This device is not compatible.

PROJECT


Build an Interactive PDF Reader using LangChain and Streamlit

In this project, we’ll learn to create an interactive PDF reader that allows users to upload custom PDFs and features a chatbot for answering questions on the content of the PDF.

Build an Interactive PDF Reader using LangChain and Streamlit

You will learn to:

Create embeddings for a PDF using Chroma.

Use GPT-3.5/GPT-4 LLM to answer questions.

Build a simple frontend for the chat app using Streamlit.

Display the relevant pages of the PDF in an iframe.

Skills

Deep Learning

Natural Language Processing

Prompt Engineering

Prerequisites

Basic understanding of working with Streamlit

Basic prompt engineering skills with LangChain

Intermediate Python programming skills

Technologies

OpenAI

Chroma logo

Chroma

Streamlit

LangChain logo

LangChain

Project Description

The Natural Language Processing market has gained a lot of momentum recently and is projected to continue this upward trend. This growth is expected due to a number of favorable factors, but the most important of these is the ability to process language at a semantic level in LLMs such as GPT-3.

In this project, we’ll create an interactive PDF reader using LangChain and Streamlit. The LangChain framework will enable us to seamlessly integrate a chatbot into our application. The application will allow a user to upload any PDF document, and then the chatbot will answer questions the user may ask by looking up the relevant text in the PDF. The referenced pages are extracted and displayed to provide context to the answer.

We’ll use Streamlit to create the web application, the HuggingFace models for creating embeddings, and the GPT 3.5 LLM for language generation. To implement our NLP pipeline, we’ll use LangChain. The basic functionality of the web application is shown in the figure below:

The chatbot answers questions by looking up the relevant portion of the book. A few pages before and after the referenced page are displayed in the browser.
The chatbot answers questions by looking up the relevant portion of the book. A few pages before and after the referenced page are displayed in the browser.

Project Tasks

1

Get Started

Task 0: Introduction

Task 1: Import the Libraries

Task 2: Set Up the API Keys

2

Develop the Web Application

Task 3: Create the Web Page Layout

Task 4: Define a Function to Process the Input File

Task 5: Get and Process the File

3

The Chat Bot

Task 6: Set Up the Chatbot

Task 7: Respond to User Queries

Congratulations!