diff --git a/app/backend/main.py b/app/backend/main.py index 59c35864..e91968b0 100644 --- a/app/backend/main.py +++ b/app/backend/main.py @@ -15,8 +15,7 @@ from starlette.routing import Route from haiku.rag.agents.chat import ( AGUI_STATE_KEY, ChatDeps, - ChatSessionState, - QAResponse, + ToolContext, create_chat_agent, ) from haiku.rag.client import HaikuRAG @@ -54,96 +53,48 @@ db_path = Path(db_path_str) logger.info(f"Database path: {db_path}") logger.info(f"QA Provider: {Config.qa.model.provider}, Model: {Config.qa.model.name}") -# Create the chat agent -chat_agent = create_chat_agent(Config) - -# Client cache for proper lifecycle -_client_cache: dict[str, HaikuRAG] = {} +# Only HaikuRAG client is a singleton (expensive to create) +_client: HaikuRAG | None = None -def get_client(effective_db_path: Path) -> HaikuRAG: +def get_client() -> HaikuRAG: """Get or create cached client.""" - path_key = str(effective_db_path) - if path_key not in _client_cache: - _client_cache[path_key] = HaikuRAG( - db_path=effective_db_path, config=Config, create=True - ) - return _client_cache[path_key] + global _client + if _client is None: + _client = HaikuRAG(db_path=db_path, config=Config, create=True) + return _client async def stream_chat(request: Request) -> Response: - """Chat streaming endpoint with AG-UI protocol.""" - body = await request.body() + """Chat streaming endpoint with AG-UI protocol. - # Parse request to build run_input + This endpoint is stateless - all state flows via AG-UI protocol: + - Fresh ToolContext created per request + - AGUIAdapter restores state via ChatDeps.state setter + - ChatDeps generates session_id if not provided + - Ask tool triggers background summarization internally + - ChatDeps.state getter emits final state in response + """ + body = await request.body() accept = request.headers.get("accept", SSE_CONTENT_TYPE) run_input = AGUIAdapter.build_run_input(body) - # Restore session state from incoming AG-UI state - session_state = ChatSessionState(session_id="") # New session: empty session_id - state = getattr(run_input, "state", None) - if state and AGUI_STATE_KEY in state: - chat_state = state[AGUI_STATE_KEY] - if chat_state and chat_state.get("session_id"): - session_state = ChatSessionState( - session_id=chat_state["session_id"], - qa_history=[ - QAResponse(**qa) for qa in chat_state.get("qa_history", []) - ], - document_filter=chat_state.get("document_filter", []), - initial_context=chat_state.get("initial_context"), - citation_registry=chat_state.get("citation_registry", {}), - ) - logger.info( - f"Incoming state: session={session_state.session_id[:8]}, " - f"qa_history={len(session_state.qa_history)}, " - f"citations={len(session_state.citation_registry)}" - ) - else: - logger.info("Incoming state: new session") - else: - logger.info("Incoming state: new session") + # Fresh context per request - state restored by AGUIAdapter via ChatDeps.state setter + context = ToolContext() + agent = create_chat_agent(Config, get_client(), context) deps = ChatDeps( - client=get_client(db_path), config=Config, - session_state=session_state, + tool_context=context, state_key=AGUI_STATE_KEY, ) # Use AGUIAdapter for streaming - adapter = AGUIAdapter(agent=chat_agent, run_input=run_input, accept=accept) + # State restoration happens automatically via ChatDeps.state setter + # Background summarization triggered by ask() tool internally + adapter = AGUIAdapter(agent=agent, run_input=run_input, accept=accept) event_stream = adapter.run_stream(deps=deps) - - async def logged_event_stream(): - async for event in event_stream: - event_type = getattr(event, "type", None) - if event_type and "state" in str(event_type).lower(): - delta = getattr(event, "delta", None) - snapshot = getattr(event, "snapshot", None) - if delta is not None: - logger.info(f"Outgoing StateDeltaEvent: {len(delta)} ops") - for op in delta: - # Extract key from path like /haiku.rag.chat/qa_history/0 - parts = op["path"].split("/") - key = "/".join(parts[2:]) if len(parts) > 2 else op["path"] - logger.info(f" {op['op']} {key}") - elif snapshot is not None: - chat_state = snapshot.get(AGUI_STATE_KEY, {}) - sid = chat_state.get("session_id", "")[:8] if chat_state else "" - qa_len = len(chat_state.get("qa_history", [])) if chat_state else 0 - reg_len = ( - len(chat_state.get("citation_registry", {})) - if chat_state - else 0 - ) - logger.info( - f"Outgoing StateSnapshotEvent: session={sid}, " - f"qa={qa_len}, keys={reg_len}" - ) - yield event - - sse_event_stream = adapter.encode_stream(logged_event_stream()) + sse_event_stream = adapter.encode_stream(event_stream) return StreamingResponse( sse_event_stream, @@ -158,10 +109,13 @@ async def stream_chat(request: Request) -> Response: async def health_check(_: Request) -> JSONResponse: """Health check endpoint.""" + # Create a temporary agent just for health check + context = ToolContext() + agent = create_chat_agent(Config, get_client(), context) return JSONResponse( { "status": "healthy", - "agent_model": str(chat_agent.model), + "agent_model": str(agent.model), "qa_provider": Config.qa.model.provider, "qa_model": Config.qa.model.name, "db_path": str(db_path), @@ -175,7 +129,7 @@ async def list_documents(_: Request) -> JSONResponse: if not db_path.exists(): return JSONResponse({"documents": [], "error": "Database not found"}) - client = get_client(db_path) + client = get_client() docs = await client.document_repository.list_all() return JSONResponse( { @@ -198,7 +152,7 @@ async def db_info(_: Request) -> JSONResponse: } ) - client = get_client(db_path) + client = get_client() stats = client.store.get_stats() return JSONResponse( @@ -224,7 +178,7 @@ async def visualize_chunk(request: Request) -> JSONResponse: if not db_path.exists(): return JSONResponse({"error": "Database not found"}, status_code=404) - client = get_client(db_path) + client = get_client() chunk = await client.chunk_repository.get_by_id(chunk_id) if not chunk: diff --git a/haiku_rag_slim/haiku/rag/agents/chat/__init__.py b/haiku_rag_slim/haiku/rag/agents/chat/__init__.py index cde34e81..2506da21 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/__init__.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/__init__.py @@ -1,4 +1,9 @@ -from haiku.rag.agents.chat.agent import create_chat_agent +from haiku.rag.agents.chat.agent import ( + ChatDeps, + create_chat_agent, + run_chat_agent, + trigger_background_summarization, +) from haiku.rag.agents.chat.context import ( summarize_session, update_session_context, @@ -6,7 +11,6 @@ from haiku.rag.agents.chat.context import ( from haiku.rag.agents.chat.search import SearchAgent from haiku.rag.agents.chat.state import ( AGUI_STATE_KEY, - ChatDeps, ChatSessionState, DocumentInfo, DocumentListResponse, @@ -14,10 +18,13 @@ from haiku.rag.agents.chat.state import ( SearchDeps, SessionContext, ) +from haiku.rag.tools.context import ToolContext __all__ = [ "AGUI_STATE_KEY", "create_chat_agent", + "run_chat_agent", + "trigger_background_summarization", "SearchAgent", "ChatDeps", "ChatSessionState", @@ -26,6 +33,7 @@ __all__ = [ "QAResponse", "SearchDeps", "SessionContext", + "ToolContext", "summarize_session", "update_session_context", ] diff --git a/haiku_rag_slim/haiku/rag/agents/chat/agent.py b/haiku_rag_slim/haiku/rag/agents/chat/agent.py index 140711ce..b84e3150 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/agent.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/agent.py @@ -1,452 +1,285 @@ -import asyncio -import math import uuid +from dataclasses import dataclass +from typing import Any, cast -from pydantic_ai import Agent, RunContext, ToolReturn +from pydantic_ai import Agent from haiku.rag.agents.chat.context import ( get_cached_session_context, - update_session_context, ) -from haiku.rag.agents.chat.prompts import CHAT_SYSTEM_PROMPT, DOCUMENT_SUMMARY_PROMPT -from haiku.rag.agents.chat.search import SearchAgent +from haiku.rag.agents.chat.context import ( + trigger_background_summarization as _trigger_summarization, +) +from haiku.rag.agents.chat.prompts import CHAT_SYSTEM_PROMPT from haiku.rag.agents.chat.state import ( - MAX_QA_HISTORY, - ChatDeps, + AGUI_STATE_KEY, ChatSessionState, DocumentInfo, - DocumentListResponse, QAResponse, + SearchDeps, + SessionContext, emit_state_event, ) -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 -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.filters import ( - build_document_filter, - build_multi_document_filter, - combine_filters, +from haiku.rag.tools.context import ToolContext +from haiku.rag.tools.document import DocumentListResponse, create_document_toolset +from haiku.rag.tools.qa import ( + QA_SESSION_NAMESPACE, + QASessionState, + create_qa_toolset, ) +from haiku.rag.tools.search import create_search_toolset +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState from haiku.rag.utils import get_model -# Similarity threshold for finding relevant prior answers -PRIOR_ANSWER_RELEVANCE_THRESHOLD = 0.7 +@dataclass +class ChatDeps: + """Dependencies for chat agent. -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) + Implements StateHandler protocol for AG-UI state management. + """ + config: AppConfig + tool_context: ToolContext + session_id: str = "" + state_key: str | None = None -# Track summarization tasks per session to allow cancellation -_summarization_tasks: dict[str, asyncio.Task[None]] = {} + @property + def state(self) -> dict[str, Any]: + """Get current state for AG-UI protocol. + Combines SessionState and QASessionState into a single state dict + matching the ChatSessionState schema expected by AG-UI clients. + """ + snapshot: dict[str, Any] = {"session_id": self.session_id} -async def _update_context_background( - qa_history: list[QAResponse], - config: AppConfig, - session_state: ChatSessionState, -) -> None: - """Background task to update session context after an ask.""" - try: - await update_session_context( - qa_history=qa_history, - config=config, - session_state=session_state, + # Add SessionState fields + session_state = self.tool_context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None: + snapshot["document_filter"] = session_state.document_filter + snapshot["citation_registry"] = session_state.citation_registry + snapshot["citations"] = [c.model_dump() for c in session_state.citations] + + # Add QASessionState fields + qa_session_state = self.tool_context.get_typed( + QA_SESSION_NAMESPACE, QASessionState ) - except asyncio.CancelledError: - pass + if qa_session_state is not None: + snapshot["qa_history"] = [ + qa.model_dump() for qa in qa_session_state.qa_history + ] + # Convert string to SessionContext model for frontend + if qa_session_state.session_context: + snapshot["session_context"] = SessionContext( + summary=qa_session_state.session_context + ).model_dump(mode="json") + else: + snapshot["session_context"] = None + + if self.state_key: + return {self.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 + + # Extract from namespaced key if present + state_data: dict[str, Any] = value + if self.state_key and self.state_key in value: + nested = value[self.state_key] + if isinstance(nested, dict): + state_data = nested + + # Update SessionState from incoming state + session_state = self.tool_context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None: + if "document_filter" in state_data: + session_state.document_filter = state_data.get("document_filter", []) + if "citation_registry" in state_data: + session_state.citation_registry = state_data["citation_registry"] + if "citations" in state_data: + from haiku.rag.agents.research.models import Citation + + session_state.citations = [ + Citation(**c) if isinstance(c, dict) else c + for c in state_data.get("citations", []) + ] + + # Extract session_id if present, or generate one + # Track what the client sent (for delta computation) + incoming_session_id = state_data.get("session_id", "") + + if incoming_session_id: + self.session_id = incoming_session_id + elif not self.session_id: + # Generate session_id now so ask() tool can use it + self.session_id = str(uuid.uuid4()) + + # Sync session_id to SessionState (track incoming for delta computation) + if session_state is not None: + session_state.session_id = self.session_id + session_state.incoming_session_id = incoming_session_id + + # Update QASessionState from incoming state + qa_session_state = self.tool_context.get_typed( + QA_SESSION_NAMESPACE, QASessionState + ) + if qa_session_state is not None: + if "qa_history" in state_data: + from haiku.rag.tools.qa import QAHistoryEntry + + qa_session_state.qa_history = [ + QAHistoryEntry(**qa) if isinstance(qa, dict) else qa + for qa in state_data.get("qa_history", []) + ] + + # Track what client sent for delta computation + incoming_session_context = state_data.get("session_context") + if isinstance(incoming_session_context, dict): + qa_session_state.incoming_session_context = SessionContext( + **incoming_session_context + ) + qa_session_state.session_context = ( + qa_session_state.incoming_session_context.summary + ) + elif incoming_session_context is None: + qa_session_state.incoming_session_context = None + qa_session_state.session_context = None + + # Check cache for fresher session_context from background summarization + # Cache is authoritative - always use it if available + if self.session_id: + cached = get_cached_session_context(self.session_id) + if cached and cached.summary: + qa_session_state.session_context = cached.render_markdown() + + # Handle initial_context -> session_context for first message + # Only applies if session_context is still empty after restoring and cache check + if "initial_context" in state_data: + initial = state_data.get("initial_context") + if initial and not qa_session_state.session_context: + qa_session_state.session_context = initial -def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]: - """Create the chat agent with search and ask tools.""" +def create_chat_agent( + config: AppConfig, + client: HaikuRAG, + context: ToolContext, +) -> Agent[ChatDeps, str]: + """Create the chat agent with composed toolsets. + + Args: + config: Application configuration. + client: HaikuRAG client for database operations. + context: ToolContext for shared state across toolsets. + Should have SessionState and QASessionState registered + (will be auto-registered if not present). + + Returns: + The configured chat agent. + + Example: + async with HaikuRAG(db_path, create=True) as client: + context = ToolContext() + agent = create_chat_agent(config, client, context) + deps = ChatDeps(config=config, tool_context=context) + result = await agent.run("Search for X", deps=deps) + """ + # Ensure session states are registered with proper AG-UI state key + existing = context.get_typed(SESSION_NAMESPACE, SessionState) + if existing is None: + context.register(SESSION_NAMESPACE, SessionState(state_key=AGUI_STATE_KEY)) + elif existing.state_key is None: + existing.state_key = AGUI_STATE_KEY + if context.get_typed(QA_SESSION_NAMESPACE, QASessionState) is None: + context.register(QA_SESSION_NAMESPACE, QASessionState()) + + # Create toolsets - these capture client, config, and context in closures + search_toolset = create_search_toolset(client, config, context=context) + document_toolset = create_document_toolset(client, config, context=context) + qa_toolset = create_qa_toolset(client, config, context=context) + + # Create the agent with composed toolsets model = get_model(config.qa.model, config) - agent: Agent[ChatDeps, str] = Agent( - model, - deps_type=ChatDeps, - output_type=str, - instructions=CHAT_SYSTEM_PROMPT, - retries=3, + agent = cast( + Agent[ChatDeps, str], + Agent( + model, + deps_type=ChatDeps, + output_type=str, + instructions=CHAT_SYSTEM_PROMPT, + toolsets=[search_toolset, document_toolset, qa_toolset], # type: ignore[arg-type] + retries=3, + ), ) - @agent.tool - async def search( - ctx: RunContext[ChatDeps], - query: str, - document_name: str | None = None, - limit: int | None = None, - ) -> ToolReturn: - """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. - - Args: - query: The search query (what to search for) - document_name: Optional document name/title to search within - limit: Number of results to return (default: 5) - """ - # Build session filter from document_filter - session_filter = build_multi_document_filter( - ctx.deps.session_state.document_filter - ) - - # Build tool filter from document_name parameter - tool_filter = build_document_filter(document_name) if document_name else None - - # Combine filters: session AND tool - doc_filter = combine_filters(session_filter, tool_filter) - - # Use search agent for query expansion and deduplication - search_agent = SearchAgent(ctx.deps.client, ctx.deps.config) - results = await search_agent.search(query, filter=doc_filter, limit=limit) - - # Store for potential citation resolution - ctx.deps.search_results = results - - if not results: - return ToolReturn(return_value="No results found.") - - new_state = ctx.deps.session_state.model_copy(deep=True) - if not new_state.session_id: - new_state.session_id = str(uuid.uuid4()) - - # Build citation infos using the copy's registry - citation_infos = [] - for r in results: - chunk_id = r.chunk_id or "" - if chunk_id: - index = new_state.get_or_assign_index(chunk_id) - else: - index = len(citation_infos) + 1 - citation_infos.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, - ) - ) - - # Update new_state with citations and fresh session_context - new_state.citations = citation_infos - if new_state.session_id: - new_state.session_context = get_cached_session_context(new_state.session_id) - - # Return detailed results for the agent to present - result_lines = [] - for c in citation_infos: - title = c.document_title or c.document_uri or "Unknown" - # Truncate content for display - snippet = c.content[:300].replace("\n", " ").strip() - if len(c.content) > 300: - snippet += "..." - - line = f"[{c.index}] **{title}**" - if c.page_numbers: - line += f" (pages {', '.join(map(str, c.page_numbers))})" - line += f"\n {snippet}" - result_lines.append(line) - - state_event = emit_state_event( - ctx.deps.session_state, new_state, ctx.deps.state_key - ) - - return ToolReturn( - return_value=f"Found {len(results)} results:\n\n" - + "\n\n".join(result_lines), - metadata=[state_event] if state_event else None, - ) - - @agent.tool - async def ask( - ctx: RunContext[ChatDeps], - question: str, - document_name: str | None = None, - ) -> ToolReturn: - """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. - - Args: - question: The question to answer - document_name: Optional document name/title to search within (e.g., "tbmed593", "army manual") - """ - # Build session filter from document_filter - session_filter = build_multi_document_filter( - ctx.deps.session_state.document_filter - ) - - # Build tool filter from document_name parameter - tool_filter = build_document_filter(document_name) if document_name else None - - # Combine filters: session AND tool - doc_filter = combine_filters(session_filter, tool_filter) - - # Build and run the conversational research graph - graph = build_research_graph( - config=ctx.deps.config, output_mode="conversational" - ) - session_id = ctx.deps.session_state.session_id - - # Get session context from server cache for planning, fallback to initial_context - cached_context = get_cached_session_context(session_id) - session_context = ( - cached_context.render_markdown() - if cached_context and cached_context.summary - else ctx.deps.session_state.initial_context - ) - - # Find relevant prior answers from qa_history - prior_answers = [] - if ctx.deps.session_state.qa_history: - embedder = get_embedder(ctx.deps.config) - question_embedding = await embedder.embed_query(question) - - # Collect questions that need embedding (not cached) - to_embed = [] - to_embed_indices = [] - for i, qa in enumerate(ctx.deps.session_state.qa_history): - if qa.question_embedding is None: - to_embed.append(qa.question) - to_embed_indices.append(i) - - # Batch embed uncached questions - if to_embed: - new_embeddings = await embedder.embed_documents(to_embed) - for i, idx in enumerate(to_embed_indices): - ctx.deps.session_state.qa_history[ - idx - ].question_embedding = new_embeddings[i] - - # Compare against all questions and collect relevant prior answers - for qa in ctx.deps.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: - prior_answers.append(qa.to_search_answer()) - - context = ResearchContext( - original_question=question, - session_context=session_context, - qa_responses=prior_answers, - ) - state = ResearchState( - context=context, - max_iterations=1, - search_filter=doc_filter, - max_concurrency=ctx.deps.config.research.max_concurrency, - ) - deps = ResearchDeps( - client=ctx.deps.client, - ) - - result = await graph.run(state=state, deps=deps) - - new_state = ctx.deps.session_state.model_copy(deep=True) - if not new_state.session_id: - new_state.session_id = str(uuid.uuid4()) - - # Build citation infos using the copy's registry - citation_infos = [] - for c in result.citations: - index = new_state.get_or_assign_index(c.chunk_id) - citation_infos.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, - ) - ) - - # Add Q&A to the copy's history - qa_response = QAResponse( - question=question, - answer=result.answer, - confidence=result.confidence, - citations=citation_infos, - ) - new_state.qa_history.append(qa_response) - # Enforce FIFO limit - if len(new_state.qa_history) > MAX_QA_HISTORY: - new_state.qa_history = new_state.qa_history[-MAX_QA_HISTORY:] - - # Update citations and session_context - new_state.citations = citation_infos - if new_state.session_id: - new_state.session_context = get_cached_session_context(new_state.session_id) - - # Spawn background task to update session context - if new_state.session_id in _summarization_tasks: - _summarization_tasks[new_state.session_id].cancel() - - task = asyncio.create_task( - _update_context_background( - qa_history=list(new_state.qa_history), - config=ctx.deps.config, - session_state=new_state, - ) - ) - _summarization_tasks[new_state.session_id] = task - task.add_done_callback( - lambda t, sid=new_state.session_id: _summarization_tasks.pop(sid, None) - ) - - # Format answer with citation references using stable indices - answer_text = result.answer - if citation_infos: - citation_refs = " ".join(f"[{c.index}]" for c in citation_infos) - answer_text = f"{answer_text}\n\nSources: {citation_refs}" - - state_event = emit_state_event( - ctx.deps.session_state, new_state, ctx.deps.state_key - ) - - return ToolReturn( - return_value=answer_text, - metadata=[state_event] if state_event else None, - ) - - @agent.tool - async def list_documents( - ctx: RunContext[ChatDeps], - page: int = 1, - ) -> DocumentListResponse: - """List available documents in the knowledge base. - - Use this when the user wants to browse or see what documents are available. - - Args: - page: Page number (default: 1, 50 documents per page) - """ - page_size = 50 - offset = (page - 1) * page_size - - doc_filter = build_multi_document_filter(ctx.deps.session_state.document_filter) - - docs = await ctx.deps.client.list_documents( - limit=page_size, offset=offset, filter=doc_filter - ) - total = await ctx.deps.client.count_documents(filter=doc_filter) - total_pages = (total + page_size - 1) // page_size if total > 0 else 1 - - return DocumentListResponse( - documents=[ - DocumentInfo( - title=doc.title or "Untitled", - uri=doc.uri or "", - created=doc.created_at.strftime("%Y-%m-%d"), - ) - for doc in docs - ], - page=page, - total_pages=total_pages, - total_documents=total, - ) - - async def _find_document(client: HaikuRAG, query: str): - """Find a document by exact URI, partial URI, or partial title match.""" - # Try exact URI match first - doc = await client.get_document_by_uri(query) - if doc is not None: - return doc - - escaped_query = query.replace("'", "''") - # Also try without spaces for matching "TB MED 593" to "tbmed593" - no_spaces = escaped_query.replace(" ", "") - - # Try partial URI match (with and without spaces) - docs = await client.list_documents( - limit=1, - filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%') OR LOWER(uri) LIKE LOWER('%{no_spaces}%')", - ) - if docs: - return docs[0] - - # Try partial title match (with and without spaces) - docs = await client.list_documents( - limit=1, - filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')", - ) - if docs: - return docs[0] - - return None - - @agent.tool - async def get_document( - ctx: RunContext[ChatDeps], - query: str, - ) -> str: - """Retrieve a specific document by title or URI. - - Use this when the user wants to fetch/get/retrieve a specific document. - - Args: - query: The document title or URI to look up - """ - doc = await _find_document(ctx.deps.client, query) - - if doc is None: - return f"Document not found: {query}" - - return ( - f"**{doc.title or 'Untitled'}**\n\n" - f"- ID: {doc.id}\n" - f"- URI: {doc.uri}\n" - f"- Created: {doc.created_at.strftime('%Y-%m-%d %H:%M')}\n\n" - f"**Content:**\n{doc.content}" - ) - - @agent.tool - async def summarize_document( - ctx: RunContext[ChatDeps], - query: str, - ) -> str: - """Generate a summary of a specific document. - - Use this when the user wants an overview or summary of a document's content. - - Args: - query: The document title or URI to summarize - """ - doc = await _find_document(ctx.deps.client, query) - - if doc is None: - return f"Document not found: {query}" - - # Use LLM to generate summary - summary_model = get_model(ctx.deps.config.qa.model, ctx.deps.config) - summary_agent: Agent[None, str] = Agent( - summary_model, - output_type=str, - ) - result = await summary_agent.run( - DOCUMENT_SUMMARY_PROMPT.format(content=doc.content or "") - ) - - return f"**Summary of {doc.title or doc.uri}:**\n\n{result.output}" - return agent + + +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. + + Note: The ask() tool now triggers summarization internally, so this + function is primarily for explicit triggering when needed. + + Args: + deps: Chat dependencies with tool_context containing QASessionState. + """ + qa_session_state = deps.tool_context.get_typed(QA_SESSION_NAMESPACE, QASessionState) + if qa_session_state is None or not qa_session_state.qa_history: + return + if not deps.session_id: + return + + _trigger_summarization( + qa_session_state=qa_session_state, + config=deps.config, + session_id=deps.session_id, + ) + + +async def run_chat_agent( + agent: Agent[ChatDeps, str], + deps: ChatDeps, + message: str, +) -> str: + """Run the chat agent and trigger background summarization. + + This wrapper handles post-processing like background summarization. + + Args: + agent: The chat agent. + deps: Chat dependencies. + message: User message. + + Returns: + Agent response. + """ + result = await agent.run(message, deps=deps) + trigger_background_summarization(deps) + return result.output + + +__all__ = [ + "create_chat_agent", + "run_chat_agent", + "trigger_background_summarization", + "ChatDeps", + "ChatSessionState", + "DocumentInfo", + "DocumentListResponse", + "QAResponse", + "SearchDeps", + "SessionContext", + "emit_state_event", + "AGUI_STATE_KEY", +] diff --git a/haiku_rag_slim/haiku/rag/agents/chat/context.py b/haiku_rag_slim/haiku/rag/agents/chat/context.py