edureka / app /main.py
Hugging Face Sync
Deploy: Clinical RAG Assistant (Clean Push)
481554c
"""FastAPI application entry point."""
from __future__ import annotations
import logging
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.config import get_settings
from app.core.vector_store import VectorStoreManager
from app.api.routes import health, documents, chat
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s | %(levelname)-8s | %(name)s | %(message)s",
)
logger = logging.getLogger(__name__)
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Startup: eagerly initialise the singleton vector store (loads embedding model)."""
logger.info("Starting up β€” initialising vector store & embedding model...")
VectorStoreManager.get_instance()
logger.info("Vector store ready βœ“")
yield
logger.info("Shutting down")
def create_app() -> FastAPI:
settings = get_settings()
app = FastAPI(
title="Agentic RAG API",
description=(
"Upload enterprise documents (PDF, TXT, CSV, Excel) and query them "
"with an AI agent powered by LangGraph + LLM + ChromaDB."
),
version="1.0.0",
lifespan=lifespan,
)
# CORS β€” allow Streamlit frontend
app.add_middleware(
CORSMiddleware,
allow_origins=[settings.frontend_origin, "http://localhost:8501", "http://127.0.0.1:8501"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Routers
prefix = "/api/v1"
app.include_router(health.router, prefix=prefix)
app.include_router(documents.router, prefix=prefix)
app.include_router(chat.router, prefix=prefix)
@app.get("/", include_in_schema=False)
async def root():
return {"message": "Agentic RAG API β€” visit /docs for the Swagger UI"}
return app
app = create_app()
if __name__ == "__main__":
import uvicorn
settings = get_settings()
uvicorn.run("app.main:app", host=settings.app_host, port=settings.app_port, reload=True)