code

import os
import streamlit as st
from openai import OpenAI

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

# OpenAI API Key
OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"

client = OpenAI(api_key=OPENAI_API_KEY)

st.title("📚 RAG Chatbot")

uploaded_file = st.file_uploader(
    "Upload a PDF",
    type=["pdf"]
)

if uploaded_file:

    with open("temp.pdf", "wb") as f:
        f.write(uploaded_file.read())

    loader = PyPDFLoader("temp.pdf")
    documents = loader.load()

    splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50
    )

    chunks = splitter.split_documents(documents)

    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )

    vector_db = FAISS.from_documents(
        chunks,
        embeddings
    )

    st.success("PDF Indexed Successfully!")

    question = st.text_input(
        "Ask a Question"
    )

    if question:

        docs = vector_db.similarity_search(
            question,
            k=3
        )

        context = "\n".join(
            [doc.page_content for doc in docs]
        )

        prompt = f"""
You are a helpful assistant.

Use only the information below.

Context:
{context}

Question:
{question}

Answer:
"""

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "user",
                    "content": prompt
                }
            ]
        )

        answer = response.choices[0].message.content

        st.subheader("Answer")
        st.write(answer)

        st.subheader("Retrieved Chunks")

        for i, doc in enumerate(docs):
            st.write(f"Chunk {i+1}")
            st.write(doc.page_content)
            st.divider()