HuggingFaceAPITextEmbedder

Embed strings using Hugging Face APIs.

Key Features

• Embeds query text using the Hugging Face Serverless Inference API, Inference Endpoints, or self-hosted Text Embeddings Inference.
• Use with the same model as HuggingFaceAPIDocumentEmbedder to ensure compatible embeddings.
• Outputs a float vector embedding for use with embedding retrievers.
• Optional truncation and normalization for TEI and Inference Endpoints backends.

info

This component embeds plain text. To embed a list of documents, use HuggingFaceAPIDocumentEmbedder.

Configuration

Authentication

Connect Haystack Platform to your Hugging Face account on the Integrations page. For detailed instructions, see Use Hugging Face Models. A token is required for the Serverless Inference API and Inference Endpoints.

1. Drag the HuggingFaceAPITextEmbedder component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Set api_type to one of:
      • serverless_inference_api: Uses the free Hugging Face Serverless Inference API.
      • inference_endpoints: Uses a paid Hugging Face Inference Endpoint.
      • text_embeddings_inference: Uses a self-hosted Text Embeddings Inference service.
   2. Set api_params. For serverless_inference_api, provide {"model": "BAAI/bge-small-en-v1.5"}. For inference_endpoints or text_embeddings_inference, provide {"url": "<your-endpoint-url>"}.
4. Go to the Advanced tab to configure the token, prefix, suffix, truncate, and normalize.

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Connections

HuggingFaceAPITextEmbedder accepts a text string as input (text). It outputs the computed embedding (embedding).

In a query pipeline, connect the pipeline's query input to text, then connect embedding to an embedding retriever's query_embedding input.

Usage Example

Using the component in a pipeline

This query pipeline uses HuggingFaceAPITextEmbedder to embed a query and retrieve documents using semantic search:

components:
  query_embedder:
    type: haystack.components.embedders.hugging_face_api_text_embedder.HuggingFaceAPITextEmbedder
    init_parameters:
      api_type: serverless_inference_api
      api_params:
        model: BAAI/bge-small-en-v1.5
      token:
        type: env_var
        env_vars:
          - HF_API_TOKEN
          - HF_TOKEN
        strict: false
      prefix:
      suffix:
      truncate: true
      normalize: false

  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          use_ssl: true
          verify_certs: false
      top_k: 20

connections:
  - sender: query_embedder.embedding
    receiver: embedding_retriever.query_embedding

inputs:
  query:
    - query_embedder.text
  filters:
    - embedding_retriever.filters

outputs:
  documents: embedding_retriever.documents

Parameters

Inputs

Parameter	Type	Default	Description
text	str		Text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		The embedding of the input text.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_type	Union[HFEmbeddingAPIType, str]		The type of Hugging Face API to use. Options: serverless_inference_api, inference_endpoints, text_embeddings_inference.
api_params	Dict[str, str]		A dictionary containing either model (Hugging Face model ID, required for serverless_inference_api) or url (URL of the inference endpoint, required for inference_endpoints or text_embeddings_inference).
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	The Hugging Face token to use as HTTP bearer authorization. Check your HF token in your account settings.
prefix	str		A string to add at the beginning of each text.
suffix	str		A string to add at the end of each text.
truncate	Optional[bool]	True	Truncates the input text to the maximum length supported by the model. Applicable when api_type is text_embeddings_inference or inference_endpoints if the backend uses Text Embeddings Inference. Ignored for serverless_inference_api.
normalize	Optional[bool]	False	Normalizes the embeddings to unit length. Applicable when api_type is text_embeddings_inference or inference_endpoints if the backend uses Text Embeddings Inference. Ignored for serverless_inference_api.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
text	str		Text to embed.