embedding=prior_embedding,
-            )
+    def test_sources_uses_uri_as_fallback(self):
+        """sources uses uri when title is None."""
+        citations = [
+            Citation(
+                document_id="d1",
+                chunk_id="c1",
+                document_uri="test.md",
+                document_title=None,
+                content="Content",
+            ),
         ]
+        entry = QAHistoryEntry(question="Q", answer="A", citations=citations)
+        assert entry.sources == ["test.md"]
 
-        # Mock the embedder to return a near-identical embedding for the new question
-        mock_embedder = AsyncMock()
-        mock_embedder.embed_query = AsyncMock(return_value=[0.5] * 2560)
-
-        with patch("haiku.rag.tools.qa.get_embedder", return_value=mock_embedder):
-            result = await run_qa_core(
-                client=qa_client,
-                config=qa_config,
-                question="Tell me about Python",
-                context=context,
-            )
-
-        assert isinstance(result, QAResult)
-        assert result.answer
-
-
-@pytest.mark.vcr()
-class TestAskTool:
-    """Tests for the ask tool in create_qa_toolset."""
-
-    @pytest.mark.asyncio
-    async def test_ask_without_tool_context(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """ask tool without tool context returns raw QAResult."""
-        toolset = create_qa_toolset(qa_config)
-        ask_tool = toolset.tools["ask"]
-
-        ctx = make_ctx(qa_client, None)
-        result = await ask_tool.function(ctx, "What is Python?")
-
-        assert isinstance(result, QAResult)
-        assert result.answer
-
-    @pytest.mark.asyncio
-    async def test_ask_with_tool_context_returns_tool_return(
-        self, allow_model_requests, qa_client, qa_config
-    ):
-        """ask tool with tool context returns ToolReturn with state snapshot."""
-        context = ToolContext()
-        prepare_context(context, features=["qa"])
-
-        toolset = create_qa_toolset(qa_config)
-        ask_tool = toolset.tools["ask"]
-
-        ctx = make_ctx(qa_client, context)
-        result = await ask_tool.function(ctx, "What is Python?")
-
-        assert isinstance(result, ToolReturn)
-        assert result.metadata is not None
-        assert len(result.metadata) > 0
-
-
-@pytest.fixture
-async def qa_client(temp_db_path):
-    """Create a HaikuRAG client with test documents for QA tests."""
-    from haiku.rag.client import HaikuRAG
-
-    async with HaikuRAG(temp_db_path, create=True) as rag:
-        await rag.create_document(
-            "Python is a programming language. It is widely used for web development.",
-            uri="test://python",
-            title="Python Guide",
+    def test_to_search_answer(self):
+        """to_search_answer converts to SearchAnswer."""
+        citation = Citation(
+            document_id="d1",
+            chunk_id="c1",
+            document_uri="doc1.md",
+            document_title="Doc",
+            content="Content",
         )
-        yield rag
+        entry = QAHistoryEntry(
+            question="What is X?",
+            answer="X is Y.",
+            confidence=0.85,
+            citations=[citation],
+        )
+        sa = entry.to_search_answer()
+        assert sa.query == "What is X?"
+        assert sa.answer == "X is Y."
+        assert sa.confidence == 0.85
+        assert sa.cited_chunks == ["c1"]
+        assert len(sa.citations) == 1
+
+    def test_question_embedding_excluded_from_serialization(self):
+        """question_embedding is excluded from model_dump."""
+        entry = QAHistoryEntry(
+            question="Q",
+            answer="A",
+            question_embedding=[0.1, 0.2],
+        )
+        data = entry.model_dump()
+        assert "question_embedding" not in data
 
