Retrieval-Augmented Generation (RAG) Based Large Language Model (LLM) Chatbot

This repository contains the code and resources for a chatbot project, implemented in a Jupyter Notebook.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Project Structure
• Dependencies
• License
• Acknowledgements

Introduction

This project aims to develop a chatbot using advanced natural language processing (NLP) techniques. The chatbot is designed to interact with users, providing meaningful responses based on the input it receives. The project utilizes various libraries and APIs to achieve its functionality.

Features

• Natural Language Understanding (NLU)
• Contextual response generation
• Integration with external APIs for enhanced capabilities
• Customizable and extendable architecture

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/AnuragB2004/RAG-Based-LLM-Chatbot
   cd RAG-Based-LLM-Chatbot

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have Jupyter Notebook installed. If not, install it using:

   pip install jupyter

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open the Chatbot-Main.ipynb file in Jupyter Notebook.

Usage

To use the chatbot, run all the cells in the Jupyter Notebook. The notebook contains detailed comments and instructions on how to interact with the chatbot.

Project Structure

chatbot-project/
├── data/
│   ├── intents.json        # Sample data for chatbot training
│   └── ...
├── models/
│   ├── chatbot_model.h5    # Trained model file
│   └── ...
├── notebooks/
│   └── Chatbot-Main.ipynb  # Main Jupyter Notebook with code
├── requirements.txt        # List of dependencies
├── README.md               # Project documentation
└── ...

Dependencies

The project relies on the following libraries and tools:

• TensorFlow
• NLTK
• NumPy
• Flask
• Jupyter Notebook
• OpenAI
• Azure Cognitive Services
• Other libraries as specified in requirements.txt

License

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

Acknowledgements

• Thanks to all the contributors and open-source projects that made this project possible.