ElasticRAG

ElasticRAG is a project designed to provide a flexible and scalable solution for integrating Elasticsearch with LlamaIndex for Retrieval-Augmented Generation (RAG) applications.

Features

• Integration with Elasticsearch for scalable vector storage
• Support for LlamaIndex and Langchain for advanced query processing
• FastAPI-based API for easy deployment and interaction

Installation

To install the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/elasticrag.git

2. Navigate to the project directory:

   cd elasticrag

3. Install the dependencies:

   pip install -r requirements.txt

Usage

To use the project, follow these steps:

1. Start the development server:

   uvicorn main:app --reload

2. Open your browser and navigate to http://localhost:8000.

Endpoints

• GET / - Home endpoint to check if the server is running.
• GET /query - Endpoint to query the LLM with a question. Example:

  curl -X GET "http://localhost:8000/query?question=Your+question+here"

Contributing

We welcome contributions! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Description of your changes"

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

License

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