 
-@pytest.fixture
-def qa_config():
-    """Default AppConfig for QA tests."""
-    from haiku.rag.config import Config
-
-    return Config
+def test_prior_answer_relevance_threshold():
+    """PRIOR_ANSWER_RELEVANCE_THRESHOLD is a sensible value."""
+    assert 0 < PRIOR_ANSWER_RELEVANCE_THRESHOLD < 1
diff --git a/tests/tools/test_search.py b/tests/tools/test_search.py
index 8a131502..69ee447f 100644
--- a/tests/tools/test_search.py
+++ b/tests/tools/test_search.py
@@ -2,11 +2,9 @@ from pathlib import Path
 from types import SimpleNamespace
 
 import pytest
-from pydantic_ai import ToolReturn
 
-from haiku.rag.tools import ToolContext, prepare_context
-from haiku.rag.tools.search import SEARCH_NAMESPACE, SearchState, create_search_toolset
-from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
+from haiku.rag.store.models import SearchResult
+from haiku.rag.tools.search import create_search_toolset
 
 
 @pytest.fixture(scope="module")
@@ -14,52 +12,9 @@ def vcr_cassette_dir():
     return str(Path(__file__).parent.parent / "cassettes" / "test_search_tools")
 
 
-def make_ctx(client, context=None):
+def make_ctx(client):
     """Create a lightweight RunContext-like object for direct tool function calls."""
-    return SimpleNamespace(deps=SimpleNamespace(client=client, tool_context=context))
-
-
-class TestSearchState:
-    """Tests for SearchState model."""
-
-    def test_search_state_defaults(self):
-        """SearchState initializes with empty results."""
-        state = SearchState()
-        assert state.results == []
-
-    def test_search_state_add_results(self):
-        """Can add results to SearchState."""
-        from haiku.rag.store.models import SearchResult
-
-        state = SearchState()
-        result = SearchResult(content="test content", score=0.9, chunk_id="chunk1")
-        state.results.append(result)
-        assert len(state.results) == 1
-        assert state.results[0].chunk_id == "chunk1"
-
-    def test_search_state_serialization(self):
-        """SearchState serializes and deserializes correctly."""
-        from haiku.rag.store.models import SearchResult
-
-        state = SearchState()
-        state.results.append(
-            SearchResult(
-                content="test",
-                score=0.8,
-                chunk_id="c1",
-                document_title="Doc Title",
-            )
-        )
-
-        # Serialize
-        data = state.model_dump()
-        assert "results" in data
-        assert len(data["results"]) == 1
-
-        # Deserialize
-        restored = SearchState.model_validate(data)
-        assert len(restored.results) == 1
-        assert restored.results[0].chunk_id == "c1"
+    return SimpleNamespace(deps=SimpleNamespace(client=client))
 
 
 @pytest.mark.vcr()
@@ -88,34 +43,15 @@ class TestSearchToolExecution:
     @pytest.mark.asyncio
     async def test_search_returns_formatted_results(self, search_client, search_config):
         """Search tool returns formatted results."""
-        context = ToolContext()
         toolset = create_search_toolset(search_config)
 
-        # Get the search function
         search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
+        ctx = make_ctx(search_client)
         result = await search_tool.function(ctx, "Python")
 
         assert "Python" in result or "programming" in result
         assert "No results found" not in result
 
-    @pytest.mark.asyncio
-    async def test_search_accumulates_in_state(self, search_client, search_config):
-        """Search tool accumulates results in SearchState."""
-        context = ToolContext()
-        toolset = create_search_toolset(search_config)
-
-        # Run search
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        await search_tool.function(ctx, "Python")
-
-        # Check state was updated
-        state = context.get(SEARCH_NAMESPACE)
-        assert isinstance(state, SearchState)
-        assert len(state.results) > 0
-        assert any("Python" in r.content for r in state.results)
-
     @pytest.mark.asyncio
     async def test_search_with_no_results(self, temp_db_path, search_config):
         """Search tool returns appropriate message when no results."""
@@ -123,11 +59,10 @@ class TestSearchToolExecution:
 
