SentenceTransformersTextEmbedder

Embed strings, such as user queries, using Sentence Transformers models.

Basic Information

• Type: haystack_integrations.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
• Components it can connect with:
  • Input: SentenceTransformersTextEmbedder can receive a string to embed from the Input component.
  • Retrievers: SentenceTransformersTextEmbedder can send the embedded text to Retrievers that use the embeddings to retrieve documents from a document store.

Inputs

Parameter	Type	Default	Description
text	str		Text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		A dictionary with the following keys: - embedding: The embedding of the input text.

Overview

Use SentenceTransformersTextEmbedder to calculate embeddings for strings, such as user queries, using Sentence Transformers models. This component is used in query pipelines when you want to perform semantic search.

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.

Usage Example

This is a query pipeline that uses SentenceTransformersTextEmbedder to embed a query and retrieve documents:

components:
  query_embedder:
    type: haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
    init_parameters:
      model: intfloat/e5-base-v2

  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
    - embedding_retriever.query
  filters:
    - embedding_retriever.filters

outputs:
  documents: embedding_retriever.documents

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	sentence-transformers/all-mpnet-base-v2	The model to use for calculating embeddings. Specify the path to a local model or the ID of the model on Hugging Face.
device	Optional[ComponentDevice]	None	Overrides the default device used to load the model.
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	An API token to use private models from Hugging Face.
prefix	str		A string to add at the beginning of each text to be embedded. You can use it to prepend the text with an instruction, as required by some embedding models, such as E5 and bge.
suffix	str		A string to add at the end of each text to embed.
batch_size	int	32	Number of texts to embed at once.
progress_bar	bool	True	If True, shows a progress bar for calculating embeddings. If False, disables the progress bar.
normalize_embeddings	bool	False	If True, the embeddings are normalized using L2 normalization, so that the embeddings have a norm of 1.
trust_remote_code	bool	False	If False, permits only Hugging Face verified model architectures. If True, permits custom models and scripts.
local_files_only	bool	False	If True, does not attempt to download the model from Hugging Face Hub and only looks at local files.
truncate_dim	Optional[int]	None	The dimension to truncate sentence embeddings to. None does no truncation. If the model has not been trained with Matryoshka Representation Learning, truncation of embeddings can significantly affect performance.
model_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoModelForSequenceClassification.from_pretrained when loading the model. Refer to specific model documentation for available kwargs.
tokenizer_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoTokenizer.from_pretrained when loading the tokenizer. Refer to specific model documentation for available kwargs.
config_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for AutoConfig.from_pretrained when loading the model configuration.
precision	Literal['float32', 'int8', 'uint8', 'binary', 'ubinary']	float32	The precision to use for the embeddings. All non-float32 precisions are quantized embeddings. Quantized embeddings are smaller in size and faster to compute, but may have a lower accuracy. They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks.
encode_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for SentenceTransformer.encode when embedding texts. This parameter is provided for fine customization. Be careful not to clash with already set parameters and avoid passing parameters that change the output type.
backend	Literal['torch', 'onnx', 'openvino']	torch	The backend to use for the Sentence Transformers model. Choose from "torch", "onnx", or "openvino". Refer to the Sentence Transformers documentation for more information on acceleration and quantization options.
revision	Optional[str]	None	The specific model version to use. It can be a branch name, a tag name, or a commit ID for a stored model on Hugging Face. This enables pinning to a particular model version for reproducibility and stability.

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.