Building a PDF Chatbot with Streamlit and LangChain

In this tutorial, we’ll walk you through the process of creating a simple PDF chatbot using Streamlit and LangChain libraries. The chatbot will allow users to upload a PDF file and ask questions related to its content.

from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from PyPDF2 import PdfReader
import streamlit as st
import os
import fitz
from PIL import Image

In this step, We import the necessary libraries for our chatbot, including those from LangChain and Streamlit.

• langchain: This library provides tools for natural language processing tasks like text splitting, embeddings, and more.
• FAISS: A library for efficient similarity search and clustering of dense vectors. It's used to store and retrieve chunks of text.
• ChatOpenAI: A chat model from LangChain used for generating responses.
• ConversationalRetrievalChain: A chain that combines a chat model, retriever, and memory to create a conversational retrieval system.
• ConversationBufferMemory: A memory system that stores and retrieves conversations for the chatbot.
• RecursiveCharacterTextSplitter: A text splitter for dividing text…