         # Use empty database
         async with HaikuRAG(temp_db_path, create=True) as empty_client:
-            context = ToolContext()
             toolset = create_search_toolset(search_config)
 
             search_tool = toolset.tools["search"]
-            ctx = make_ctx(empty_client, context)
+            ctx = make_ctx(empty_client)
             result = await search_tool.function(ctx, "anything")
 
             assert result == "No results found."
@@ -135,73 +70,74 @@ class TestSearchToolExecution:
     @pytest.mark.asyncio
     async def test_search_with_filter(self, search_client, search_config):
         """Search tool respects filter parameter."""
-        context = ToolContext()
-        toolset = create_search_toolset(search_config)
+        accumulated: list[SearchResult] = []
+        toolset = create_search_toolset(search_config, on_results=accumulated.extend)
 
         search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        # Filter to only Python documents
+        ctx = make_ctx(search_client)
         await search_tool.function(ctx, "programming", filter="title LIKE '%Python%'")
 
-        # Should find Python but not JavaScript
-        state = context.get(SEARCH_NAMESPACE)
-        assert isinstance(state, SearchState)
-        for r in state.results:
+        for r in accumulated:
             assert "JavaScript" not in (r.document_title or "")
 
-    @pytest.mark.asyncio
-    async def test_search_without_context(self, search_client, search_config):
-        """Search tool works without ToolContext."""
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, None)
-        result = await search_tool.function(ctx, "Python")
-
-        # Should still return results
-        assert "Python" in result or "programming" in result
-
-    @pytest.mark.asyncio
-    async def test_search_multiple_accumulates(self, search_client, search_config):
-        """Multiple searches accumulate results in state."""
-        context = ToolContext()
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        await search_tool.function(ctx, "Python")
-        state = context.get(SEARCH_NAMESPACE)
-        assert isinstance(state, SearchState)
-        first_count = len(state.results)
-
-        await search_tool.function(ctx, "JavaScript")
-        state = context.get(SEARCH_NAMESPACE)
-        assert isinstance(state, SearchState)
-        second_count = len(state.results)
-
-        assert second_count > first_count
-
     @pytest.mark.asyncio
     async def test_search_with_base_filter(self, search_client, search_config):
         """Search toolset respects base_filter parameter."""
-        context = ToolContext()
-        # Create toolset with base_filter for Python documents only
+        accumulated: list[SearchResult] = []
         toolset = create_search_toolset(
             search_config,
             base_filter="title LIKE '%Python%'",
+            on_results=accumulated.extend,
         )
 
         search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
+        ctx = make_ctx(search_client)
         await search_tool.function(ctx, "programming")
 
-        # Should only find Python documents
-        state = context.get(SEARCH_NAMESPACE)
-        assert isinstance(state, SearchState)
-        assert len(state.results) > 0
-        for r in state.results:
+        assert len(accumulated) > 0
+        for r in accumulated:
             assert "JavaScript" not in (r.document_title or "")
 
+    @pytest.mark.asyncio
+    async def test_search_on_results_callback(self, search_client, search_config):
+        """on_results callback receives search results."""
+        accumulated: list[SearchResult] = []
+        toolset = create_search_toolset(search_config, on_results=accumulated.extend)
+
+        search_tool = toolset.tools["search"]
+        ctx = make_ctx(search_client)
+        await search_tool.function(ctx, "Python")
+
+        assert len(accumulated) > 0
+        assert any("Python" in r.content for r in accumulated)
+
+    @pytest.mark.asyncio
+    async def test_search_on_results_accumulates_across_calls(
+        self, search_client, search_config
+    ):
+        """Multiple searches accumulate results via on_results callback."""
+        accumulated: list[SearchResult] = []
+        toolset = create_search_toolset(search_config, on_results=accumulated.extend)
+
+        search_tool = toolset.tools["search"]
+        ctx = make_ctx(search_client)
+        await search_tool.function(ctx, "Python")
+        first_count = len(accumulated)
+
+        await search_tool.function(ctx, "JavaScript")
+        assert len(accumulated) > first_count
+
+    @pytest.mark.asyncio
+    async def test_search_without_on_results(self, search_client, search_config):
+        """Search works without on_results callback."""
+        toolset = create_search_toolset(search_config)
+
+        search_tool = toolset.tools["search"]
+        ctx = make_ctx(search_client)
+        result = await search_tool.function(ctx, "Python")
+
+        assert "Python" in result or "programming" in result
+
 
