RAG-ChatBot

A Retrieval-Augmented Generation (RAG) based chatbot that combines the power of GPT-4 with custom knowledge retrieval capabilities. The chatbot can process and answer questions based on provided URLs and generate quizzes from the content.

Features

• RAG Implementation: Combines vector embeddings with GPT-4 for context-aware responses
• Custom Knowledge Base: Build vector store from provided URLs
• Quiz Generation: Generate quizzes in Russian from the knowledge base
• Smart Text Chunking: Intelligent text segmentation for optimal context retrieval
• Vector Caching: Local caching of vector embeddings for improved performance

Prerequisites

• Python 3.8+
• OpenAI API key
• Required Python packages (see Installation)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/RAG-ChatBot.git
cd RAG-ChatBot

2. Install required packages:

pip install -r requirements.txt

3. Set up your environment variables: Create a .env file in the root directory with:

OPENAI_API_KEY=your_api_key_here
EMBEDDING_MODEL=text-embedding-ada-002  # or your preferred model

Usage

Basic Usage

from src.chatbot.service import ChatBotService

# Initialize the chatbot with your URLs
urls = ["https://example.com/page1", "https://example.com/page2"]
chatbot = ChatBotService(urls)

# Get a response
response = chatbot.get_response("What is the main topic?")
print(response)

# Generate a quiz
quiz = chatbot.generate_test("specific topic")
print(quiz)

Quiz Generation

The chatbot can generate quizzes in Russian from the provided content:

# Generate a quiz on a specific topic
quiz = chatbot.generate_test("artificial intelligence")

# Generate a random quiz
quiz = chatbot.generate_test()

How It Works

1. Text Processing:

   • URLs are scraped for content
   • Text is chunked into manageable pieces
   • Chunks are converted to vector embeddings

2. Query Processing:

   • User query is converted to vector embedding
   • Similar chunks are retrieved using cosine similarity
   • Retrieved context is sent to GPT-4 for response generation

3. Quiz Generation:

   • Content is retrieved based on topic
   • GPT-4 generates structured quiz questions
   • Questions are formatted and presented to the user

Testing

Run the test suite:

python -m unittest tests/test_chatbot.py

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

Acknowledgments

• OpenAI for providing the GPT-4 and embedding APIs
• Contributors and maintainers of the project