index 27086a78..966a9b03 100644 --- a/haiku_rag_slim/haiku/rag/agents/chat/context.py +++ b/haiku_rag_slim/haiku/rag/agents/chat/context.py @@ -1,4 +1,7 @@ +import asyncio +from dataclasses import dataclass, field from datetime import datetime, timedelta +from typing import TYPE_CHECKING from pydantic_ai import Agent @@ -7,33 +10,72 @@ from haiku.rag.agents.chat.state import ChatSessionState, QAResponse, SessionCon from haiku.rag.config.models import AppConfig from haiku.rag.utils import get_model -# Cache for session contexts (session_id -> SessionContext) -# Used to persist async summarization results between requests -_session_context_cache: dict[str, SessionContext] = {} +if TYPE_CHECKING: + from haiku.rag.tools.qa import QASessionState + + +@dataclass +class SessionCache: + """Per-session cache for context and embeddings.""" + + context: SessionContext | None = None + embeddings: dict[str, list[float]] = field(default_factory=dict) + + +# Cache for session data (session_id -> SessionCache) +# Used to persist async summarization results and embeddings between requests +_session_cache: dict[str, SessionCache] = {} _cache_timestamps: dict[str, datetime] = {} _CACHE_TTL = timedelta(hours=1) +# Track summarization tasks per session to allow cancellation +_summarization_tasks: dict[str, asyncio.Task[None]] = {} + def _cleanup_stale_cache() -> None: """Remove cache entries older than TTL.""" now = datetime.now() stale = [sid for sid, ts in _cache_timestamps.items() if now - ts > _CACHE_TTL] for sid in stale: - _session_context_cache.pop(sid, None) + _session_cache.pop(sid, None) _cache_timestamps.pop(sid, None) +def _get_or_create_session_cache(session_id: str) -> SessionCache: + """Get or create session cache for a given session_id.""" + _cleanup_stale_cache() + if session_id not in _session_cache: + _session_cache[session_id] = SessionCache() + _cache_timestamps[session_id] = datetime.now() + return _session_cache[session_id] + + def cache_session_context(session_id: str, context: SessionContext) -> None: """Store session context in cache.""" - _cleanup_stale_cache() - _session_context_cache[session_id] = context - _cache_timestamps[session_id] = datetime.now() + cache = _get_or_create_session_cache(session_id) + cache.context = context def get_cached_session_context(session_id: str) -> SessionContext | None: """Get session context from server cache.""" _cleanup_stale_cache() - return _session_context_cache.get(session_id) + cache = _session_cache.get(session_id) + return cache.context if cache else None + + +def cache_question_embedding( + session_id: str, question: str, embedding: list[float] +) -> None: + """Store question embedding in session cache.""" + cache = _get_or_create_session_cache(session_id) + cache.embeddings[question] = embedding + + +def get_cached_embedding(session_id: str, question: str) -> list[float] | None: + """Get cached embedding for a question in this session.""" + _cleanup_stale_cache() + cache = _session_cache.get(session_id) + return cache.embeddings.get(question) if cache else None async def summarize_session( @@ -114,3 +156,78 @@ def _format_qa_history(qa_history: list[QAResponse]) -> str: lines.append("") return "\n".join(lines) + + +async def _update_context_background( + qa_session_state: "QASessionState", + config: AppConfig, + session_id: str, +) -> None: + """Background task to update session context after an ask.""" + try: + # Convert QAHistoryEntry to QAResponse format for update_session_context + qa_history = [ + QAResponse( + question=entry.question, + answer=entry.answer, + confidence=entry.confidence, + citations=list(entry.citations), + ) + for entry in qa_session_state.qa_history + ] + + session_state = ChatSessionState( + session_id=session_id, + qa_history=qa_history, + ) + + await update_session_context( + qa_history=qa_history, + config=config, + session_state=session_state, + ) + + # Update the QASessionState with the new context + cached = get_cached_session_context(session_id) + if cached and cached.summary: + qa_session_state.session_context = cached.render_markdown() + + except asyncio.CancelledError: + pass + except Exception as e: + import logging + + logging.getLogger(__name__).exception(f"Background summarization failed: {e}") + + +def trigger_background_summarization( + qa_session_state: "QASessionState", + config: AppConfig, + session_id: str, +) -> 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. + session_id: Session ID for caching results. + """ + if not qa_session_state.qa_history or not session_id: + return + + # Cancel any existing summarization task for this session + if session_id in _summarization_tasks: + _summarization_tasks[session_id].cancel() + + # Spawn background task + task = asyncio.create_task( + _update_context_background( + qa_session_state=qa_session_state, + config=config, + session_id=session_id, + ) + ) + _summarization_tasks[session_id] = task + task.add_done_callback( + lambda _t, sid=session_id: _summarization_tasks.pop(sid, None) + ) diff --git a/haiku_rag_slim/haiku/rag/chat/app.py b/haiku_rag_slim/haiku/rag/chat/app.py index a53c8a47..87ae91d7 100644 --- a/haiku_rag_slim/haiku/rag/chat/app.py +++ b/haiku_rag_slim/haiku/rag/chat/app.py @@ -17,14 +17,19 @@ from pydantic_ai import ( ) from pydantic_ai.messages import ModelMessage -from haiku.rag.agents.chat.agent import create_chat_agent +from haiku.rag.agents.chat.agent import ( + ChatDeps, + create_chat_agent, + trigger_background_summarization, +) from haiku.rag.agents.chat.state import ( AGUI_STATE_KEY, - ChatDeps, ChatSessionState, ) from haiku.rag.client import HaikuRAG from haiku.rag.config import get_config +from haiku.rag.tools.context import ToolContext +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState if TYPE_CHECKING: from textual.app import ComposeResult @@ -153,8 +158,16 @@ class ChatApp(App): ) await self.client.__aenter__() - # Create agent and session state - self.agent = create_chat_agent(self.config) + # Create tool context and agent + self.tool_context = ToolContext() + self.agent = create_chat_agent(self.config, self.client, self.tool_context) + + # Initialize session state in tool context + session_state = self.tool_context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None: + session_state.document_filter = self._document_filter + + # Keep ChatSessionState for UI state sync (used by _sync_session_state) self.session_state = ChatSessionState( session_id=str(uuid.uuid4()), initial_context=self._initial_context, @@ -170,8 +183,40 @@ class ChatApp(App): await self.client.__aexit__(None, None, None) def _sync_session_state(self, chat_state: dict[str, Any]) -> None: - """Sync session_state from AG-UI state.""" - self.session_state = ChatSessionState.model_validate(chat_state) + """Sync session_state from AG-UI state. + + Updates existing session_state with fields from incoming state, + preserving fields not present in the update (e.g., initial_context). + """ + from haiku.rag.agents.research.models import Citation + + # Update specific fields rather than replacing the entire state + if "session_id" in chat_state: + self.session_state.session_id = chat_state["session_id"] + if "document_filter" in chat_state: + self.session_state.document_filter = chat_state["document_filter"] + if "citation_registry" in chat_state: + self.session_state.citation_registry = chat_state["citation_registry"] + if "citations" in chat_state: + self.session_state.citations = [ + Citation(**c) if isinstance(c, dict) else c + for c in chat_state["citations"] + ] + if "qa_history" in chat_state: + from haiku.rag.agents.chat.state import QAResponse + + self.session_state.qa_history = [ + QAResponse(**qa) if isinstance(qa, dict) else qa + for qa in chat_state["qa_history"] + ] + if "session_context" in chat_state: + from haiku.rag.agents.chat.state import SessionContext + + ctx = chat_state["session_context"] + if ctx is not None: + self.session_state.session_context = ( + SessionContext(**ctx) if isinstance(ctx, dict) else ctx + ) async def _handle_stream_event(self, event: AgentStreamEvent) -> None: """Handle streaming events from the agent.""" @@ -274,10 +319,33 @@ class ChatApp(App): AGUI_STATE_KEY: self.session_state.model_dump(mode="json") } + # Sync session state to tool context before running + session_state = self.tool_context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None: + session_state.session_id = self.session_state.session_id + session_state.document_filter = self.session_state.document_filter + session_state.citation_registry = self.session_state.citation_registry + session_state.citations = list(self.session_state.citations) + + # Sync initial_context to QA session state + from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState + + qa_session_state = self.tool_context.get_typed( + QA_SESSION_NAMESPACE, QASessionState + ) + if qa_session_state is not None: + if ( + not qa_session_state.session_context + and self.session_state.initial_context + ): + qa_session_state.session_context = ( + self.session_state.initial_context + ) + deps = ChatDeps( - client=self.client, config=self.config, - session_state=self.session_state, + tool_context=self.tool_context, + session_id=self.session_state.session_id, state_key=AGUI_STATE_KEY, ) @@ -307,6 +375,23 @@ class ChatApp(App): if self.session_state.citations: await chat_history.add_citations(self.session_state.citations) + # Trigger background summarization + trigger_background_summarization(deps) + + # Sync session context from QASessionState to ChatSessionState for modal + qa_session_state = self.tool_context.get_typed( + QA_SESSION_NAMESPACE, QASessionState + ) + if qa_session_state is not None and qa_session_state.session_context: + from datetime import datetime + + from haiku.rag.agents.chat.state import SessionContext + + self.session_state.session_context = SessionContext( + summary=qa_session_state.session_context, + last_updated=datetime.now(), + ) + except asyncio.CancelledError: chat_history.hide_thinking() await chat_history.add_message("assistant", "*Cancelled*") @@ -383,6 +468,21 @@ class ChatApp(App): 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 + + # Sync session context from QASessionState before showing modal + qa_session_state = self.tool_context.get_typed( + QA_SESSION_NAMESPACE, QASessionState + ) + if qa_session_state is not None and qa_session_state.session_context: + from datetime import datetime + + from haiku.rag.agents.chat.state import SessionContext + + self.session_state.session_context = SessionContext( + summary=qa_session_state.session_context, + last_updated=datetime.now(), + ) await self.push_screen( ContextModal(self.session_state, is_locked=self._context_locked) diff --git a/haiku_rag_slim/haiku/rag/tools/__init__.py b/haiku_rag_slim/haiku/rag/tools/__init__.py index c8e1cb59..d2a89bd2 100644 --- a/haiku_rag_slim/haiku/rag/tools/__init__.py +++ b/haiku_rag_slim/haiku/rag/tools/__init__.py @@ -18,8 +18,20 @@ from haiku.rag.tools.filters import ( combine_filters, ) from haiku.rag.tools.models import AnalysisResult, QAResult -from haiku.rag.tools.qa import QA_NAMESPACE, QAState, create_qa_toolset +from haiku.rag.tools.qa import ( + QA_NAMESPACE, + QA_SESSION_NAMESPACE, + QAHistoryEntry, + QASessionState, + QAState, + create_qa_toolset, +) from haiku.rag.tools.search import SEARCH_NAMESPACE, SearchState, create_search_toolset +from haiku.rag.tools.session import ( + SESSION_NAMESPACE, + SessionState, + compute_state_delta, +) __all__ = [ "ToolContext", @@ -38,9 +50,15 @@ __all__ = [ "create_document_toolset", "find_document", "QA_NAMESPACE", + "QA_SESSION_NAMESPACE", "QAState", + "QASessionState", + "QAHistoryEntry", "create_qa_toolset", "ANALYSIS_NAMESPACE", "AnalysisState", "create_analysis_toolset", + "SESSION_NAMESPACE", + "SessionState", + "compute_state_delta", ] diff --git a/haiku_rag_slim/haiku/rag/tools/analysis.py b/haiku_rag_slim/haiku/rag/tools/analysis.py index 26258cec..e95386c8 100644 --- a/haiku_rag_slim/haiku/rag/tools/analysis.py +++ b/haiku_rag_slim/haiku/rag/tools/analysis.py @@ -7,8 +7,13 @@ from haiku.rag.agents.rlm.models import CodeExecution from haiku.rag.client import HaikuRAG from haiku.rag.config.models import AppConfig from haiku.rag.tools.context import ToolContext -from haiku.rag.tools.filters import build_document_filter, combine_filters +from haiku.rag.tools.filters import ( + build_document_filter, + build_multi_document_filter, + combine_filters, +) from haiku.rag.tools.models import AnalysisResult +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState ANALYSIS_NAMESPACE = "haiku.rag.analysis" @@ -36,6 +41,8 @@ def create_analysis_toolset( config: Application configuration. context: Optional ToolContext for state accumulation. If provided, code executions are tracked in AnalysisState. + If SessionState is registered, it will be used for dynamic + document filtering. base_filter: Optional base SQL WHERE clause applied to searches. tool_name: Name for the analyze tool. Defaults to "analyze". @@ -63,9 +70,20 @@ def create_analysis_toolset( Returns: AnalysisResult with answer and execution metadata. """ - # Build filter from base_filter and document_name + # Get session filter from session state + session_filter = None + if context is not None: + session_state = context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None and session_state.document_filter: + session_filter = build_multi_document_filter( + session_state.document_filter + ) + + # Build filter from base_filter, session_filter, and document_name doc_filter = build_document_filter(document_name) if document_name else None - effective_filter = combine_filters(base_filter, doc_filter) + effective_filter = combine_filters( + combine_filters(base_filter, session_filter), doc_filter + ) # Create RLM context and deps 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 450800e8..1e4fdc0c 100644 --- a/haiku_rag_slim/haiku/rag/tools/context.py +++ b/haiku_rag_slim/haiku/rag/tools/context.py @@ -62,6 +62,16 @@ class ToolContext(BaseModel): """Get state for a namespace, or None if not registered.""" return self._namespaces.get(namespace) + def get_typed(self, namespace: str, expected_type: type[T]) -> T | None: + """Get state for a namespace with type checking. + + Returns the state cast to expected_type if it matches, None otherwise. + """ + state = self._namespaces.get(namespace) + if isinstance(state, expected_type): + return state + return None + def get_or_create(self, namespace: str, factory: Callable[[], T]) -> T: """Get state for a namespace, creating it if not registered. diff --git a/haiku_rag_slim/haiku/rag/tools/document.py b/haiku_rag_slim/haiku/rag/tools/document.py index f798a1b8..c96a6d0d 100644 --- a/haiku_rag_slim/haiku/rag/tools/document.py +++ b/haiku_rag_slim/haiku/rag/tools/document.py @@ -4,6 +4,8 @@ from pydantic_ai import Agent, FunctionToolset from haiku.rag.client import HaikuRAG from haiku.rag.config.models import AppConfig from haiku.rag.tools.context import ToolContext +from haiku.rag.tools.filters import build_multi_document_filter, combine_filters +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState from haiku.rag.utils import get_model DOCUMENT_NAMESPACE = "haiku.rag.document" @@ -91,6 +93,8 @@ def create_document_toolset( config: Application configuration (used for summarization LLM). context: Optional ToolContext for state tracking. If provided, accessed documents are tracked in DocumentState. + If SessionState is registered, it will be used for dynamic + document filtering. base_filter: Optional base SQL WHERE clause applied to list operations. Returns: @@ -113,10 +117,21 @@ def create_document_toolset( page_size = 50 offset = (page - 1) * page_size + # Get session filter from session state + session_filter = None + if context is not None: + session_state = context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None and session_state.document_filter: + session_filter = build_multi_document_filter( + session_state.document_filter + ) + + effective_filter = combine_filters(base_filter, session_filter) + docs = await client.list_documents( - limit=page_size, offset=offset, filter=base_filter + limit=page_size, offset=offset, filter=effective_filter ) - total = await client.count_documents(filter=base_filter) + total = await client.count_documents(filter=effective_filter) total_pages = (total + page_size - 1) // page_size if total > 0 else 1 return DocumentListResponse( diff --git a/haiku_rag_slim/haiku/rag/tools/qa.py b/haiku_rag_slim/haiku/rag/tools/qa.py index 9161111e..ac899ef8 100644 --- a/haiku_rag_slim/haiku/rag/tools/qa.py +++ b/haiku_rag_slim/haiku/rag/tools/qa.py @@ -1,18 +1,64 @@ -from pydantic import BaseModel -from pydantic_ai import FunctionToolset +import math +from pydantic import BaseModel, Field +from pydantic_ai import FunctionToolset, ToolReturn + +from haiku.rag.agents.chat.context import ( + cache_question_embedding, + get_cached_embedding, + trigger_background_summarization, +) +from haiku.rag.agents.chat.state import SessionContext 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 ToolContext from haiku.rag.tools.filters import build_document_filter, combine_filters from haiku.rag.tools.models import QAResult +from haiku.rag.tools.session import ( + SESSION_NAMESPACE, + SessionState, + compute_combined_state_delta, +) QA_NAMESPACE = "haiku.rag.qa" +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] = [] + question_embedding: list[float] | None = Field(default=None, exclude=True) + + def to_search_answer(self) -> SearchAnswer: + """Convert to SearchAnswer for research graph context.""" + return SearchAnswer( + query=self.question, + answer=self.answer, + confidence=self.confidence, + cited_chunks=[c.chunk_id for c in self.citations], + citations=self.citations, + ) + class QAState(BaseModel): """State for QA toolset. @@ -23,6 +69,20 @@ class QAState(BaseModel): history: list[QAResult] = [] +class QASessionState(BaseModel): + """Extended session state for QA with embedding cache.""" + + qa_history: list[QAHistoryEntry] = [] + session_context: str | None = None + incoming_session_context: SessionContext | None = Field( + default=None, exclude=True + ) # Track what client sent + + +QA_SESSION_NAMESPACE = "haiku.rag.qa_session" +MAX_QA_HISTORY = 50 + + def create_qa_toolset( client: HaikuRAG, config: AppConfig, @@ -39,10 +99,14 @@ def create_qa_toolset( config: Application configuration. context: Optional ToolContext for state accumulation. If provided, Q&A results are accumulated in QAState. + If SessionState is registered, it will be used for dynamic + document filtering and citation indexing. base_filter: Optional base SQL WHERE clause applied to searches. tool_name: Name for the ask tool. Defaults to "ask". session_context: Optional session context for the research graph. + Overridden by QASessionState.session_context if available. prior_answers: Optional list of prior answers for context. + Overridden by similarity-matched answers from QASessionState if available. Returns: FunctionToolset with an ask tool. @@ -55,7 +119,7 @@ def create_qa_toolset( async def ask( question: str, document_name: str | None = None, - ) -> QAResult: + ) -> ToolReturn | QAResult: """Answer a question using the knowledge base. Uses a research graph for searching and synthesizing answers. @@ -67,17 +131,110 @@ def create_qa_toolset( Returns: QAResult with answer, confidence, and citations. """ - # Build filter from base_filter and document_name + # Get session states + session_state: SessionState | None = None + qa_session_state: QASessionState | None = None + old_state_snapshot: dict | None = None + + if context is not None: + session_state = context.get_typed(SESSION_NAMESPACE, SessionState) + qa_session_state = context.get_typed(QA_SESSION_NAMESPACE, QASessionState) + + # Capture combined state snapshot before changes + # Use incoming values (what client sent) so delta shows server-side updates + if session_state is not None: + old_state_snapshot = { + "session_id": session_state.incoming_session_id, + "document_filter": session_state.document_filter.copy(), + "citation_registry": session_state.citation_registry.copy(), + "citations": [c.model_dump() for c in session_state.citations], + } + if qa_session_state is not None: + old_state_snapshot["qa_history"] = [ + qa.model_dump() for qa in qa_session_state.qa_history + ] + # Use incoming_session_context so delta shows what client sent + if qa_session_state.incoming_session_context is not None: + old_state_snapshot["session_context"] = ( + qa_session_state.incoming_session_context.model_dump( + mode="json" + ) + ) + else: + old_state_snapshot["session_context"] = None + + # Build filter from session state, base_filter, and document_name + session_filter = None + if session_state is not None and session_state.document_filter: + from haiku.rag.tools.filters import build_multi_document_filter + + session_filter = build_multi_document_filter(session_state.document_filter) + doc_filter = build_document_filter(document_name) if document_name else None - effective_filter = combine_filters(base_filter, doc_filter) + effective_filter = combine_filters( + combine_filters(base_filter, session_filter), doc_filter + ) + + # Determine session context + effective_session_context = session_context + if qa_session_state is not None and qa_session_state.session_context: + effective_session_context = qa_session_state.session_context + + # Find relevant prior answers via similarity matching + effective_prior_answers = prior_answers or [] + session_id = session_state.session_id if session_state is not None else "" + if qa_session_state is not None and qa_session_state.qa_history: + embedder = get_embedder(config) + question_embedding = await embedder.embed_query(question) + + # Collect questions that need embedding + to_embed = [] + to_embed_indices = [] + for i, qa in enumerate(qa_session_state.qa_history): + if qa.question_embedding is None: + # Check per-session cache first + if session_id: + cached = get_cached_embedding(session_id, qa.question) + if cached: + qa.question_embedding = cached + continue + to_embed.append(qa.question) + to_embed_indices.append(i) + + # Batch embed uncached questions + if to_embed: + new_embeddings = await embedder.embed_documents(to_embed) + for i, idx in enumerate(to_embed_indices): + embedding = new_embeddings[i] + qa_session_state.qa_history[idx].question_embedding = embedding + # Cache per-session for next request + if session_id: + cache_question_embedding( + session_id, + qa_session_state.qa_history[idx].question, + embedding, + ) + + # Find similar prior answers + 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 # Build and run the research graph graph = build_research_graph(config=config, output_mode="conversational") research_context = ResearchContext( original_question=question, - session_context=session_context, - qa_responses=prior_answers or [], + session_context=effective_session_context, + qa_responses=effective_prior_answers, ) research_state = ResearchState( context=research_context, @@ -89,20 +246,26 @@ def create_qa_toolset( result = await graph.run(state=research_state, deps=deps) - # Convert to QAResult - citations = [ - Citation( - index=i + 1, - 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, + # 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, + ) ) - for i, c in enumerate(result.citations) - ] qa_result = QAResult( question=question, @@ -111,10 +274,71 @@ def create_qa_toolset( citations=citations, ) - # Accumulate in state if context provided + # Accumulate in QA state if context provided if state is not None: state.history.append(qa_result) + # Update session state with citations + if session_state is not None: + session_state.citations = citations + + # Update QA session state with history entry + 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: + ] + # Trigger background summarization + trigger_background_summarization( + qa_session_state=qa_session_state, + config=config, + session_id=session_id, + ) + + # Compute and return state delta if session state changed + if session_state is not None and old_state_snapshot is not None: + # Build new combined state snapshot + new_state_snapshot = { + "session_id": session_state.session_id, + "document_filter": session_state.document_filter, + "citation_registry": session_state.citation_registry, + "citations": [c.model_dump() for c in session_state.citations], + } + if qa_session_state is not None: + new_state_snapshot["qa_history"] = [ + qa.model_dump() for qa in qa_session_state.qa_history + ] + if qa_session_state.session_context: + new_state_snapshot["session_context"] = SessionContext( + summary=qa_session_state.session_context + ).model_dump(mode="json") + else: + new_state_snapshot["session_context"] = None + + state_event = compute_combined_state_delta( + old_state_snapshot, + new_state_snapshot, + state_key=session_state.state_key, + ) + + # Format answer with citation references + answer_text = result.answer + if citations: + citation_refs = " ".join(f"[{c.index}]" for c in citations) + answer_text = f"{answer_text}\n\nSources: {citation_refs}" + + metadata = [state_event] if state_event is not None else None + return ToolReturn(return_value=answer_text, metadata=metadata) + return qa_result toolset = FunctionToolset() diff --git a/haiku_rag_slim/haiku/rag/tools/search.py b/haiku_rag_slim/haiku/rag/tools/search.py index 44945ac9..2e9ba7c2 100644 --- a/haiku_rag_slim/haiku/rag/tools/search.py +++ b/haiku_rag_slim/haiku/rag/tools/search.py @@ -1,11 +1,13 @@ from pydantic import BaseModel -from pydantic_ai import FunctionToolset +from pydantic_ai import FunctionToolset, ToolReturn +from haiku.rag.agents.research.models import Citation from haiku.rag.client import HaikuRAG from haiku.rag.config.models import AppConfig from haiku.rag.store.models import SearchResult from haiku.rag.tools.context import ToolContext -from haiku.rag.tools.filters import combine_filters +from haiku.rag.tools.filters import build_multi_document_filter, combine_filters +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState, compute_state_delta SEARCH_NAMESPACE = "haiku.rag.search" @@ -34,6 +36,8 @@ def create_search_toolset( config: Application configuration. context: Optional ToolContext for state accumulation. If provided, search results are accumulated in SearchState. + If SessionState is registered, it will be used for dynamic + document filtering and citation indexing. expand_context: Whether to expand search results with surrounding context. Defaults to True. base_filter: Optional base SQL WHERE clause applied to all searches. @@ -44,15 +48,15 @@ def create_search_toolset( FunctionToolset with a search tool. """ # Get or create search state if context provided - state: SearchState | None = None + search_state: SearchState | None = None if context is not None: - state = context.get_or_create(SEARCH_NAMESPACE, SearchState) + search_state = context.get_or_create(SEARCH_NAMESPACE, SearchState) async def search( query: str, limit: int | None = None, filter: str | None = None, - ) -> str: + ) -> ToolReturn | str: """Search the knowledge base for relevant documents. Args: @@ -63,8 +67,25 @@ def create_search_toolset( Returns: Formatted search results with content and metadata. """ + # Get session state for