 @pytest.fixture
 async def search_client(temp_db_path):
@@ -222,124 +158,6 @@ async def search_client(temp_db_path):
         yield rag
 
 
-@pytest.mark.vcr()
-class TestSearchWithSessionState:
-    """Tests for search tool with session state (citation indexing path)."""
-
-    @pytest.mark.asyncio
-    async def test_search_with_session_state_returns_tool_return(
-        self, search_client, search_config
-    ):
-        """Search with SessionState returns ToolReturn with StateDeltaEvent."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        result = await search_tool.function(ctx, "Python")
-
-        assert isinstance(result, ToolReturn)
-        assert result.metadata is not None
-        assert len(result.metadata) > 0
-
-    @pytest.mark.asyncio
-    async def test_search_with_session_state_populates_citations(
-        self, search_client, search_config
-    ):
-        """Search populates SessionState.citation_registry and citations."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        await search_tool.function(ctx, "Python")
-
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        assert len(session_state.citation_registry) > 0
-        assert len(session_state.citations) > 0
-
-    @pytest.mark.asyncio
-    async def test_search_with_session_state_formatted_output(
-        self, search_client, search_config
-    ):
-        """Search with SessionState formats results with [index] **Title**."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        result = await search_tool.function(ctx, "Python")
-
-        assert isinstance(result, ToolReturn)
-        output = result.return_value
-        assert "Found" in output
-        assert "[1]" in output
-        assert "**" in output
-
-    @pytest.mark.asyncio
-    async def test_search_citations_accumulate_across_calls(
-        self, search_client, search_config
-    ):
-        """Multiple searches accumulate citation indices across calls."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-
-        await search_tool.function(ctx, "Python")
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        first_count = len(session_state.citation_registry)
-
-        await search_tool.function(ctx, "JavaScript")
-        # New chunks should get higher indices
-        assert len(session_state.citation_registry) >= first_count
-
-    @pytest.mark.asyncio
-    async def test_search_populates_citations_history(
-        self, search_client, search_config
-    ):
-        """Search appends to SessionState.citations_history."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        await search_tool.function(ctx, "Python")
-
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        assert len(session_state.citations_history) == 1
-        assert session_state.citations_history[0] == session_state.citations
-
-    @pytest.mark.asyncio
-    async def test_search_multiple_appends_separate_entries(
-        self, search_client, search_config
-    ):
-        """Multiple searches append separate entries to citations_history."""
-        context = ToolContext()
-        prepare_context(context, features=["search"])
-        toolset = create_search_toolset(search_config)
-
-        search_tool = toolset.tools["search"]
-        ctx = make_ctx(search_client, context)
-        await search_tool.function(ctx, "Python")
-        await search_tool.function(ctx, "JavaScript")
-
-        session_state = context.get(SESSION_NAMESPACE, SessionState)
-        assert session_state is not None
-        assert len(session_state.citations_history) == 2
-        # Latest citations should match the last entry
-        assert session_state.citations_history[1] == session_state.citations
-
-
 @pytest.fixture
 def search_config():
     """Default AppConfig for search tests."""
diff --git a/tests/tools/test_session.py b/tests/tools/test_session.py
deleted file mode 100644
index c3d0a768..00000000
--- a/tests/tools/test_session.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from ag_ui.core import EventType, StateDeltaEvent
-
-from haiku.rag.agents.research.models import Citation
-from haiku.rag.tools.session import (
-    SessionState,
-    compute_combined_state_delta,
-    compute_state_delta,
-)
-
-
-class TestSessionState:
-    """Tests for SessionState model."""
-
-    def test_citations_history_defaults_to_empty(self):
-        """SessionState.citations_history defaults to empty list."""
-        state = SessionState()
-        assert state.citations_history == []
-
-    def test_citations_history_serialization_roundtrip(self):
-        """citations_history survives serialize/deserialize."""
-        citation = Citation(
-            index=1,
-            document_id="d1",
-            chunk_id="c1",
-            document_uri="test://doc",
-            document_title="Doc",
-            page_numbers=[],
-            headings=None,
-            content="some content",
-        )
-        state = SessionState(citations_history=[[citation]])
-        data = state.model_dump(mode="json")
-        restored = SessionState.model_validate(data)
-        assert len(restored.citations_history) == 1
-        assert len(restored.citations_history[0]) == 1
-        assert restored.citations_history[0][0].chunk_id == "c1"
-
-
-class TestComputeStateDelta:
-    """Tests for compute_state_delta."""
-
-    def test_returns_delta_on_change(self):
-        """compute_state_delta returns StateDeltaEvent when state changed."""
-        old = SessionState()
-        new = SessionState(citation_registry={"chunk-a": 1})
-
-        result = compute_state_delta(old, new)
-
-        assert isinstance(result, StateDeltaEvent)
-        assert result.type == EventType.STATE_DELTA
-        assert len(result.delta) > 0
-
-    def test_returns_none_on_no_change(self):
-        """compute_state_delta returns None when states are identical."""
-        state = SessionState(document_filter=["doc1"])
-
-        result = compute_state_delta(state, state.model_copy(deep=True))
-
-        assert result is None
-
-    def test_with_state_key(self):
-        """compute_state_delta wraps delta under state_key."""
-        old = SessionState()
-        new = SessionState(document_filter=["doc1"])
-
-        result = compute_state_delta(old, new, state_key="my.key")
-
-        assert isinstance(result, StateDeltaEvent)
-        # The delta paths should be prefixed with /my.key/
