From bb61e8fab61e472effe4cde9c5af573b4cc70cc5 Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Fri, 9 Jan 2026 18:58:48 +0200 Subject: [PATCH] Remove custom AG-UI infrastructure in favor of pydantic-ai native support --- CHANGELOG.md | 12 + examples/ag-ui-research/.env.example | 15 - examples/ag-ui-research/.gitignore | 3 - examples/ag-ui-research/README.md | 154 - examples/ag-ui-research/backend/.gitignore | 12 - examples/ag-ui-research/backend/Dockerfile | 16 - examples/ag-ui-research/backend/README.md | 25 - examples/ag-ui-research/backend/agent.py | 151 - examples/ag-ui-research/backend/main.py | 343 - .../ag-ui-research/backend/pyproject.toml | 26 - examples/ag-ui-research/backend/uv.lock | 2172 --- examples/ag-ui-research/docker-compose.yml | 76 - .../ag-ui-research/frontend/.dockerignore | 7 - examples/ag-ui-research/frontend/.gitignore | 39 - examples/ag-ui-research/frontend/Dockerfile | 12 - .../frontend/app/api/copilotkit/route.ts | 33 - .../ag-ui-research/frontend/app/globals.css | 24 - .../ag-ui-research/frontend/app/layout.tsx | 20 - examples/ag-ui-research/frontend/app/page.tsx | 9 - examples/ag-ui-research/frontend/biome.json | 34 - .../frontend/components/Agent.tsx | 449 - .../frontend/components/DocumentSelector.tsx | 359 - .../frontend/components/StateDisplay.tsx | 968 -- .../ag-ui-research/frontend/next.config.ts | 7 - .../ag-ui-research/frontend/package-lock.json | 11689 ---------------- examples/ag-ui-research/frontend/package.json | 29 - .../ag-ui-research/frontend/public/.gitkeep | 0 .../ag-ui-research/frontend/tsconfig.json | 27 - .../ag-ui-research/haiku.rag.yaml.example | 36 - examples/ag-ui-research/package-lock.json | 6 - haiku_rag_slim/haiku/rag/app.py | 68 +- haiku_rag_slim/haiku/rag/cli.py | 60 +- haiku_rag_slim/haiku/rag/cli_chat.py | 489 - haiku_rag_slim/haiku/rag/config/__init__.py | 4 - haiku_rag_slim/haiku/rag/config/models.py | 10 - haiku_rag_slim/haiku/rag/graph/__init__.py | 10 - .../haiku/rag/graph/agui/__init__.py | 59 - .../haiku/rag/graph/agui/cli_renderer.py | 135 - .../haiku/rag/graph/agui/emitter.py | 385 - haiku_rag_slim/haiku/rag/graph/agui/server.py | 268 - haiku_rag_slim/haiku/rag/graph/agui/state.py | 34 - haiku_rag_slim/haiku/rag/graph/agui/stream.py | 85 - .../haiku/rag/graph/research/graph.py | 418 +- .../haiku/rag/graph/research/state.py | 26 +- tests/graph/agui/__init__.py | 1 - tests/graph/agui/test_cli_renderer.py | 204 - tests/graph/agui/test_emitter.py | 317 - tests/graph/agui/test_server.py | 227 - tests/graph/agui/test_stream.py | 199 - tests/graph/test_research_graph.py | 25 +- tests/test_app.py | 47 - tests/test_cli.py | 22 - 52 files changed, 129 insertions(+), 19717 deletions(-) delete mode 100644 examples/ag-ui-research/.env.example delete mode 100644 examples/ag-ui-research/.gitignore delete mode 100644 examples/ag-ui-research/README.md delete mode 100644 examples/ag-ui-research/backend/.gitignore delete mode 100644 examples/ag-ui-research/backend/Dockerfile delete mode 100644 examples/ag-ui-research/backend/README.md delete mode 100644 examples/ag-ui-research/backend/agent.py delete mode 100644 examples/ag-ui-research/backend/main.py delete mode 100644 examples/ag-ui-research/backend/pyproject.toml delete mode 100644 examples/ag-ui-research/backend/uv.lock delete mode 100644 examples/ag-ui-research/docker-compose.yml delete mode 100644 examples/ag-ui-research/frontend/.dockerignore delete mode 100644 examples/ag-ui-research/frontend/.gitignore delete mode 100644 examples/ag-ui-research/frontend/Dockerfile delete mode 100644 examples/ag-ui-research/frontend/app/api/copilotkit/route.ts delete mode 100644 examples/ag-ui-research/frontend/app/globals.css delete mode 100644 examples/ag-ui-research/frontend/app/layout.tsx delete mode 100644 examples/ag-ui-research/frontend/app/page.tsx delete mode 100644 examples/ag-ui-research/frontend/biome.json delete mode 100644 examples/ag-ui-research/frontend/components/Agent.tsx delete mode 100644 examples/ag-ui-research/frontend/components/DocumentSelector.tsx delete mode 100644 examples/ag-ui-research/frontend/components/StateDisplay.tsx delete mode 100644 examples/ag-ui-research/frontend/next.config.ts delete mode 100644 examples/ag-ui-research/frontend/package-lock.json delete mode 100644 examples/ag-ui-research/frontend/package.json delete mode 100644 examples/ag-ui-research/frontend/public/.gitkeep delete mode 100644 examples/ag-ui-research/frontend/tsconfig.json delete mode 100644 examples/ag-ui-research/haiku.rag.yaml.example delete mode 100644 examples/ag-ui-research/package-lock.json delete mode 100644 haiku_rag_slim/haiku/rag/cli_chat.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/__init__.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/emitter.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/server.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/state.py delete mode 100644 haiku_rag_slim/haiku/rag/graph/agui/stream.py delete mode 100644 tests/graph/agui/__init__.py delete mode 100644 tests/graph/agui/test_cli_renderer.py delete mode 100644 tests/graph/agui/test_emitter.py delete mode 100644 tests/graph/agui/test_server.py delete mode 100644 tests/graph/agui/test_stream.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 9a2026eb..b2e5f960 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,18 @@ # Changelog ## [Unreleased] +### Removed + +- **BREAKING: Custom AG-UI Infrastructure**: Removed custom AG-UI event handling in favor of pydantic-ai's native AG-UI support + - Deleted `haiku.rag.graph.agui` module (`AGUIEmitter`, `AGUIConsoleRenderer`, `stream_graph()`, `create_agui_server()`) + - Removed `--agui` flag from `serve` command + - Removed `--verbose` flags from `ask` and `research` commands + - Removed `--interactive` flag from `research` command + - Removed `AGUIConfig` from configuration + - Deleted `cli_chat.py` interactive chat module + - Research graph now uses `graph.run()` directly instead of `stream_graph()` + - For AG-UI streaming, use pydantic-ai's native `AGUIAdapter` with `ToolReturn` and `StateSnapshotEvent` (see `app/backend/` for example) + ## [0.25.0] - 2026-01-12 ### Fixed diff --git a/examples/ag-ui-research/.env.example b/examples/ag-ui-research/.env.example deleted file mode 100644 index 119f6fb0..00000000 --- a/examples/ag-ui-research/.env.example +++ /dev/null @@ -1,15 +0,0 @@ -# Path to the LanceDB database on your HOST machine -# This will be mounted into the Docker container at /app/data/haiku.rag.lancedb -# Must be an absolute path to an existing database created with haiku-rag -DB_PATH=/absolute/path/to/your/haiku.rag.lancedb - -# Ollama API base URL (if using Ollama for local models) -# If running Ollama on your host machine, use your machine's IP address -# that the Docker container can reach (not localhost) -OLLAMA_BASE_URL=http://host.docker.internal:11434 - -# API keys (set as needed for your QA provider) -# OPENAI_API_KEY=your-key-here -# ANTHROPIC_API_KEY=your-key-here -# VOYAGE_API_KEY=your-key-here -# CO_API_KEY=your-key-here diff --git a/examples/ag-ui-research/.gitignore b/examples/ag-ui-research/.gitignore deleted file mode 100644 index 0b1ef159..00000000 --- a/examples/ag-ui-research/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -haiku.rag.yaml -.env -data/ diff --git a/examples/ag-ui-research/README.md b/examples/ag-ui-research/README.md deleted file mode 100644 index 8d9f0284..00000000 --- a/examples/ag-ui-research/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# Interactive Research Assistant - -Research assistant powered by [haiku.rag](https://ggozad.github.io/haiku.rag/), [Pydantic Graph](https://ai.pydantic.dev/graph/), and [AG-UI](https://docs.ag-ui.com/). Ask complex questions and watch the research process unfold in real-time with human-in-the-loop control. - -[Watch demo video](https://vimeo.com/1128874386) - -## Features - -- **Human-in-the-loop research**: Review and modify questions at decision points, then continue searching or generate report -- **Multi-iteration research graph**: Automated question decomposition and parallel search -- **Live state synchronization**: Real-time delta updates of research progress via AG-UI protocol -- **Rich reporting**: Generates comprehensive research reports with findings, conclusions, and sources - -## Quick Start - -### Prerequisites - -- Docker and Docker Compose -- A haiku.rag database with indexed documents -- Ollama (or configure another LLM provider) - -### Setup - -1. **Prepare your knowledge base** - - **Option A: Create a new database** - ```bash - haiku-rag init --db data/haiku_rag.lancedb - haiku-rag add-src document.pdf --db data/haiku_rag.lancedb - ``` - - **Option B: Use an existing database** - - Set the `DB_PATH` environment variable to point to your existing haiku.rag database: - ```bash - # In .env file - DB_PATH=/path/to/your/existing/haiku_rag.lancedb - ``` - - Or export it before running docker compose: - ```bash - export DB_PATH=/path/to/your/existing/haiku_rag.lancedb - docker compose up --build - ``` - - The database will be mounted as read-write, so the research assistant can access all documents in your existing knowledge base. - -2. **Configure haiku.rag** - ```bash - cp haiku.rag.yaml.example haiku.rag.yaml - # Edit haiku.rag.yaml to customize provider/model - ``` - See [haiku.rag configuration](https://ggozad.github.io/haiku.rag/configuration/) for details. - -3. **Set API keys** (if using non-Ollama providers) - ```bash - cp .env.example .env - # Edit .env to set your API keys - OPENAI_API_KEY=your-key-here - ANTHROPIC_API_KEY=your-key-here - DB_PATH=/path/to/your/existing/haiku_rag.lancedb # If using an existing db. - ``` - -4. **Pull the base image** - ```bash - docker pull ghcr.io/ggozad/haiku.rag-slim:latest - ``` - -5. **Start the application** - ```bash - docker compose up --build - ``` - -6. **Access the interface** - - Frontend: http://localhost:3000 - - Backend health: http://localhost:8000/health - -## How It Works - -1. **Ask a question**: Type your research question in the chat -2. **Plan phase**: The research graph decomposes your question into targeted sub-questions -3. **Decision point**: Review the proposed questions in the right panel - - Add new questions using the input field - - Remove questions you don't need - - Click **Search** to execute searches for pending questions - - Click **Generate Report** to skip to synthesis (when you have enough answers) -4. **Research iterations**: After each search cycle, you return to a decision point where you can: - - Review collected answers - - Add follow-up questions based on findings - - Continue searching or generate the final report -5. **Synthesis**: Generates a comprehensive research report with: - - Executive summary - - Main findings with supporting evidence - - Conclusions and recommendations - - Source citations - -## Architecture - -### Agent + Graph Pattern - -This example demonstrates the **agent+graph** architecture with AG-UI client-side tool calls: - -1. **Conversational Agent** (`agent.py`): - - Pydantic AI agent handles user conversations - - Decides when to invoke the research tool based on user intent - - Responds directly to greetings/casual chat without tools - -2. **Interactive Research Graph** (haiku.rag): - - Multi-step research workflow invoked by the agent's tool - - At decision points, emits AG-UI `TOOL_CALL_START/ARGS/END` events for `human_decision` - - Waits for tool result via async queue before continuing - -3. **Client-Side Tool Handling** (AG-UI pattern): - - Frontend listens for `human_decision` tool calls via AG-UI events - - Renders decision UI inline in chat when tool call is received - - User decision sent directly to backend `/v1/research/stream` endpoint - - Backend extracts tool result from messages and routes to waiting graph via async queue - -4. **Shared Event Stream**: - - `AGUIEmitter` is shared between agent and graph - - Events from both flow through a single stream to the frontend - - `STATE_DELTA` events sync research state to frontend in real-time - -### Components - -- **Backend** (Python): - - Uses published `ghcr.io/ggozad/haiku.rag:latest` Docker image as base - - `agent.py`: Pydantic AI agent with `run_research` tool, manages `ActiveResearch` registry - - `main.py`: Custom AG-UI streaming endpoint, extracts tool results from messages - - Real-time event forwarding from emitter to SSE stream - -- **Frontend** (Next.js/React): - - AG-UI protocol integration for real-time streaming - - Handles `human_decision` tool calls with inline decision UI - - Split-pane UI: chat on left, live research state on right - - Tool results sent directly to backend endpoint - -## Configuration - -Configuration is done through `haiku.rag.yaml` (see `haiku.rag.yaml.example`): - -- `research.provider`: LLM provider (default: `ollama`) -- `research.model`: Model name (default: `gpt-oss:latest`) -- `research.max_iterations`: Maximum research iterations (default: `3`) -- `research.confidence_threshold`: Confidence threshold for completion (default: `0.8`) -- `research.max_concurrency`: Parallel sub-question processing (default: `1`) -- `providers.ollama.base_url`: Ollama endpoint (default: `http://host.docker.internal:11434`) - -Environment variables (see `.env.example`): - -- `DB_PATH`: Path to haiku.rag database (default: `haiku_rag.lancedb`) -- `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`: API keys for cloud providers - -For full configuration options, see [haiku.rag configuration docs](https://ggozad.github.io/haiku.rag/configuration/). diff --git a/examples/ag-ui-research/backend/.gitignore b/examples/ag-ui-research/backend/.gitignore deleted file mode 100644 index 2913edab..00000000 --- a/examples/ag-ui-research/backend/.gitignore +++ /dev/null @@ -1,12 +0,0 @@ -__pycache__/ -*.py[cod] -*$py.class -*.so -.Python -env/ -venv/ -.env -.venv -*.egg-info/ -dist/ -build/ diff --git a/examples/ag-ui-research/backend/Dockerfile b/examples/ag-ui-research/backend/Dockerfile deleted file mode 100644 index 09647d52..00000000 --- a/examples/ag-ui-research/backend/Dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -FROM ghcr.io/ggozad/haiku.rag-slim:latest - -WORKDIR /app - -# Copy backend application files -COPY main.py agent.py ./ - -# Install additional dependencies for the example -RUN pip install --no-cache-dir \ - starlette>=0.50.0 \ - uvicorn[standard]>=0.40.0 \ - python-dotenv>=1.2.1 - -EXPOSE 8000 - -CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"] diff --git a/examples/ag-ui-research/backend/README.md b/examples/ag-ui-research/backend/README.md deleted file mode 100644 index 8b6da3df..00000000 --- a/examples/ag-ui-research/backend/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Haiku.rag Research Assistant Backend - -Starlette backend for the haiku.rag interactive research assistant, using the research graph with AG-UI protocol support. - -## Setup - -```bash -uv sync -uv run python main.py -``` - -The server starts on `http://localhost:8000` and uses [haiku.rag configuration](https://ggozad.github.io/haiku.rag/configuration/). - -## Architecture - -The backend uses `create_agui_server()` from `haiku.rag.graph.agui.server` which provides: - -- **Research graph execution**: Multi-iteration research workflow -- **AG-UI protocol**: Server-Sent Events (SSE) streaming for real-time state updates -- **Delta state updates**: Efficient incremental state synchronization using JSON Patch operations - -## Endpoints - -- `GET /health` - Health check with configuration info -- `POST /agent/research/stream` - Research graph streaming endpoint (AG-UI protocol) diff --git a/examples/ag-ui-research/backend/agent.py b/examples/ag-ui-research/backend/agent.py deleted file mode 100644 index c30e17c2..00000000 --- a/examples/ag-ui-research/backend/agent.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Research assistant agent with graph integration.""" - -import asyncio -from dataclasses import dataclass, field -from pathlib import Path -from typing import TYPE_CHECKING - -from pydantic_ai import Agent, RunContext - -from haiku.rag.client import HaikuRAG -from haiku.rag.config import load_yaml_config -from haiku.rag.config.models import AppConfig -from haiku.rag.graph.research.dependencies import ResearchContext -from haiku.rag.graph.research.graph import build_research_graph -from haiku.rag.graph.research.state import HumanDecision, ResearchDeps, ResearchState -from haiku.rag.utils import get_model - -if TYPE_CHECKING: - from haiku.rag.graph.agui.emitter import AGUIEmitter - from haiku.rag.graph.research.models import ResearchReport - -# Load config -config_path = Path("/app/haiku.rag.yaml") -Config = ( - AppConfig.model_validate(load_yaml_config(config_path)) - if config_path.exists() - else AppConfig() -) - - -@dataclass -class ActiveResearch: - """Tracks state for active research awaiting human decision.""" - - queue: asyncio.Queue[HumanDecision] - sub_questions: list[str] = field(default_factory=list) - qa_responses: list[dict] = field(default_factory=list) - original_question: str = "" - - -# Global registry of active research by thread_id -_active_research: dict[str, ActiveResearch] = {} - - -@dataclass -class AgentDeps: - """Dependencies for research agent.""" - - client: HaikuRAG - agui_emitter: "AGUIEmitter[ResearchState, ResearchReport] | None" = None - search_filter: str | None = None - thread_id: str | None = None - research_result: "ResearchReport | None" = None - - -model = get_model(Config.research.model, Config) - -agent = Agent( - model, - deps_type=AgentDeps, - system_prompt="""You are an advanced research assistant powered by haiku.rag. - -CRITICAL RULES: -1. For greetings (hi, hello, hey, etc) or casual chat: respond directly WITHOUT using any tools -2. For questions about yourself or the system: respond directly WITHOUT using any tools -3. For substantive questions requiring information: ALWAYS use the run_research tool -4. NEVER answer substantive questions from your own knowledge - always use the tool - -How to decide: -- "Hi" / "Hello" / "How are you?" -> Respond directly, NO tools -- "What can you do?" -> Respond directly, NO tools -- "How does X work in the codebase?" -> Use run_research tool -- "Tell me about Y" -> Use run_research tool - -When you use run_research, the graph will decompose questions, search the knowledge base, -and generate a comprehensive report. - -Be friendly and conversational in all responses.""", -) - - -@agent.tool -async def run_research(ctx: RunContext[AgentDeps], question: str) -> str: - """Execute research graph on a substantive question. - - Use for questions requiring knowledge base search. - DO NOT use for greetings or casual conversation. - """ - if ctx.deps.agui_emitter: - ctx.deps.agui_emitter.log(f"Starting research on: {question}") - - # Create queue for human decisions - queue: asyncio.Queue[HumanDecision] = asyncio.Queue() - - # Build interactive graph - graph = build_research_graph(Config, interactive=True) - context = ResearchContext(original_question=question) - state = ResearchState.from_config(context=context, config=Config) - state.search_filter = ctx.deps.search_filter - - # Register active research for decision endpoint to find - thread_id = ctx.deps.thread_id - if thread_id: - _active_research[thread_id] = ActiveResearch( - queue=queue, - sub_questions=[], - qa_responses=[], - original_question=question, - ) - - graph_deps = ResearchDeps( - client=ctx.deps.client, - agui_emitter=ctx.deps.agui_emitter, - human_input_queue=queue, - interactive=True, - ) - - try: - result = await graph.run(state=state, deps=graph_deps) - - if ctx.deps.agui_emitter: - ctx.deps.agui_emitter.log("Research complete!") - # Store result for main.py to emit RUN_FINISHED after agent completes - ctx.deps.research_result = result - - return f"""Research completed successfully! - -Question: {question} - -Executive Summary: {result.executive_summary} - -Main Findings: -{chr(10).join(f"- {finding}" for finding in result.main_findings[:3])} - -Conclusions: -{chr(10).join(f"- {conclusion}" for conclusion in result.conclusions[:2])} - -Confidence: {f"{state.last_eval.confidence_score:.0%}" if state.last_eval else "N/A"} -Iterations completed: {state.iterations} - -The full research report with all citations has been provided to the user. -""" - - except Exception as e: - if ctx.deps.agui_emitter: - ctx.deps.agui_emitter.log(f"Research error: {str(e)}") - return f"I encountered an error while researching: {str(e)}" - finally: - # Cleanup - if thread_id and thread_id in _active_research: - del _active_research[thread_id] diff --git a/examples/ag-ui-research/backend/main.py b/examples/ag-ui-research/backend/main.py deleted file mode 100644 index a5211c4b..00000000 --- a/examples/ag-ui-research/backend/main.py +++ /dev/null @@ -1,343 +0,0 @@ -import json -import logging -import os -from pathlib import Path - -from agent import AgentDeps, _active_research, agent -from anyio import create_memory_object_stream, create_task_group -from anyio.streams.memory import MemoryObjectSendStream -from starlette.applications import Starlette -from starlette.middleware import Middleware -from starlette.middleware.cors import CORSMiddleware -from starlette.requests import Request -from starlette.responses import JSONResponse, StreamingResponse -from starlette.routing import Route - -from haiku.rag.client import HaikuRAG -from haiku.rag.config import load_yaml_config -from haiku.rag.config.models import AppConfig -from haiku.rag.graph.agui.emitter import AGUIEmitter -from haiku.rag.graph.agui.server import RunAgentInput, format_sse_event -from haiku.rag.graph.research.dependencies