model.py

import os
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.llms import HuggingFaceHub
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
import uuid
import faiss

vectorstore = None

def load_vectorstore(pdf_path):
    global vectorstore

    reader = PdfReader(pdf_path)
    text = "".join([page.extract_text() or "" for page in reader.pages])
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    chunks = splitter.split_text(text)

    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
    dim = len(embeddings.embed_query("test"))
    index = faiss.IndexFlatL2(dim)

    vectorstore = FAISS(
        embedding_function=embeddings,
        index=index,
        docstore=InMemoryDocstore({}),
        index_to_docstore_id={}
    )
    uuids = [str(uuid.uuid4()) for _ in chunks]
    vectorstore.add_texts(chunks, ids=uuids)


def ask_question(query):
    global vectorstore
    if not vectorstore:
        return "Please upload and index a document first."

    llm = HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
        huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN"),
        model_kwargs={"temperature": 0.7, "max_length": 512}
    )

    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
    prompt = PromptTemplate(
        template="Use the context to answer the question:
Context: {context}
Question: {question}
Answer:",
        input_variables=["context", "question"]
    )

    chain = RetrievalQA.from_chain_type(
        llm=llm,
        retriever=retriever,
        return_source_documents=False,
        chain_type_kwargs={"prompt": prompt}
    )
    return chain({"query": query})["result"]