---
title: "HuggingFaceAPIDocumentEmbedder"
id: huggingfaceapidocumentembedder
slug: "/huggingfaceapidocumentembedder"
description: "Use this component to compute document embeddings using various Hugging Face APIs."
---

# HuggingFaceAPIDocumentEmbedder

Use this component to compute document embeddings using various Hugging Face APIs.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx)  in an indexing pipeline |
| **Mandatory init variables** | "api_type": The type of Hugging Face API to use  <br /> <br />"api_params": A dictionary with one of the following keys:  <br /> <br />- `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.**OR** - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or `TEXT_EMBEDDINGS_INFERENCE`.  <br /> <br />"token": The Hugging Face API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var. |
| **Mandatory run variables** | “documents”: A list of documents to be embedded |
| **Output variables** | “documents”: A list of documents to be embedded (enriched with embeddings) |
| **API reference** | [Embedders](/reference/embedders-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_api_document_embedder.py |

## Overview

`HuggingFaceAPIDocumentEmbedder` can be used to compute document embeddings using different Hugging Face APIs:

- [Free Serverless Inference API](https://huggingface.co/inference-api)
- [Paid Inference Endpoints](https://huggingface.co/inference-endpoints)
- [Self-hosted Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)

:::note
This component should be used to embed a list of documents. To embed a string, use [`HuggingFaceAPITextEmbedder`](huggingfaceapitextembedder.mdx).

:::

The component uses a `HF_API_TOKEN` environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with `token` – see code examples below.
The token is needed:

- If you use the Serverless Inference API, or
- If you use the Inference Endpoints.

## Usage

Similarly to other Document Embedders, this component allows adding prefixes (and postfixes) to include instruction and embedding metadata.
For more fine-grained details, refer to the component’s [API reference](/reference/embedders-api#huggingfaceapidocumentembedder).

### On its own

#### Using Free Serverless Inference API

Formerly known as (free) Hugging Face Inference API, this API allows you to quickly experiment with many models hosted on the Hugging Face Hub, offloading the inference to Hugging Face servers. It’s rate-limited and not meant for production.

To use this API, you need a [free Hugging Face token](https://huggingface.co/settings/tokens).
The Embedder expects the `model` in `api_params`.

```python
from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.utils import Secret
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

document_embedder = HuggingFaceAPIDocumentEmbedder(
    api_type="serverless_inference_api",
    api_params={"model": "BAAI/bge-small-en-v1.5"},
    token=Secret.from_token("<your-api-key>"),
)

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

#### Using Paid Inference Endpoints

In this case, a private instance of the model is deployed by Hugging Face, and you typically pay per hour.

To understand how to spin up an Inference Endpoint, visit [Hugging Face documentation](https://huggingface.co/inference-endpoints/dedicated).

Additionally, in this case, you need to provide your Hugging Face token.
The Embedder expects the `url` of your endpoint in `api_params`.

```python
from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.utils import Secret
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

document_embedder = HuggingFaceAPIDocumentEmbedder(
    api_type="inference_endpoints",
    api_params={"url": "<your-inference-endpoint-url>"},
    token=Secret.from_token("<your-api-key>"),
)

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

#### Using Self-Hosted Text Embeddings Inference (TEI)

[Hugging Face Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference) is a toolkit for efficiently deploying and serving text embedding models.

While it powers the most recent versions of Serverless Inference API and Inference Endpoints, it can be used easily on-premise through Docker.

For example, you can run a TEI container as follows:

```shell
model=BAAI/bge-large-en-v1.5
revision=refs/pr/5
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
```

For more information, refer to the [official TEI repository](https://github.com/huggingface/text-embeddings-inference).

The Embedder expects the `url` of your TEI instance in `api_params`.

```python
from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder
from haystack.dataclasses import Document

doc = Document(content="I love pizza!")

document_embedder = HuggingFaceAPIDocumentEmbedder(
    api_type="text_embeddings_inference",
    api_params={"url": "http://localhost:8080"},
)

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

### In a pipeline

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import HuggingFaceAPITextEmbedder, HuggingFaceAPIDocumentEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

document_embedder = HuggingFaceAPIDocumentEmbedder(api_type="serverless_inference_api",
				                                           api_params={"model": "BAAI/bge-small-en-v1.5"})

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("document_embedder", document_embedder)
indexing_pipeline.add_component("doc_writer", DocumentWriter(document_store=document_store)
indexing_pipeline.connect("document_embedder", "doc_writer")
indexing_pipeline.run({"document_embedder": {"documents": documents}})

text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
                                           api_params={"model": "BAAI/bge-small-en-v1.5"})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", text_embedder)
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

## Document(id=..., content: 'My name is Wolfgang and I live in Berlin', ...)
```