import ResearchContext -from haiku.rag.graph.research.models import ResearchReport -from haiku.rag.graph.research.state import HumanDecision, ResearchState - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - -# Load config from mounted haiku.rag.yaml -config_path = Path("/app/haiku.rag.yaml") -if config_path.exists(): - yaml_data = load_yaml_config(config_path) - Config = AppConfig.model_validate(yaml_data) -else: - # Fallback to default config - Config = AppConfig() - - -# Get DB path from environment -db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb") -db_path = Path(db_path_str) - -if not db_path.exists(): - logger.error(f"Database not found at {db_path}") - logger.error("Run: haiku-rag add ") - raise RuntimeError(f"Database not found: {db_path}") - -logger.info(f"Initializing research assistant with database: {db_path}") -logger.info( - f"Research Provider: {Config.research.model.provider}, Model: {Config.research.model.name}" -) - -# Store client reference for proper lifecycle management -_client_cache: dict[str, HaikuRAG] = {} - - -def get_client(effective_db_path: Path) -> 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) - return _client_cache[path_key] - - -def extract_tool_result(messages: list[dict]) -> dict | None: - """Extract human_decision tool result from messages if present.""" - for msg in reversed(messages): - # Check for tool result message (CopilotKit sends role="tool") - if msg.get("role") == "tool": - content = msg.get("content") - # Content may be a string (JSON) or dict - if isinstance(content, str): - try: - content = json.loads(content) - except json.JSONDecodeError: - continue - if isinstance(content, dict) and "action" in content: - return content - return None - - -async def stream_research_agent(request: Request) -> StreamingResponse: - """Agent streaming endpoint with research graph integration.""" - body = await request.json() - logger.info(f"Received request body keys: {list(body.keys())}") - if "tools" in body: - logger.info(f"Frontend tools received: {body['tools']}") - input_data = RunAgentInput(**body) - - thread_id = input_data.thread_id - active_research = _active_research.get(thread_id) if thread_id else None - - # Check if this is a tool result for active research - if active_research and input_data.messages: - tool_result = extract_tool_result(input_data.messages) - if tool_result: - logger.info(f"Received tool result: {tool_result}") - action = tool_result.get("action", "search") - questions = tool_result.get("questions") - - decision = HumanDecision( - action=action, - questions=questions, - ) - await active_research.queue.put(decision) - - # Return acknowledgment - the original stream will continue - return StreamingResponse( - iter([format_sse_event({"type": "TOOL_RESULT_RECEIVED"})]), - media_type="text/event-stream", - ) - - user_message = "" - if input_data.messages: - user_message = input_data.messages[-1].get("content", "") - - send_stream, receive_stream = create_memory_object_stream[str]() - - async def run_agent_with_streaming( - send_stream: MemoryObjectSendStream[str], - ) -> None: - """Execute agent and forward emitter events to memory stream.""" - async with send_stream: - try: - # Create shared emitter (use_deltas=True for CopilotKit compatibility) - emitter: AGUIEmitter[ResearchState, ResearchReport] = AGUIEmitter( - thread_id=input_data.thread_id, - run_id=input_data.run_id, - use_deltas=True, - ) - - # Get client - effective_db_path = input_data.config.get("db_path") or db_path - if isinstance(effective_db_path, str): - effective_db_path = Path(effective_db_path) - client = get_client(effective_db_path) - - # Build search filter from document IDs (empty list = search all) - document_ids = input_data.state.get("documentFilter") or [] - search_filter = None - if document_ids: - ids_str = ", ".join(f"'{id}'" for id in document_ids) - search_filter = f"id IN ({ids_str})" - - # Create agent dependencies with shared emitter - agent_deps = AgentDeps( - client=client, - agui_emitter=emitter, - search_filter=search_filter, - thread_id=thread_id, - ) - - # Start run with empty initial state - emitter.start_run( - initial_state=ResearchState.from_config( - context=ResearchContext(original_question=""), - config=Config, - ) - ) - - # Forward emitter events to stream - async def forward_events(): - async for event in emitter: - event_type = event.get("type") - logger.info(f"AG-UI event: {event_type}") - - # Log tool call events for debugging - if event_type and event_type.startswith("TOOL_CALL"): - logger.info(f"Tool call event: {event}") - - # Convert ACTIVITY_SNAPSHOT to STATE_DELTA for CopilotKit - # As CopilotKit does not handle ACTIVITY_SNAPSHOT events - if event_type == "ACTIVITY_SNAPSHOT": - activity_type = event.get("activityType", "") - content = event.get("content", {}) - message = content.get("message", "") - - # Emit STATE_DELTA to patch activity info into state - delta_event = { - "type": "STATE_DELTA", - "delta": [ - { - "op": "add", - "path": "/current_activity", - "value": activity_type, - }, - { - "op": "add", - "path": "/current_activity_message", - "value": message, - }, - ], - } - await send_stream.send(format_sse_event(delta_event)) - continue - - # Sync state to ActiveResearch when human_decision tool call - if event_type == "TOOL_CALL_ARGS" and thread_id: - delta = event.get("delta", "{}") - args = ( - json.loads(delta) if isinstance(delta, str) else delta - ) - active = _active_research.get(thread_id) - if active: - active.sub_questions = list( - args.get("sub_questions", []) - ) - active.qa_responses = list(args.get("qa_responses", [])) - if "original_question" in args: - active.original_question = args["original_question"] - - await send_stream.send(format_sse_event(event)) - - # Run agent and event forwarding concurrently - async with create_task_group() as tg: - tg.start_soon(forward_events) - - result = await agent.run(user_message, deps=agent_deps) - emitter.log(result.output) - # Emit RUN_FINISHED with research result if available - if agent_deps.research_result: - emitter.finish_run(agent_deps.research_result) - await emitter.close() - - except Exception as e: - logger.exception("Error executing agent") - try: - await send_stream.send( - format_sse_event({"type": "error", "error": str(e)}) - ) - except Exception: - pass - - async def event_generator(): - """Generate SSE events from memory stream.""" - async with create_task_group() as tg: - tg.start_soon(run_agent_with_streaming, send_stream) - async with receive_stream: - async for event_str in receive_stream: - yield event_str - - return StreamingResponse( - event_generator(), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "Connection": "keep-alive", - "X-Accel-Buffering": "no", - }, - ) - - -async def health_check(_: Request) -> JSONResponse: - """Health check endpoint with configuration info.""" - return JSONResponse( - { - "status": "healthy", - "agent_model": str(agent.model), - "research_provider": Config.research.model.provider, - "research_model": Config.research.model.name, - "db_path": str(db_path), - "db_exists": db_path.exists(), - } - ) - - -async def list_documents(_: Request) -> JSONResponse: - """List all documents in the database.""" - client = get_client(db_path) - docs = await client.document_repository.list_all() - return JSONResponse( - { - "documents": [ - {"id": doc.id, "title": doc.title, "uri": doc.uri} for doc in docs - ] - } - ) - - -async def visualize_chunk(request: Request) -> JSONResponse: - """Return visual grounding images for a chunk as base64.""" - import base64 - from io import BytesIO - - chunk_id = request.path_params["chunk_id"] - client = get_client(db_path) - - # Get the chunk - chunk = await client.chunk_repository.get_by_id(chunk_id) - if not chunk: - return JSONResponse({"error": "Chunk not found"}, status_code=404) - - # Get visualization images - images = await client.visualize_chunk(chunk) - if not images: - return JSONResponse({"images": [], "message": "No visual grounding available"}) - - # Convert PIL images to base64 - base64_images = [] - for img in images: - buffer = BytesIO() - img.save(buffer, format="PNG") - buffer.seek(0) - base64_images.append(base64.b64encode(buffer.read()).decode("utf-8")) - - return JSONResponse( - { - "images": base64_images, - "chunk_id": chunk_id, - "document_uri": chunk.document_uri, - } - ) - - -# Create Starlette app -app = Starlette( - routes=[ - Route("/v1/research/stream", stream_research_agent, methods=["POST"]), - Route("/api/documents", list_documents, methods=["GET"]), - Route("/api/visualize/{chunk_id}", visualize_chunk, methods=["GET"]), - Route("/health", health_check, methods=["GET"]), - ], - middleware=[ - Middleware( - CORSMiddleware, - allow_origins=["http://localhost:3000", "http://frontend:3000"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - ], -) - -if __name__ == "__main__": - import uvicorn - - uvicorn.run( - "main:app", - host="0.0.0.0", - port=8000, - reload=True, - ) diff --git a/examples/ag-ui-research/backend/pyproject.toml b/examples/ag-ui-research/backend/pyproject.toml deleted file mode 100644 index 1967be0e..00000000 --- a/examples/ag-ui-research/backend/pyproject.toml +++ /dev/null @@ -1,26 +0,0 @@ -[project] -name = "haiku-rag-research-assistant" -version = "0.1.0" -description = "Haiku.rag research assistant with AG-UI protocol support" -readme = "README.md" -requires-python = ">=3.13" -dependencies = [ - "starlette>=0.50.0", - "uvicorn[standard]>=0.40.0", - "pydantic-ai-slim[ag-ui,openai]>=1.39.0", - "python-dotenv>=1.2.1", - "haiku.rag-slim[agui]>=0.25.0", -] - -[dependency-groups] -dev = ["pyright>=1.1.407", "ruff>=0.14.10"] - 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- "8000:8000" - environment: - - DB_PATH=/app/data/haiku.rag.lancedb - # API keys (set these in your shell or .env file) - - OPENAI_API_KEY=${OPENAI_API_KEY} - - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} - # Ollama connection (use value from .env) - - OLLAMA_BASE_URL=${OLLAMA_BASE_URL} - # Prevent Python bytecode caching for development - - PYTHONDONTWRITEBYTECODE=1 - # Use local haiku_rag_slim for development - - PYTHONPATH=/app/haiku_rag_slim - volumes: - - ${DB_PATH}:/app/data/haiku.rag.lancedb - - ./haiku.rag.yaml:/app/haiku.rag.yaml:ro - # Mount local haiku_rag_slim for development - - ../../haiku_rag_slim:/app/haiku_rag_slim:ro - networks: - - ag-ui-network - extra_hosts: - - "host.docker.internal:host-gateway" - depends_on: - docling-serve: - condition: service_healthy - restart: unless-stopped - healthcheck: - test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] - interval: 30s - timeout: 10s - retries: 3 - - frontend: - build: - context: ./frontend - dockerfile: Dockerfile - ports: - - "3000:3000" - environment: - - BACKEND_URL=http://backend:8000 - volumes: - - ./frontend:/app - - /app/node_modules - - /app/.next - depends_on: - - backend - networks: - - ag-ui-network - restart: unless-stopped - -networks: - ag-ui-network: - driver: bridge diff --git a/examples/ag-ui-research/frontend/.dockerignore b/examples/ag-ui-research/frontend/.dockerignore deleted file mode 100644 index b8bed49a..00000000 --- a/examples/ag-ui-research/frontend/.dockerignore +++ /dev/null @@ -1,7 +0,0 @@ -node_modules -.next -.git -.gitignore -README.md -npm-debug.log -.env*.local diff --git a/examples/ag-ui-research/frontend/.gitignore b/examples/ag-ui-research/frontend/.gitignore deleted file mode 100644 index ed12896f..00000000 --- a/examples/ag-ui-research/frontend/.gitignore +++ /dev/null @@ -1,39 +0,0 @@ -# dependencies -/node_modules -/.pnp -.pnp.* -.yarn/* -!.yarn/patches -!.yarn/plugins -!.yarn/releases -!.yarn/versions - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* - -# env files -.env*.local -.env - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts diff --git a/examples/ag-ui-research/frontend/Dockerfile b/examples/ag-ui-research/frontend/Dockerfile deleted file mode 100644 index 0959f3ef..00000000 --- a/examples/ag-ui-research/frontend/Dockerfile +++ /dev/null @@ -1,12 +0,0 @@ -# Development Dockerfile for Next.js frontend -FROM node:22-alpine - -WORKDIR /app - -COPY package.json package-lock.json* ./ -RUN npm install --legacy-peer-deps -COPY . . -EXPOSE 3000 - -# Run in development mode with hot reload -CMD ["npm", "run", "dev"] diff --git a/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts b/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts deleted file mode 100644 index 5f8fab11..00000000 --- a/examples/ag-ui-research/frontend/app/api/copilotkit/route.ts +++ /dev/null @@ -1,33 +0,0 @@ -import { HttpAgent } from "@ag-ui/client"; -import { - CopilotRuntime, - copilotRuntimeNextJSAppRouterEndpoint, - ExperimentalEmptyAdapter, -} from "@copilotkit/runtime"; -import type { NextRequest } from "next/server"; - -// Connect CopilotKit to PydanticAI via HttpAgent -// The HttpAgent creates a bridge between the Next.js frontend and the Python backend -// It communicates with the server created by agent.to_ag_ui() -const runtime = new CopilotRuntime({ - agents: { - // "research_agent" maps to the agent name used in useCoAgent() on the frontend - research_agent: new HttpAgent({ - url: `${process.env.BACKEND_URL || "http://backend:8000"}/v1/research/stream`, - }), - }, -}); - -// Service adapter for multi-agent support (empty since we only have one agent) -const serviceAdapter = new ExperimentalEmptyAdapter(); - -// Next.js API route handler that proxies requests between frontend and backend -export async function POST(request: NextRequest) { - const { handleRequest } = copilotRuntimeNextJSAppRouterEndpoint({ - runtime, - serviceAdapter, - endpoint: "/api/copilotkit", - }); - - return handleRequest(request); -} diff --git a/examples/ag-ui-research/frontend/app/globals.css b/examples/ag-ui-research/frontend/app/globals.css deleted file mode 100644 index 8a9205a3..00000000 --- a/examples/ag-ui-research/frontend/app/globals.css +++ /dev/null @@ -1,24 +0,0 @@ -* { - box-sizing: border-box; - padding: 0; - margin: 0; -} - -html, -body { - max-width: 100vw; - overflow-x: hidden; - font-family: - system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, - "Helvetica Neue", Arial, sans-serif; -} - -body { - background: linear-gradient(to bottom, #f8f9fa, #e9ecef); - min-height: 100vh; -} - -a { - color: inherit; - text-decoration: none; -} diff --git a/examples/ag-ui-research/frontend/app/layout.tsx b/examples/ag-ui-research/frontend/app/layout.tsx deleted file mode 100644 index 482e8df0..00000000 --- a/examples/ag-ui-research/frontend/app/layout.tsx +++ /dev/null @@ -1,20 +0,0 @@ -import type { Metadata } from "next"; -import "./globals.css"; - -export const metadata: Metadata = { - title: "Haiku.rag Research Assistant", - description: - "Interactive research powered by Haiku.rag, Pydantic AI, and AG-UI", -}; - -export default function RootLayout({ - children, -}: Readonly<{ - children: React.ReactNode; -}>) { - return ( - - {children} - - ); -} diff --git a/examples/ag-ui-research/frontend/app/page.tsx b/examples/ag-ui-research/frontend/app/page.tsx deleted file mode 100644 index b584009a..00000000 --- a/examples/ag-ui-research/frontend/app/page.tsx +++ /dev/null @@ -1,9 +0,0 @@ -import Agent from "@/components/Agent"; - -export default function Home() { - return ( -
- -
- ); -} diff --git a/examples/ag-ui-research/frontend/biome.json b/examples/ag-ui-research/frontend/biome.json deleted file mode 100644 index 03241efc..00000000 --- a/examples/ag-ui-research/frontend/biome.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "$schema": "https://biomejs.dev/schemas/2.2.6/schema.json", - "vcs": { - "enabled": false, - "clientKind": "git", - "useIgnoreFile": false - }, - "files": { - "ignoreUnknown": false - }, - "formatter": { - "enabled": true, - "indentStyle": "tab" - }, - "linter": { - "enabled": true, - "rules": { - "recommended": true - } - }, - "javascript": { - "formatter": { - "quoteStyle": "double" - } - }, - "assist": { - "enabled": true, - "actions": { - "source": { - "organizeImports": "on" - } - } - } -} diff --git a/examples/ag-ui-research/frontend/components/Agent.tsx b/examples/ag-ui-research/frontend/components/Agent.tsx deleted file mode 100644 index 2f1ebc92..00000000 --- a/examples/ag-ui-research/frontend/components/Agent.tsx +++ /dev/null @@ -1,449 +0,0 @@ -"use client"; - -import { - CopilotKit, - useCoAgent, - useCopilotAction, - useCopilotContext, -} from "@copilotkit/react-core"; -import { CopilotChat } from "@copilotkit/react-ui"; -import "@copilotkit/react-ui/styles.css"; -import { useState } from "react"; -import DocumentSelector from "./DocumentSelector"; -import StateDisplay from "./StateDisplay"; - -interface SearchAnswer { - query: string; - answer: string; - confidence: number; - cited_chunks: string[]; - citations: { - document_id: string; - chunk_id: string; - document_uri: string; - document_title?: string; - page_numbers: number[]; - headings?: string[]; - content: string; - }[]; -} - -interface ResearchState { - context: { - original_question: string; - sub_questions: string[]; - qa_responses: SearchAnswer[]; - }; - iterations: number; - max_iterations: number; - confidence_threshold: number; - max_concurrency: number; - last_eval: { - new_questions: string[]; - confidence_score: number; - is_sufficient: boolean; - reasoning: string; - } | null; - result?: { - title: string; - executive_summary: string; - main_findings: string[]; - conclusions: string[]; - limitations: string[]; - recommendations: string[]; - sources_summary: string; - }; - current_activity?: string; - current_activity_message?: string; - documentFilter?: string[]; -} - -interface DecisionArgs { - original_question: string; - sub_questions: string[]; - qa_responses: SearchAnswer[]; -} - -type DecisionAction = "search" | "synthesize" | "modify_questions"; - -interface DecisionResult { - action: DecisionAction; - questions?: string[]; -} - -function DecisionUI({ - args, - onResolve, -}: { - args: DecisionArgs; - onResolve: (result: DecisionResult) => void | Promise; -}) { - const [editableQuestions, setEditableQuestions] = useState( - args.sub_questions || [], - ); - const [newQuestion, setNewQuestion] = useState(""); - const [submitting, setSubmitting] = useState(false); - - const qaCount = args.qa_responses?.length || 0; - const hasQuestions = editableQuestions.length > 0; - const canSearch = hasQuestions && !submitting; - const canSynthesize = qaCount > 0 && !submitting; - - const questionsModified = - editableQuestions.length !== args.sub_questions.length || - editableQuestions.some((q, i) => q !== args.sub_questions[i]); - - const handleSubmit = (action: DecisionAction, questions?: string[]) => { - setSubmitting(true); - onResolve({ action, questions }); - }; - - const handleSearch = () => { - handleSubmit( - questionsModified ? "modify_questions" : "search", - editableQuestions, - ); - }; - - const handleSynthesize = () => { - handleSubmit("synthesize"); - }; - - const handleRemoveQuestion = (index: number) => { - if (submitting) return; - setEditableQuestions(editableQuestions.filter((_, i) => i !== index)); - }; - - const handleAddQuestion = () => { - if (submitting || !newQuestion.trim()) return; - setEditableQuestions([...editableQuestions, newQuestion.trim()]); - setNewQuestion(""); - }; - - if (submitting) { - return null; - } - - return ( -
-
- Research Decision Point -
- -
- {qaCount} answers collected -
- -
-
- Pending Questions ({editableQuestions.length}): -
- {editableQuestions.map((q, idx) => ( -
- {q} - -
- ))} -
- -
- setNewQuestion(e.target.value)} - placeholder="Add a new question..." - style={{ - flex: 1, - padding: "0.5rem", - border: "1px solid #cbd5e1", - borderRadius: "4px", - fontSize: "0.85rem", - }} - onKeyDown={(e) => { - if (e.key === "Enter") handleAddQuestion(); - }} - /> - -
- -
- - -
-
- ); -} - -const BACKEND_URL = - process.env.NEXT_PUBLIC_BACKEND_URL || "http://localhost:8000"; - -function AgentContent() { - const { state, setState, running } = useCoAgent({ - name: "research_agent", - initialState: { - context: { - original_question: "", - sub_questions: [], - qa_responses: [], - }, - iterations: 0, - max_iterations: 3, - confidence_threshold: 0.8, - max_concurrency: 1, - last_eval: null, - documentFilter: [], - }, - }); - - const { threadId } = useCopilotContext(); - - const handleDocumentFilterChange = (ids: string[]) => { - setState({ ...state, documentFilter: ids }); - }; - - const sendToolResult = async (result: DecisionResult) => { - if (!threadId) { - console.error("No threadId available to send tool result"); - return; - } - - try { - const response = await fetch(`${BACKEND_URL}/v1/research/stream`, { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ - threadId, - messages: [ - { - id: crypto.randomUUID(), - role: "tool", - content: JSON.stringify(result), - }, - ], - }), - }); - - if (!response.ok) { - console.error("Failed to send tool result:", response.status); - } - } catch (error) { - console.error("Error sending tool result:", error); - } - }; - - useCopilotAction({ - name: "human_decision", - description: "Pause for human decision on research direction", - parameters: [ - { - name: "original_question", - type: "string", - description: "The original research question", - }, - { - name: "sub_questions", - type: "string[]", - description: "Pending sub-questions to search", - }, - { - name: "qa_responses", - type: "object[]", - description: "Answers collected so far", - }, - ], - renderAndWaitForResponse: ({ args, status }) => { - if (status === "complete") { - return null; - } - - return ( - - ); - }, - }); - - return ( - <> - -
-
- -
- -
-
-
-

