Simple Realtime Augmented Generation (RAG) with LangChain and Chainlit

This repository demonstrates a simple implementation of Realtime Augmented Generation using Chainlit. It showcases how to integrate Chainlit with LLMs through LangChain to generate and augment content in real-time. The project includes examples and documentation to help you get started quickly.

Features

• Integration with multiple AI models (OpenAI GPT and VertexAI Gemini)

Getting Started

To get started with this project, follow the instructions below:

1. Clone the repository:

   git clone https://github.com/ghif/llm-rag.git

2. Navigate to the project directory:

   cd llm-rag

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application: Without RAG:

   chainlit run app_plain.py

   With RAG:

   chainlit run app.py

Contributing

We welcome contributions! Please read our contributing guidelines for more details.

License

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