dynamic filters and citation indexing + session_state: SessionState | None = None + old_session_state: SessionState | None = None + if context is not None: + session_state = context.get_typed(SESSION_NAMESPACE, SessionState) + if session_state is not None: + old_session_state = session_state.model_copy(deep=True) + + # Build session filter from session state's document_filter + session_filter = None + if session_state is not None and session_state.document_filter: + session_filter = build_multi_document_filter(session_state.document_filter) + + # Combine all filters: base_filter AND session_filter AND tool filter + effective_filter = combine_filters( + combine_filters(base_filter, session_filter), filter + ) + effective_limit = limit or config.search.limit - effective_filter = combine_filters(base_filter, filter) results = await client.search( query, limit=effective_limit, filter=effective_filter ) @@ -72,14 +93,64 @@ def create_search_toolset( if expand_context: results = await client.expand_context(results) - # Accumulate results in state if context provided - if state is not None: - state.results.extend(results) + # Accumulate results in search state if context provided + if search_state is not None: + search_state.results.extend(results) if not results: return "No results found." - # Format results for agent context + # Build citations if session state is available + 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: + 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 + + # Format results with citation indices + 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: + 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) + + # Compute state delta if session state changed + if old_session_state is not None: + state_event = compute_state_delta(old_session_state, session_state) + if state_event is not None: + return ToolReturn( + return_value=formatted, + metadata=[state_event], + ) + + return formatted + + # Format results without citation indexing (standalone use) total = len(results) formatted = [ r.format_for_agent(rank=i + 1, total=total) for i, r in enumerate(results) diff --git a/haiku_rag_slim/haiku/rag/tools/session.py b/haiku_rag_slim/haiku/rag/tools/session.py new file mode 100644 index 00000000..4777a59c --- /dev/null +++ b/haiku_rag_slim/haiku/rag/tools/session.py @@ -0,0 +1,89 @@ +from typing import Any + +import jsonpatch +from ag_ui.core import EventType, StateDeltaEvent +from pydantic import BaseModel, Field + +from haiku.rag.agents.research.models import Citation + +SESSION_NAMESPACE = "haiku.rag.session" + + +class SessionState(BaseModel): + """Session-level state for AG-UI integration. + + This state is shared across toolsets and enables: + - Session identification + - Dynamic document filtering + - Stable citation indices across tool calls + - AG-UI state synchronization + """ + + session_id: str = "" + incoming_session_id: str = Field(default="", exclude=True) # Track what client sent + document_filter: list[str] = [] + citation_registry: dict[str, int] = {} + citations: list[Citation] = [] + state_key: str | None = Field(default=None, exclude=True) + + 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, +) -> StateDeltaEvent | None: + """Compute state delta between old and new session state. + + Returns a StateDeltaEvent if there are changes, None otherwise. + The state_key from new_state is used for namespacing. + """ + return compute_combined_state_delta( + old_state.model_dump(mode="json"), + new_state.model_dump(mode="json"), + state_key=new_state.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/tests/agents/chat/test_chat_agent.py b/tests/agents/chat/test_chat_agent.py index de1f4989..c53c91b5 100644 --- a/tests/agents/chat/test_chat_agent.py +++ b/tests/agents/chat/test_chat_agent.py @@ -9,6 +9,7 @@ from haiku.rag.agents.chat import ( ChatSessionState, QAResponse, SearchAgent, + ToolContext, create_chat_agent, ) from haiku.rag.agents.chat.context import get_cached_session_context @@ -16,6 +17,7 @@ from haiku.rag.agents.chat.state import MAX_QA_HISTORY from haiku.rag.agents.research.models import Citation from haiku.rag.client import HaikuRAG from haiku.rag.config import Config +from haiku.rag.tools.session import SESSION_NAMESPACE, SessionState def extract_state_from_result(result, state_key: str = AGUI_STATE_KEY) -> dict | None: @@ -49,26 +51,25 @@ def vcr_cassette_dir(): return str(Path(__file__).parent.parent.parent / "cassettes" / "test_chat_agent") -def test_create_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) + client = HaikuRAG(temp_db_path, create=True) + context = ToolContext() + agent = create_chat_agent(Config, client, context) assert agent is not None assert agent.name == "chat_agent" or agent.name is None + client.close() def test_chat_deps_initialization(temp_db_path): """Test ChatDeps can be initialized with required fields.""" - client = HaikuRAG(temp_db_path, create=True) - deps = ChatDeps(client=client, config=Config) + context = ToolContext() + deps = ChatDeps(config=Config, tool_context=context) - assert deps.client is client assert deps.config is Config - assert deps.search_results is None - assert deps.session_state is not None - assert deps.session_state.qa_history == [] - assert deps.session_state.citations == [] - - client.close() + assert deps.tool_context is context + assert deps.session_id == "" + assert deps.state_key is None def test_agui_state_key_constant(): @@ -76,26 +77,126 @@ def test_agui_state_key_constant(): assert AGUI_STATE_KEY == "haiku.rag.chat" -def test_chat_deps_with_state_key(temp_db_path): +def test_chat_deps_with_state_key(): """Test ChatDeps can be initialized with state_key for keyed state emission.""" - client = HaikuRAG(temp_db_path, create=True) - deps = ChatDeps(client=client, config=Config, state_key="my_state") + context = ToolContext() + deps = ChatDeps(config=Config, tool_context=context, state_key="my_state") - assert deps.client is client assert deps.config is Config assert deps.state_key == "my_state" - client.close() - -def test_chat_deps_state_key_default_none(temp_db_path): +def test_chat_deps_state_key_default_none(): """Test ChatDeps state_key defaults to None.""" - client = HaikuRAG(temp_db_path, create=True) - deps = ChatDeps(client=client, config=Config) + context = ToolContext() + deps = ChatDeps(config=Config, tool_context=context) assert deps.state_key is None - client.close() + +def test_chat_deps_state_setter_handles_initial_context(): + """Test ChatDeps.state setter transfers initial_context to qa_session_state.""" + from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState + + context = ToolContext() + # Register QASessionState (normally done by create_chat_agent) + context.register(QA_SESSION_NAMESPACE, QASessionState()) + context.register(SESSION_NAMESPACE, SessionState()) + + deps = ChatDeps(config=Config, tool_context=context, state_key=AGUI_STATE_KEY) + + # Client sends initial_context with no session_context + incoming_state = { + AGUI_STATE_KEY: { + "session_id": "", + "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 == "Background info about the project" + + +def test_chat_deps_state_setter_parses_session_context_dict(): + """Test ChatDeps.state setter parses session_context dict into SessionContext model.""" + from haiku.rag.agents.chat.state import SessionContext + from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState + + context = ToolContext() + context.register(QA_SESSION_NAMESPACE, QASessionState()) + context.register(SESSION_NAMESPACE, SessionState()) + + deps = ChatDeps(config=Config, tool_context=context, state_key=AGUI_STATE_KEY) + + # Client sends session_context as a dict (as it comes from JSON) + incoming_state = { + AGUI_STATE_KEY: { + "session_id": "test-session", + "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 model + qa_session_state = context.get(QA_SESSION_NAMESPACE) + assert isinstance(qa_session_state, QASessionState) + assert qa_session_state.session_context == "Previous conversation summary" + assert isinstance(qa_session_state.incoming_session_context, SessionContext) + assert ( + qa_session_state.incoming_session_context.summary + == "Previous conversation summary" + ) + + +def test_chat_deps_state_setter_generates_session_id(): + """Test ChatDeps.state setter generates session_id if client sends empty.""" + from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState + + context = ToolContext() + context.register(QA_SESSION_NAMESPACE, QASessionState()) + context.register(SESSION_NAMESPACE, SessionState()) + + deps = ChatDeps(config=Config, tool_context=context, state_key=AGUI_STATE_KEY) + + # Client sends empty session_id + incoming_state = { + AGUI_STATE_KEY: { + "session_id": "", + "session_context": None, + "qa_history": [], + "citations": [], + "document_filter": [], + "citation_registry": {}, + } + } + + deps.state = incoming_state + + # session_id should be generated (UUID format) + assert deps.session_id != "" + assert len(deps.session_id) == 36 # UUID length with dashes + + # Should also be synced to SessionState + session_state = context.get(SESSION_NAMESPACE) + assert isinstance(session_state, SessionState) + assert session_state.session_id == deps.session_id def test_chat_session_state(): @@ -263,12 +364,12 @@ async def test_chat_agent_search_tool(allow_model_requests, temp_db_path): title="DocLayNet Annotation", ) - agent = create_chat_agent(Config) - session_state = ChatSessionState(session_id="test-search") + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, - session_state=session_state, + tool_context=context, + session_id="test-search", ) # Ask something that should trigger the search tool @@ -298,12 +399,12 @@ async def test_chat_agent_search_tool_with_filter(allow_model_requests, temp_db_ title="DocLayNet Sources", ) - agent = create_chat_agent(Config) - session_state = ChatSessionState(session_id="test-search-filter") + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, - session_state=session_state, + tool_context=context, + session_id="test-search-filter", ) # Ask to search within a specific document @@ -327,10 +428,11 @@ async def test_chat_agent_get_document_tool(allow_model_requests, temp_db_path): title="DocLayNet Class Labels", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask to get a specific document @@ -349,10 +451,11 @@ async def test_chat_agent_get_document_tool(allow_model_requests, temp_db_path): 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: - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask for a document that doesn't exist @@ -469,7 +572,7 @@ async def test_search_agent_no_results(allow_model_requests, temp_db_path): @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 adds citations to the response.""" + """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( @@ -478,14 +581,15 @@ async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path) title="DocLayNet Class Labels", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, state_key=AGUI_STATE_KEY, ) - # Ask a question that should use the ask tool with citations + # 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, @@ -493,11 +597,20 @@ async def test_chat_agent_ask_adds_citations(allow_model_requests, temp_db_path) assert result.output is not None - # Extract emitted state from result metadata - emitted_state = extract_state_from_result(result) - assert emitted_state is not None - # The qa_history should have been updated with the new Q&A - assert len(emitted_state.get("qa_history", [])) >= 1 + # 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 @@ -508,6 +621,8 @@ async def test_chat_agent_ask_triggers_background_summarization( """Test that the ask tool triggers background session context summarization.""" import asyncio + from haiku.rag.agents.chat.agent import run_chat_agent + async with HaikuRAG(temp_db_path, create=True) as client: await client.create_document( content=DOCLAYNET_CLASS_LABELS, @@ -515,30 +630,27 @@ async def test_chat_agent_ask_triggers_background_summarization( title="DocLayNet Class Labels", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, + session_id="test-summarization", state_key=AGUI_STATE_KEY, ) - # Ask a question - result = await agent.run( + # Ask a question using run_chat_agent to trigger background summarization + result = await run_chat_agent( + agent, + deps, "What is the highest count class in the DocLayNet dataset?", - deps=deps, ) - assert result.output is not None - - # Extract emitted state to get the session_id - emitted_state = extract_state_from_result(result) - assert emitted_state is not None - session_id = emitted_state.get("session_id") - assert session_id is not None - assert len(emitted_state.get("qa_history", [])) >= 1 + assert result is not None # Wait for background task to complete # The task caches session_context server-side + session_id = deps.session_id cached_context = None for _ in range(50): # Wait up to 5 seconds cached_context = get_cached_session_context(session_id) @@ -559,12 +671,10 @@ async def test_chat_agent_ask_with_prior_answer_retrieval( ): """Test that ask tool retrieves relevant prior answers from qa_history. - This exercises the prior answer retrieval logic (agent.py lines 231-257): + 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 """ - import asyncio - async with HaikuRAG(temp_db_path, create=True) as client: await client.create_document( content=DOCLAYNET_CLASS_LABELS, @@ -572,10 +682,11 @@ async def test_chat_agent_ask_with_prior_answer_retrieval( title="DocLayNet Class Labels", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps1 = ChatDeps( - client=client, config=Config, + tool_context=context, state_key=AGUI_STATE_KEY, ) @@ -586,47 +697,26 @@ async def test_chat_agent_ask_with_prior_answer_retrieval( ) assert result1.output is not None - # Extract emitted state from first call - state1 = extract_state_from_result(result1) - assert state1 is not None - assert len(state1.get("qa_history", [])) == 1 - session_id = state1.get("session_id") - assert session_id is not None - - # Wait for background summarization to complete - for _ in range(50): - if get_cached_session_context(session_id) is not None: - break - await asyncio.sleep(0.1) - - # Create new session state from emitted state for second call - # (simulating client sending state back to server) - session_state2 = ChatSessionState( - session_id=session_id, - qa_history=[QAResponse(**qa) for qa in state1.get("qa_history", [])], - citation_registry=state1.get("citation_registry", {}), - ) - deps2 = ChatDeps( - client=client, - config=Config, - session_state=session_state2, - state_key=AGUI_STATE_KEY, - ) + # 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 - # This will embed the first question and compare similarity result2 = await agent.run( "Tell me about DocLayNet class labels", - deps=deps2, + deps=deps1, ) assert result2.output is not None - # The important thing is that prior answer retrieval happened - # We can verify this by checking session_state2 was used (embedding added) - assert session_state2.qa_history[0].question_embedding is not None - # Embedding should be a list of floats - assert isinstance(session_state2.qa_history[0].question_embedding, list) - assert len(session_state2.qa_history[0].question_embedding) > 0 + # 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(): @@ -697,16 +787,18 @@ async def test_chat_agent_search_with_session_filter( title="DocLayNet Sources", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) + # Set session filter to only include the labels document - session_state = ChatSessionState( - session_id="test-session-filter", - document_filter=["DocLayNet Class Labels"], - ) + session_state = context.get(SESSION_NAMESPACE) + assert isinstance(session_state, SessionState) + session_state.document_filter = ["DocLayNet Class Labels"] + deps = ChatDeps( - client=client, config=Config, - session_state=session_state, + tool_context=context, + session_id="test-session-filter", ) # Search should only return results from the filtered document @@ -716,8 +808,15 @@ async def test_chat_agent_search_with_session_filter( ) assert result.output is not None - # Results should only reference the Labels document, not Sources - assert "Labels" in result.output or "class" in result.output.lower() + + # 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 "" + ) @pytest.mark.asyncio @@ -861,7 +960,7 @@ def test_search_tool_citation_registry_logic(): def test_cosine_similarity_identical_vectors(): """Test cosine similarity returns 1.0 for identical vectors.""" - from haiku.rag.agents.chat.agent import _cosine_similarity + from haiku.rag.tools.qa import _cosine_similarity vec = [1.0, 2.0, 3.0] assert _cosine_similarity(vec, vec) == pytest.approx(1.0) @@ -869,7 +968,7 @@ def test_cosine_similarity_identical_vectors(): def test_cosine_similarity_orthogonal_vectors(): """Test cosine similarity returns 0.0 for orthogonal vectors.""" - from haiku.rag.agents.chat.agent import _cosine_similarity + from haiku.rag.tools.qa import _cosine_similarity vec1 = [1.0, 0.0, 0.0] vec2 = [0.0, 1.0, 0.0] @@ -878,7 +977,7 @@ def test_cosine_similarity_orthogonal_vectors(): def test_cosine_similarity_opposite_vectors(): """Test cosine similarity returns -1.0 for opposite vectors.""" - from haiku.rag.agents.chat.agent import _cosine_similarity + from haiku.rag.tools.qa import _cosine_similarity vec1 = [1.0, 2.0, 3.0] vec2 = [-1.0, -2.0, -3.0] @@ -887,7 +986,7 @@ def test_cosine_similarity_opposite_vectors(): def test_cosine_similarity_zero_vector(): """Test cosine similarity handles zero vectors gracefully.""" - from haiku.rag.agents.chat.agent import _cosine_similarity + from haiku.rag.tools.qa import _cosine_similarity vec = [1.0, 2.0, 3.0] zero = [0.0, 0.0, 0.0] @@ -898,14 +997,14 @@ def test_cosine_similarity_zero_vector(): def test_prior_answer_relevance_threshold_constant(): """Test PRIOR_ANSWER_RELEVANCE_THRESHOLD is set to expected value.""" - from haiku.rag.agents.chat.agent import PRIOR_ANSWER_RELEVANCE_THRESHOLD + 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.agents.chat.agent import ( + from haiku.rag.tools.qa import ( PRIOR_ANSWER_RELEVANCE_THRESHOLD, _cosine_similarity, ) @@ -920,7 +1019,7 @@ def test_prior_answer_matching_above_threshold(): def test_prior_answer_matching_below_threshold(): """Test that dissimilar questions (below threshold) are not matched.""" - from haiku.rag.agents.chat.agent import ( + from haiku.rag.tools.qa import ( PRIOR_ANSWER_RELEVANCE_THRESHOLD, _cosine_similarity, ) @@ -970,7 +1069,7 @@ async def test_summarization_task_cancellation(): """Test that new summarization tasks cancel previous ones for same session.""" import asyncio - from haiku.rag.agents.chat.agent import _summarization_tasks + from haiku.rag.agents.chat.context import _summarization_tasks # Clear any existing tasks _summarization_tasks.clear() @@ -1033,10 +1132,11 @@ async def test_list_documents_basic(allow_model_requests, temp_db_path): title="DocLayNet Annotation", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask to list documents @@ -1067,16 +1167,17 @@ async def test_list_documents_with_session_filter(allow_model_requests, temp_db_ title="DocLayNet Sources", ) - agent = create_chat_agent(Config) # Set session filter to only include the labels document - session_state = ChatSessionState( - session_id="test-list-filter", - document_filter=["DocLayNet Class Labels"], + context = ToolContext() + context.register( + SESSION_NAMESPACE, + SessionState(document_filter=["DocLayNet Class Labels"]), ) + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, - session_state=session_state, + tool_context=context, + session_id="test-list-filter", ) # Ask to list documents - should only show filtered documents @@ -1112,10 +1213,11 @@ async def test_list_documents_pagination(allow_model_requests, temp_db_path): title="DocLayNet Sources", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask to list first 2 documents @@ -1144,10 +1246,11 @@ async def test_summarize_document_found(allow_model_requests, temp_db_path): title="DocLayNet Class Labels", ) - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask to summarize a specific document @@ -1166,10 +1269,11 @@ async def test_summarize_document_found(allow_model_requests, temp_db_path): 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: - agent = create_chat_agent(Config) + context = ToolContext() + agent = create_chat_agent(Config, client, context) deps = ChatDeps( - client=client, config=Config, + tool_context=context, ) # Ask to summarize a document that doesn't exist @@ -1246,7 +1350,7 @@ 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.agent import _summarization_tasks + from haiku.rag.agents.chat.context import _summarization_tasks _summarization_tasks.clear() diff --git a/tests/agents/chat/test_context.py b/tests/agents/chat/test_context.py index 0d7ffb9b..96830b0e 100644 --- a/tests/agents/chat/test_context.py +++ b/tests/agents/chat/test_context.py @@ -261,13 +261,13 @@ class TestSessionContextCache: def test_cache_and_retrieve_session_context(self): """Test caching and retrieving a session context.""" from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, cache_session_context, get_cached_session_context, ) # Clear cache - _session_context_cache.clear() + _session_cache.clear() now = datetime.now() ctx = SessionContext(summary="Test summary", last_updated=now) @@ -282,11 +282,11 @@ class TestSessionContextCache: def test_get_cached_session_context_returns_none_when_not_cached(self): """Test get_cached_session_context returns None when nothing cached.""" from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, get_cached_session_context, ) - _session_context_cache.clear() + _session_cache.clear() result = get_cached_session_context("nonexistent-session") assert result is None @@ -298,12 +298,12 @@ class TestSessionContextCache: from haiku.rag.agents.chat.context import ( _CACHE_TTL, _cache_timestamps, - _session_context_cache, + _session_cache, cache_session_context, get_cached_session_context, ) - _session_context_cache.clear() + _session_cache.clear() _cache_timestamps.clear() # Add an entry @@ -320,21 +320,21 @@ class TestSessionContextCache: # Should be None because the entry was cleaned up assert result is None - assert "stale-session" not in _session_context_cache + assert "stale-session" not in _session_cache @pytest.mark.asyncio - async def test_update_session_context_caches_result(self): + async def test_update_session_caches_result(self): """Test update_session_context stores result in cache.""" from unittest.mock import AsyncMock, patch from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, get_cached_session_context, update_session_context, ) from haiku.rag.agents.chat.state import ChatSessionState - _session_context_cache.clear() + _session_cache.clear() session_state = ChatSessionState(session_id="cache-test-session") @@ -368,13 +368,13 @@ class TestSessionContextCache: async def test_update_session_context_no_cache_without_session_id(self): """Test update_session_context doesn't cache without session_id.""" from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, get_cached_session_context, update_session_context, ) from haiku.rag.agents.chat.state import ChatSessionState - _session_context_cache.clear() + _session_cache.clear() # No session_id session_state = ChatSessionState() @@ -395,12 +395,12 @@ class TestSessionContextCache: from unittest.mock import patch from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, update_session_context, ) from haiku.rag.agents.chat.state import ChatSessionState - _session_context_cache.clear() + _session_cache.clear() # Create session_state with initial_context but no session_context session_state = ChatSessionState( @@ -446,12 +446,12 @@ class TestSessionContextCache: from unittest.mock import patch from haiku.rag.agents.chat.context import ( - _session_context_cache, + _session_cache, update_session_context, ) from haiku.rag.agents.chat.state import ChatSessionState, SessionContext - _session_context_cache.clear() + _session_cache.clear() # Create session_state with BOTH initial_context and session_context session_state = ChatSessionState( 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 index 90f4f30f..e8bf4cfd 100644 --- 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 @@ -62,7 +62,7 @@ interactions: connection: - keep-alive content-length: - - '5237' + - '5339' content-type: - application/json host: @@ -111,28 +111,28 @@ interactions: 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. + Search the knowledge base for relevant documents. + + Formatted search results with content and metadata. + name: search parameters: additionalProperties: false properties: - document_name: + filter: anyOf: - type: string - type: 'null' default: null - description: Optional document name/title to search within + description: Optional SQL WHERE clause to filter documents. limit: anyOf: - type: integer - type: 'null' default: null - description: 'Number of results to return (default: 5)' + description: 'Number of results to return (default: from config).' query: - description: The search query (what to search for) + description: The search query (what to search for). type: string required: - query @@ -140,32 +140,10 @@ interactions: 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. + List available documents in the knowledge base. + + Paginated list of documents with metadata. + name: list_documents parameters: additionalProperties: false @@ -178,15 +156,16 @@ interactions: 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. + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + name: get_document parameters: additionalProperties: false properties: query: - description: The document title or URI to look up + description: The document title or URI to look up. type: string required: - query @@ -195,21 +174,47 @@ interactions: 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. + Generate a summary of a specific document. + + Generated summary or not found message. + name: summarize_document parameters: additionalProperties: false properties: query: - description: The document title or URI to summarize + description: The document title or URI to summarize. type: string required: - query type: object strict: true type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + 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: @@ -229,18 +234,18 @@ interactions: - function: arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}' name: ask - id: call_eolhkv9k + id: call_k66a34yj index: 0 type: function - created: 1769804649 - id: chatcmpl-937 + created: 1770037789 + id: chatcmpl-710 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 43 - prompt_tokens: 1033 - total_tokens: 1076 + completion_tokens: 45 + prompt_tokens: 1055 + total_tokens: 1100 status: code: 200 message: OK @@ -323,7 +328,7 @@ interactions: response: headers: content-length: - - '1032' + - '992' content-type: - application/json parsed_body: @@ -332,28 +337,28 @@ interactions: index: 0 message: content: '' - reasoning: 'Need first sub-question: what are class definitions? highest count means class with most examples. Ask: - "What are the class labels and their example counts in DocLayNet?" That gives data.' + 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: - function: - arguments: '{"is_complete":false,"next_question":"Provide the list of all class labels in the DocLayNet dataset - along with the number of examples for each class.","reasoning":"The user asks for the highest count class; - we need the class counts first. The next question should gather class distributions from the DocLayNet dataset. - This is a concise, self‑contained query that directly addresses the missing information."