Multi AI Agents with RAG

This project demonstrates the development of a multi-agent AI chatbot using Retrieval-Augmented Generation (RAG) with LangGraph and AstraDB. The system enables dynamic routing of user queries between a vector database and external tools like Wikipedia, leveraging Large Language Models (LLMs) for natural language processing.

Key Features

• Multi-Agent System: Dynamically routes user queries to the appropriate AI agent.
• RAG Implementation: Combines retrieval and generation for accurate responses.
• External Integration: Utilizes Wikipedia and other external resources to enhance AI capabilities.

Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/multi-ai-agents-with-rag.git
   cd multi-ai-agents-with-rag

2. Open the Jupyter Notebook (multi_ai_agents_with_rag.ipynb) in Jupyter Notebook or Google Colab to run the project.

Requirements

• Python 3.x
• Libraries listed in the Colab notebook

License

This project is licensed under the MIT License.