-        paths = [op["path"] for op in result.delta]
-        assert all(p.startswith("/my.key/") for p in paths)
-
-
-class TestComputeCombinedStateDelta:
-    """Tests for compute_combined_state_delta."""
-
-    def test_returns_delta_on_change(self):
-        """compute_combined_state_delta returns StateDeltaEvent when snapshots differ."""
-        old = {"citations": []}
-        new = {"citations": [{"index": 1, "chunk_id": "c1"}]}
-
-        result = compute_combined_state_delta(old, new)
-
-        assert isinstance(result, StateDeltaEvent)
-        assert result.type == EventType.STATE_DELTA
-
-    def test_returns_none_on_no_change(self):
-        """compute_combined_state_delta returns None when snapshots are identical."""
-        snapshot = {"citations": [], "document_filter": []}
-
-        result = compute_combined_state_delta(snapshot, snapshot.copy())
-
-        assert result is None
-
-    def test_with_state_key_wraps(self):
-        """compute_combined_state_delta wraps under state_key."""
-        old = {"value": 1}
-        new = {"value": 2}
-
-        result = compute_combined_state_delta(old, new, state_key="ns")
-
-        assert isinstance(result, StateDeltaEvent)
-        paths = [op["path"] for op in result.delta]
-        assert all(p.startswith("/ns/") for p in paths)
-
-    def test_without_state_key(self):
-        """compute_combined_state_delta works without state_key."""
-        old = {"value": 1}
-        new = {"value": 2}
-
-        result = compute_combined_state_delta(old, new)
-
-        assert isinstance(result, StateDeltaEvent)
-        paths = [op["path"] for op in result.delta]
-        assert any(p == "/value" for p in paths)
diff --git a/tests/tools/test_toolkit.py b/tests/tools/test_toolkit.py
deleted file mode 100644
index 2b8dbbd9..00000000
--- a/tests/tools/test_toolkit.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import pytest
-
-from haiku.rag.config import Config
-from haiku.rag.tools.prompts import build_tools_prompt
-from haiku.rag.tools.toolkit import (
-    FEATURE_ANALYSIS,
-    FEATURE_DOCUMENTS,
-    FEATURE_QA,
-    FEATURE_SEARCH,
-    build_toolkit,
-)
-
-
-def test_build_toolkit_default_features():
-    """Defaults to ["search", "documents"], producing 2 toolsets."""
-    toolkit = build_toolkit(Config)
-    assert len(toolkit.toolsets) == 2
-    assert toolkit.features == [FEATURE_SEARCH, FEATURE_DOCUMENTS]
-
-
-def test_build_toolkit_all_features():
-    """All 4 features produce 4 toolsets."""
-    features = [FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA, FEATURE_ANALYSIS]
-    toolkit = build_toolkit(Config, features=features)
-    assert len(toolkit.toolsets) == 4
-    assert toolkit.features == features
-
-
-def test_build_toolkit_single_feature():
-    """Single feature produces 1 toolset."""
-    toolkit = build_toolkit(Config, features=[FEATURE_SEARCH])
-    assert len(toolkit.toolsets) == 1
-    assert toolkit.features == [FEATURE_SEARCH]
-
-
-def test_build_toolkit_prompt_matches_features():
-    """Toolkit prompt matches build_tools_prompt for the same features."""
-    features = [FEATURE_SEARCH, FEATURE_DOCUMENTS, FEATURE_QA]
-    toolkit = build_toolkit(Config, features=features)
-    expected = build_tools_prompt(features)
-    assert toolkit.prompt == expected
-
-
-def test_toolkit_create_context_registers_namespaces():
-    """create_context registers correct namespaces for the features."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-    from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-    toolkit = build_toolkit(Config, features=[FEATURE_SEARCH, FEATURE_QA])
-    context = toolkit.create_context()
-
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is not None
-
-
-def test_toolkit_create_context_no_qa_skips_qa_state():
-    """create_context without QA feature skips QASessionState."""
-    from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
-    from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-    toolkit = build_toolkit(Config, features=[FEATURE_SEARCH, FEATURE_DOCUMENTS])
-    context = toolkit.create_context()
-
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.get(QA_SESSION_NAMESPACE, QASessionState) is None
-
-
-def test_toolkit_create_context_sets_state_key():
-    """create_context propagates state_key to the ToolContext."""
-    toolkit = build_toolkit(Config)
-    context = toolkit.create_context(state_key="my.state.key")
-    assert context.state_key == "my.state.key"
-
-
-def test_toolkit_create_context_no_state_key():
-    """create_context without state_key leaves it None."""
-    toolkit = build_toolkit(Config)
-    context = toolkit.create_context()
-    assert context.state_key is None
-
-
-def test_toolkit_prepare_existing_context():
-    """prepare registers namespaces on an existing ToolContext."""
-    from haiku.rag.tools.context import ToolContext
-    from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState
-
-    toolkit = build_toolkit(Config, features=[FEATURE_SEARCH])
-    context = ToolContext()
-    toolkit.prepare(context, state_key="test.key")
-
-    assert context.get(SESSION_NAMESPACE, SessionState) is not None
-    assert context.state_key == "test.key"
-
-
-def test_toolkit_frozen():
-    """Toolkit is immutable after creation."""
-    toolkit = build_toolkit(Config)
-    with pytest.raises(AttributeError):
-        toolkit.features = ["search"]  # type: ignore[misc]
diff --git a/uv.lock b/uv.lock
index 229f13b1..731e0ec4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1449,6 +1449,7 @@ source = { editable = "haiku_rag_slim" }
 dependencies = [
     { name = "cachetools" },
     { name = "docling-core" },
+    { name = "haiku-skills" },
     { name = "httpx" },
     { name = "jsonpatch" },
     { name = "lancedb" },
@@ -1512,6 +1513,7 @@ requires-dist = [
     { name = "cohere", marker = "extra == 'cohere'", specifier = ">=5.20.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = "==2.73.1" },
     { name = "docling-core", specifier = "==2.65.1" },
+    { name = "haiku-skills", specifier = ">=0.4.2" },
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "jsonpatch", specifier = ">=1.33" },
     { name = "lancedb", specifier = "==0.29.2" },
@@ -1540,6 +1542,21 @@ requires-dist = [
 ]
 provides-extras = ["docling", "voyageai", "mxbai", "cohere", "zeroentropy", "jina", "tui", "anthropic", "groq", "google", "mistral", "bedrock", "vertexai"]
 