}' + arguments: '{"is_complete":false,"next_question":"What are the class names and their respective document counts + 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_wubtp1az + id: call_bq73y6jw index: 0 type: function - created: 1769804655 - id: chatcmpl-168 + created: 1770037791 + id: chatcmpl-880 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 140 + completion_tokens: 133 prompt_tokens: 374 - total_tokens: 514 + total_tokens: 507 status: code: 200 message: OK @@ -366,7 +371,7 @@ interactions: connection: - keep-alive content-length: - - '2901' + - '2877' content-type: - application/json host: @@ -419,8 +424,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Results are ordered by relevance, with rank 1 being most relevant. role: system - - content: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each - class. + - content: What are the class names and their respective document counts in the DocLayNet dataset? role: user model: gpt-oss reasoning_effort: low @@ -476,7 +480,7 @@ interactions: response: headers: content-length: - - '507' + - '511' content-type: - application/json parsed_body: @@ -489,20 +493,20 @@ interactions: role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}' name: search_and_answer - id: call_z9248skz + id: call_n08pybym index: 0 type: function - created: 1769804658 - id: chatcmpl-497 + created: 1770037792 + id: chatcmpl-47 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: completion_tokens: 39 - prompt_tokens: 638 - total_tokens: 677 + prompt_tokens: 632 + total_tokens: 671 status: code: 200 message: OK @@ -515,7 +519,7 @@ interactions: connection: - keep-alive content-length: - - '111' + - '115' content-type: - application/json host: @@ -524,7 +528,7 @@ interactions: parsed_body: encoding_format: base64 input: - - DocLayNet class labels number of examples + - DocLayNet dataset class names document counts model: qwen3-embedding:4b uri: http://localhost:11434/v1/embeddings response: @@ -535,7 +539,7 @@ interactions: - chunked parsed_body: data: - - embedding: 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 + - embedding: 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 index: 0 object: embedding model: qwen3-embedding:4b @@ -555,7 +559,7 @@ interactions: connection: - keep-alive content-length: - - '3764' + - '3745' content-type: - application/json host: @@ -608,8 +612,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Results are ordered by relevance, with rank 1 being most relevant. role: system - - content: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each - class. + - content: What are the class names and their respective document counts in the DocLayNet dataset? role: user - content: |- @@ -618,12 +621,12 @@ interactions: role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}' name: search_and_answer - id: call_z9248skz + id: call_n08pybym type: function - content: |- - [387dce4d-4568-4471-9078-5b92788627fa] [rank 1 of 1] + [b40c5a39-4b39-4da9-b431-2fcfe2e5944b] [rank 1 of 1] Source: "DocLayNet Class Labels" Type: list_item Content: @@ -647,7 +650,7 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_z9248skz + tool_call_id: call_n08pybym model: gpt-oss reasoning_effort: low stream: false @@ -702,270 +705,7 @@ interactions: response: headers: content-length: - - '507' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: tool_calls - index: 0 - message: - content: '' - reasoning: Need counts. try. - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' - name: search_and_answer - id: call_0r2kc49s - index: 0 - type: function - created: 1769804661 - id: chatcmpl-201 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 40 - prompt_tokens: 826 - total_tokens: 866 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '106' - content-type: - - application/json - host: - - localhost:11434 - method: POST - parsed_body: - encoding_format: base64 - input: - - DocLayNet dataset examples 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: - - '4627' - 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: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each - class. - role: user - - content: |- - - Need search. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' - name: search_and_answer - id: call_z9248skz - type: function - - content: |- - [387dce4d-4568-4471-9078-5b92788627fa] [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_z9248skz - - content: |- - - Need counts. try. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' - name: search_and_answer - id: call_0r2kc49s - type: function - - content: |- - [387dce4d-4568-4471-9078-5b92788627fa] [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_0r2kc49s - 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: - - '412' + - '789' content-type: - application/json parsed_body: @@ -973,18 +713,21 @@ interactions: - finish_reason: stop index: 0 message: - content: I’m sorry, but I couldn’t find the requested information on the number of examples for each class in the - DocLayNet dataset. + 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: 1769804663 - id: chatcmpl-39 + created: 1770037796 + id: chatcmpl-453 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 31 - prompt_tokens: 1015 - total_tokens: 1046 + completion_tokens: 129 + prompt_tokens: 823 + total_tokens: 952 status: code: 200 message: OK @@ -997,7 +740,7 @@ interactions: connection: - keep-alive content-length: - - '4916' + - '4411' content-type: - application/json host: @@ -1050,8 +793,7 @@ interactions: - Be concise and direct; avoid meta commentary about the process. - Results are ordered by relevance, with rank 1 being most relevant. role: system - - content: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each - class. + - content: What are the class names and their respective document counts in the DocLayNet dataset? role: user - content: |- @@ -1060,12 +802,12 @@ interactions: role: assistant tool_calls: - function: - arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + arguments: '{"limit":10,"query":"DocLayNet dataset class names document counts"}' name: search_and_answer - id: call_z9248skz + id: call_n08pybym type: function - content: |- - [387dce4d-4568-4471-9078-5b92788627fa] [rank 1 of 1] + [b40c5a39-4b39-4da9-b431-2fcfe2e5944b] [rank 1 of 1] Source: "DocLayNet Class Labels" Type: list_item Content: @@ -1089,46 +831,12 @@ interactions: Table - Tabular data role: tool - tool_call_id: call_z9248skz - - content: |- - - Need counts. try. - - role: assistant - tool_calls: - - function: - arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' - name: search_and_answer - id: call_0r2kc49s - type: function - - content: |- - [387dce4d-4568-4471-9078-5b92788627fa] [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_0r2kc49s - - content: I’m sorry, but I couldn’t find the requested information on the number of examples for each class in the - DocLayNet dataset. + 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: @@ -1190,7 +898,7 @@ interactions: response: headers: content-length: - - '768' + - '979' content-type: - application/json parsed_body: @@ -1199,26 +907,27 @@ interactions: index: 0 message: content: '' - reasoning: Need to call functions.final_result. + reasoning: We need to return in function call final_result. role: assistant tool_calls: - function: - arguments: '{"answer":"I’m sorry, but I couldn’t find the requested information on the number of examples for - each class in the DocLayNet dataset.","cited_chunks":[],"confidence":0.3,"query":"Provide the list of all - class labels in the DocLayNet dataset along with the number of examples for each class."}' + 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_plq2bo5u + id: call_njvex9t3 index: 0 type: function - created: 1769804667 - id: chatcmpl-519 + created: 1770037800 + id: chatcmpl-580 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 93 - prompt_tokens: 1071 - total_tokens: 1164 + completion_tokens: 160 + prompt_tokens: 977 + total_tokens: 1137 status: code: 200 message: OK @@ -1231,7 +940,7 @@ interactions: connection: - keep-alive content-length: - - '2905' + - '3072' content-type: - application/json host: @@ -1265,10 +974,20 @@ interactions: What is the highest count class in the DocLayNet dataset? - Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each class. - I’m sorry, but I couldn’t find the requested information on the number of examples for each class in the DocLayNet dataset. - 0.3 - null + What are the class names and their respective document counts in the DocLayNet dataset? + 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.) + 0.4 + DocLayNet Class Labels @@ -1350,7 +1069,7 @@ interactions: response: headers: content-length: - - '748' + - '753' content-type: - application/json parsed_body: @@ -1359,26 +1078,26 @@ interactions: index: 0 message: content: '' - reasoning: 'Need answer: highest count class in DocLayNet dataset. We need evidence. No evidence given. Likely can''t - answer. Provide acknowledgement.' + 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’m sorry, but I couldn’t find the information on which class has the highest count in - the DocLayNet dataset based on the information available to me.","confidence":0.2}' + 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_mb10jcub + id: call_nfwmamn7 index: 0 type: function - created: 1769804670 - id: chatcmpl-788 + created: 1770037803 + id: chatcmpl-137 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: completion_tokens: 88 - prompt_tokens: 466 - total_tokens: 554 + prompt_tokens: 509 + total_tokens: 597 status: code: 200 message: OK @@ -1391,89 +1110,7 @@ interactions: connection: - keep-alive content-length: - - '1665' - 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: 20%): - I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset based on the information available to me. - role: user - model: gpt-oss - reasoning_effort: low - stream: false - uri: http://localhost:11434/v1/chat/completions - response: - headers: - content-length: - - '719' - content-type: - - application/json - parsed_body: - choices: - - finish_reason: stop - index: 0 - message: - content: |- - ### Key Facts Established - - The user inquired about the **highest count class** in the **DocLayNet** dataset. - - The assistant was unable to locate or provide this information, citing a lack of available data. - - ### Documents Referenced - - None mentioned or cited. - - ### Current Focus - - Determining the class with the highest frequency/count within the DocLayNet dataset. - reasoning: We need summarize conversation. just one Q/A. - role: assistant - created: 1769804674 - id: chatcmpl-573 - model: gpt-oss - object: chat.completion - system_fingerprint: fp_ollama - usage: - completion_tokens: 97 - prompt_tokens: 381 - total_tokens: 478 - status: - code: 200 - message: OK -- request: - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate, zstd - connection: - - keep-alive - content-length: - - '5709' + - '5840' content-type: - application/json host: @@ -1524,12 +1161,14 @@ interactions: - function: arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}' name: ask - id: call_eolhkv9k + id: call_k66a34yj type: function - - content: I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset - based on the information available to me. + - 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_eolhkv9k + tool_call_id: call_k66a34yj model: gpt-oss reasoning_effort: low stream: false @@ -1537,28 +1176,28 @@ interactions: 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. + Search the knowledge base for relevant documents. + + Formatted search results with content and metadata. + name: search parameters: additionalProperties: false properties: - document_name: + filter: anyOf: - type: string - type: 'null' default: null - description: Optional document name/title to search within + description: Optional SQL WHERE clause to filter documents. limit: anyOf: - type: integer - type: 'null' default: null - description: 'Number of results to return (default: 5)' + description: 'Number of results to return (default: from config).' query: - description: The search query (what to search for) + description: The search query (what to search for). type: string required: - query @@ -1566,32 +1205,10 @@ interactions: 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. + List available documents in the knowledge base. + + Paginated list of documents with metadata. + name: list_documents parameters: additionalProperties: false @@ -1604,15 +1221,16 @@ interactions: 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. + Retrieve a specific document by title or URI. + + Document content and metadata, or not found message. + name: get_document parameters: additionalProperties: false properties: query: - description: The document title or URI to look up + description: The document title or URI to look up. type: string required: - query @@ -1621,26 +1239,52 @@ interactions: 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. + Generate a summary of a specific document. + + Generated summary or not found message. + name: summarize_document parameters: additionalProperties: false properties: query: - description: The document title or URI to summarize + description: The document title or URI to summarize. type: string required: - query type: object strict: true type: function + - function: + description: |- + Answer a question using the knowledge base. + + Uses a research graph for searching and synthesizing answers. + + QAResult with answer, confidence, and citations. + + 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: - - '399' + - '587' content-type: - application/json parsed_body: @@ -1648,17 +1292,19 @@ interactions: - finish_reason: stop index: 0 message: - content: I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset. + 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: 1769804678 - id: chatcmpl-740 + created: 1770037806 + id: chatcmpl-124 model: gpt-oss object: chat.completion system_fingerprint: fp_ollama usage: - completion_tokens: 29 - prompt_tokens: 1124 - total_tokens: 1153 + completion_tokens: 67 + prompt_tokens: 1153 + total_tokens: 1220 status: code: 200 message: OK diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_emits_qa_state.