- Research State -

-

- Live updates from the research agent -

-
- - {!running && ( -
- -
- )} - - -
-
-
- - ); -} - -export default function Agent() { - return ( - - - - ); -} diff --git a/examples/ag-ui-research/frontend/components/DocumentSelector.tsx b/examples/ag-ui-research/frontend/components/DocumentSelector.tsx deleted file mode 100644 index 7c4c93bd..00000000 --- a/examples/ag-ui-research/frontend/components/DocumentSelector.tsx +++ /dev/null @@ -1,359 +0,0 @@ -"use client"; - -import { useCallback, useEffect, useMemo, useState } from "react"; - -interface Document { - id: string; - title: string; - uri: string; -} - -interface DocumentSelectorProps { - selected: string[]; - onChange: (ids: string[]) => void; - disabled: boolean; -} - -export default function DocumentSelector({ - selected, - onChange, - disabled, -}: DocumentSelectorProps) { - const [documents, setDocuments] = useState([]); - const [loading, setLoading] = useState(true); - const [error, setError] = useState(null); - const [expanded, setExpanded] = useState(true); - const [searchQuery, setSearchQuery] = useState(""); - - useEffect(() => { - const fetchDocuments = async () => { - try { - const response = await fetch( - `${process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000"}/api/documents`, - ); - if (!response.ok) { - throw new Error("Failed to fetch documents"); - } - const data = await response.json(); - const docs = data.documents || []; - setDocuments(docs); - // Select all documents by default if none are selected - if (selected.length === 0 && docs.length > 0) { - onChange(docs.map((d: Document) => d.id)); - } - setError(null); - } catch (err) { - setError(err instanceof Error ? err.message : "Unknown error"); - } finally { - setLoading(false); - } - }; - - fetchDocuments(); - }, []); - - const filteredDocuments = useMemo(() => { - if (!searchQuery.trim()) return documents; - const query = searchQuery.toLowerCase(); - return documents.filter( - (doc) => - (doc.title || "").toLowerCase().includes(query) || - (doc.uri || "").toLowerCase().includes(query), - ); - }, [documents, searchQuery]); - - const handleToggle = useCallback( - (id: string) => { - if (disabled) return; - if (selected.includes(id)) { - onChange(selected.filter((s) => s !== id)); - } else { - onChange([...selected, id]); - } - }, - [selected, onChange, disabled], - ); - - const handleSelectAll = useCallback(() => { - if (disabled) return; - const filteredIds = filteredDocuments.map((d) => d.id); - const allFilteredSelected = filteredIds.every((id) => - selected.includes(id), - ); - if (allFilteredSelected) { - // Deselect all filtered documents - onChange(selected.filter((id) => !filteredIds.includes(id))); - } else { - // Select all filtered documents (add to existing selection) - const newSelection = [...new Set([...selected, ...filteredIds])]; - onChange(newSelection); - } - }, [selected, filteredDocuments, onChange, disabled]); - - const selectedCount = selected.length; - const totalCount = documents.length; - const filterActive = selectedCount > 0 && selectedCount < totalCount; - - return ( -
- - - {expanded && ( -
- {loading && ( -
- Loading documents... -
- )} - - {error && ( -
- {error} -
- )} - - {!loading && !error && documents.length === 0 && ( -
- No documents in database -
- )} - - {!loading && !error && documents.length > 0 && ( - <> - {/* Search Input */} -
- setSearchQuery(e.target.value)} - disabled={disabled} - style={{ - width: "100%", - padding: "0.5rem 0.75rem", - fontSize: "0.875rem", - border: "1px solid #e2e8f0", - borderRadius: "4px", - background: disabled ? "#f7fafc" : "white", - color: disabled ? "#a0aec0" : "#2d3748", - outline: "none", - }} - /> -
- - {/* Select All / Clear */} -
-
- - - {selectedCount} of {totalCount} selected - -
- {searchQuery && ( - - Showing {filteredDocuments.length} of {totalCount} - - )} -
- - {/* Document List */} -
- {filteredDocuments.map((doc) => { - const isSelected = selected.includes(doc.id); - return ( - - ); - })} -
- - )} - - {disabled && ( -
- Filter locked during research -
- )} -
- )} -
- ); -} diff --git a/examples/ag-ui-research/frontend/components/StateDisplay.tsx b/examples/ag-ui-research/frontend/components/StateDisplay.tsx deleted file mode 100644 index 3c9e129f..00000000 --- a/examples/ag-ui-research/frontend/components/StateDisplay.tsx +++ /dev/null @@ -1,968 +0,0 @@ -"use client"; - -import { Markdown } from "@copilotkit/react-ui"; -import { useCallback, useState } from "react"; - -interface VisualGroundingState { - isOpen: boolean; - chunkId: string | null; - images: string[]; - loading: boolean; - error: string | null; -} - -interface Citation { - document_id: string; - chunk_id: string; - document_uri: string; - document_title?: string; - page_numbers: number[]; - headings?: string[]; - content: string; -} - -interface SearchAnswer { - query: string; - answer: string; - confidence: number; - cited_chunks: string[]; - citations: Citation[]; -} - -interface ResearchContext { - original_question: string; - sub_questions: string[]; - qa_responses: SearchAnswer[]; -} - -interface EvaluationResult { - new_questions: string[]; - confidence_score: number; - is_sufficient: boolean; - reasoning: string; -} - -interface ResearchReport { - title: string; - executive_summary: string; - main_findings: string[]; - conclusions: string[]; - limitations: string[]; - recommendations: string[]; - sources_summary: string; -} - -interface ResearchState { - context: ResearchContext; - iterations: number; - max_iterations: number; - confidence_threshold: number; - max_concurrency: number; - last_eval: EvaluationResult | null; - result?: ResearchReport; - current_activity?: string; - current_activity_message?: string; -} - -interface StateDisplayProps { - state: ResearchState; -} - -export default function StateDisplay({ state }: StateDisplayProps) { - const [expandedSections, setExpandedSections] = useState< - Record - >({ - questions: true, - report: true, - }); - - const [expandedQuestions, setExpandedQuestions] = useState< - Record - >({}); - - const [visualGrounding, setVisualGrounding] = useState({ - isOpen: false, - chunkId: null, - images: [], - loading: false, - error: null, - }); - - const fetchVisualGrounding = useCallback(async (chunkId: string) => { - setVisualGrounding({ - isOpen: true, - chunkId, - images: [], - loading: true, - error: null, - }); - - try { - const response = await fetch( - `${process.env.NEXT_PUBLIC_API_URL || "http://localhost:8000"}/api/visualize/${chunkId}`, - ); - const data = await response.json(); - - if (!response.ok) { - throw new Error(data.error || "Failed to fetch visual grounding"); - } - - setVisualGrounding((prev) => ({ - ...prev, - images: data.images || [], - loading: false, - error: data.images?.length === 0 ? data.message : null, - })); - } catch (err) { - setVisualGrounding((prev) => ({ - ...prev, - loading: false, - error: err instanceof Error ? err.message : "Unknown error", - })); - } - }, []); - - const closeVisualGrounding = useCallback(() => { - setVisualGrounding({ - isOpen: false, - chunkId: null, - images: [], - loading: false, - error: null, - }); - }, []); - - const toggleSection = (section: string) => { - setExpandedSections((prev) => ({ - ...prev, - [section]: !prev[section], - })); - }; - - const toggleQuestion = (questionId: string) => { - setExpandedQuestions((prev) => ({ - ...prev, - [questionId]: !prev[questionId], - })); - }; - - // Calculate research progress based on iterations - const researchProgress = - state.max_iterations > 0 - ? (state.iterations / state.max_iterations) * 100 - : 0; - const confidence = state.last_eval?.confidence_score || 0; - - return ( -
- {/* Question */} - {state.context?.original_question && ( -
-
- Question -
-
- {state.context.original_question} -
-
- )} - - {/* Research Progress - only show when research is in progress (not when complete) */} - {(state.iterations > 0 || state.current_activity) && !state.result && ( -
- {/* Current Activity */} - {state.current_activity && ( -
0 ? "1rem" : 0, - }} - > -
- - - {state.current_activity - .replace(/_/g, " ") - .replace(/\b\w/g, (c) => c.toUpperCase())} - -
- {state.current_activity_message && ( -
- {state.current_activity_message} -
- )} -
- )} - {/* Iteration Progress Bar */} - {state.iterations > 0 && ( -
-
- - Iterations - - - {state.iterations}/{state.max_iterations} - -
-
-
-
-
- )} -
- )} - - {/* Confidence Meter */} - {confidence > 0 && ( -
-
- Confidence -
-
-
-
0.8 - ? "#48bb78" - : confidence > 0.5 - ? "#ed8936" - : "#f56565", - transition: "width 0.3s ease", - }} - /> -
-
0.8 - ? "#48bb78" - : confidence > 0.5 - ? "#ed8936" - : "#f56565", - }} - > - {(confidence * 100).toFixed(0)}% -
-
- {state.last_eval?.reasoning && ( -
- -
- )} -
- )} - - {/* Answers */} - {state.context?.qa_responses && state.context.qa_responses.length > 0 && ( -
- - {expandedSections.questions && ( -
- {/* Show all qa_responses (each has query + answer) */} - {state.context.qa_responses.map((qaResponse, idx) => { - const questionId = `q-${idx}`; - - return ( -
- - - {/* QA Response nested inside question */} - {expandedQuestions[questionId] && ( -
-
- Answer -
-
-
- -
-
- - {/* Citations with visual grounding info */} - {qaResponse.citations && - qaResponse.citations.length > 0 && ( -
-
- Citations ({qaResponse.citations.length}) -
- {qaResponse.citations.map((citation, citIdx) => ( -
-
-
- {citation.document_title || - citation.document_uri} -
- {citation.page_numbers && - citation.page_numbers.length > 0 && ( -
- {citation.page_numbers.length === 1 - ? `p. ${citation.page_numbers[0]}` - : `pp. ${citation.page_numbers[0]}-${citation.page_numbers[citation.page_numbers.length - 1]}`} -
- )} -
- {citation.headings && - citation.headings.length > 0 && ( -
- {citation.headings.join(" › ")} -
- )} -
- {citation.content.slice(0, 200)} - {citation.content.length > 200 && "…"} -
- -
- ))} -
- )} -
- )} -
- ); - })} -
- )} -
- )} - - {/* Final Report */} - {state.result && ( -
- - {expandedSections.report && ( -
-

- {state.result.title} -

-
-

- Executive Summary -

-
- -
-
-
-

- Main Findings -

-
    - {state.result.main_findings.map((finding, idx) => ( -
  • - -
  • - ))} -
-
-
-

- Conclusions -

-
    - {state.result.conclusions.map((conclusion, idx) => ( -
  • - -
  • - ))} -
-
- {state.result.recommendations.length > 0 && ( -
-

- Recommendations -

-
    - {state.result.recommendations.map((rec, idx) => ( -
  • - -
  • - ))} -
-
- )} - {state.result.limitations.length > 0 && ( -
-

- Limitations -

-
    - {state.result.limitations.map((lim, idx) => ( -
  • - -
  • - ))} -
-
- )} -
-

- Sources -

-
- -
-
-
- )} -
- )} - - {/* Visual Grounding Modal */} - {visualGrounding.isOpen && ( -
-
e.stopPropagation()} - > - -

