Sentence Encoder bge-m3

FastAPI implementation of BAAI/bge-m3 encoder, containerized for scalable Kubernetes deployment.

Description

This project provides a high-performance API for generating sentence embeddings using the BAAI/bge-m3 model. It's built with FastAPI for efficient handling of requests and containerized for easy deployment and scaling in Kubernetes environments.

Features

• Fast and efficient sentence encoding using BAAI/bge-m3 model
• RESTful API built with FastAPI
• Docker containerization for consistent environments
• Kubernetes deployment ready
• Scalable architecture suitable for high-load environments
• Health check endpoints for Kubernetes probes

Prerequisites

• Python 3.9+
• Docker
• Kubernetes cluster (for production deployment)

Quick Start

Local Development

1. Clone the repository:

   git clone [email protected]:jeroenherczeg/sentence-encoder-bge-m3.git
   cd sentence-encoder-bge-m3
   

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

3. Run the FastAPI server:

   uvicorn main:app --reload
   

4. Access the API at http://localhost:8000 and the interactive docs at http://localhost:8000/docs

Docker

1. Build the Docker image:

   docker build -t sentence-encoder-bge-m3:latest .
   

2. Run the container:

   docker run -p 8000:8000 sentence-encoder-bge-m3:latest
   

Kubernetes Deployment

1. Apply the Kubernetes manifests:

   kubectl apply -f kubernetes/
   

2. Access the service (method depends on your Kubernetes setup)

API Usage

Encode Sentences

Endpoint: POST /encode

curl -X POST "http://localhost:8000/encode" -H "Content-Type: application/json" -d '{"sentences": ["Hello, world!", "This is a test sentence."]}'

Request Body:

{
  "sentences": ["Hello, world!", "Another sentence to encode."]
}

Response:

{
  "encodings": [
    [0.1, 0.2, 0.3, ...],
    [0.4, 0.5, 0.6, ...]
  ]
}

Kubernetes Health Probes

Endpoint: GET /readiness

curl http://localhost:8000/readiness

Endpoint: GET /liveness

curl http://localhost:8000/liveness

Model Information

This API uses the BAAI/bge-m3 model, which is a state-of-the-art sentence embedding model. It's designed to generate high-quality vector representations of sentences that capture semantic meaning, making it ideal for various natural language processing tasks such as semantic search, text classification, and similarity comparison.

Performance

The BAAI/bge-m3 model offers a good balance between performance and accuracy. In our testing, it processes approximately 457 sentences per second on a standard CPU. For production environments, we recommend GPU acceleration for higher throughput.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• BAAI for the bge-m3 model
• FastAPI for the web framework
• Sentence Transformers for the embedding framework
• Docker for containerization
• Kubernetes for orchestration