---
id: hugging-face-server
name: 'Hugging Face Server'
---

# Hugging Face Server

Chroma provides a convenient wrapper for HuggingFace Text Embedding Server, a standalone server that provides text embeddings via a REST API. You can read more about it [**here**](https://github.com/huggingface/text-embeddings-inference).

## Setting Up The Server

To run the embedding server locally you can run the following command from the root of the Chroma repository. The docker compose command will run Chroma and the embedding server together.

```terminal
docker compose -f examples/server_side_embeddings/huggingface/docker-compose.yml up -d
```

or

```terminal
docker run -p 8001:80 -d -rm --name huggingface-embedding-server ghcr.io/huggingface/text-embeddings-inference:cpu-0.3.0 --model-id BAAI/bge-small-en-v1.5 --revision -main
```

The above docker command will run the server with the `BAAI/bge-small-en-v1.5` model. You can find more information about running the server in docker [**here**](https://github.com/huggingface/text-embeddings-inference#docker).

## Usage

### python

```python
from chromadb.utils.embedding_functions import HuggingFaceEmbeddingServer
huggingface_ef = HuggingFaceEmbeddingServer(url="http://localhost:8001/embed")
```

### typescript

```typescript
// npm install @chroma-core/huggingface-server

import { HuggingFaceEmbeddingServerFunction } from '@chroma-core/huggingface-server';

const embedder = new HuggingFaceEmbeddingServerFunction({ url: "http://localhost:8001/embed" })

// use directly
const embeddings = embedder.generate(["document1","document2"])

// pass documents to query for .add and .query
let collection = await client.createCollection({name: "name", embeddingFunction: embedder})
collection = await client.getCollection({name: "name", embeddingFunction: embedder})
```

The embedding model is configured on the server side. Check the docker-compose file in `examples/server_side_embeddings/huggingface/docker-compose.yml` for an example of how to configure the server.

## Authentication

The embedding server can be configured to only allow usage with API keys. 
You can use authentication in the chroma clients:

### python

```python
from chromadb.utils.embedding_functions import HuggingFaceEmbeddingServer
huggingface_ef = HuggingFaceEmbeddingServer(url="http://localhost:8001/embed", api_key="your secret key")
```

### typescript

```typescript
import  {HuggingFaceEmbeddingServerFunction} from 'chromadb';
const embedder = new HuggingFaceEmbeddingServerFunction({ url: "http://localhost:8001/embed", apiKey: "your secret key" })
```