RAG-Based AI Chat Bot

This project is a Retrieval-Augmented Generation (RAG)-based chatbot powered by OpenAI's GPT model, which uses a vector database to enhance search and response capabilities. The bot processes data stored in the data folder using OpenAI's embeddings, with a text processing pipeline built on RecursiveCharacterTextSplitter to manage larger documents. The Chroma vector database is employed to store and query the processed data.

Features

• Data Source: The bot processes text files from the data folder.
• Embedding Creation: It generates embeddings using OpenAI's OPENAIEmbeddings.
• Vector Database: The bot stores and retrieves vectors using Chroma.
• Text Preprocessing: It uses RecursiveCharacterTextSplitter to handle and split large text documents.

Installation

1. Clone the repository

Clone the repository using the command:

git clone <repository-url>
cd <repository-directory>

2. Install the required packages

To install all dependencies, use the command:

pip install -r requirements.txt

3. Set up OpenAI API Key

You will need an OpenAI API key to run the chatbot. Create a .env file in the root directory and add your API key in the following format:

OPENAI_API_KEY=your_openai_api_key_here

Replace your_openai_api_key_here with your actual OpenAI API key.

Usage

Once the environment is set up, you can run the bot using the command:

python app.py

The bot will use the data stored in the data folder as its source for generating responses. It will preprocess the text files, create embeddings, and use the Chroma vector database to handle vector storage and retrieval.

File Structure

├── data/                  # Folder containing source text files
├── app.py                 # Main script to run the bot
├── requirements.txt       # List of dependencies
├── .env                   # Environment file for OpenAI API key
├── README.md              # Project documentation
└── other necessary scripts or files

Requirements

• Python 3.8+
• OpenAI API Key

Key Libraries

• Chroma: Vector database for storing and querying embeddings.
• OpenAI: Used to access the GPT model and create embeddings.
• LangChain: For handling embedding creation and text splitting.

Contributing

Feel free to submit a pull request or open an issue if you find a bug or have a suggestion!