VertexAITextEmbedder

Embed text using VertexAI Text Embeddings API.

Basic Information

• Type: haystack_integrations.components.embedders.google_vertex.text_embedder.VertexAITextEmbedder

Inputs

Parameter	Type	Default	Description
text	Union[List[Document], List[str], str]		The 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

Work in Progress

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

Embed text using VertexAI Text Embeddings API.

See available models in the official Google documentation.

Usage example:

from haystack_integrations.components.embedders.google_vertex import VertexAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = VertexAITextEmbedder(model="text-embedding-005")

print(text_embedder.run(text_to_embed))
# {'embedding': [-0.08127457648515701, 0.03399784862995148, -0.05116401985287666, ...]

Usage Example

components:
  VertexAITextEmbedder:
    type: google_vertex.src.haystack_integrations.components.embedders.google_vertex.text_embedder.VertexAITextEmbedder
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	Literal['text-embedding-004', 'text-embedding-005', 'textembedding-gecko-multilingual@001', 'text-multilingual-embedding-002', 'text-embedding-large-exp-03-07']		Name of the model to use.
task_type	Literal['RETRIEVAL_DOCUMENT', 'RETRIEVAL_QUERY', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'QUESTION_ANSWERING', 'FACT_VERIFICATION', 'CODE_RETRIEVAL_QUERY']	RETRIEVAL_QUERY	The type of task for which the embeddings are being generated. For more information see the official Google documentation.
gcp_region_name	Optional[Secret]	Secret.from_env_var('GCP_DEFAULT_REGION', strict=False)	The default location to use when making API calls, if not set uses us-central-1.
gcp_project_id	Optional[Secret]	Secret.from_env_var('GCP_PROJECT_ID', strict=False)	ID of the GCP project to use. By default, it is set during Google Cloud authentication.
progress_bar	bool	True	Whether to display a progress bar during processing.
truncate_dim	Optional[int]	None	The dimension to truncate the embeddings to, if specified.

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	Union[List[Document], List[str], str]		The text to embed.