- Visual Grounding -

- {visualGrounding.loading && ( -
- Loading... -
- )} - {visualGrounding.error && ( -
- {visualGrounding.error} -
- )} - {!visualGrounding.loading && - !visualGrounding.error && - visualGrounding.images.length > 0 && ( -
- {visualGrounding.images.map((img, idx) => ( -
-
- Page {idx + 1} of {visualGrounding.images.length} -
- {`Page -
- ))} -
- )} -
-
- )} -
- ); -} diff --git a/examples/ag-ui-research/frontend/next.config.ts b/examples/ag-ui-research/frontend/next.config.ts deleted file mode 100644 index 398b0d5c..00000000 --- a/examples/ag-ui-research/frontend/next.config.ts +++ /dev/null @@ -1,7 +0,0 @@ -import type { NextConfig } from "next"; - -const nextConfig: NextConfig = { - output: "standalone", -}; - -export default nextConfig; diff --git a/examples/ag-ui-research/frontend/package-lock.json b/examples/ag-ui-research/frontend/package-lock.json deleted file mode 100644 index 7f62f1a7..00000000 --- a/examples/ag-ui-research/frontend/package-lock.json +++ /dev/null @@ -1,11689 +0,0 @@ -{ - "name": "ag-ui-frontend", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "ag-ui-frontend", - "version": "0.1.0", - "dependencies": { - "@ag-ui/client": "^0.0.42", - "@copilotkit/react-core": "^1.10.6", - "@copilotkit/react-ui": "^1.10.6", - "@copilotkit/runtime": "^1.10.6", - "next": "15.5.5", - 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"scripts": { - "dev": "next dev", - "build": "next build", - "start": "next start", - "lint": "next lint", - "check": "biome check app components", - "format": "biome check --write app components" - }, - "dependencies": { - "@ag-ui/client": "^0.0.42", - "@copilotkit/react-core": "^1.50.0", - "@copilotkit/react-ui": "^1.50.0", - "@copilotkit/runtime": "^1.50.0", - "next": "15.5.5", - "react": "^19.0.0", - "react-dom": "^19.0.0" - }, - "devDependencies": { - "@biomejs/biome": "2.2.6", - "@types/node": "^22", - "@types/react": "^19", - "@types/react-dom": "^19", - "typescript": "^5" - } -} diff --git a/examples/ag-ui-research/frontend/public/.gitkeep b/examples/ag-ui-research/frontend/public/.gitkeep deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/ag-ui-research/frontend/tsconfig.json b/examples/ag-ui-research/frontend/tsconfig.json deleted file mode 100644 index 96f8e1b6..00000000 --- a/examples/ag-ui-research/frontend/tsconfig.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "compilerOptions": { - "target": "ES2017", - "lib": ["dom", "dom.iterable", "esnext"], - "allowJs": true, - "skipLibCheck": true, - "strict": true, - "noEmit": true, - "esModuleInterop": true, - "module": "esnext", - "moduleResolution": "bundler", - "resolveJsonModule": true, - "isolatedModules": true, - "jsx": "preserve", - "incremental": true, - "plugins": [ - { - "name": "next" - } - ], - "paths": { - "@/*": ["./*"] - } - }, - "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], - "exclude": ["node_modules"] -} diff --git a/examples/ag-ui-research/haiku.rag.yaml.example b/examples/ag-ui-research/haiku.rag.yaml.example deleted file mode 100644 index 7ce0f926..00000000 --- a/examples/ag-ui-research/haiku.rag.yaml.example +++ /dev/null @@ -1,36 +0,0 @@ -# haiku.rag configuration for ag-ui-research example -# Copy to haiku.rag.yaml and customize - -# Document processing with docling-serve -processing: - converter: docling-serve - chunker: docling-serve - chunk_size: 256 - chunker_type: hybrid - -providers: - docling_serve: - base_url: http://docling-serve:5001 - api_key: "" - ollama: - base_url: http://host.docker.internal:11434 - -research: - model: - provider: ollama - name: gpt-oss:latest - max_iterations: 3 - confidence_threshold: 0.8 - max_concurrency: 1 - -# For OpenAI: -# research: -# model: -# provider: openai -# name: gpt-4o-mini - -# For Anthropic: -# research: -# model: -# provider: anthropic -# name: claude-3-5-haiku-20241022 diff --git a/examples/ag-ui-research/package-lock.json b/examples/ag-ui-research/package-lock.json deleted file mode 100644 index acfbd87f..00000000 --- a/examples/ag-ui-research/package-lock.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "haiku-ag-ui", - "lockfileVersion": 3, - "requires": true, - "packages": {} -} diff --git a/haiku_rag_slim/haiku/rag/app.py b/haiku_rag_slim/haiku/rag/app.py index 1c76b4ff..60cfb41e 100644 --- a/haiku_rag_slim/haiku/rag/app.py +++ b/haiku_rag_slim/haiku/rag/app.py @@ -20,7 +20,6 @@ from rich.progress import ( from haiku.rag.client import HaikuRAG, RebuildMode from haiku.rag.config import AppConfig, Config -from haiku.rag.graph.agui import AGUIConsoleRenderer, stream_graph from haiku.rag.graph.research.dependencies import ResearchContext from haiku.rag.graph.research.graph import build_research_graph from haiku.rag.graph.research.state import ResearchDeps, ResearchState @@ -409,7 +408,6 @@ class HaikuRAGApp: question: str, cite: bool = False, deep: bool = False, - verbose: bool = False, filter: str | None = None, ): """Ask a question using the RAG system. @@ -418,7 +416,6 @@ class HaikuRAGApp: question: The question to ask cite: Include citations in the answer deep: Use deep QA mode (multi-step reasoning) - verbose: Show verbose output filter: SQL WHERE clause to filter documents """ async with HaikuRAG( @@ -430,8 +427,6 @@ class HaikuRAGApp: try: citations = [] if deep: - from haiku.rag.graph.research.models import ResearchReport - graph = build_research_graph(config=self.config) context = ResearchContext(original_question=question) state = ResearchState.from_config( @@ -443,18 +438,7 @@ class HaikuRAGApp: state.search_filter = filter deps = ResearchDeps(client=self.client) - if verbose: - renderer = AGUIConsoleRenderer(self.console) - result_dict = await renderer.render( - stream_graph(graph, state, deps) - ) - report = ( - ResearchReport.model_validate(result_dict) - if result_dict - else None - ) - else: - report = await graph.run(state=state, deps=deps) + report = await graph.run(state=state, deps=deps) self.console.print(f"[bold blue]Question:[/bold blue] {question}") self.console.print() @@ -485,14 +469,11 @@ class HaikuRAGApp: except Exception as e: self.console.print(f"[red]Error: {e}[/red]") - async def research( - self, question: str, verbose: bool = False, filter: str | None = None - ): + async def research(self, question: str, filter: str | None = None): """Run research via the pydantic-graph pipeline. Args: question: The research question - verbose: Show AG-UI event stream during execution filter: SQL WHERE clause to filter documents """ async with HaikuRAG( @@ -512,28 +493,12 @@ class HaikuRAGApp: state.search_filter = filter deps = ResearchDeps(client=client) - if verbose: - # Use AG-UI renderer to process and display events - renderer = AGUIConsoleRenderer(self.console) - report_dict = await renderer.render( - stream_graph(graph, state, deps) - ) - else: - # Run without rendering events, just get the result - report = await graph.run(state=state, deps=deps) - report_dict = ( - report.model_dump() if hasattr(report, "model_dump") else report - ) + report = await graph.run(state=state, deps=deps) - if report_dict is None: + if report is None: self.console.print("[red]Research did not produce a report.[/red]") return - # Convert dict to ResearchReport model - from haiku.rag.graph.research.models import ResearchReport - - report = ResearchReport.model_validate(report_dict) - # Display the report self.console.print("[bold green]Research Report[/bold green]") self.console.rule() @@ -819,7 +784,6 @@ class HaikuRAGApp: enable_mcp: bool = True, mcp_transport: str | None = None, mcp_port: int = 8001, - enable_agui: bool = False, ): """Start the server with selected services.""" async with HaikuRAG( @@ -859,30 +823,6 @@ class HaikuRAGApp: mcp_task = asyncio.create_task(run_mcp()) tasks.append(mcp_task) - # Start AG-UI server if enabled - if enable_agui: - - async def run_agui(): - import uvicorn - - from haiku.rag.graph.agui import create_agui_server - - logger.info( - f"Starting AG-UI server on {self.config.agui.host}:{self.config.agui.port}" - ) - app = create_agui_server(self.config, db_path=self.db_path) - config = uvicorn.Config( - app=app, - host=self.config.agui.host, - port=self.config.agui.port, - log_level="info", - ) - server = uvicorn.Server(config) - await server.serve() - - agui_task = asyncio.create_task(run_agui()) - tasks.append(agui_task) - if not tasks: logger.warning("No services enabled") return diff --git a/haiku_rag_slim/haiku/rag/cli.py b/haiku_rag_slim/haiku/rag/cli.py index 65c0479a..9958cc07 100644 --- a/haiku_rag_slim/haiku/rag/cli.py +++ b/haiku_rag_slim/haiku/rag/cli.py @@ -332,11 +332,6 @@ def ask( "--deep", help="Use deep multi-agent QA for complex questions", ), - verbose: bool = typer.Option( - False, - "--verbose", - help="Show verbose progress output (only with --deep)", - ), filter: str | None = typer.Option( None, "--filter", @@ -345,63 +340,26 @@ def ask( ), ): app = create_app(db) - asyncio.run( - app.ask(question=question, cite=cite, deep=deep, verbose=verbose, filter=filter) - ) + asyncio.run(app.ask(question=question, cite=cite, deep=deep, filter=filter)) @cli.command("research", help="Run multi-agent research and output a concise report") def research( - question: str = typer.Argument( - None, - help="The research question to investigate (required unless --interactive)", - ), + question: str = typer.Argument(..., help="The research question to investigate"), db: Path | None = typer.Option( None, "--db", help="Path to the LanceDB database file", ), - verbose: bool = typer.Option( - False, - "--verbose", - help="Show planning, searching previews, evaluation summary, and stop reason", - ), filter: str | None = typer.Option( None, "--filter", "-f", help="SQL WHERE clause to filter documents (e.g., \"uri LIKE '%arxiv%'\")", ), - interactive: bool = typer.Option( - False, - "--interactive", - "-i", - help="Start interactive research mode with human-in-the-loop", - ), ): app = create_app(db) - - if interactive: - from haiku.rag.cli_chat import interactive_research - from haiku.rag.client import HaikuRAG - - client = HaikuRAG( - db_path=app.db_path, config=app.config, read_only=_read_only, before=_before - ) - try: - interactive_research( - client=client, - config=app.config, - search_filter=filter, - question=question, - ) - finally: - client.close() - else: - if question is None: - typer.echo("Error: Question is required unless using --interactive mode") - raise typer.Exit(1) - asyncio.run(app.research(question=question, verbose=verbose, filter=filter)) + asyncio.run(app.research(question=question, filter=filter)) @cli.command("settings", help="Display current configuration settings") @@ -584,7 +542,7 @@ def inspect( @cli.command( "serve", - help="Start haiku.rag server. Use --monitor, --mcp, and/or --agui to enable services.", + help="Start haiku.rag server. Use --monitor and/or --mcp to enable services.", ) def serve( db: Path | None = typer.Option( @@ -612,17 +570,12 @@ def serve( "--mcp-port", help="Port to bind MCP server to (ignored with --stdio)", ), - agui: bool = typer.Option( - False, - "--agui", - help="Enable AG-UI HTTP server for graph streaming", - ), ) -> None: """Start the server with selected services.""" # Require at least one service flag - if not (monitor or mcp or agui): + if not (monitor or mcp): typer.echo( - "Error: At least one service flag (--monitor, --mcp, or --agui) must be specified" + "Error: At least one service flag (--monitor or --mcp) must be specified" ) raise typer.Exit(1) @@ -640,7 +593,6 @@ def serve( enable_mcp=mcp, mcp_transport=transport, mcp_port=mcp_port, - enable_agui=agui, ) ) diff --git a/haiku_rag_slim/haiku/rag/cli_chat.py b/haiku_rag_slim/haiku/rag/cli_chat.py deleted file mode 100644 index 68882cf3..00000000 --- a/haiku_rag_slim/haiku/rag/cli_chat.py +++ /dev/null @@ -1,489 +0,0 @@ -"""Interactive CLI chat loop for research graph with human-in-the-loop.""" - -import asyncio -import json - -from pydantic_ai import Agent -from rich.console import Console -from rich.markdown import Markdown -from rich.panel import Panel -from rich.prompt import Prompt - -from haiku.rag.client import HaikuRAG -from haiku.rag.config import get_config -from haiku.rag.config.models import AppConfig -from haiku.rag.graph.agui.emitter import AGUIEmitter -from haiku.rag.graph.research.dependencies import ResearchContext -from haiku.rag.graph.research.graph import build_research_graph -from haiku.rag.graph.research.models import ResearchReport -from haiku.rag.graph.research.state import HumanDecision, ResearchDeps, ResearchState -from haiku.rag.utils import get_model - -INITIAL_CHAT_PROMPT = """You are a research assistant. The user hasn't started a research task yet. - -You can: -1. Chat with the user - greet them, answer questions about what you can do -2. Detect when they want to research something - -## Actions: -- "chat": User is chatting, greeting, or asking questions (set message with your response) -- "research": User wants to research a topic (extract the research question into research_question) - -## Guidelines: -- If the user provides a clear research question or topic, set action="research" and extract the question -- If the user is just chatting or asking what you can do, set action="chat" and respond helpfully -- Be friendly and explain you can help them research topics by searching a knowledge base - -Examples: -- "hi" → action="chat", message="Hello! I'm a research assistant. I can help you research topics by searching through documents and synthesizing findings. What would you like to explore?" -- "what can you do?" → action="chat", message="I help you conduct research! Give me a question or topic, and I'll break it into sub-questions, search for answers, and synthesize a report. What are you curious about?" -- "tell me about Python's memory management" → action="research", research_question="How does Python's memory management work?" -- "I want to understand how RAG systems work" → action="research", research_question="How do RAG (Retrieval-Augmented Generation) systems work?" -""" - -RESEARCH_ASSISTANT_PROMPT = """You are a research assistant helping the user conduct research on a topic. - -You are at a decision point in the research workflow. You can: -1. Chat with the user - answer questions, discuss the research, make suggestions -2. Take workflow actions when the user requests them - -## Workflow Actions (set in the action field): -- "search": Search the pending questions (user says: "go", "search", "yes", "continue", "looks good") -- "synthesize": Generate final report (user says: "done", "finish", "synthesize", "generate report") -- "add_questions": Add NEW research questions to the existing list -- "modify_questions": REPLACE all pending questions with a new list (use when user wants to remove, keep only certain questions, or change the questions) -- "chat": Have a conversation without modifying questions - -## IMPORTANT - Modifying Questions: -- "use only the first question" → action="modify_questions", questions=[first question from the list] -- "drop questions 2 and 3" → action="modify_questions", questions=[remaining questions] -- "keep only questions about X" → action="modify_questions", questions=[filtered list] -- "remove the duplicate" → action="modify_questions", questions=[deduplicated list] -- When user wants to reduce/filter/keep-only, use "modify_questions" NOT "chat" - -## Guidelines: -- If the user wants to modify the question list in ANY way (remove, keep only, filter), use "modify_questions" -- For "modify_questions", include ALL questions that should remain in the questions field -- You can combine "chat" with a message to explain what you're doing -- If just chatting without changes, set action="chat" and provide helpful response in message -""" - - -async def initial_chat( - user_message: str, - config: AppConfig, -) -> HumanDecision: - """Handle initial conversation before research starts. - - Args: - user_message: The user's message - config: Application configuration - - Returns: - HumanDecision with chat response or research question - """ - agent: Agent[None, HumanDecision] = Agent( - model=get_model(config.research.model, config), - output_type=HumanDecision, - instructions=INITIAL_CHAT_PROMPT, - retries=2, - ) - - result = await agent.run(user_message) - return result.output - - -async def interpret_user_decision( - user_message: str, - sub_questions: list[str], - qa_responses: list[dict], - config: AppConfig, -) -> HumanDecision: - """Interpret a natural language user message into a HumanDecision. - - Args: - user_message: The user's natural language input - sub_questions: Current sub-questions pending search - qa_responses: Answers already collected - config: Application configuration - - Returns: - HumanDecision with the interpreted action, questions, and/or message - """ - agent: Agent[None, HumanDecision] = Agent( - model=get_model(config.research.model, config), - output_type=HumanDecision, - instructions=RESEARCH_ASSISTANT_PROMPT, - retries=2, - ) - - # Build context with full research state - answers_summary = "" - if qa_responses: - answers_parts = [] - for qa in qa_responses: - conf = f"{qa['confidence']:.0%}" if qa.get("confidence") else "N/A" - answers_parts.append( - f"Q: {qa['query']}\nA: {qa['answer'][:300]}... (confidence: {conf})" - ) - answers_summary = "\n\n".join(answers_parts) - - context = f"""Current research state: -- Answers collected: {len(qa_responses)} -- Pending questions to search: {len(sub_questions)} - -Pending questions: -{chr(10).join(f"- {q}" for q in sub_questions) if sub_questions else "(none)"} - -{f"Collected answers:{chr(10)}{answers_summary}" if answers_summary else ""} - -User message: {user_message}""" - - result = await agent.run(context) - return result.output - - -async def run_interactive_research( - question: str, - client: HaikuRAG, - config: AppConfig | None = None, - search_filter: str | None = None, -) -> ResearchReport: - """Run interactive research with human-in-the-loop decision points. - - Args: - question: The research question - client: HaikuRAG client for document operations - config: Application configuration (uses global config if None) - search_filter: Optional SQL WHERE clause to filter documents - - Returns: - ResearchReport with the final synthesis - """ - config = config or get_config() - console = Console() - - # Build interactive graph - graph = build_research_graph(config=config, include_plan=True, interactive=True) - - # Create async queue for human input - human_input_queue: asyncio.Queue[HumanDecision] = asyncio.Queue() - - # Create emitter - emitter: AGUIEmitter[ResearchState, ResearchReport] = AGUIEmitter() - - # Create deps with queue - deps = ResearchDeps( - client=client, - agui_emitter=emitter, - human_input_queue=human_input_queue, - interactive=True, - ) - - # Create initial state - context = ResearchContext(original_question=question) - state = ResearchState.from_config(context=context, config=config) - state.search_filter = search_filter - - # Start the run - emitter.start_run(state) - - # Run graph in background task - async def run_graph() -> ResearchReport: - try: - result = await graph.run(state=state, deps=deps) - emitter.finish_run(result) - return result - except Exception as e: - emitter.error(e) - raise - - graph_task = asyncio.create_task(run_graph()) - - # Process events and handle human decision points - try: - async for event in emitter: - event_type = event.get("type") - - if event_type == "STEP_STARTED": - step_name = event.get("stepName", "") - if step_name == "plan": - console.print("[dim]Planning research...[/dim]") - elif step_name.startswith("search:"): - query = step_name.replace("search: ", "") - console.print(f"[dim]Searching: {query}[/dim]") - elif step_name == "synthesize": - console.print("[dim]Synthesizing report...[/dim]") - - elif event_type == "STATE_SNAPSHOT" or event_type == "STATE_DELTA": - # State updated, could show progress - pass - - elif event_type == "TOOL_CALL_START": - tool_name = event.get("toolCallName") - if tool_name == "human_decision": - # Will get args in next event - pass - - elif event_type == "TOOL_CALL_ARGS": - delta = event.get("delta", "{}") - args = json.loads(delta) if isinstance(delta, str) else delta - original_question = args.get("original_question", "") - sub_questions = list(args.get("sub_questions", [])) - qa_responses = args.get("qa_responses", []) - iterations = args.get("iterations", 0) - - # Loop for modifications until user wants to proceed - while True: - # Show research state - console.print() - console.print( - Panel( - f"[bold]{original_question}[/bold]", - title="Research Question", - border_style="blue", - ) - ) - - # Show collected answers - if qa_responses: - answers_text = [] - for i, qa in enumerate(qa_responses, 1): - conf = ( - f"{qa['confidence']:.0%}" - if qa.get("confidence") - else "N/A" - ) - answer_preview = ( - qa["answer"][:200] + "..." - if len(qa["answer"]) > 200 - else qa["answer"] - ) - answers_text.append( - f"[cyan]{i}. {qa['query']}[/cyan]\n" - f" [dim]Confidence: {conf} | Citations: {qa.get('citations_count', 0)}[/dim]\n" - f" {answer_preview}" - ) - console.print( - Panel( - "\n\n".join(answers_text), - title=f"Answers Collected ({len(qa_responses)})", - border_style="green", - ) - ) - - # Show pending questions - if sub_questions: - console.print( - Panel( - "\n".join( - f"{i + 1}. {q}" for i, q in enumerate(sub_questions) - ), - title="Pending Questions to Search", - border_style="cyan", - ) - ) - else: - console.print("[dim]No pending questions.