> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Hugging Face Server

export const Warning = ({title, children}) => <div className="my-6">
    <div className="relative pr-1.5 pb-1.5">
      <div className="absolute top-1.5 left-1.5 right-0 bottom-0 bg-yellow-500 dark:bg-yellow-600" />
      <div className="relative border border-black dark:border-gray-500 px-5 py-4 bg-white dark:bg-neutral-900">
        {title && <p className="block mb-2"><strong>{title}</strong></p>}
        {children}
      </div>
    </div>
  </div>;

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 theme={null}
docker compose -f examples/server_side_embeddings/huggingface/docker-compose.yml up -d
```

or

```terminal theme={null}
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
```

<Warning>
  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).
</Warning>

## Usage

<CodeGroup>
  ```python Python theme={null}
  from chromadb.utils.embedding_functions import HuggingFaceEmbeddingServer
  huggingface_ef = HuggingFaceEmbeddingServer(url="http://localhost:8001/embed")
  ```

  ```typescript TypeScript theme={null}
  // 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,
  });
  ```
</CodeGroup>

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:

<CodeGroup>
  ```python Python theme={null}
  from chromadb.utils.embedding_functions import HuggingFaceEmbeddingServer
  huggingface_ef = HuggingFaceEmbeddingServer(url="http://localhost:8001/embed", api_key="your secret key")
  ```

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