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---
title: Text Embedding
emoji: 🚀
colorFrom: purple
colorTo: yellow
sdk: docker
pinned: false
license: apache-2.0
---
# Embedding API
API to call an embedding model ([intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)) for generating multilingual text embeddings.<br>
The embedding model takes a text string and converts it into 1024 dimension vector.<br>
Using a `POST` request to the `/embed` endpoint with a list of texts, the API returns their corresponding embeddings.<br>
A maximum of 2000 characters per text is enforced to avoid truncation, and thereby loss of information, by the tokenizer.<br>
Each text must start with either "query: " or "passage: ".<br>
The API is deployed at a Hugging Face Docker space where the Swagger UI can be acccessed at:<br>
[https://emilbm-text-embedding.hf.space/docs](https://emilbm-text-embedding.hf.space/docs)
## Features
- FastAPI-based REST API
- `/embed` endpoint for generating embeddings from a list of texts
- `/health` endpoint for checking the API status
- Uses HuggingFace Transformers and PyTorch
- Includes linting and unit tests
- Dockerfile for containerization
- CI/CD with GitHub Actions to build, lint, test, and deploy to Hugging Face
## Local Development
### Requirements
- Python 3.12+
- [UV](https://docs.astral.sh/uv/)
- (Optional) Docker
### Installation
1. **Clone the repository:**
```sh
git clone https://github.com/EmilbMadsen/embedding-api.git
cd embedding-api
```
2. **Create a virtual environment and activate it:**
```sh
uv venv
source .venv/bin/activate
```
3. **Install dependencies:**
```sh
uv sync
```
### Formatting, Linting and Unit Tests
- **Formatting (with Black and Ruff) and linting (with Black, Ruff, and MyPy):**
```sh
make format
make lint
```
- **Run unit tests:**
```sh
make test
```
### Running Locally (without Docker)
Start the API server with Uvicorn:
```sh
uvicorn app.main:app --reload --port 7860
```
### Running Locally (with Docker)
Build and start the API server with Docker:
```sh
docker build -t embedding-api .
docker run -p 7860:7860 embedding-api
```
### Test the endpoint
Test the endpoint with either:
```sh
curl -X 'POST' \
'http://127.0.0.1:7860/embed' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"texts": [
"query: what is the capital of France?",
"passage: Paris is the capital of France."
]
}'
```
Or through the Swagger UI.
## Usage
### Embed Endpoint
- **POST** `/embed`
- **Request Body:**
```json
{
"texts": [
"query: what is the capital of France?",
"passage: Paris is the capital of France."
]
}
```
- **Response:**
```json
{
"embeddings": [[...], [...]]
}
```
### Health Endpoint
- **GET** `/health`
- **Response:**
```json
{
"status": "ok"
}
```
## Project Structure
```
app/
main.py # FastAPI app
embeddings.py # Embedding logic
models.py # Request/response models
logger.py # Logging setup
tests/
test_api.py # API tests
test_embeddings.py # Embedding tests
```
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