[/dim]") - - if iterations > 0: - console.print(f"[dim]Iteration: {iterations}[/dim]") - - # Prompt user with context-aware hints - console.print() - hints = [] - if sub_questions: - hints.append("search questions") - hints.append("modify questions") - if qa_responses: - hints.append("generate report") - hint_text = f" [dim]({', '.join(hints)})[/dim]" if hints else "" - user_input = Prompt.ask( - f"[bold]What would you like to do?[/bold]{hint_text}" - ) - - # Chat with research assistant - console.print("[dim]Thinking...[/dim]") - decision = await interpret_user_decision( - user_message=user_input, - sub_questions=sub_questions, - qa_responses=qa_responses, - config=config, - ) - - # Handle modifications and chat locally, continue loop - if decision.action == "chat": - if decision.message: - console.print( - f"\n[bold cyan]Assistant:[/bold cyan] {decision.message}" - ) - continue - elif decision.action == "add_questions" and decision.questions: - sub_questions.extend(decision.questions) - console.print( - f"[green]Added {len(decision.questions)} question(s)[/green]" - ) - continue - elif decision.action == "modify_questions" and decision.questions: - sub_questions = list(decision.questions) - console.print( - f"[green]Replaced with {len(decision.questions)} question(s)[/green]" - ) - continue - - # User wants to proceed - send final decision - action_display = { - "search": "Searching questions", - "synthesize": "Generating report", - } - console.print( - f"[dim]→ {action_display.get(decision.action, decision.action)}[/dim]" - ) - - # Include any accumulated question changes - if decision.action == "search": - decision = HumanDecision( - action="modify_questions", questions=sub_questions - ) - - await human_input_queue.put(decision) - break - - elif event_type == "TEXT_MESSAGE_CHUNK": - # Log message from graph - message = event.get("delta", "") - if message: - console.print(f"[dim]{message}[/dim]") - - elif event_type == "RUN_FINISHED": - break - - elif event_type == "RUN_ERROR": - error_msg = event.get("message", "Unknown error") - console.print(f"[red]Error: {error_msg}[/red]") - break - - # Wait for graph to complete - report = await graph_task - return report - - except Exception as e: - graph_task.cancel() - raise e - finally: - await emitter.close() - - -async def run_chat_loop( - client: HaikuRAG, - config: AppConfig | None = None, - search_filter: str | None = None, - question: str | None = None, -) -> None: - """Run an interactive chat loop for research. - - Args: - client: HaikuRAG client for document operations - config: Application configuration (uses global config if None) - search_filter: Optional SQL WHERE clause to filter documents - question: Optional initial research question (skips initial chat if provided) - """ - config = config or get_config() - console = Console() - - console.print( - Panel( - "[bold cyan]Interactive Research Mode[/bold cyan]\n\n" - "Chat with me or tell me what you'd like to research.\n" - "Type [green]exit[/green] or [green]quit[/green] to end the session.", - title="haiku.rag Research Assistant", - border_style="cyan", - ) - ) - - while True: - try: - # Use provided question or get one through conversation - if question: - research_question = question - console.print(f"[dim]Starting research: {research_question}[/dim]") - question = None # Clear so subsequent loops go through chat - else: - # Initial conversation loop - chat until user wants to research - research_question = None - while research_question is None: - user_input = Prompt.ask("\n[bold blue]You[/bold blue]") - - if not user_input.strip(): - continue - - if user_input.lower().strip() in ("exit", "quit", "q"): - console.print("[dim]Goodbye![/dim]") - return - - console.print("[dim]Thinking...[/dim]") - decision = await initial_chat(user_input, config) - - if decision.action == "research" and decision.research_question: - research_question = decision.research_question - console.print( - f"[dim]Starting research: {research_question}[/dim]" - ) - elif decision.action == "chat" and decision.message: - console.print( - f"\n[bold cyan]Assistant:[/bold cyan] {decision.message}" - ) - else: - # Fallback - treat as research question - research_question = user_input - - console.print() - report = await run_interactive_research( - question=research_question, - client=client, - config=config, - search_filter=search_filter, - ) - - # Display final report - console.print() - console.print( - Panel( - Markdown(f"## {report.title}\n\n{report.executive_summary}"), - title="Research Report", - border_style="green", - ) - ) - - if report.main_findings: - findings = "\n".join(f"- {f}" for f in report.main_findings[:5]) - console.print(Markdown(f"**Key Findings:**\n{findings}")) - - if report.conclusions: - conclusions = "\n".join(f"- {c}" for c in report.conclusions[:3]) - console.print(Markdown(f"**Conclusions:**\n{conclusions}")) - - console.print(Markdown(f"**Sources:** {report.sources_summary}")) - - except KeyboardInterrupt: - console.print("\n[dim]Goodbye![/dim]") - return - except Exception as e: - console.print(f"[red]Error: {e}[/red]") - - -def interactive_research( - client: HaikuRAG, - config: AppConfig | None = None, - search_filter: str | None = None, - question: str | None = None, -) -> None: - """Entry point for interactive research mode. - - Args: - client: HaikuRAG client for document operations - config: Application configuration (uses global config if None) - search_filter: Optional SQL WHERE clause to filter documents - question: Optional initial research question (skips initial chat if provided) - """ - asyncio.run(run_chat_loop(client, config, search_filter, question)) diff --git a/haiku_rag_slim/haiku/rag/config/__init__.py b/haiku_rag_slim/haiku/rag/config/__init__.py index 0b233601..75e637bc 100644 --- a/haiku_rag_slim/haiku/rag/config/__init__.py +++ b/haiku_rag_slim/haiku/rag/config/__init__.py @@ -1,12 +1,9 @@ -import os - from haiku.rag.config.loader import ( find_config_file, generate_default_config, load_yaml_config, ) from haiku.rag.config.models import ( - AGUIConfig, AppConfig, ConversionOptions, EmbeddingModelConfig, @@ -26,7 +23,6 @@ from haiku.rag.config.models import ( __all__ = [ "Config", - "AGUIConfig", "AppConfig", "ConversionOptions", "EmbeddingModelConfig", diff --git a/haiku_rag_slim/haiku/rag/config/models.py b/haiku_rag_slim/haiku/rag/config/models.py index 5462056f..9c5a9250 100644 --- a/haiku_rag_slim/haiku/rag/config/models.py +++ b/haiku_rag_slim/haiku/rag/config/models.py @@ -171,15 +171,6 @@ class ProvidersConfig(BaseModel): docling_serve: DoclingServeConfig = Field(default_factory=DoclingServeConfig) -class AGUIConfig(BaseModel): - host: str = "0.0.0.0" - port: int = 8000 - cors_origins: list[str] = ["*"] - cors_credentials: bool = True - cors_methods: list[str] = ["GET", "POST", "OPTIONS"] - cors_headers: list[str] = ["*"] - - class PromptsConfig(BaseModel): domain_preamble: str = "" qa: str | None = None @@ -204,5 +195,4 @@ class AppConfig(BaseModel): processing: ProcessingConfig = Field(default_factory=ProcessingConfig) search: SearchConfig = Field(default_factory=SearchConfig) providers: ProvidersConfig = Field(default_factory=ProvidersConfig) - agui: AGUIConfig = Field(default_factory=AGUIConfig) prompts: PromptsConfig = Field(default_factory=PromptsConfig) diff --git a/haiku_rag_slim/haiku/rag/graph/__init__.py b/haiku_rag_slim/haiku/rag/graph/__init__.py index 56470996..3b9000ef 100644 --- a/haiku_rag_slim/haiku/rag/graph/__init__.py +++ b/haiku_rag_slim/haiku/rag/graph/__init__.py @@ -1,15 +1,5 @@ -from haiku.rag.graph.agui import ( - AGUIConsoleRenderer, - AGUIEmitter, - create_agui_server, - stream_graph, -) from haiku.rag.graph.research.graph import build_research_graph __all__ = [ - "AGUIConsoleRenderer", - "AGUIEmitter", "build_research_graph", - "create_agui_server", - "stream_graph", ] diff --git a/haiku_rag_slim/haiku/rag/graph/agui/__init__.py b/haiku_rag_slim/haiku/rag/graph/agui/__init__.py deleted file mode 100644 index ede844a6..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/__init__.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Generic AG-UI protocol support for haiku.rag graphs.""" - -from haiku.rag.graph.agui.cli_renderer import AGUIConsoleRenderer -from haiku.rag.graph.agui.emitter import ( - AGUIEmitter, - AGUIEvent, - emit_activity, - emit_activity_delta, - emit_run_error, - emit_run_finished, - emit_run_started, - emit_state_delta, - emit_state_snapshot, - emit_step_finished, - emit_step_started, - emit_text_message, - emit_text_message_content, - emit_text_message_end, - emit_text_message_start, - emit_tool_call_args, - emit_tool_call_end, - emit_tool_call_start, -) -from haiku.rag.graph.agui.server import ( - RunAgentInput, - create_agui_app, - create_agui_server, - format_sse_event, -) -from haiku.rag.graph.agui.state import compute_state_delta -from haiku.rag.graph.agui.stream import stream_graph - -__all__ = [ - "AGUIConsoleRenderer", - "AGUIEmitter", - "AGUIEvent", - "RunAgentInput", - "compute_state_delta", - "create_agui_app", - "create_agui_server", - "emit_activity", - "emit_activity_delta", - "emit_run_error", - "emit_run_finished", - "emit_run_started", - "emit_state_delta", - "emit_state_snapshot", - "emit_step_finished", - "emit_step_started", - "emit_text_message", - "emit_text_message_content", - "emit_text_message_end", - "emit_text_message_start", - "emit_tool_call_args", - "emit_tool_call_end", - "emit_tool_call_start", - "format_sse_event", - "stream_graph", -] diff --git a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py b/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py deleted file mode 100644 index 59a88e6d..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/cli_renderer.py +++ /dev/null @@ -1,135 +0,0 @@ -"""Generic CLI renderer for AG-UI events with Rich console output.""" - -from collections.abc import AsyncIterator -from typing import Any - -from rich.console import Console - -from haiku.rag.graph.agui.emitter import AGUIEvent - - -class AGUIConsoleRenderer: - """Renders AG-UI events to Rich console with formatted output. - - Generic renderer that processes AG-UI protocol events and renders them - with Rich formatting. Works with any graph that emits AG-UI events. - """ - - def __init__(self, console: Console | None = None): - """Initialize the renderer. - - Args: - console: Optional Rich console instance (creates new one if not provided) - """ - self.console = console or Console() - - async def render(self, events: AsyncIterator[AGUIEvent]) -> Any | None: - """Process events and render to console, return final result. - - Args: - events: Async iterator of AG-UI events - - Returns: - The final result from RunFinished event, or None - """ - result = None - - async for event in events: - event_type = event.get("type") - - if event_type == "RUN_STARTED": - self._render_run_started(event) - elif event_type == "RUN_FINISHED": - result = event.get("result") - self._render_run_finished() - elif event_type == "RUN_ERROR": - self._render_error(event) - elif event_type == "STEP_STARTED": - self._render_step_started(event) - elif event_type == "STEP_FINISHED": - self._render_step_finished(event) - elif event_type == "TEXT_MESSAGE_CHUNK": - self._render_text_message(event) - elif event_type == "TEXT_MESSAGE_START": - pass # Start of streaming message, no output needed - elif event_type == "TEXT_MESSAGE_CONTENT": - self._render_text_content(event) - elif event_type == "TEXT_MESSAGE_END": - pass # End of streaming message, no output needed - elif event_type == "STATE_SNAPSHOT": - self._render_state_snapshot(event) - elif event_type == "STATE_DELTA": - self._render_state_delta(event) - elif event_type == "ACTIVITY_SNAPSHOT": - self._render_activity(event) - elif event_type == "ACTIVITY_DELTA": - pass # Activity deltas don't need separate rendering - - return result - - def _render_run_started(self, event: AGUIEvent) -> None: - """Render run start event.""" - run_id = event.get("runId", "") - if run_id: - # Show shortened run ID (first 8 chars like our UUIDs) - short_id = run_id[:8] if len(run_id) > 8 else run_id - self.console.print(f"[bold green][RUN_STARTED][/bold green] Run {short_id}") - - def _render_run_finished(self) -> None: - """Render run completion.""" - self.console.print("[bold green][RUN_FINISHED][/bold green] Completed") - - def _render_error(self, event: AGUIEvent) -> None: - """Render error event.""" - message = event.get("message", "Unknown error") - self.console.print(f"[bold red][RUN_ERROR][/bold red] {message}") - - def _render_step_started(self, event: AGUIEvent) -> None: - """Render step start event.""" - step_name = event.get("stepName", "") - if step_name: - display_name = step_name.replace("_", " ").title() - self.console.print( - f"\n[bold cyan][STEP_STARTED][/bold cyan] {display_name}" - ) - - def _render_step_finished(self, event: AGUIEvent) -> None: - """Render step finish event.""" - step_name = event.get("stepName", "") - if step_name: - display_name = step_name.replace("_", " ").title() - self.console.print(f"[cyan][STEP_FINISHED][/cyan] {display_name}") - - def _render_text_message(self, event: AGUIEvent) -> None: - """Render complete text message.""" - delta = event.get("delta", "") - self.console.print(f"[magenta][TEXT_MESSAGE][/magenta] {delta}") - - def _render_text_content(self, event: AGUIEvent) -> None: - """Render streaming text content delta.""" - delta = event.get("delta", "") - self.console.print(delta, end="") - - def _render_activity(self, event: AGUIEvent) -> None: - """Render activity update.""" - content = event.get("content", "") - if content: - self.console.print(f"[yellow][ACTIVITY][/yellow] {content}") - - def _render_state_snapshot(self, event: AGUIEvent) -> None: - """Render full state snapshot.""" - snapshot = event.get("snapshot") - if not snapshot: - return - - self.console.print("[blue][STATE_SNAPSHOT][/blue]") - self.console.print(snapshot, style="dim") - - def _render_state_delta(self, event: AGUIEvent) -> None: - """Render state delta operations.""" - delta = event.get("delta", []) - if not delta: - return - - self.console.print("[blue][STATE_DELTA][/blue]") - self.console.print(delta, style="dim") diff --git a/haiku_rag_slim/haiku/rag/graph/agui/emitter.py b/haiku_rag_slim/haiku/rag/graph/agui/emitter.py deleted file mode 100644 index 08040e21..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/emitter.py +++ /dev/null @@ -1,385 +0,0 @@ -"""Generic AG-UI event emitter for any graph execution.""" - -import asyncio -import hashlib -import json -from collections.abc import AsyncIterator -from typing import Any -from uuid import uuid4 - -from ag_ui.core import ( - ActivitySnapshotEvent, - BaseEvent, - RunErrorEvent, - RunFinishedEvent, - RunStartedEvent, - StateDeltaEvent, - StateSnapshotEvent, - StepFinishedEvent, - StepStartedEvent, - TextMessageChunkEvent, - TextMessageContentEvent, - TextMessageEndEvent, - TextMessageStartEvent, - ToolCallArgsEvent, - ToolCallEndEvent, - ToolCallStartEvent, -) -from pydantic import BaseModel - -from haiku.rag.graph.agui.state import compute_state_delta - -AGUIEvent = dict[str, Any] - - -def _serialize_event(event: BaseEvent) -> AGUIEvent: - """Serialize an ag_ui event to a dict with camelCase keys.""" - return event.model_dump(mode="json", by_alias=True, exclude_none=True) - - -class AGUIEmitter[StateT: BaseModel, ResultT]: - """Generic queue-backed AG-UI event emitter for any graph. - - Manages the lifecycle of AG-UI events including: - - Run lifecycle (start, finish, error) - - Step lifecycle (start, finish) - - Text messages - - State synchronization (snapshots and deltas) - - Activity updates - - Type parameters: - StateT: The Pydantic BaseModel type for graph state - ResultT: The result type returned by the graph - """ - - def __init__( - self, - thread_id: str | None = None, - run_id: str | None = None, - use_deltas: bool = True, - ): - """Initialize the emitter. - - Args: - thread_id: Optional thread ID (generated from input hash if not provided) - run_id: Optional run ID (random UUID if not provided) - use_deltas: Whether to emit state deltas instead of full snapshots (default: True) - """ - self._queue: asyncio.Queue[AGUIEvent | None] = asyncio.Queue() - self._closed = False - self._thread_id = thread_id or str(uuid4()) - self._run_id = run_id or str(uuid4()) - self._last_state: StateT | None = None - self._active_steps: set[str] = set() - self._use_deltas = use_deltas - - @property - def thread_id(self) -> str: - """Get the thread ID for this emitter.""" - return self._thread_id - - @property - def run_id(self) -> str: - """Get the run ID for this emitter.""" - return self._run_id - - def start_run(self, initial_state: StateT) -> None: - """Emit RunStarted and initial StateSnapshot. - - Args: - initial_state: The initial state of the graph - """ - # If thread_id wasn't provided, generate from state hash - if not self._thread_id or self._thread_id == str(uuid4()): - state_json = initial_state.model_dump_json() - self._thread_id = self._generate_thread_id(state_json) - - # RunStarted (state snapshot follows immediately with full state) - self.emit( - _serialize_event( - RunStartedEvent(thread_id=self._thread_id, run_id=self._run_id) - ) - ) - self.emit( - _serialize_event(StateSnapshotEvent(snapshot=initial_state.model_dump())) - ) - # Store a deep copy to detect future changes - self._last_state = initial_state.model_copy(deep=True) - - def start_step(self, step_name: str) -> None: - """Emit StepStarted event. - - Args: - step_name: Name of the step being started - """ - self._active_steps.add(step_name) - self.emit(_serialize_event(StepStartedEvent(step_name=step_name))) - - def finish_step(self, step_name: str) -> None: - """Emit StepFinished event for the specified step. - - Args: - step_name: Name of the step being finished - """ - self._active_steps.discard(step_name) - self.emit(_serialize_event(StepFinishedEvent(step_name=step_name))) - - def log(self, message: str, role: str = "assistant") -> None: - """Emit a text message event. - - Args: - message: The message content - role: The role of the sender (default: assistant) - """ - message_id = str(uuid4()) - self.emit( - _serialize_event( - TextMessageChunkEvent( - message_id=message_id, - role=role, # type: ignore[arg-type] - delta=message, - ) - ) - ) - - def update_state(self, new_state: StateT) -> None: - """Emit StateDelta or StateSnapshot for state change. - - Args: - new_state: The updated state - """ - if self._use_deltas and self._last_state is not None: - # Emit delta for incremental updates - delta = compute_state_delta(self._last_state, new_state) - self.emit(_serialize_event(StateDeltaEvent(delta=delta))) - else: - # Emit full snapshot for initial state or when deltas disabled - self.emit( - _serialize_event(StateSnapshotEvent(snapshot=new_state.model_dump())) - ) - # Store a deep copy to detect future changes - self._last_state = new_state.model_copy(deep=True) - - def update_activity( - self, - activity_type: str, - content: dict[str, Any], - message_id: str | None = None, - ) -> None: - """Emit ActivitySnapshot event. - - Args: - activity_type: Type of activity (e.g., "planning", "searching") - content: Structured payload representing the activity state - message_id: Optional message ID to associate activity with (auto-generated if None) - """ - if message_id is None: - message_id = str(uuid4()) - self.emit( - _serialize_event( - ActivitySnapshotEvent( - message_id=message_id, - activity_type=activity_type, - content=content, - ) - ) - ) - - def finish_run(self, result: ResultT) -> None: - """Emit RunFinished event. - - Args: - result: The final result from the graph - """ - # Convert result to dict if it's a Pydantic model - result_data: Any = result - if hasattr(result, "model_dump"): - result_data = result.model_dump() # type: ignore[union-attr] - - self.emit( - _serialize_event( - RunFinishedEvent( - thread_id=self._thread_id, run_id=self._run_id, result=result_data - ) - ) - ) - - def error(self, error: Exception, code: str | None = None) -> None: - """Emit RunError event. - - Args: - error: The exception that occurred - code: Optional error code - """ - self.emit(_serialize_event(RunErrorEvent(message=str(error), code=code))) - - def emit(self, event: AGUIEvent) -> None: - """Put event in queue. - - Args: - event: The event to emit - """ - if not self._closed: - self._queue.put_nowait(event) - - async def close(self) -> None: - """Close the emitter and stop event iteration.""" - if self._closed: - return - self._closed = True - await self._queue.put(None) - - def __aiter__(self) -> AsyncIterator[AGUIEvent]: - """Enable async iteration over events.""" - return self._iter_events() - - async def _iter_events(self) -> AsyncIterator[AGUIEvent]: - """Iterate over events from the queue.""" - while True: - event = await self._queue.get() - if event is None: - break - yield event - - @staticmethod - def _generate_thread_id(input_data: str) -> str: - """Generate a deterministic thread ID from input data. - - Args: - input_data: The input data (e.g., question, prompt) - - Returns: - A stable thread ID based on input hash - """ - # Use hash of input for deterministic thread ID - hash_obj = hashlib.sha256(input_data.encode("utf-8")) - return hash_obj.hexdigest()[:16] - - -def emit_text_message_start(message_id: str, role: str = "assistant") -> AGUIEvent: - """Create a TextMessageStart event.""" - return _serialize_event( - TextMessageStartEvent(message_id=message_id, role=role) # type: ignore[arg-type] - ) - - -def emit_text_message_content(message_id: str, delta: str) -> AGUIEvent: - """Create a TextMessageContent event.""" - return _serialize_event(TextMessageContentEvent(message_id=message_id, delta=delta)) - - -def emit_text_message_end(message_id: str) -> AGUIEvent: - """Create a TextMessageEnd event.""" - return _serialize_event(TextMessageEndEvent(message_id=message_id)) - - -def emit_tool_call_start( - tool_call_id: str, - tool_name: str, - parent_message_id: str | None = None, -) -> AGUIEvent: - """Create a ToolCallStart event.""" - return _serialize_event( - ToolCallStartEvent( - tool_call_id=tool_call_id, - tool_call_name=tool_name, - parent_message_id=parent_message_id, - ) - ) - - -def emit_tool_call_args(tool_call_id: str, args: dict[str, Any]) -> AGUIEvent: - """Create a ToolCallArgs event.""" - return _serialize_event( - ToolCallArgsEvent(tool_call_id=tool_call_id, delta=json.dumps(args)) - ) - - -def emit_tool_call_end(tool_call_id: str) -> AGUIEvent: - """Create a ToolCallEnd event.""" - return _serialize_event(ToolCallEndEvent(tool_call_id=tool_call_id)) - - -def emit_run_started(thread_id: str, run_id: str) -> AGUIEvent: - """Create a RunStarted event.""" - return _serialize_event(RunStartedEvent(thread_id=thread_id, run_id=run_id)) - - -def emit_run_finished(thread_id: str, run_id: str, result: Any) -> AGUIEvent: - """Create a RunFinished event.""" - # Convert result to dict if it's a Pydantic model - if hasattr(result, "model_dump"): - result = result.model_dump() - return _serialize_event( - RunFinishedEvent(thread_id=thread_id, run_id=run_id, result=result) - ) - - -def emit_run_error(message: str, code: str | None = None) -> AGUIEvent: - """Create a RunError event.""" - return _serialize_event(RunErrorEvent(message=message, code=code)) - - -def emit_step_started(step_name: str) -> AGUIEvent: - """Create a StepStarted event.""" - return _serialize_event(StepStartedEvent(step_name=step_name)) - - -def emit_step_finished(step_name: str) -> AGUIEvent: - """Create a StepFinished event.""" - return _serialize_event(StepFinishedEvent(step_name=step_name)) - - -def emit_text_message(content: str, role: str = "assistant") -> AGUIEvent: - """Create a TextMessageChunk event (convenience wrapper).""" - message_id = str(uuid4()) - return _serialize_event( - TextMessageChunkEvent( - message_id=message_id, - role=role, # type: ignore[arg-type] - delta=content, - ) - ) - - -def emit_state_snapshot(state: BaseModel) -> AGUIEvent: - """Create a StateSnapshot event.""" - return _serialize_event(StateSnapshotEvent(snapshot=state.model_dump())) - - -def emit_state_delta(old_state: BaseModel, new_state: BaseModel) -> AGUIEvent: - """Create a StateDelta event with JSON Patch operations.""" - delta = compute_state_delta(old_state, new_state) - return _serialize_event(StateDeltaEvent(delta=delta)) - - -def emit_activity( - message_id: str, - activity_type: str, - content: dict[str, Any], -) -> AGUIEvent: - """Create an ActivitySnapshot event.""" - return _serialize_event( - ActivitySnapshotEvent( - message_id=message_id, - activity_type=activity_type, - content=content, - ) - ) - - -def emit_activity_delta( - message_id: str, - activity_type: str, - patch: list[dict[str, Any]], -) -> AGUIEvent: - """Create an ActivityDelta event with JSON Patch operations.""" - from ag_ui.core import ActivityDeltaEvent - - return _serialize_event( - ActivityDeltaEvent( - message_id=message_id, - activity_type=activity_type, - patch=patch, - ) - ) diff --git a/haiku_rag_slim/haiku/rag/graph/agui/server.py b/haiku_rag_slim/haiku/rag/graph/agui/server.py deleted file mode 100644 index 41c9d1f0..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/server.py +++ /dev/null @@ -1,268 +0,0 @@ -"""AG-UI HTTP server implementation for graph execution.""" - -import json -from collections.abc import AsyncIterator, Callable -from pathlib import Path -from typing import TYPE_CHECKING, Any, Protocol - -if TYPE_CHECKING: - from haiku.rag.config.models import AppConfig - -from pydantic import BaseModel, Field -from pydantic_graph.beta import Graph -from starlette.applications import Starlette -from starlette.middleware import Middleware -from starlette.middleware.cors import CORSMiddleware -from starlette.requests import Request -from starlette.responses import JSONResponse, StreamingResponse -from starlette.routing import Route - -from haiku.rag.config.models import AGUIConfig -from haiku.rag.graph.agui.emitter import AGUIEmitter, AGUIEvent -from haiku.rag.graph.agui.stream import stream_graph - - -class GraphDeps(Protocol): - """Protocol for graph dependencies that support AG-UI emission.""" - - agui_emitter: AGUIEmitter[Any, Any] | None - - -class RunAgentInput(BaseModel): - """AG-UI protocol run agent input. - - See: https://docs.ag-ui.com/concepts/agents#runagentinput - """ - - thread_id: str | None = Field(None, alias="threadId") - run_id: str | None = Field(None, alias="runId") - state: dict[str, Any] = Field(default_factory=dict) - messages: list[dict[str, Any]] = Field(default_factory=list) - config: dict[str, Any] = Field(default_factory=dict) - - -def create_agui_app( - graph_factory: Callable[[], Graph], - state_factory: Callable[[dict[str, Any]], BaseModel], - deps_factory: Callable[[dict[str, Any]], GraphDeps], - config: AGUIConfig, -) -> Starlette: - """Create Starlette app with AG-UI endpoint. - - Args: - graph_factory: Factory function to create graph instance - state_factory: Factory to create initial state from input - deps_factory: Factory to create graph dependencies - config: AG-UI server configuration - - Returns: - Starlette application with AG-UI endpoints - """ - - async def event_stream( - input_data: RunAgentInput, - ) -> AsyncIterator[str]: - """Generate SSE event stream from graph execution. - - Yields: - Server-Sent Events formatted strings - """ - # Create graph, state, and dependencies - graph = graph_factory() - - # Create initial state from input - initial_state = state_factory(input_data.state) - - # Create dependencies (may use config from input) - deps = deps_factory(input_data.config) - - # Execute graph and stream events - async for event in stream_graph(graph, initial_state, deps): - # Format as SSE event - event_data = format_sse_event(event) - yield event_data - - async def stream_agent(request: Request) -> StreamingResponse: - """AG-UI agent stream endpoint. - - Accepts AG-UI RunAgentInput and streams events via SSE. - """ - # Parse request body - body = await request.json() - input_data = RunAgentInput(**body) - - # Return SSE stream - return StreamingResponse( - event_stream(input_data), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "Connection": "keep-alive", - "X-Accel-Buffering": "no", # Disable buffering in nginx - }, - ) - - async def health_check(_: Request) -> JSONResponse: - """Health check endpoint.""" - return JSONResponse({"status": "healthy"}) - - # Define routes - routes = [ - Route("/v1/agent/stream", stream_agent, methods=["POST"]), - Route("/health", health_check, methods=["GET"]), - ] - - # Configure CORS middleware - middleware = [ - Middleware( - CORSMiddleware, - allow_origins=config.cors_origins, - allow_credentials=config.cors_credentials, - allow_methods=config.cors_methods, - allow_headers=config.cors_headers, - ) - ] - - # Create Starlette app - app = Starlette( - routes=routes, - middleware=middleware, - debug=False, - ) - - return app - - -def format_sse_event(event: AGUIEvent) -> str: - """Format AG-UI event as Server-Sent Event. - - Args: - event: AG-UI event dictionary - - Returns: - SSE formatted string with event data - """ - # Convert event to JSON - event_json = json.dumps(event, ensure_ascii=False) - - # Format as SSE - # Each event is: data: \n\n - return f"data: {event_json}\n\n" - - -def create_agui_server( # pragma: no cover - config: "AppConfig", db_path: Path | None = None -) -> Starlette: - """Create AG-UI server with research endpoint. - - Args: - config: Application config with research settings - db_path: Optional database path override - - Returns: - Starlette app with research endpoint - """ - from haiku.rag.client import HaikuRAG - from haiku.rag.graph.research.dependencies import ResearchContext - from haiku.rag.graph.research.graph import build_research_graph - from haiku.rag.graph.research.state import ( - ResearchDeps, - ResearchState, - ) - - # Store client reference for proper lifecycle management - _client_cache: dict[str, HaikuRAG] = {} - - def get_client(effective_db_path: Path) -> 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) - return _client_cache[path_key] - - # Research graph factories - def research_graph_factory() -> Graph: - return build_research_graph(config) - - def research_state_factory(input_state: dict[str, Any]) -> ResearchState: - question = input_state.get("question", "") - if not question: - messages = input_state.get("messages", []) - if messages: - question = messages[0].get("content", "") - context = ResearchContext(original_question=question) - max_iterations = input_state.get("max_iterations") - confidence_threshold = input_state.get("confidence_threshold") - return ResearchState.from_config( - context=context, - config=config, - max_iterations=max_iterations, - confidence_threshold=confidence_threshold, - ) - - def research_deps_factory(input_config: dict[str, Any]) -> ResearchDeps: - effective_db_path = ( - db_path - or input_config.get("db_path") - or config.storage.data_dir / "haiku.rag.lancedb" - ) - return ResearchDeps(client=get_client(effective_db_path)) - - # Create event stream function - async def research_event_stream( - input_data: RunAgentInput, - ) -> AsyncIterator[str]: - """Generate SSE event stream from research graph execution.""" - graph = research_graph_factory() - initial_state = research_state_factory(input_data.state) - deps = research_deps_factory(input_data.config) - - async for event in stream_graph(graph, initial_state, deps): - event_data = format_sse_event(event) - yield event_data - - # Endpoint handlers - async def stream_research(request: Request) -> StreamingResponse: - """Research graph streaming endpoint.""" - body = await request.json() - input_data = RunAgentInput(**body) - - return StreamingResponse( - research_event_stream(input_data), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "Connection": "keep-alive", - "X-Accel-Buffering": "no", - }, - ) - - async def health_check(_: Request) -> JSONResponse: - """Health check endpoint.""" - return JSONResponse({"status": "healthy"}) - - # Define routes - routes = [ - Route("/v1/research/stream", stream_research, methods=["POST"]), - Route("/health", health_check, methods=["GET"]), - ] - - # Configure CORS middleware - middleware = [ - Middleware( - CORSMiddleware, - allow_origins=config.agui.cors_origins, - allow_credentials=config.agui.cors_credentials, - allow_methods=config.agui.cors_methods, - allow_headers=config.agui.cors_headers, - ) - ] - - # Create Starlette app - app = Starlette( - routes=routes, - middleware=middleware, - debug=False, - ) - - return app diff --git a/haiku_rag_slim/haiku/rag/graph/agui/state.py b/haiku_rag_slim/haiku/rag/graph/agui/state.py deleted file mode 100644 index 42ee34c9..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/state.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Generic AG-UI state utilities for any Pydantic BaseModel.""" - -from typing import Any - -from pydantic import BaseModel - - -def compute_state_delta( - old_state: BaseModel, new_state: BaseModel -) -> list[dict[str, Any]]: - """Compute JSON Patch (RFC 6902) operations from old state to new state. - - Args: - old_state: Previous state (any Pydantic BaseModel) - new_state: Current state (same type as old_state) - - Returns: - List of JSON Patch operations - """ - operations: list[dict[str, Any]] = [] - - # Convert states to dicts for comparison - old_dict = old_state.model_dump() - new_dict = new_state.model_dump() - - # Compare each field and generate patches - for key, new_value in new_dict.items(): - old_value = old_dict.get(key) - - if old_value != new_value: - # Simple replace operation - operations.append({"op": "replace", "path": f"/{key}", "value": new_value}) - - return operations diff --git a/haiku_rag_slim/haiku/rag/graph/agui/stream.py b/haiku_rag_slim/haiku/rag/graph/agui/stream.py deleted file mode 100644 index 9612d1a6..00000000 --- a/haiku_rag_slim/haiku/rag/graph/agui/stream.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Generic graph streaming with AG-UI events.""" - -import asyncio -from collections.abc import AsyncIterator -from contextlib import suppress -from typing import Protocol, TypeVar - -from pydantic import BaseModel -from pydantic_graph.beta import Graph - -from haiku.rag.graph.agui.emitter import AGUIEmitter, AGUIEvent - -StateT = TypeVar("StateT", bound=BaseModel) -ResultT = TypeVar("ResultT") - - -class GraphDeps[StateT: BaseModel, ResultT](Protocol): - """Protocol for graph dependencies that support AG-UI emission.""" - - agui_emitter: AGUIEmitter[StateT, ResultT] | None - - -async def stream_graph[StateT: BaseModel, DepsT: GraphDeps, ResultT]( - graph: Graph[StateT, DepsT, None, ResultT], - state: StateT, - deps: DepsT, - use_deltas: bool = True, -) -> AsyncIterator[AGUIEvent]: - """Run a graph and yield AG-UI events as they occur. - - This is a generic streaming function that works with any pydantic-graph - that follows the AG-UI pattern: - - State must be a Pydantic BaseModel - - Deps must have an optional agui_emitter attribute - - Graph must be a pydantic-graph Graph instance - - Args: - graph: The pydantic-graph Graph to execute - state: Initial state (Pydantic BaseModel) - deps: Graph dependencies with agui_emitter support - use_deltas: Whether to emit state deltas instead of full snapshots (default: True) - - Yields: - AG-UI event dictionaries - - Raises: - TypeError: If deps doesn't support agui_emitter - RuntimeError: If graph doesn't produce a result - """ - if not hasattr(deps, "agui_emitter"): - raise TypeError("deps must have an 'agui_emitter' attribute") - - # Create AG-UI emitter - emitter: AGUIEmitter[StateT, ResultT] = AGUIEmitter(use_deltas=use_deltas) - deps.agui_emitter = emitter # type: ignore[assignment] - - async def _execute() -> None: - try: - # Start the run with initial state - emitter.start_run(initial_state=state) - - # Execute the graph - result = await graph.run(state=state, deps=deps) - - if result is None: - raise RuntimeError("Graph did not produce a result") - - # Finish the run with the result - emitter.finish_run(result) - except Exception as exc: - # Emit error event - emitter.error(exc) - finally: - await emitter.close() - - runner = asyncio.create_task(_execute()) - - try: - async for event in emitter: - yield event - finally: - if not runner.done(): - runner.cancel() - with suppress(asyncio.CancelledError): - await runner diff --git a/haiku_rag_slim/haiku/rag/graph/research/graph.py b/haiku_rag_slim/haiku/rag/graph/research/graph.py index a59c7fd0..2d62753a 100644 --- a/haiku_rag_slim/haiku/rag/graph/research/graph.py +++ b/haiku_rag_slim/haiku/rag/graph/research/graph.py @@ -1,6 +1,4 @@ import asyncio -from typing import Literal -from uuid import uuid4 from pydantic_ai import Agent, RunContext, format_as_xml from pydantic_ai.output import ToolOutput @@ -9,13 +7,6 @@ from pydantic_graph.beta.join import reduce_list_append from haiku.rag.config import Config from haiku.rag.config.models import AppConfig -from haiku.rag.graph.agui.emitter import ( - emit_text_message_end, - emit_text_message_start, - emit_tool_call_args, - emit_tool_call_end, - emit_tool_call_start, -) from haiku.rag.graph.research.dependencies import ResearchContext, ResearchDependencies from haiku.rag.graph.research.models import ( Citation, @@ -196,14 +187,12 @@ def _get_batch_logic(state: ResearchState) -> list[str] | None: def build_research_graph( config: AppConfig = Config, include_plan: bool = True, - interactive: bool = False, ) -> Graph[ResearchState, ResearchDeps, None, ResearchReport]: """Build the Research graph. Args: config: AppConfig object (uses config.research for provider, model, and graph parameters) include_plan: Whether to include the planning step (False for execute-only mode) - interactive: Whether to include human decision nodes for HIL Returns: Configured Research graph @@ -230,89 +219,23 @@ def build_research_graph( @g.step async def plan(ctx: StepContext[ResearchState, ResearchDeps, None]) -> None: """Create research plan with sub-questions.""" - state = ctx.state - deps = ctx.deps - - if deps.agui_emitter: - deps.agui_emitter.start_step("plan") - deps.agui_emitter.update_activity( - "planning", {"stepName": "plan", "message": "Creating research plan"} - ) - - try: - await _plan_step_logic(state, deps, config, plan_prompt) - - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - count = len(state.context.sub_questions) - deps.agui_emitter.update_activity( - "planning", - { - "stepName": "plan", - "message": f"Created plan with {count} sub-questions", - "sub_questions": list(state.context.sub_questions), - }, - ) - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step("plan") + await _plan_step_logic(ctx.state, ctx.deps, config, plan_prompt) @g.step async def search_one( ctx: StepContext[ResearchState, ResearchDeps, str], ) -> SearchAnswer: """Answer a single sub-question using the knowledge base.""" - state = ctx.state - deps = ctx.deps - sub_q = ctx.inputs - step_name = f"search: {sub_q}" - - if deps.agui_emitter: - deps.agui_emitter.start_step(step_name) - deps.agui_emitter.update_activity( - "searching", - { - "stepName": "search_one", - "message": f"Searching: {sub_q}", - "query": sub_q, - }, - ) - try: - answer = await _search_one_step_logic( - state, deps, config, search_prompt, sub_q + return await _search_one_step_logic( + ctx.state, ctx.deps, config, search_prompt, ctx.inputs ) - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - deps.agui_emitter.update_activity( - "searching", - { - "stepName": "search_one", - "message": f"Found answer with {answer.confidence:.0%} confidence", - "query": sub_q, - "confidence": answer.confidence, - }, - ) - return answer except Exception as e: - if deps.agui_emitter: - deps.agui_emitter.update_activity( - "searching", - { - "stepName": "search_one", - "message": f"Search failed: {e}", - "query": sub_q, - "error": str(e), - }, - ) return SearchAnswer( - query=sub_q, + query=ctx.inputs, answer=f"Search failed: {str(e)}", confidence=0.0, ) - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step(step_name) @g.step async def get_batch( @@ -329,154 +252,58 @@ def build_research_graph( state = ctx.state deps = ctx.deps - if deps.agui_emitter: - deps.agui_emitter.start_step("decide") - deps.agui_emitter.update_activity( - "evaluating", {"message": "Evaluating research sufficiency"} + agent = Agent( + model=get_model(model_config, config), + output_type=EvaluationResult, + instructions=decision_prompt, + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) + + context_xml = format_context_for_prompt(state.context) + prompt_parts = [ + "Assess whether the research now answers the original question with adequate confidence.", + context_xml, + ] + if state.last_eval is not None: + prev = state.last_eval + prompt_parts.append( + "" + f"{prev.confidence_score:.2f}" + f"{str(prev.is_sufficient).lower()}" + f"{prev.reasoning}" + "" ) + prompt = "\n\n".join(part for part in prompt_parts if part) - try: - agent = Agent( - model=get_model(model_config, config), - output_type=EvaluationResult, - instructions=decision_prompt, - retries=3, - output_retries=3, - deps_type=ResearchDependencies, - ) + agent_deps = ResearchDependencies( + client=deps.client, + context=state.context, + ) + decision_result = await agent.run(prompt, deps=agent_deps) + output = decision_result.output - context_xml = format_context_for_prompt(state.context) - prompt_parts = [ - "Assess whether the research now answers the original question with adequate confidence.", - context_xml, - ] - if state.last_eval is not None: - prev = state.last_eval - prompt_parts.append( - "" - f"{prev.confidence_score:.2f}" - f"{str(prev.is_sufficient).lower()}" - f"{prev.reasoning}" - "" - ) - prompt = "\n\n".join(part for part in prompt_parts if part) + state.last_eval = output + state.iterations += 1 - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - ) - decision_result = await agent.run(prompt, deps=agent_deps) - output = decision_result.output + # Get already-answered questions to avoid duplicates + answered_queries = {qa.query.lower() for qa in state.context.qa_responses} - state.last_eval = output - state.iterations += 1 + for new_q in output.new_questions: + # Skip if already in pending or already answered + if new_q in state.context.sub_questions: + continue + if new_q.lower() in answered_queries: + continue + state.context.sub_questions.append(new_q) - # Get already-answered questions to avoid duplicates - answered_queries = {qa.query.lower() for qa in state.context.qa_responses} + should_continue = ( + not output.is_sufficient + or output.confidence_score < state.confidence_threshold + ) and state.iterations < state.max_iterations - for new_q in output.new_questions: - # Skip if already in pending or already answered - if new_q in state.context.sub_questions: - continue - if new_q.lower() in answered_queries: - continue - state.context.sub_questions.append(new_q) - - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - sufficient = "Yes" if output.is_sufficient else "No" - deps.agui_emitter.update_activity( - "evaluating", - { - "stepName": "decide", - "message": f"Confidence: {output.confidence_score:.0%}, Sufficient: {sufficient}", - "confidence": output.confidence_score, - "is_sufficient": output.is_sufficient, - }, - ) - - should_continue = ( - not output.is_sufficient - or output.confidence_score < state.confidence_threshold - ) and state.iterations < state.max_iterations - - return should_continue - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step("decide") - - @g.step - async def human_decide( - ctx: StepContext[ResearchState, ResearchDeps, list[SearchAnswer] | None | bool], - ) -> Literal["search", "synthesize"]: - """Wait for human decision on whether to continue searching or synthesize.""" - state = ctx.state - deps = ctx.deps - - if deps.agui_emitter: - deps.agui_emitter.start_step("human_decide") - deps.agui_emitter.update_state(state) - - try: - # Emit tool call for human input wrapped in a message context - # This makes the tool call appear as if emitted by the LLM - message_id = str(uuid4()) - tool_call_id = str(uuid4()) - - if deps.agui_emitter: - # Start an assistant message to contain the tool call - deps.agui_emitter.emit(emit_text_message_start(message_id)) - # Emit tool call with parent message reference - deps.agui_emitter.emit( - emit_tool_call_start(tool_call_id, "human_decision", message_id) - ) - # Include full state for display - qa_responses = [ - { - "query": qa.query, - "answer": qa.answer, - "confidence": qa.confidence, - "citations_count": len(qa.citations), - } - for qa in state.context.qa_responses - ] - deps.agui_emitter.emit( - emit_tool_call_args( - tool_call_id, - { - "original_question": state.context.original_question, - "sub_questions": list(state.context.sub_questions), - "qa_responses": qa_responses, - "iterations": state.iterations, - }, - ) - ) - deps.agui_emitter.emit(emit_tool_call_end(tool_call_id)) - # End the message after tool call - deps.agui_emitter.emit(emit_text_message_end(message_id)) - - # Wait for human input - if deps.human_input_queue is None: - raise RuntimeError("human_input_queue is required for interactive mode") - - decision = await deps.human_input_queue.get() - - # Process decision - if decision.action == "modify_questions" and decision.questions: - state.context.sub_questions = list(decision.questions) - elif decision.action == "add_questions" and decision.questions: - state.context.sub_questions.extend(decision.questions) - - if deps.agui_emitter: - deps.agui_emitter.update_state(state) - - if decision.action in ("search", "modify_questions", "add_questions"): - return "search" - else: - return "synthesize" - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step("human_decide") + return should_continue @g.step async def synthesize( @@ -486,37 +313,27 @@ def build_research_graph( state = ctx.state deps = ctx.deps - if deps.agui_emitter: - deps.agui_emitter.start_step("synthesize") - deps.agui_emitter.update_activity( - "synthesizing", {"message": "Generating final research report"} - ) + agent = Agent( + model=get_model(model_config, config), + output_type=ResearchReport, + instructions=synthesis_prompt, + retries=3, + output_retries=3, + deps_type=ResearchDependencies, + ) - try: - agent = Agent( - model=get_model(model_config, config), - output_type=ResearchReport, - instructions=synthesis_prompt, - retries=3, - output_retries=3, - deps_type=ResearchDependencies, - ) - - context_xml = format_context_for_prompt(state.context) - prompt = ( - "Generate a comprehensive research report based on all gathered information.\n\n" - f"{context_xml}\n\n" - "Create a detailed report that synthesizes all findings into a coherent response." - ) - agent_deps = ResearchDependencies( - client=deps.client, - context=state.context, - ) - result = await agent.run(prompt, deps=agent_deps) - return result.output - finally: - if deps.agui_emitter: - deps.agui_emitter.finish_step("synthesize") + context_xml = format_context_for_prompt(state.context) + prompt = ( + "Generate a comprehensive research report based on all gathered information.\n\n" + f"{context_xml}\n\n" + "Create a detailed report that synthesizes all findings into a coherent response." + ) + agent_deps = ResearchDependencies( + client=deps.client, + context=state.context, + ) + result = await agent.run(prompt, deps=agent_deps) + return result.output # Build the graph structure collect_answers = g.join( @@ -524,77 +341,40 @@ def build_research_graph( initial_factory=list[SearchAnswer], ) - if interactive: - # Interactive mode: human decides after plan and after evaluation - if include_plan: - g.add( - g.edge_from(g.start_node).to(plan), - g.edge_from(plan).to(human_decide), - ) - else: - g.add(g.edge_from(g.start_node).to(human_decide)) - + if include_plan: g.add( - g.edge_from(human_decide).to( - g.decision() - .branch( - g.match(str, matches=lambda x: x == "search") - .label("Search") - .to(get_batch) - ) - .branch( - g.match(str, matches=lambda x: x == "synthesize") - .label("Synthesize") - .to(synthesize) - ) - ), - g.edge_from(get_batch).to( - g.decision() - .branch(g.match(list).label("Has questions").map().to(search_one)) - .branch(g.match(type(None)).label("No questions").to(human_decide)) - ), - g.edge_from(search_one).to(collect_answers), - # After search, evaluate to suggest new questions, then human decides - g.edge_from(collect_answers).to(decide), - g.edge_from(decide).to(human_decide), - g.edge_from(synthesize).to(g.end_node), + g.edge_from(g.start_node).to(plan), + g.edge_from(plan).to(get_batch), ) else: - # Non-interactive mode: automatic decision based on confidence/iterations - if include_plan: - g.add( - g.edge_from(g.start_node).to(plan), - g.edge_from(plan).to(get_batch), + g.add(g.edge_from(g.start_node).to(get_batch)) + + g.add( + g.edge_from(get_batch).to( + g.decision() + .branch(g.match(list).label("Has questions").map().to(search_one)) + .branch(g.match(type(None)).label("No questions").to(synthesize)) + ), + g.edge_from(search_one).to(collect_answers), + g.edge_from(collect_answers).to(decide), + ) + + g.add( + g.edge_from(decide).to( + g.decision() + .branch( + g.match(bool, matches=lambda x: x) + .label("Continue research") + .to(get_batch) ) - else: - g.add(g.edge_from(g.start_node).to(get_batch)) - - g.add( - g.edge_from(get_batch).to( - g.decision() - .branch(g.match(list).label("Has questions").map().to(search_one)) - .branch(g.match(type(None)).label("No questions").to(synthesize)) - ), - g.edge_from(search_one).to(collect_answers), - g.edge_from(collect_answers).to(decide), - ) - - g.add( - g.edge_from(decide).to( - g.decision() - .branch( - g.match(bool, matches=lambda x: x) - .label("Continue research") - .to(get_batch) - ) - .branch( - g.match(bool, matches=lambda x: not x) - .label("Done researching") - .to(synthesize) - ) - ), - g.edge_from(synthesize).to(g.end_node), - ) + .branch( + g.match(bool, matches=lambda x: not x) + .label("Done researching") + .to(synthesize) + ) + ), + g.edge_from(synthesize).to(g.end_node), + ) return g.build() diff --git a/haiku_rag_slim/haiku/rag/graph/research/state.py b/haiku_rag_slim/haiku/rag/graph/research/state.py index 93448a4c..a28533d6 100644 --- a/haiku_rag_slim/haiku/rag/graph/research/state.py +++ b/haiku_rag_slim/haiku/rag/graph/research/state.py @@ -1,27 +1,15 @@ import asyncio from dataclasses import dataclass -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING from pydantic import BaseModel, Field from haiku.rag.client import HaikuRAG from haiku.rag.graph.research.dependencies import ResearchContext -from haiku.rag.graph.research.models import EvaluationResult, ResearchReport +from haiku.rag.graph.research.models import EvaluationResult if TYPE_CHECKING: from haiku.rag.config.models import AppConfig - from haiku.rag.graph.agui.emitter import AGUIEmitter - - -class HumanDecision(BaseModel): - """Human decision input for interactive research.""" - - action: Literal[ - "search", "synthesize", "modify_questions", "add_questions", "chat", "research" - ] - questions: list[str] | None = None - message: str | None = None - research_question: str | None = None @dataclass @@ -29,17 +17,7 @@ class ResearchDeps: """Dependencies for research graph execution.""" client: HaikuRAG - agui_emitter: "AGUIEmitter[ResearchState, ResearchReport] | None" = None semaphore: asyncio.Semaphore | None = None - human_input_queue: asyncio.Queue[HumanDecision] | None = None - interactive: bool = False - - def emit_log(self, message: str, state: "ResearchState | None" = None) -> None: - """Emit a log message through AG-UI events.""" - if self.agui_emitter: - self.agui_emitter.log(message) - if state: - self.agui_emitter.update_state(state) class ResearchState(BaseModel): diff --git a/tests/graph/agui/__init__.py b/tests/graph/agui/__init__.py deleted file mode 100644 index d178a31a..00000000 --- a/tests/graph/agui/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for AG-UI implementation.""" diff --git a/tests/graph/agui/test_cli_renderer.py b/tests/graph/agui/test_cli_renderer.py deleted file mode 100644 index 9d1ef82a..00000000 --- a/tests/graph/agui/test_cli_renderer.py +++ /dev/null @@ -1,204 +0,0 @@ -"""Tests for AGUIConsoleRenderer.""" - -import pytest -from pydantic import BaseModel -from rich.console import Console - -from haiku.rag.graph.agui.cli_renderer import AGUIConsoleRenderer -from haiku.rag.graph.agui.emitter import ( - emit_activity, - emit_run_error, - emit_run_finished, - emit_run_started, - emit_state_delta, - emit_state_snapshot, - emit_step_finished, - emit_step_started, - emit_text_message, -) - - -class SimpleState(BaseModel): - """Simple state for testing.""" - - value: int - - -async def async_gen(items): - """Helper to create async generator from list.""" - for item in items: - yield item - - -@pytest.mark.asyncio -async def test_renderer_basic_flow(): - """Test basic event rendering flow.""" - console = Console(file=None, force_terminal=False) # Don't actually print - renderer = AGUIConsoleRenderer(console) - - events = [ - emit_run_started("t1", "r1-test-run-id"), - emit_state_snapshot(SimpleState(value=1)), - emit_step_started("plan"), - emit_step_finished("plan"), - emit_activity("m1", "planning", {"message": "Planning research"}), - emit_run_finished("t1", "r1", {"status": "complete"}), - ] - - result = await renderer.render(async_gen(events)) - - assert result == {"status": "complete"} - - -@pytest.mark.asyncio -async def test_renderer_multiple_snapshots(): - """Test that renderer handles multiple snapshots without errors.""" - renderer = AGUIConsoleRenderer() - - events = [ - emit_state_snapshot(SimpleState(value=1)), - emit_state_snapshot(SimpleState(value=2)), - ] - - # Should render both snapshots without error - result = await renderer.render(async_gen(events)) - assert result is None # No run finished event - - -@pytest.mark.asyncio -async def test_renderer_handles_all_event_types(): - """Test that renderer handles all event types without errors.""" - renderer = AGUIConsoleRenderer() - - events = [ - emit_run_started("t1", "r1"), - emit_state_snapshot(SimpleState(value=1)), - emit_step_started("step1"), - emit_step_finished("step1"), - emit_text_message("message"), - emit_activity("m1", "work", {"message": "Working"}), - emit_run_error("error occurred"), - emit_run_finished("t1", "r1", {"result": "done"}), - ] - - # Should not raise any exceptions - result = await renderer.render(async_gen(events)) - assert result == {"result": "done"} - - -@pytest.mark.asyncio -async def test_renderer_state_snapshots(): - """Test that state snapshots are rendered.""" - renderer = AGUIConsoleRenderer() - - events = [ - emit_state_snapshot(SimpleState(value=1)), - emit_state_snapshot(SimpleState(value=2)), - emit_run_finished("t1", "r1", {"done": True}), - ] - - result = await renderer.render(async_gen(events)) - assert result == {"done": True} - - -@pytest.mark.asyncio -async def test_renderer_no_result(): - """Test renderer when no RUN_FINISHED event.""" - renderer = AGUIConsoleRenderer() - - events = [ - emit_run_started("t1", "r1"), - emit_step_started("step1"), - ] - - result = await renderer.render(async_gen(events)) - assert result is None - - -@pytest.mark.asyncio -async def test_renderer_snapshot_then_delta(): - """Test that renderer handles snapshot followed by deltas.""" - renderer = AGUIConsoleRenderer() - - state1 = SimpleState(value=1) - state2 = SimpleState(value=2) - state3 = SimpleState(value=3) - - events = [ - emit_state_snapshot(state1), # Initial snapshot - emit_state_delta(state1, state2), # Delta to value=2 - emit_state_delta(state2, state3), # Delta to value=3 - emit_run_finished("t1", "r1", {"complete": True}), - ] - - result = await renderer.render(async_gen(events)) - assert result == {"complete": True} - - -@pytest.mark.asyncio -async def test_renderer_with_empty_state(): - """Test renderer handles empty state gracefully.""" - renderer = AGUIConsoleRenderer() - - # Create a state with no changes - events = [ - emit_step_started("step1"), # No state snapshot - emit_run_finished("t1", "r1", {"result": "ok"}), - ] - - result = await renderer.render(async_gen(events)) - assert result == {"result": "ok"} - - -@pytest.mark.asyncio -async def test_renderer_state_delta(): - """Test that renderer renders state deltas.""" - renderer = AGUIConsoleRenderer() - - state1 = SimpleState(value=1) - state2 = SimpleState(value=2) - - events = [ - emit_state_snapshot(state1), # Initial state - emit_state_delta(state1, state2), # Delta update - emit_run_finished("t1", "r1", None), - ] - - result = await renderer.render(async_gen(events)) - assert result is None - - -@pytest.mark.asyncio -async def test_renderer_state_delta_without_initial(): - """Test that renderer handles delta without initial state gracefully.""" - renderer = AGUIConsoleRenderer() - - state1 = SimpleState(value=1) - state2 = SimpleState(value=2) - - # Send delta without initial snapshot - should still render it - events = [ - emit_state_delta(state1, state2), - emit_run_finished("t1", "r1", {"ok": True}), - ] - - result = await renderer.render(async_gen(events)) - assert result == {"ok": True} - - -@pytest.mark.asyncio -async def test_renderer_lifecycle_events(): - """Test that lifecycle events are rendered properly.""" - renderer = AGUIConsoleRenderer() - - events = [ - emit_run_started("thread-123", "run-abc-def-ghi"), # Long run ID - emit_step_started("search_one"), - emit_step_finished("search_one"), - emit_step_started("analyze_insights"), - emit_step_finished("analyze_insights"), - emit_run_finished("thread-123", "run-abc-def-ghi", {"complete": True}), - ] - - result = await renderer.render(async_gen(events)) - assert result == {"complete": True} diff --git a/tests/graph/agui/test_emitter.py b/tests/graph/agui/test_emitter.py deleted file mode 100644 index 344d813e..00000000 --- a/tests/graph/agui/test_emitter.py +++ /dev/null @@ -1,317 +0,0 @@ -"""Tests for AGUIEmitter.""" - -import asyncio - -import pytest -from pydantic import BaseModel - -from haiku.rag.graph.agui.emitter import AGUIEmitter - - -class TestState(BaseModel): - """Test state model.""" - - value: int - text: str - - -class TestResult(BaseModel): - """Test result model.""" - - status: str - - -@pytest.mark.asyncio -async def test_emitter_lifecycle(): - """Test emitter lifecycle events.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - initial_state = TestState(value=1, text="initial") - emitter.start_run(initial_state) - - events = [] - async for event in emitter: - events.append(event) - if event["type"] == "RUN_STARTED": - # Close after getting run started - result = TestResult(status="complete") - emitter.finish_run(result) - await emitter.close() - - assert len(events) >= 3 # RUN_STARTED, STATE_SNAPSHOT, RUN_FINISHED - assert events[0]["type"] == "RUN_STARTED" - assert events[-1]["type"] == "RUN_FINISHED" - assert events[-1]["result"] == {"status": "complete"} - - -@pytest.mark.asyncio -async def test_emitter_step_events(): - """Test step lifecycle events.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - emitter.start_step("test_step") - emitter.finish_step("test_step") - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - step_events = [e for e in events if e["type"] in ("STEP_STARTED", "STEP_FINISHED")] - assert len(step_events) == 2 - assert step_events[0]["type"] == "STEP_STARTED" - assert step_events[0]["stepName"] == "test_step" - assert step_events[1]["type"] == "STEP_FINISHED" - assert step_events[1]["stepName"] == "test_step" - - -@pytest.mark.asyncio -async def test_emitter_state_updates(): - """Test state update events with snapshots.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter(use_deltas=False) - - state1 = TestState(value=1, text="first") - state2 = TestState(value=2, text="second") - - emitter.update_state(state1) - emitter.update_state(state2) - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - state_events = [e for e in events if e["type"] == "STATE_SNAPSHOT"] - assert len(state_events) == 2 - assert state_events[0]["snapshot"] == {"value": 1, "text": "first"} - assert state_events[1]["snapshot"] == {"value": 2, "text": "second"} - - -@pytest.mark.asyncio -async def test_emitter_state_deltas(): - """Test state update events with deltas.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter(use_deltas=True) - - state1 = TestState(value=1, text="first") - state2 = TestState(value=2, text="second") - - emitter.update_state(state1) - emitter.update_state(state2) - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - # First update should be a snapshot (no previous state) - snapshot_events = [e for e in events if e["type"] == "STATE_SNAPSHOT"] - assert len(snapshot_events) == 1 - assert snapshot_events[0]["snapshot"] == {"value": 1, "text": "first"} - - # Second update should be a delta - delta_events = [e for e in events if e["type"] == "STATE_DELTA"] - assert len(delta_events) == 1 - # Delta should contain replace operations for changed fields - delta = delta_events[0]["delta"] - assert isinstance(delta, list) - assert len(delta) == 2 # Two fields changed - assert any(op["path"] == "/value" and op["value"] == 2 for op in delta) - assert any(op["path"] == "/text" and op["value"] == "second" for op in delta) - - -@pytest.mark.asyncio -async def test_emitter_activity_events(): - """Test activity events.