Rag-Chat-Bot

Rag-Chat-Bot is a Retrieval-Augmented Generation (RAG) chatbot that uses OpenAI's GPT-4 plus text-embedding-ada-002 to answer questions strictly from a custom chat dataset stored in a persistent ChromaDB collection.

Features

• Loads chat data from a CSV file.
• Generates embeddings with OpenAI and stores them in a persistent ChromaDB collection on disk.
• Retrieves the top-k most relevant chats for each query via semantic search.
• Generates answers with GPT-4, constrained to the retrieved context.

Setup

1. Clone the repository and open it in VS Code (recommended — a Dev Container config is included).

2. Install dependencies:

   pip install -r requirements.txt

3. Set your OpenAI API key by creating a .env file in the project root:

   OPENAI_API_KEY=your_openai_api_key
   

4. Provide a chat CSV at data/chat_training_2.csv. The script reads column index 1 as the chat id and column index 4 as the chat text.

5. Run the chatbot:

   python chatbot.py

Usage

When started, the chatbot will:

1. Load the chats from data/chat_training_2.csv.
2. Embed each chat and store it in a persistent ChromaDB collection (under ./chroma_db).
3. Prompt you for a query — type exit or press Enter on an empty prompt to quit.
4. For each query, retrieve the 3 most similar chats and have GPT-4 answer using only that context.

Files

• chatbot.py — Main script (CLI loop, embedding, retrieval, generation).
• data/ — Place your chat CSV here.
• requirements.txt — Python dependencies.
• .devcontainer/ — VS Code Dev Container configuration.