OptimumTextEmbedder

A component to embed text using models loaded with the

Basic Information

• Type: haystack_integrations.optimum.src.haystack_integrations.components.embedders.optimum.optimum_text_embedder.OptimumTextEmbedder

Inputs

Parameter	Type	Default	Description
text	str		The text to embed.

Outputs

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

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

A component to embed text using models loaded with the HuggingFace Optimum library, leveraging the ONNX runtime for high-speed inference.

Usage example:

from haystack_integrations.components.embedders.optimum import OptimumTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = OptimumTextEmbedder(model="sentence-transformers/all-mpnet-base-v2")
text_embedder.warm_up()

print(text_embedder.run(text_to_embed))

# {'embedding': [-0.07804739475250244, 0.1498992145061493,, ...]}

Usage Example

components:
  OptimumTextEmbedder:
    type: optimum.src.haystack_integrations.components.embedders.optimum.optimum_text_embedder.OptimumTextEmbedder
    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	A string representing the model id on HF Hub.
token	Optional[Secret]	Secret.from_env_var('HF_API_TOKEN', strict=False)	The HuggingFace token to use as HTTP bearer authorization.
prefix	str		A string to add to the beginning of each text.
suffix	str		A string to add to the end of each text.
normalize_embeddings	bool	True	Whether to normalize the embeddings to unit length.
onnx_execution_provider	str	CPUExecutionProvider	The execution provider to use for ONNX models. Note: Using the TensorRT execution provider TensorRT requires to build its inference engine ahead of inference, which takes some time due to the model optimization and nodes fusion. To avoid rebuilding the engine every time the model is loaded, ONNX Runtime provides a pair of options to save the engine: trt_engine_cache_enable and trt_engine_cache_path. We recommend setting these two provider options using the model_kwargs parameter, when using the TensorRT execution provider. The usage is as follows: python embedder = OptimumDocumentEmbedder( model="sentence-transformers/all-mpnet-base-v2", onnx_execution_provider="TensorrtExecutionProvider", model_kwargs={ "provider_options": { "trt_engine_cache_enable": True, "trt_engine_cache_path": "tmp/trt_cache", } }, )
pooling_mode	Optional[Union[str, OptimumEmbedderPooling]]	None	The pooling mode to use. When None, pooling mode will be inferred from the model config.
model_kwargs	Optional[Dict[str, Any]]	None	Dictionary containing additional keyword arguments to pass to the model. In case of duplication, these kwargs override model, onnx_execution_provider and token initialization parameters.
working_dir	Optional[str]	None	The directory to use for storing intermediate files generated during model optimization/quantization. Required for optimization and quantization.
optimizer_settings	Optional[OptimumEmbedderOptimizationConfig]	None	Configuration for Optimum Embedder Optimization. If None, no additional optimization is be applied.
quantizer_settings	Optional[OptimumEmbedderQuantizationConfig]	None	Configuration for Optimum Embedder Quantization. If None, no quantization is be applied.

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		The text to embed.