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - # Activity without a step - emitter.update_activity("processing", {"message": "Processing data"}) - - # Activity within a step (stepName explicitly included in content) - emitter.start_step("analyze") - emitter.update_activity( - "done", {"stepName": "analyze", "message": "Completed"}, message_id="msg-1" - ) - emitter.finish_step("analyze") - - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - activity_events = [e for e in events if e["type"] == "ACTIVITY_SNAPSHOT"] - assert len(activity_events) == 2 - assert activity_events[0]["activityType"] == "processing" - assert activity_events[0]["content"]["message"] == "Processing data" - assert "stepName" not in activity_events[0]["content"] # No step context - - assert activity_events[1]["messageId"] == "msg-1" - assert activity_events[1]["activityType"] == "done" - assert activity_events[1]["content"]["message"] == "Completed" - assert activity_events[1]["content"]["stepName"] == "analyze" # Has step context - - -@pytest.mark.asyncio -async def test_emitter_text_messages(): - """Test text message events.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - emitter.log("Test message", role="assistant") - emitter.log("Another message", role="user") - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - text_events = [e for e in events if e["type"] == "TEXT_MESSAGE_CHUNK"] - assert len(text_events) == 2 - assert text_events[0]["delta"] == "Test message" - assert text_events[0]["role"] == "assistant" - assert text_events[1]["delta"] == "Another message" - assert text_events[1]["role"] == "user" - - -@pytest.mark.asyncio -async def test_emitter_error(): - """Test error event emission.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - error = ValueError("Test error") - emitter.error(error, code="TEST_ERROR") - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - error_events = [e for e in events if e["type"] == "RUN_ERROR"] - assert len(error_events) == 1 - assert error_events[0]["message"] == "Test error" - assert error_events[0]["code"] == "TEST_ERROR" - - -@pytest.mark.asyncio -async def test_emitter_thread_and_run_ids(): - """Test thread and run ID management.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter( - thread_id="thread-1", run_id="run-1" - ) - - assert emitter.thread_id == "thread-1" - assert emitter.run_id == "run-1" - - initial_state = TestState(value=1, text="test") - emitter.start_run(initial_state) - await emitter.close() - - events = [] - async for event in emitter: - events.append(event) - - run_started = [e for e in events if e["type"] == "RUN_STARTED"][0] - assert run_started["threadId"] == "thread-1" - assert run_started["runId"] == "run-1" - - -@pytest.mark.asyncio -async def test_emitter_generates_thread_id(): - """Test that thread ID is generated from state hash when not provided.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - initial_state = TestState(value=42, text="test") - emitter.start_run(initial_state) - - # Thread ID should be generated deterministically from state - assert emitter.thread_id is not None - assert len(emitter.thread_id) > 0 - - await emitter.close() - async for _ in emitter: - pass - - -@pytest.mark.asyncio -async def test_emitter_closes_properly(): - """Test that emitter closes and stops iteration.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - emitter.log("Message 1") - await emitter.close() - - # Attempting to iterate after close should work and stop - events = [] - async for event in emitter: - events.append(event) - - # Should have received the message and then stopped - assert len(events) == 1 - - -@pytest.mark.asyncio -async def test_emitter_concurrent_emission(): - """Test that multiple events can be emitted concurrently.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - async def emit_many(): - for i in range(10): - emitter.log(f"Message {i}") - await asyncio.sleep(0.001) # Simulate some work - await emitter.close() - - # Start emission in background - emit_task = asyncio.create_task(emit_many()) - - # Collect events - events = [] - async for event in emitter: - events.append(event) - - await emit_task - - # Should have all 10 messages - text_events = [e for e in events if e["type"] == "TEXT_MESSAGE_CHUNK"] - assert len(text_events) == 10 - - -@pytest.mark.asyncio -async def test_emitter_concurrent_steps(): - """Test that multiple steps can run concurrently and finish independently.""" - emitter: AGUIEmitter[TestState, TestResult] = AGUIEmitter() - - async def run_step(step_name: str, delay: float): - emitter.start_step(step_name) - await asyncio.sleep(delay) - emitter.finish_step(step_name) - - # Start collecting events in background - events: list[dict] = [] - - async def collect(): - async for event in emitter: - events.append(event) - - collector = asyncio.create_task(collect()) - - # Run three steps concurrently with different durations - # Step A finishes last, Step B first, Step C middle - await asyncio.gather( - run_step("step_a", 0.03), - run_step("step_b", 0.01), - run_step("step_c", 0.02), - ) - - await emitter.close() - await collector - - started = [e for e in events if e["type"] == "STEP_STARTED"] - finished = [e for e in events if e["type"] == "STEP_FINISHED"] - - # All three steps should have started - started_names = {e["stepName"] for e in started} - assert started_names == {"step_a", "step_b", "step_c"} - - # All three steps should have finished - finished_names = {e["stepName"] for e in finished} - assert finished_names == {"step_a", "step_b", "step_c"} diff --git a/tests/graph/agui/test_server.py b/tests/graph/agui/test_server.py deleted file mode 100644 index 24ca0d06..00000000 --- a/tests/graph/agui/test_server.py +++ /dev/null @@ -1,227 +0,0 @@ -"""Tests for AG-UI server.""" - -import pytest -from pydantic import BaseModel -from starlette.testclient import TestClient - -from haiku.rag.config.models import AGUIConfig -from haiku.rag.graph.agui.server import RunAgentInput, create_agui_app, format_sse_event - - -class SimpleState(BaseModel): - """Simple state for testing.""" - - question: str - - -class SimpleResult(BaseModel): - """Simple result for testing.""" - - answer: str - - -class MockGraph: - """Mock graph that returns immediately.""" - - async def run(self, state, deps): # type: ignore[no-untyped-def] - """Return a simple result.""" - return SimpleResult(answer=f"Answer to: {state.question}") - - -def test_run_agent_input_parsing(): - """Test RunAgentInput model parsing.""" - data = { - "threadId": "thread-1", - "runId": "run-1", - "state": {"question": "What is AI?"}, - "messages": [], - "config": {}, - } - - input_data = RunAgentInput(**data) - - assert input_data.thread_id == "thread-1" - assert input_data.run_id == "run-1" - assert input_data.state == {"question": "What is AI?"} - - -def test_format_sse_event(): - """Test SSE event formatting.""" - event = {"type": "TEST_EVENT", "data": "test"} - - sse = format_sse_event(event) - - assert sse.startswith("data: ") - assert sse.endswith("\n\n") - assert '{"type": "TEST_EVENT"' in sse - - -def test_create_agui_app_basic(): - """Test basic app creation.""" - config = AGUIConfig( - host="localhost", - port=8000, - cors_origins=["http://localhost"], - ) - - def graph_factory(): - return MockGraph() - - def state_factory(input_state): - return SimpleState(question=input_state.get("question", "")) - - def deps_factory(input_config): - from dataclasses import dataclass - - @dataclass - class SimpleDeps: - agui_emitter: None = None - - return SimpleDeps() - - app = create_agui_app( - graph_factory=graph_factory, # type: ignore[arg-type] - state_factory=state_factory, - deps_factory=deps_factory, # type: ignore[arg-type] - config=config, - ) - - # Should return a Starlette app - assert app is not None - assert hasattr(app, "routes") - - -def test_server_health_endpoint(): - """Test health check endpoint.""" - config = AGUIConfig() - - def graph_factory(): - return MockGraph() - - def state_factory(input_state): - return SimpleState(question="") - - def deps_factory(input_config): - from dataclasses import dataclass - - @dataclass - class SimpleDeps: - agui_emitter: None = None - - return SimpleDeps() - - app = create_agui_app( - graph_factory=graph_factory, # type: ignore[arg-type] - state_factory=state_factory, - deps_factory=deps_factory, # type: ignore[arg-type] - config=config, - ) - - client = TestClient(app) - response = client.get("/health") - - assert response.status_code == 200 - assert response.json() == {"status": "healthy"} - - -@pytest.mark.asyncio -async def test_server_stream_endpoint(): - """Test AG-UI streaming endpoint.""" - config = AGUIConfig() - - def graph_factory(): - return MockGraph() - - def state_factory(input_state): - question = input_state.get("question", "") - return SimpleState(question=question) - - def deps_factory(input_config): - from dataclasses import dataclass - - @dataclass - class SimpleDeps: - agui_emitter: None = None - - return SimpleDeps() - - app = create_agui_app( - graph_factory=graph_factory, # type: ignore[arg-type] - state_factory=state_factory, - deps_factory=deps_factory, # type: ignore[arg-type] - config=config, - ) - - client = TestClient(app) - - request_data = { - "threadId": "test-1", - "runId": "run-1", - "state": {"question": "What is pydantic-graph?"}, - "messages": [], - "config": {}, - } - - response = client.post("/v1/agent/stream", json=request_data) - - assert response.status_code == 200 - assert response.headers["content-type"] == "text/event-stream; charset=utf-8" - - # Read the streamed events - events = [] - for line in response.iter_lines(): - if line.startswith("data: "): - import json - - event_data = line[6:] # Remove "data: " prefix - event = json.loads(event_data) - events.append(event) - - # Should have received multiple events - assert len(events) > 0 - - # Should have RUN_STARTED and RUN_FINISHED - event_types = [e["type"] for e in events] - assert "RUN_STARTED" in event_types - assert "RUN_FINISHED" in event_types - - -def test_server_cors_headers(): - """Test CORS middleware is configured.""" - config = AGUIConfig( - cors_origins=["http://example.com"], - cors_credentials=True, - ) - - def graph_factory(): - return MockGraph() - - def state_factory(input_state): - return SimpleState(question="") - - def deps_factory(input_config): - from dataclasses import dataclass - - @dataclass - class SimpleDeps: - agui_emitter: None = None - - return SimpleDeps() - - app = create_agui_app( - graph_factory=graph_factory, # type: ignore[arg-type] - state_factory=state_factory, - deps_factory=deps_factory, # type: ignore[arg-type] - config=config, - ) - - client = TestClient(app) - - # GET request with Origin header should get CORS headers - response = client.get("/health", headers={"Origin": "http://example.com"}) - - assert response.status_code == 200 - # CORS middleware should add access-control headers - assert ( - "access-control-allow-origin" in response.headers or response.status_code == 200 - ) diff --git a/tests/graph/agui/test_stream.py b/tests/graph/agui/test_stream.py deleted file mode 100644 index 34ce71e7..00000000 --- a/tests/graph/agui/test_stream.py +++ /dev/null @@ -1,199 +0,0 @@ -"""Tests for stream_graph function.""" - -from dataclasses import dataclass - -import pytest -from pydantic import BaseModel - -from haiku.rag.graph.agui.emitter import AGUIEmitter -from haiku.rag.graph.agui.stream import stream_graph - - -class TestState(BaseModel): - """Test state model.""" - - value: int - - -@dataclass -class TestDeps: - """Test dependencies.""" - - agui_emitter: AGUIEmitter | None = None - - -class MockGraph: - """Mock graph for testing.""" - - def __init__(self, result: str | dict[str, str | int] = "done"): - self.result = result - self.run_called = False - - async def run(self, state, deps): # type: ignore[no-untyped-def] - """Mock run method.""" - self.run_called = True - - # Emit some events through the emitter - if deps.agui_emitter: - deps.agui_emitter.start_step("mock_step") - deps.agui_emitter.update_activity("working", {"message": "Doing work"}) - deps.agui_emitter.finish_step("mock_step") - - return self.result - - -@pytest.mark.asyncio -async def test_stream_graph_basic(): - """Test basic graph streaming.""" - graph = MockGraph(result="success") - state = TestState(value=1) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Should have collected events - assert len(events) > 0 - - # Should have RUN_STARTED and RUN_FINISHED - event_types = [e["type"] for e in events] - assert "RUN_STARTED" in event_types - assert "RUN_FINISHED" in event_types - - # Graph should have been executed - assert graph.run_called - - -@pytest.mark.asyncio -async def test_stream_graph_emits_initial_state(): - """Test that initial state is emitted.""" - graph = MockGraph() - state = TestState(value=42) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Should have initial state snapshot - state_snapshots = [e for e in events if e["type"] == "STATE_SNAPSHOT"] - assert len(state_snapshots) > 0 - # First snapshot should be initial state - assert state_snapshots[0]["snapshot"] == {"value": 42} - - -@pytest.mark.asyncio -async def test_stream_graph_emits_step_events(): - """Test that step events from graph are emitted.""" - graph = MockGraph() - state = TestState(value=1) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Should have step events from MockGraph - step_started = [e for e in events if e["type"] == "STEP_STARTED"] - assert len(step_started) > 0 - assert step_started[0]["stepName"] == "mock_step" - - -@pytest.mark.asyncio -async def test_stream_graph_emits_activity(): - """Test that activity events from graph are emitted.""" - graph = MockGraph() - state = TestState(value=1) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Should have activity events from MockGraph - activities = [e for e in events if e["type"] == "ACTIVITY_SNAPSHOT"] - assert len(activities) > 0 - assert activities[0]["content"] == {"message": "Doing work"} - - -@pytest.mark.asyncio -async def test_stream_graph_handles_error(): - """Test that graph errors are captured and emitted.""" - - class ErrorGraph: - async def run(self, state, deps): - raise ValueError("Test error") - - graph = ErrorGraph() - state = TestState(value=1) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Should have error event - errors = [e for e in events if e["type"] == "RUN_ERROR"] - assert len(errors) > 0 - assert "Test error" in errors[0]["message"] - - -@pytest.mark.asyncio -async def test_stream_graph_closes_emitter(): - """Test that emitter is properly closed.""" - - class NeverReturnsGraph: - async def run(self, state, deps): - # Don't return anything - pass - - graph = NeverReturnsGraph() - state = TestState(value=1) - deps = TestDeps() - - events = [] - try: - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - except RuntimeError: - # Expected - graph didn't return a result - pass - - # Should have error event about no result - errors = [e for e in events if e["type"] == "RUN_ERROR"] - assert len(errors) > 0 - - -@pytest.mark.asyncio -async def test_stream_graph_without_emitter_support(): - """Test error when deps doesn't support agui_emitter.""" - - @dataclass - class BadDeps: - pass - - graph = MockGraph() - state = TestState(value=1) - deps = BadDeps() - - with pytest.raises(TypeError, match="agui_emitter"): - async for _ in stream_graph(graph, state, deps): # type: ignore[arg-type] - pass - - -@pytest.mark.asyncio -async def test_stream_graph_result_in_finish_event(): - """Test that graph result is included in RUN_FINISHED event.""" - graph = MockGraph(result={"status": "complete", "count": 42}) - state = TestState(value=1) - deps = TestDeps() - - events = [] - async for event in stream_graph(graph, state, deps): # type: ignore[arg-type] - events.append(event) - - # Find RUN_FINISHED event - finished = [e for e in events if e["type"] == "RUN_FINISHED"] - assert len(finished) == 1 - assert finished[0]["result"] == {"status": "complete", "count": 42} diff --git a/tests/graph/test_research_graph.py b/tests/graph/test_research_graph.py index 376cd6f3..ca1df9d3 100644 --- a/tests/graph/test_research_graph.py +++ b/tests/graph/test_research_graph.py @@ -1,9 +1,9 @@ import pytest from haiku.rag.client import HaikuRAG -from haiku.rag.graph.agui.stream import stream_graph from haiku.rag.graph.research.dependencies import ResearchContext from haiku.rag.graph.research.graph import build_research_graph +from haiku.rag.graph.research.models import ResearchReport from haiku.rag.graph.research.state import ResearchDeps, ResearchState @@ -27,24 +27,11 @@ async def test_graph_end_to_end(allow_model_requests, temp_db_path, qa_corpus): deps = ResearchDeps(client=client) - events = [] - result = None - async for event in stream_graph(graph, state, deps): - events.append(event) - if event["type"] == "RUN_FINISHED": - result = event["result"] - elif event["type"] == "RUN_ERROR": - pytest.fail(f"Graph execution failed: {event['message']}") + result = await graph.run(state=state, deps=deps) - assert result is not None, ( - f"No result. Events collected: {[e['type'] for e in events]}" - ) - assert isinstance(result, dict) - assert "title" in result - assert "executive_summary" in result - - event_types = [e["type"] for e in events] - assert "RUN_STARTED" in event_types - assert "RUN_FINISHED" in event_types + assert result is not None + assert isinstance(result, ResearchReport) + assert result.title + assert result.executive_summary client.close() diff --git a/tests/test_app.py b/tests/test_app.py index d0e472f2..998a68b8 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -338,24 +338,6 @@ async def test_ask_with_cite(app: HaikuRAGApp, monkeypatch): assert mock_print.call_count >= 1 -@pytest.mark.asyncio -async def test_ask_with_verbose(app: HaikuRAGApp, monkeypatch): - """Test asking a question with verbose (should be ignored for non-deep).""" - mock_answer = "Test answer" - mock_citations = [] - mock_client = AsyncMock() - mock_client.ask.return_value = (mock_answer, mock_citations) - mock_client.__aenter__.return_value = mock_client - - mock_print = MagicMock() - monkeypatch.setattr(app.console, "print", mock_print) - - with patch("haiku.rag.app.HaikuRAG", return_value=mock_client): - await app.ask("test question", verbose=True) - - mock_client.ask.assert_called_once_with("test question", filter=None) - - @pytest.mark.asyncio async def test_ask_with_deep(app: HaikuRAGApp, monkeypatch): """Test asking a question with deep mode uses research graph.""" @@ -427,35 +409,6 @@ async def test_ask_with_deep_and_cite(app: HaikuRAGApp, monkeypatch): assert call_kwargs["state"].context.original_question == "test question" -@pytest.mark.asyncio -async def test_ask_with_deep_and_verbose(app: HaikuRAGApp, monkeypatch): - """Test asking a question with deep mode and verbose output.""" - import haiku.rag.app as app_module - - mock_output = {"executive_summary": "Deep research answer"} - - mock_renderer = AsyncMock() - mock_renderer.render.return_value = mock_output - - mock_graph = AsyncMock() - - mock_client = AsyncMock() - - mock_print = MagicMock() - monkeypatch.setattr(app.console, "print", mock_print) - monkeypatch.setattr(app_module, "build_research_graph", lambda **kwargs: mock_graph) - - with patch("haiku.rag.app.HaikuRAG") as mock_rag_class: - mock_rag_class.return_value.__aenter__ = AsyncMock(return_value=mock_client) - mock_rag_class.return_value.__aexit__ = AsyncMock(return_value=None) - with patch("haiku.rag.app.AGUIConsoleRenderer", return_value=mock_renderer): - await app.ask("test question", deep=True, verbose=True) - - # With verbose, it should use AGUIConsoleRenderer.render, not graph.run - mock_renderer.render.assert_called_once() - mock_graph.run.assert_not_called() - - @pytest.mark.asyncio async def test_history_all_tables(tmp_path, monkeypatch): """Test history command shows version history for all tables.""" diff --git a/tests/test_cli.py b/tests/test_cli.py index ac7da1fc..933d35e5 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -282,7 +282,6 @@ def test_ask(): question="What is Python?", cite=False, deep=False, - verbose=False, filter=None, ) @@ -300,7 +299,6 @@ def test_ask_with_cite(): question="What is Python?", cite=True, deep=False, - verbose=False, filter=None, ) @@ -318,7 +316,6 @@ def test_ask_with_deep(): question="What is Python?", cite=False, deep=True, - verbose=False, filter=None, ) @@ -336,25 +333,6 @@ def test_ask_with_deep_and_cite(): question="What is Python?", cite=True, deep=True, - verbose=False, - filter=None, - ) - - -def test_ask_with_deep_and_verbose(): - with patch("haiku.rag.cli.HaikuRAGApp") as mock_app: - mock_app_instance = MagicMock() - mock_app_instance.ask = AsyncMock() - mock_app.return_value = mock_app_instance - - result = runner.invoke(cli, ["ask", "What is Python?", "--deep", "--verbose"]) - - assert result.exit_code == 0 - mock_app_instance.ask.assert_called_once_with( - question="What is Python?", - cite=False, - deep=True, - verbose=True, filter=None, )