main.py

from fastapi import FastAPI, File, UploadFile, Form
import os
from langchain_community.document_loaders import PyMuPDFLoader
from LoadLLM import Loadllm
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain

DB_FAISS_PATH = 'vectorstore/db_faiss'

app = FastAPI()

@app.get('/')
async def home():
    return "API Server Running"

@app.post('/PromptBuddy')
async def PromptLLM(file: UploadFile = File(...), query: str = Form(...)):

    with open(f"uploads/{file.filename}", "wb") as f:
        f.write(file.file.read())

    loader = PyMuPDFLoader(file_path=f"uploads/{file.filename}")
    data = loader.load()

    # Create embeddings using Sentence Transformers
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    # Create a FAISS vector store and save embeddings
    db = FAISS.from_documents(data, embeddings)
    db.save_local(DB_FAISS_PATH)

    # Load the language model
    llm = Loadllm.load_llm()

    # Create a conversational chain
    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())

    result = chain({"question": query, "chat_history": ''})
    return result["answer"]