haiku.rag/app/backend/main.py

import logging
import os
from pathlib import Path

from dotenv import load_dotenv
from pydantic_ai.ui import SSE_CONTENT_TYPE
from pydantic_ai.ui.ag_ui import AGUIAdapter
from starlette.applications import Starlette
from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware
from starlette.requests import Request
from starlette.responses import JSONResponse, Response, StreamingResponse
from starlette.routing import Route

from haiku.rag.agents.chat import (
    AGUI_STATE_KEY,
    ChatDeps,
    ChatSessionState,
    QAResponse,
    create_chat_agent,
)
from haiku.rag.client import HaikuRAG
from haiku.rag.config import load_yaml_config
from haiku.rag.config.models import AppConfig

load_dotenv()

# Configure logfire (only sends data if LOGFIRE_TOKEN is present)
try:
    import logfire

    logfire.configure(send_to_logfire="if-token-present", console=False)
    logfire.instrument_pydantic_ai()
except Exception:
    pass

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)

# Load config
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:
    Config = AppConfig()

# Get DB path from environment
db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb")
db_path = Path(db_path_str)

logger.info(f"Database path: {db_path}")
logger.info(f"QA Provider: {Config.qa.model.provider}, Model: {Config.qa.model.name}")

# Create the chat agent
chat_agent = create_chat_agent(Config)

# Client cache for proper lifecycle
_client_cache: dict[str, HaikuRAG] = {}


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, create=True
        )
    return _client_cache[path_key]


async def stream_chat(request: Request) -> Response:
    """Chat streaming endpoint with AG-UI protocol."""
    body = await request.body()
    logger.info("Received chat request")

    # Parse request to build run_input
    accept = request.headers.get("accept", SSE_CONTENT_TYPE)
    run_input = AGUIAdapter.build_run_input(body)

    # Restore session state from incoming AG-UI state (look under namespaced key)
    initial_qa_history: list[QAResponse] = []
    background_context: str | None = None
    state = getattr(run_input, "state", None)
    if state:
        chat_state = state.get(AGUI_STATE_KEY, state)
        if "qa_history" in chat_state:
            initial_qa_history = [
                QAResponse(**qa) for qa in chat_state.get("qa_history", [])
            ]
        background_context = chat_state.get("background_context")

    # Build deps with session state
    thread_id = getattr(run_input, "thread_id", None)
    deps = ChatDeps(
        client=get_client(db_path),
        config=Config,
        session_state=ChatSessionState(
            session_id=thread_id or "",
            qa_history=initial_qa_history,
            background_context=background_context,
        ),
        state_key=AGUI_STATE_KEY,
    )

    # Use AGUIAdapter for streaming
    adapter = AGUIAdapter(agent=chat_agent, run_input=run_input, accept=accept)
    event_stream = adapter.run_stream(deps=deps)
    sse_event_stream = adapter.encode_stream(event_stream)

    return StreamingResponse(
        sse_event_stream,
        media_type=accept,
        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",
            "agent_model": str(chat_agent.model),
            "qa_provider": Config.qa.model.provider,
            "qa_model": Config.qa.model.name,
            "db_path": str(db_path),
            "db_exists": db_path.exists(),
        }
    )


async def list_documents(_: Request) -> JSONResponse:
    """List all documents in the database."""
    if not db_path.exists():
        return JSONResponse({"documents": [], "error": "Database not found"})

    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 db_info(_: Request) -> JSONResponse:
    """Get database info and statistics."""
    if not db_path.exists():
        return JSONResponse(
            {
                "exists": False,
                "path": str(db_path),
                "documents": 0,
                "chunks": 0,
            }
        )

    client = get_client(db_path)
    stats = client.store.get_stats()

    return JSONResponse(
        {
            "exists": True,
            "path": str(db_path),
            "documents": stats.get("documents", {}).get("num_rows", 0),
            "chunks": stats.get("chunks", {}).get("num_rows", 0),
            "documents_bytes": stats.get("documents", {}).get("total_bytes", 0),
            "chunks_bytes": stats.get("chunks", {}).get("total_bytes", 0),
            "has_vector_index": stats.get("chunks", {}).get("has_vector_index", False),
        }
    )


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"]

    if not db_path.exists():
        return JSONResponse({"error": "Database not found"}, status_code=404)

    client = get_client(db_path)

    chunk = await client.chunk_repository.get_by_id(chunk_id)
    if not chunk:
        return JSONResponse({"error": "Chunk not found"}, status_code=404)

    images = await client.visualize_chunk(chunk)
    if not images:
        return JSONResponse({"images": [], "message": "No visual grounding available"})

    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/chat/stream", stream_chat, methods=["POST"]),
        Route("/api/documents", list_documents, methods=["GET"]),
        Route("/api/info", db_info, 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,
    )