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Fastembed API

Overview

Fastembed API provides a straightforward way to generate text embeddings for paragraphs, making it easier to retrieve vector embeddings for downstream tasks like semantic search or text similarity.

Context

It was developed as a companyon of Boc·ajarro which, as is written in PHP, needed some Python help in order to create text embeddings in order to feed a vector database.

As it's meant to be executed in a server without a graphic card, it doesn't rely on expensive (in terms of computation) Pytorch setups. Instead, it uses FastEmbed so it can be executed in the CPU's of a standard Virtual Private Server.

Limitations

  • Currently, the API supports multilingual embeddings using the model sentence-transformers/paraphrase-multilingual-mpnet-base-v2 and accepts text input one paragraph at a time.

  • As it's meant to be executed in a private network, it does not implement any authentication (or authorization) mechanism.

Installation

To get started, clone the repository:

git clone https://github.com/estudio-hawara/fastembed-api
cd fastembed-api

Local Environment

For a local setup using a virtual environment:

# 1. Create a Python virtual environment
python -m venv .venv

# 2. Activate the Python virtual environment
source .venv/bin/activate

# 3. Install the dependencies
pip install -r requirements.txt

# 4. Start the service
uvicorn main:app

The API should now be available at http://localhost:8000.

Docker Environment

To use Docker, follow these steps:

# 1. Build the Docker Image
docker compose build

# 2. Start the Service as a Background Container
docker compose up -d

The API should now be available at http://localhost:8000.

Endpoints

/embed Endpoint

  • Method: POST

  • Description: Accepts a paragraph of text and returns vector embeddings for that text.

  • Request:

    • Content-Type: application/json
    • JSON Body: { "paragraph": "Your paragraph here" }

  • Response:

    • JSON Body: { "embeddings": [ ... ] }
    • The embeddings attribute contains an array with the embedding vectors.

/health Endpoint

  • Method: GET
  • Description: Returns a 200 status if the service is up and ready.

Health Check in Docker Compose

The docker-compose.yml file includes the following healthcheck configuration to ensure the service is fully initialized:

healthcheck:
  test: curl --fail http://localhost:8000/health || exit 1
  interval: 2s
  timeout: 5s
  retries: 3
  start_period: 5s

This configuration ensures that Docker waits until the service is ready before marking it as "healthy."

Persistent Docker Volume

This project caches FastEmbed files in a temporary directory for quicker container restarts. The .onnx model and related files are stored in a Docker volume as specified in docker-compose.yml:

volumes:
  - fastembed_cache:/tmp/fastembed_cache

Using this volume prevents repeated downloads of model files, speeding up restarts and preserving cache data between runs.

Requirements

  • Python: 3.12 (>=3.12 and <3.13)
  • Docker: No specific version required.

Testing

To run unit tests:

pytest

These tests ensure basic functionality. Benchmark tests will be added in future updates.

License

This project is licensed under the MIT License.