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_emits_qa_state.yaml new file mode 100644 index 00000000..90f4f30f --- /dev/null +++ b/tests/cassettes/test_chat_agent/test_chat_agent_ask_emits_qa_state.yaml @@ -0,0 +1,1665 @@ +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 ", "find in ", "answer from ", or " in ": + - 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: + 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: + - '522' + 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":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_eolhkv9k + index: 0 + type: function + created: 1769804649 + id: chatcmpl-937 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 43 + prompt_tokens: 1033 + total_tokens: 1076 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1766' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + messages: + - content: |- + You are the research orchestrator planning the investigation. + + If a 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. + + + 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: 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: + - '1032' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need first sub-question: what are class definitions? highest count means class with most examples. Ask: + "What are the class labels and their example counts in DocLayNet?" That gives data.' + role: assistant + tool_calls: + - function: + arguments: '{"is_complete":false,"next_question":"Provide the list of all class labels in the DocLayNet dataset + along with the number of examples for each class.","reasoning":"The user asks for the highest count class; + we need the class counts first. The next question should gather class distributions from the DocLayNet dataset. + This is a concise, self‑contained query that directly addresses the missing information."}' + name: final_result + id: call_wubtp1az + index: 0 + type: function + created: 1769804655 + id: chatcmpl-168 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 140 + prompt_tokens: 374 + total_tokens: 514 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2901' + 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: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each + class. + 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 search. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + name: search_and_answer + id: call_z9248skz + index: 0 + type: function + created: 1769804658 + id: chatcmpl-497 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 39 + prompt_tokens: 638 + total_tokens: 677 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '111' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet class labels number of 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: 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: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each + class. + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + name: search_and_answer + id: call_z9248skz + type: function + - content: |- + [387dce4d-4568-4471-9078-5b92788627fa] [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_z9248skz + 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 counts. try. + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' + name: search_and_answer + id: call_0r2kc49s + index: 0 + type: function + created: 1769804661 + id: chatcmpl-201 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 40 + prompt_tokens: 826 + total_tokens: 866 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '106' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - DocLayNet dataset examples 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: + - '4627' + 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: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each + class. + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + name: search_and_answer + id: call_z9248skz + type: function + - content: |- + [387dce4d-4568-4471-9078-5b92788627fa] [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_z9248skz + - content: |- + + Need counts. try. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' + name: search_and_answer + id: call_0r2kc49s + type: function + - content: |- + [387dce4d-4568-4471-9078-5b92788627fa] [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_0r2kc49s + 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: + - '412' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find the requested information on the number of examples for each class in the + DocLayNet dataset. + role: assistant + created: 1769804663 + id: chatcmpl-39 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 31 + prompt_tokens: 1015 + total_tokens: 1046 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '4916' + 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: Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each + class. + role: user + - content: |- + + Need search. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet class labels number of examples"}' + name: search_and_answer + id: call_z9248skz + type: function + - content: |- + [387dce4d-4568-4471-9078-5b92788627fa] [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_z9248skz + - content: |- + + Need counts. try. + + role: assistant + tool_calls: + - function: + arguments: '{"limit":5,"query":"DocLayNet dataset examples per class"}' + name: search_and_answer + id: call_0r2kc49s + type: function + - content: |- + [387dce4d-4568-4471-9078-5b92788627fa] [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_0r2kc49s + - content: I’m sorry, but I couldn’t find the requested information on the number of examples 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: + - '768' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: Need to call functions.final_result. + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I couldn’t find the requested information on the number of examples for + each class in the DocLayNet dataset.","cited_chunks":[],"confidence":0.3,"query":"Provide the list of all + class labels in the DocLayNet dataset along with the number of examples for each class."}' + name: final_result + id: call_plq2bo5u + index: 0 + type: function + created: 1769804667 + id: chatcmpl-519 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 93 + prompt_tokens: 1071 + total_tokens: 1164 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '2905' + 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 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. + + + What is the highest count class in the DocLayNet dataset? + + + Provide the list of all class labels in the DocLayNet dataset along with the number of examples for each class. + I’m sorry, but I couldn’t find the requested information on the number of examples for each class in the DocLayNet dataset. + 0.3 + null + + + + 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: + - '748' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: tool_calls + index: 0 + message: + content: '' + reasoning: 'Need answer: highest count class in DocLayNet dataset. We need evidence. No evidence given. Likely can''t + answer. Provide acknowledgement.' + role: assistant + tool_calls: + - function: + arguments: '{"answer":"I’m sorry, but I couldn’t find the information on which class has the highest count in + the DocLayNet dataset based on the information available to me.","confidence":0.2}' + name: final_result + id: call_mb10jcub + index: 0 + type: function + created: 1769804670 + id: chatcmpl-788 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 88 + prompt_tokens: 466 + total_tokens: 554 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '1665' + 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: 20%): + I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset based on the information available to me. + role: user + model: gpt-oss + reasoning_effort: low + stream: false + uri: http://localhost:11434/v1/chat/completions + response: + headers: + content-length: + - '719' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: |- + ### Key Facts Established + - The user inquired about the **highest count class** in the **DocLayNet** dataset. + - The assistant was unable to locate or provide this information, citing a lack of available data. + + ### Documents Referenced + - None mentioned or cited. + + ### Current Focus + - Determining the class with the highest frequency/count within the DocLayNet dataset. + reasoning: We need summarize conversation. just one Q/A. + role: assistant + created: 1769804674 + id: chatcmpl-573 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 97 + prompt_tokens: 381 + total_tokens: 478 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '5709' + 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 ", "find in ", "answer from ", or " in ": + - 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: |- + + Need ask. + + role: assistant + tool_calls: + - function: + arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}' + name: ask + id: call_eolhkv9k + type: function + - content: I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset + based on the information available to me. + role: tool + tool_call_id: call_eolhkv9k + 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: + - '399' + content-type: + - application/json + parsed_body: + choices: + - finish_reason: stop + index: 0 + message: + content: I’m sorry, but I couldn’t find the information on which class has the highest count in the DocLayNet dataset. + role: assistant + created: 1769804678 + id: chatcmpl-740 + model: gpt-oss + object: chat.completion + system_fingerprint: fp_ollama + usage: + completion_tokens: 29 + prompt_tokens: 1124 + total_tokens: 1153 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/tools/test_analysis.py b/tests/tools/test_analysis.py index c474becb..f4fac3e0 100644 --- a/tests/tools/test_analysis.py +++ b/tests/tools/test_analysis.py @@ -72,18 +72,12 @@ class TestAnalysisToolset: @pytest.fixture -def analysis_client(temp_db_path): +async def analysis_client(temp_db_path): """Create a HaikuRAG client for analysis tests.""" - import asyncio - from haiku.rag.client import HaikuRAG - async def setup(): - rag = HaikuRAG(temp_db_path, create=True) - await rag.__aenter__() - return rag - - return asyncio.get_event_loop().run_until_complete(setup()) + async with HaikuRAG(temp_db_path, create=True) as rag: + yield rag @pytest.fixture diff --git a/tests/tools/test_document.py b/tests/tools/test_document.py index 4296026d..817204db 100644 --- a/tests/tools/test_document.py +++ b/tests/tools/test_document.py @@ -162,15 +162,11 @@ class TestDocumentToolExecution: @pytest.fixture -def doc_client(temp_db_path): +async def doc_client(temp_db_path): """Create a HaikuRAG client with test documents.""" - import asyncio - from haiku.rag.client import HaikuRAG - async def setup(): - rag = HaikuRAG(temp_db_path, create=True) - await rag.__aenter__() + 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", @@ -181,9 +177,7 @@ def doc_client(temp_db_path): uri="test://javascript", title="JavaScript Guide", ) - return rag - - return asyncio.get_event_loop().run_until_complete(setup()) + yield rag @pytest.fixture diff --git a/tests/tools/test_qa.py b/tests/tools/test_qa.py index c0629703..4456f388 100644 --- a/tests/tools/test_qa.py +++ b/tests/tools/test_qa.py @@ -73,18 +73,12 @@ class TestQAToolset: @pytest.fixture -def qa_client_simple(temp_db_path): +async def qa_client_simple(temp_db_path): """Create a HaikuRAG client without documents for basic tests.""" - import asyncio - from haiku.rag.client import HaikuRAG - async def setup(): - rag = HaikuRAG(temp_db_path, create=True) - await rag.__aenter__() - return rag - - return asyncio.get_event_loop().run_until_complete(setup()) + async with HaikuRAG(temp_db_path, create=True) as rag: + yield rag @pytest.fixture diff --git a/tests/tools/test_search.py b/tests/tools/test_search.py index 70bbf67e..d3d8a5a6 100644 --- a/tests/tools/test_search.py +++ b/tests/tools/test_search.py @@ -213,15 +213,11 @@ class TestSearchToolExecution: @pytest.fixture -def search_client(temp_db_path): +async def search_client(temp_db_path): """Create a HaikuRAG client with test data for search tests.""" - import asyncio - from haiku.rag.client import HaikuRAG - async def setup(): - rag = HaikuRAG(temp_db_path, create=True) - await rag.__aenter__() + 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", @@ -232,9 +228,7 @@ def search_client(temp_db_path): uri="test://javascript", title="JavaScript Guide", ) - return rag - - return asyncio.get_event_loop().run_until_complete(setup()) + yield rag @pytest.fixture