+[[package]]
+name = "haiku-skills"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic" },
+    { name = "pydantic-ai-slim", extra = ["mcp"] },
+    { name = "pyyaml" },
+    { name = "skills-ref" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3b/13/96cb8124fd47d9f7b752f835b8c751bc67908a061a53a2ba392509d1fe68/haiku_skills-0.4.2.tar.gz", hash = "sha256:4846e7ebc1c7919faa576badd77723a0d681cf3a92d90f7b97edf3f6eca8ea8d", size = 119892, upload-time = "2026-02-20T11:18:47.434Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c5/77/2f94bf41b4b0f0af62431f9c9753db9343a44d1012cadb2e277210b69dae/haiku_skills-0.4.2-py3-none-any.whl", hash = "sha256:08ec15f3a9c894ef33d989850e2b73e78a2a9364056e997bff7cd910a0c4cfe9", size = 21077, upload-time = "2026-02-20T11:18:45.593Z" },
+]
+
 [[package]]
 name = "hf-xet"
 version = "1.2.0"
@@ -3649,6 +3666,9 @@ groq = [
 logfire = [
     { name = "logfire", extra = ["httpx"] },
 ]
+mcp = [
+    { name = "mcp" },
+]
 mistral = [
     { name = "mistralai" },
 ]
@@ -4705,6 +4725,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" },
 ]
 
