SentenceTransformersDocumentEmbedder

Calculates document embeddings using Sentence Transformers models.

Basic Information

• Type: haystack_integrations.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder

Inputs

Parameter	Type	Default	Description
documents	List[Document]		Documents to embed.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A dictionary with the following keys: - documents: Documents with embeddings.

Overview

Calculates document embeddings using Sentence Transformers models.

It stores the embeddings in the embedding metadata field of each document. You can also embed documents' metadata. Use this component in indexing pipelines to embed input documents and send them to DocumentWriter to write a into a Document Store.

Usage Example

components:
  SentenceTransformersDocumentEmbedder:
    type: components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder
    init_parameters:

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. Pass a local path or ID of the model on Hugging Face.
device	Optional[ComponentDevice]	None	The device to use for loading the model. Overrides the default device.
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	The API token to download private models from Hugging Face.
prefix	str		A string to add at the beginning of each document text. Can be used 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 document text.
batch_size	int	32	Number of documents to embed at once.
progress_bar	bool	True	If True, shows a progress bar when embedding documents.
normalize_embeddings	bool	False	If True, the embeddings are normalized using L2 normalization, so that each embedding has a norm of 1.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed along with the document text.
embedding_separator	str	\n	Separator used to concatenate the metadata fields to the document text.
trust_remote_code	bool	False	If False, allows only Hugging Face verified model architectures. If True, allows 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 wasn't trained with Matryoshka Representation Learning, truncating 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 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 documents. 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.

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
documents	List[Document]		Documents to embed.