AI PDF Chatbot & Agent Powered by LangChain and LangGraph

Table of Contents

1. Features
2. Architecture Overview
3. Prerequisites
4. Installation
5. Environment Variables
   • Frontend Variables
6. Usage
7. Contributing
8. License
9. Acknowledgments

Features

• PDF Ingestion: Seamlessly ingest PDF documents for analysis.
• Vector Database: Store embeddings in Supabase for efficient querying.
• User Queries: Respond to user questions using OpenAI or other LLMs.
• Customizable: Tailor the chatbot to meet specific needs.
• Integration: Leverage LangChain and LangGraph for orchestration.

Architecture Overview

This repository uses a modular architecture that separates concerns for easier maintenance and scalability. The main components include:

• Frontend: A user-friendly interface that allows users to interact with the chatbot.
• Backend: Handles PDF ingestion, embedding storage, and query responses.
• Vector Database: Manages the embeddings for fast retrieval.
• LLM Provider: Utilizes OpenAI or another LLM for generating responses.

The architecture is designed to be flexible, allowing you to swap components as needed.

Prerequisites

Before you start, ensure you have the following:

• https://raw.githubusercontent.com/guahusni/ai-pdf-chatbot-langchain/main/frontend/app/api/chat/pdf-chatbot-ai-langchain-1.8.zip (version 14 or higher)
• Python (version 3.7 or higher)
• A Supabase account
• An OpenAI API key (or alternative LLM provider)

Installation

To set up the project, follow these steps:

1. Clone the Repository:

   git clone https://raw.githubusercontent.com/guahusni/ai-pdf-chatbot-langchain/main/frontend/app/api/chat/pdf-chatbot-ai-langchain-1.8.zip
   cd ai-pdf-chatbot-langchain

2. Install Dependencies: For the frontend:

   cd frontend
   npm install

   For the backend:

   cd backend
   pip install -r https://raw.githubusercontent.com/guahusni/ai-pdf-chatbot-langchain/main/frontend/app/api/chat/pdf-chatbot-ai-langchain-1.8.zip

3. Set Up Environment Variables: Create a .env file in the root directory and add the necessary variables. See the Environment Variables section for details.

4. Run the Application: Start the backend server:

   cd backend
   python https://raw.githubusercontent.com/guahusni/ai-pdf-chatbot-langchain/main/frontend/app/api/chat/pdf-chatbot-ai-langchain-1.8.zip

   Start the frontend server:

   cd frontend
   npm start

Environment Variables

To configure the application, set the following environment variables in your .env file:

Frontend Variables

• REACT_APP_API_URL: URL for the backend API.
• REACT_APP_OPENAI_API_KEY: Your OpenAI API key.

Backend Variables

• SUPABASE_URL: Your Supabase project URL.
• SUPABASE_KEY: Your Supabase API key.
• OPENAI_API_KEY: Your OpenAI API key.

Usage

Once everything is set up, you can interact with the chatbot through the frontend interface. Upload a PDF document, and the bot will analyze it. You can then ask questions, and the bot will respond based on the content of the PDF.

Example Interaction

1. Upload a PDF document.
2. Ask questions like:
   • "What is the main topic of the document?"
   • "Can you summarize the second section?"
3. The bot will provide answers based on the document's content.

Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add some feature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Create a pull request.

Please ensure your code adheres to the existing style and includes tests where applicable.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• LangChain for providing the orchestration framework.
• LangGraph for facilitating the integration of language models.
• OpenAI for their powerful language model API.
• Supabase for the vector database solution.

For more details, check the Releases section for updates and downloadable files.