+[[package]]
+name = "skills-ref"
+version = "0.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "strictyaml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/23/42/943d3ba8b097af7068b7178563a5062ad8a977982f4a7b4f67facfc575e9/skills_ref-0.1.1.tar.gz", hash = "sha256:6b400ca6e0049be62dca0167ff943ba2745fd67efb37fbba4d0ee341fccd2695", size = 93519, upload-time = "2026-01-10T13:23:41.423Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/25/36a43c3a61fb6cc3984e6ad5e556929b8ae71c95eba615dae4cf2f427964/skills_ref-0.1.1-py3-none-any.whl", hash = "sha256:d35db5bb8de71ae301daf5ca9cb71f8a555e8c6f83a6d40e46a5bc09f8f461b5", size = 12918, upload-time = "2026-01-10T13:23:40.106Z" },
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -4758,6 +4791,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/0d/13d1d239a25cbfb19e740db83143e95c772a1fe10202dda4b76792b114dd/starlette-0.52.1-py3-none-any.whl", hash = "sha256:0029d43eb3d273bc4f83a08720b4912ea4b071087a3b48db01b7c839f7954d74", size = 74272, upload-time = "2026-01-18T13:34:09.188Z" },
 ]
 
+[[package]]
+name = "strictyaml"
+version = "1.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b3/08/efd28d49162ce89c2ad61a88bd80e11fb77bc9f6c145402589112d38f8af/strictyaml-1.7.3.tar.gz", hash = "sha256:22f854a5fcab42b5ddba8030a0e4be51ca89af0267961c8d6cfa86395586c407", size = 115206, upload-time = "2023-03-10T12:50:27.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/96/7c/a81ef5ef10978dd073a854e0fa93b5d8021d0594b639cc8f6453c3c78a1d/strictyaml-1.7.3-py3-none-any.whl", hash = "sha256:fb5c8a4edb43bebb765959e420f9b3978d7f1af88c80606c03fb420888f5d1c7", size = 123917, upload-time = "2023-03-10T12:50:17.242Z" },
+]
+
 [[package]]
 name = "sympy"
 version = "1.14.0"