VertexAIDocumentEmbedder

Embed documents using Vertex AI Embeddings API.

Basic Information

• Type: haystack_integrations.components.embedders.google_vertex.document_embedder.VertexAIDocumentEmbedder
• Components it can connect with:
  • Preprocessors: Receives documents from Converters or DocumentSplitter in an index.
  • DocumentWriter: Sends embedded documents to DocumentWriter for storage.

Inputs

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

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A list of documents with embeddings.

Overview

VertexAIDocumentEmbedder embeds documents using the Vertex AI Embeddings API. Use this component in an index to embed documents before storing them in a document store.

Compatible Models

You can find the supported models in the official Google documentation.

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.

Authorization

This component authenticates using Google Cloud Application Default Credentials (ADCs). Create secrets with the following keys: GCP_PROJECT_ID and GCP_DEFAULT_REGION. For detailed instructions on creating secrets, see Create Secrets.

Usage Example

This index uses VertexAIDocumentEmbedder to embed documents before storing them:

components:
  TextFileToDocument:
    type: haystack.components.converters.txt.TextFileToDocument
    init_parameters:
      encoding: utf-8
      store_full_path: false

  DocumentSplitter:
    type: haystack.components.preprocessors.document_splitter.DocumentSplitter
    init_parameters:
      split_by: sentence
      split_length: 5
      split_overlap: 1

  VertexAIDocumentEmbedder:
    type: haystack_integrations.components.embedders.google_vertex.document_embedder.VertexAIDocumentEmbedder
    init_parameters:
      model: text-embedding-005
      task_type: RETRIEVAL_DOCUMENT
      gcp_region_name:
        type: env_var
        env_vars:
          - GCP_DEFAULT_REGION
        strict: false
      gcp_project_id:
        type: env_var
        env_vars:
          - GCP_PROJECT_ID
        strict: false
      batch_size: 32
      max_tokens_total: 20000
      time_sleep: 30
      retries: 3
      progress_bar: true
      truncate_dim:
      meta_fields_to_embed:
      embedding_separator: "\n"

  DocumentWriter:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'vertex-embeddings'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      policy: OVERWRITE

connections:
  - sender: TextFileToDocument.documents
    receiver: DocumentSplitter.documents
  - sender: DocumentSplitter.documents
    receiver: VertexAIDocumentEmbedder.documents
  - sender: VertexAIDocumentEmbedder.documents
    receiver: DocumentWriter.documents

inputs:
  files:
    - TextFileToDocument.sources

max_runs_per_component: 100

metadata: {}

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', ...]		Name of the model to use.
task_type	Literal['RETRIEVAL_DOCUMENT', 'RETRIEVAL_QUERY', ...]	RETRIEVAL_DOCUMENT	The type of task for which the embeddings are being generated. See 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.
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 run() method. You can pass these parameters at query time through the API, in Playground, or when running a job.

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