VertexAIDocumentEmbedder

Embed text using Vertex AI Embeddings API.

Basic Information

• Type: haystack_integrations.components.embedders.google_vertex.document_embedder.VertexAIDocumentEmbedder

Inputs

Parameter	Type	Default	Description
documents	List[Document]		A list of documents to embed.

Outputs

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

Overview

Work in Progress

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

Embed text using Vertex AI Embeddings API.

See available models in the official Google documentation.

Usage example:

from haystack import Document
from haystack_integrations.components.embedders.google_vertex import VertexAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = VertexAIDocumentEmbedder(model="text-embedding-005")

result = document_embedder.run([doc])
print(result['documents'][0].embedding)
# [-0.044606007635593414, 0.02857724390923977, -0.03549133986234665,

Usage Example

components:
  VertexAIDocumentEmbedder:
    type: integrations.google_vertex.src.haystack_integrations.components.embedders.google_vertex.document_embedder.VertexAIDocumentEmbedder
    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_DOCUMENT	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.
batch_size	int	32	The number of documents to process in a single batch.
max_tokens_total	int	20000	The maximum number of tokens to process in total.
time_sleep	int	30	The time to sleep between retries in seconds.
retries	int	3	The number of retries in case of failure.
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.
meta_fields_to_embed	Optional[List[str]]	None	A list of metadata fields to include in the embeddings.
embedding_separator	str	\n	The separator to use between different embeddings.

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]		A list of documents to embed.