app.py

import os
import logging
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
import shutil

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="RAG Chatbot API")

# Ensure directories exist
try:
    os.makedirs("documents", exist_ok=True)
    os.makedirs("vectorstore", exist_ok=True)
    logger.info("Directories 'documents' and 'vectorstore' created or already exist.")
except Exception as e:
    logger.error(f"Failed to create directories: {str(e)}")
    raise

# Check for GOOGLE_API_KEY
if not os.getenv("GOOGLE_API_KEY"):
    logger.error("GOOGLE_API_KEY environment variable not set.")
    raise ValueError("GOOGLE_API_KEY environment variable not set.")

# Initialize Gemini LLM
try:
    llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-flash",
        google_api_key=os.getenv("GOOGLE_API_KEY")
    )
    logger.info("Gemini LLM initialized successfully.")
except Exception as e:
    logger.error(f"Failed to initialize Gemini LLM: {str(e)}")
    raise

# Initialize embeddings
try:
    embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001",
        google_api_key=os.getenv("GOOGLE_API_KEY")
    )
    logger.info("Gemini embeddings initialized successfully.")
except Exception as e:
    logger.error(f"Failed to initialize Gemini embeddings: {str(e)}")
    raise

# Path for vector store
VECTOR_STORE_PATH = "vectorstore/index"

def process_pdf(pdf_path):
    """Process and index a PDF document."""
    try:
        logger.info(f"Processing PDF: {pdf_path}")
        loader = PyPDFLoader(pdf_path)
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        texts = text_splitter.split_documents(documents)
        if os.path.exists(VECTOR_STORE_PATH):
            vector_store = FAISS.load_local(VECTOR_STORE_PATH, embeddings, allow_dangerous_deserialization=True)
            vector_store.add_documents(texts)
            logger.info("Added documents to existing FAISS vector store.")
        else:
            vector_store = FAISS.from_documents(texts, embeddings)
            logger.info("Created new FAISS vector store.")
        vector_store.save_local(VECTOR_STORE_PATH)
        logger.info("Vector store saved successfully.")
        return {"status": "Document processed and indexed successfully"}
    except Exception as e:
        logger.error(f"Error processing PDF: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing document: {str(e)}")

def answer_query(query):
    """Answer a query using the RAG pipeline."""
    if not os.path.exists(VECTOR_STORE_PATH):
        logger.warning("No vector store found. Please upload a document first.")
        return {"error": "No documents indexed yet. Please upload a document first."}
    try:
        logger.info(f"Processing query: {query}")
        vector_store = FAISS.load_local(VECTOR_STORE_PATH, embeddings, allow_dangerous_deserialization=True)
        qa_chain = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=vector_store.as_retriever(search_kwargs={"k": 3}),
            return_source_documents=True
        )
        result = qa_chain({"query": query})
        logger.info("Query processed successfully.")
        return {
            "answer": result["result"],
            "source_documents": [doc.page_content[:200] for doc in result["source_documents"]]
        }
    except Exception as e:
        logger.error(f"Error answering query: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error answering query: {str(e)}")

@app.post("/upload-document")
async def upload_document(file: UploadFile = File(...)):
    """API to upload and process a PDF document."""
    if not file.filename.endswith(".pdf"):
        logger.warning(f"Invalid file type uploaded: {file.filename}")
        raise HTTPException(status_code=400, detail="Only PDF files are allowed")
    file_path = f"documents/{file.filename}"
    try:
        with open(file_path, "wb") as buffer:
            shutil.copyfileobj(file.file, buffer)
        logger.info(f"Uploaded file saved: {file_path}")
        result = process_pdf(file_path)
        return JSONResponse(content=result, status_code=200)
    except Exception as e:
        logger.error(f"Error in upload_document: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error uploading document: {str(e)}")

@app.post("/ask-question")
async def ask_question(query: str):
    """API to answer a query based on indexed documents."""
    logger.info(f"Received question: {query}")
    result = answer_query(query)
    return JSONResponse(content=result, status_code=200)

@app.get("/health")
async def health_check():
    """Health check endpoint."""
    logger.info("Health check requested.")
    return {"status": "API is running"}