""" FastAPI Application for Multimodal RAG System US Army Medical Research Papers Q&A """ import os import logging from typing import List, Dict, Optional, Union from contextlib import asynccontextmanager from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel, Field # Import from query_index (standalone) from query_index import MultimodalRAGSystem # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Global variables rag_system: Optional[MultimodalRAGSystem] = None # Lifecycle management @asynccontextmanager async def lifespan(app: FastAPI): """Initialize and cleanup RAG system""" global rag_system logger.info("Starting RAG system initialization...") try: rag_system = MultimodalRAGSystem() logger.info("RAG system initialized successfully!") except Exception as e: logger.error(f"Error during initialization: {str(e)}") rag_system = None yield logger.info("Shutting down RAG system...") rag_system = None # Create FastAPI app app = FastAPI( title="Multimodal RAG API", description="Q&A system for US Army medical research papers (Text + Images)", version="2.0.0", lifespan=lifespan ) # CORS middleware app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Mount static files app.mount("/static", StaticFiles(directory="static"), name="static") # Mount extracted images # This allows the frontend to load images via /extracted_images/filename.jpg if os.path.exists("extracted_images"): app.mount("/extracted_images", StaticFiles(directory="extracted_images"), name="images") # Mount PDF documents if os.path.exists("WHEC_Documents"): app.mount("/documents", StaticFiles(directory="WHEC_Documents"), name="documents") # Pydantic models class QueryRequest(BaseModel): question: str = Field(..., min_length=1, max_length=1000, description="Question to ask") class ImageSource(BaseModel): path: Optional[str] filename: Optional[str] score: Optional[float] page: Optional[Union[str, int]] # could be int or str depending on metadata file: Optional[str] link: Optional[str] = None class TextSource(BaseModel): text: str score: float page: Optional[Union[str, int]] file: Optional[str] link: Optional[str] = None class QueryResponse(BaseModel): answer: str images: List[ImageSource] texts: List[TextSource] question: str class HealthResponse(BaseModel): status: str rag_initialized: bool # API Endpoints @app.get("/", tags=["Root"]) async def root(): """Serve the frontend application""" return FileResponse('static/index.html') @app.get("/health", response_model=HealthResponse, tags=["Health"]) async def health_check(): """Health check endpoint""" return HealthResponse( status="healthy", rag_initialized=rag_system is not None ) @app.post("/query", response_model=QueryResponse, tags=["Query"]) async def query_rag(request: QueryRequest): """ Query the RAG system """ if not rag_system: raise HTTPException( status_code=503, detail="RAG system not initialized. Check logs for errors." ) try: # Get answer result = rag_system.ask(request.question) return QueryResponse( answer=result['answer'], images=result['images'], texts=result['texts'], question=request.question ) except Exception as e: logger.error(f"Error processing query: {str(e)}") raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}") if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)