app.py

import os
import streamlit as st
from langchain.embeddings import Embedding
from groq import Groq
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents import Tool

# Set up Groq API
groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# Define a custom embedding class for Groq
class GroqEmbedding(Embedding):
    def __init__(self, model="groq-embedding-model", api_key=None):
        self.model = model
        self.client = Groq(api_key=api_key or os.getenv("GROQ_API_KEY"))
    
    def embed_documents(self, texts):
        # Use Groq's API to generate embeddings
        embeddings = self.client.embed_documents(texts, model=self.model)
        return embeddings
    
    def embed_query(self, query):
        # Use Groq's API to generate query embedding
        return self.client.embed_query(query, model=self.model)

# Streamlit App UI
st.title("PDF Question-Answering with Groq Embeddings")

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

# Process the uploaded PDF
if uploaded_file is not None:
    loader = PyPDFLoader(uploaded_file)
    documents = loader.load()
    
    # Split documents into smaller chunks for better processing
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    split_docs = text_splitter.split_documents(documents)

    # Create embeddings using Groq
    embeddings = GroqEmbedding(api_key=os.getenv("GROQ_API_KEY"))
    
    # Create a FAISS vector store
    vector_db = FAISS.from_documents(split_docs, embeddings)
    
    # Initialize the retrieval-based QA system
    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vector_db)
    
    # User input for querying the PDF content
    query = st.text_input("Ask a question about the PDF:")

    if query:
        result = qa.run(query)
        st.write("Answer:", result)