VertexAITextEmbedder

Embed text using Vertex AI Text Embeddings API.

Key Features

• Embeds query text using the Vertex AI Text Embeddings API for semantic search.
• Authenticates using Google Cloud Application Default Credentials.
• Supports configurable task types for different use cases (for example, RETRIEVAL_QUERY).
• Use with the same model as VertexAIDocumentEmbedder to ensure compatible embeddings.
• Outputs a float vector embedding for use with embedding retrievers.

Configuration

Authentication

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

1. Drag the VertexAITextEmbedder component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the model name (for example, text-embedding-005). For supported models, see the official Google documentation.
4. Go to the Advanced tab to configure the GCP project ID, GCP region, task type, truncate dimension, and progress bar.

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.

Connections

VertexAITextEmbedder accepts a text string as input (text). It outputs the computed embedding (embedding).

In a query pipeline, connect the pipeline's query input to text, then connect embedding to an embedding retriever's query_embedding input.

Usage Example

This query pipeline uses VertexAITextEmbedder to embed queries for semantic search:

components:
  VertexAITextEmbedder:
    type: haystack_integrations.components.embedders.google_vertex.text_embedder.VertexAITextEmbedder
    init_parameters:
      model: text-embedding-005
      task_type: RETRIEVAL_QUERY
      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
      progress_bar: true
      truncate_dim:

  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    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:
      top_k: 10

  ChatPromptBuilder:
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
    init_parameters:
      template:
        - role: system
          content: "You are a helpful assistant answering questions based on the provided documents."
        - role: user
          content: "Documents:\n{% for doc in documents %}\n{{ doc.content }}\n{% endfor %}\n\nQuestion: {{ query }}"

  OpenAIChatGenerator:
    type: haystack.components.generators.chat.openai.OpenAIChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
          - OPENAI_API_KEY
        strict: false
      model: gpt-4o-mini

  OutputAdapter:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: List[str]

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

connections:
  - sender: VertexAITextEmbedder.embedding
    receiver: embedding_retriever.query_embedding
  - sender: embedding_retriever.documents
    receiver: ChatPromptBuilder.documents
  - sender: ChatPromptBuilder.prompt
    receiver: OpenAIChatGenerator.messages
  - sender: OpenAIChatGenerator.replies
    receiver: OutputAdapter.replies
  - sender: OutputAdapter.output
    receiver: answer_builder.replies
  - sender: embedding_retriever.documents
    receiver: answer_builder.documents

inputs:
  query:
    - VertexAITextEmbedder.text
    - ChatPromptBuilder.query
    - answer_builder.query
  filters:
    - embedding_retriever.filters

outputs:
  documents: embedding_retriever.documents
  answers: answer_builder.answers

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

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

Outputs

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

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_QUERY	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.
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.