main.py

import os
import uvicorn
import logging
from fastapi import FastAPI, HTTPException, status
from fastapi.responses import RedirectResponse
from pydantic import BaseModel
from dotenv import load_dotenv
from rag_core import RAGCore
from contextlib import asynccontextmanager

load_dotenv()
logging.basicConfig(level=logging.INFO)

rag_instance = None

class QueryRequest(BaseModel):
    question: str

@asynccontextmanager
async def lifespan(app: FastAPI):
    global rag_instance
    try:
        if not os.getenv("OPENAI_API_KEY"):
            logging.critical("FATAL: OPENAI_API_KEY not found in .env. Embeddings/Evals require this.")
            exit(1)
        rag_instance = RAGCore()
        logging.info("✅ RAG Core initialized successfully.")
        yield
    except Exception as e:
        logging.error(f"RAG Initialization Failed: {e}")
        rag_instance = None
        yield
    finally:
        rag_instance = None

app = FastAPI(title="Multi-Document RAG Assistant API", lifespan=lifespan)

@app.get("/")
async def root():
    """Redirect root URL to interactive API documentation."""
    return RedirectResponse(url="/docs")

@app.post("/query")
async def query_rag_endpoint(request: QueryRequest):
    """Answers a question using the multi-document knowledge base."""
    if not rag_instance:
        raise HTTPException(
            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
            detail="RAG service is initializing or unavailable. Check server logs."
        )

    try:
        response = await rag_instance.query(request.question)
        return response
    except Exception as e:
        logging.error(f"Query error: {e}", exc_info=True)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Error processing query: {e}"
        )

if __name__ == "__main__":
    port = int(os.getenv("UVICORN_PORT", 8000))
    uvicorn.run(app, host="0.0.0.0", port=port)