haiku.rag/app/backend/main.py

import logging
import os
from pathlib import Path

from dotenv import find_dotenv, load_dotenv
from pydantic_ai import Agent
from pydantic_ai.ag_ui import AGUIAdapter
from pydantic_ai.ui import SSE_CONTENT_TYPE
from 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.client import HaikuRAG
from haiku.rag.config import load_yaml_config
from haiku.rag.config.models import AppConfig
from haiku.rag.skills.rag import AGENT_PREAMBLE, create_skill
from haiku.skills import SkillDeps, SkillToolset
from haiku.skills.prompts import build_system_prompt

load_dotenv(find_dotenv(usecwd=True))

# 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}")

# Only HaikuRAG client is a singleton (expensive to create)
_client: HaikuRAG | None = None


def get_client() -> HaikuRAG:
    """Get or create cached client."""
    global _client
    if _client is None:
        _client = HaikuRAG(db_path=db_path, config=Config, create=True)
    return _client


# Create skill, toolset, and agent
skill = create_skill(db_path=db_path, config=Config)
toolset = SkillToolset(skills=[skill])

agent = Agent(
    os.getenv("HAIKU_CHAT_MODEL", "openai:gpt-4o"),
    instructions=build_system_prompt(toolset.skill_catalog, preamble=AGENT_PREAMBLE),
    toolsets=[toolset],
    deps_type=SkillDeps,
)


async def stream_chat(request: Request) -> Response:
    """Chat streaming endpoint with AG-UI protocol."""
    body = await request.body()
    accept = request.headers.get("accept", SSE_CONTENT_TYPE)
    run_input = AGUIAdapter.build_run_input(body)

    adapter = AGUIAdapter(agent=agent, run_input=run_input, accept=accept)
    event_stream = adapter.run_stream(deps=SkillDeps())
    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",
            "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()
    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()
    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()

    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,  # type: ignore[invalid-argument-type]
            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,
    )