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from fastapi import FastAPI, File, UploadFile, HTTPException |
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from fastapi.middleware.cors import CORSMiddleware |
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import os |
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from dotenv import load_dotenv |
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from utils.uploadFilePDFtoMD import convert_pdf_to_md |
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from utils.vectorDB import create_retriever, load_retriever |
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from utils.chunking import split_text_by_markdown |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from utils.llm import ask_question |
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from pydantic import BaseModel |
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class QueryRequest(BaseModel): |
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question: str |
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load_dotenv() |
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app = FastAPI() |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["https://*.streamlit.app"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") |
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@app.post("/uploadfile/") |
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async def upload_file(file: UploadFile = File(...)): |
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if not file.filename.endswith(".pdf"): |
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raise HTTPException(status_code=400, detail="Only PDF files are supported.") |
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temp_dir = "temp" |
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os.makedirs(temp_dir, exist_ok=True) |
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temp_path = os.path.join(temp_dir, file.filename) |
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with open(temp_path, "wb") as f: |
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f.write(await file.read()) |
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try: |
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md = convert_pdf_to_md(temp_path) |
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chunks = split_text_by_markdown(md) |
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retriever = create_retriever(chunks, embeddings) |
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os.remove(temp_path) |
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return {"message": "File processed and vector store created successfully."} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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@app.post("/query") |
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async def query(request: QueryRequest): |
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try: |
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retriever = load_retriever(embeddings) |
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retrieved_docs = retriever.invoke(request.question) |
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context = "\n\n".join([doc.page_content for doc in retrieved_docs]) |
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answer = ask_question(request.question, context) |
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return {"question": request.question, "answer": answer} |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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if __name__ == "__main__": |
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 8000))) |
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