GoogleGenAIDocumentEmbedder

Compute document embeddings using Google AI models.

Key Features

• Computes document embeddings using Google AI models
• Supports both the Gemini Developer API and Vertex AI
• Stores embeddings in each document's embedding field
• Batch processing for efficient embedding of large document sets
• Optional metadata field embedding alongside document text

Configuration

1. Drag the GoogleGenAIDocumentEmbedder component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   1. Choose the API to use: gemini for the Gemini Developer API or vertex for Vertex AI.
   2. For the Gemini Developer API, enter your Google API key (GOOGLE_API_KEY or GEMINI_API_KEY). For Vertex AI, enter your GCP project ID and location. For detailed instructions, see Create Secrets.
   3. Select an embedding model. For supported models, see the Google AI documentation.
4. Go to the Advanced tab to configure batch size, metadata fields to embed, and other settings.

Connections

GoogleGenAIDocumentEmbedder accepts a list of Document objects through its documents input. It outputs a list of Document objects with the embeddings stored in the embedding field, and a meta dictionary with model usage information.

Use this component in an indexing pipeline. Connect preprocessors like DocumentSplitter to its documents input, and connect its documents output to DocumentWriter.

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.

Source Code

To check this component's source code, open document_embedder.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  GoogleGenAIDocumentEmbedder:
    type: haystack_integrations.components.embedders.google_genai.document_embedder.GoogleGenAIDocumentEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - GOOGLE_API_KEY
        - GEMINI_API_KEY
        strict: false
      api: gemini
      model: text-embedding-004
      prefix: ''
      suffix: ''
      batch_size: 32
      progress_bar: true
      embedding_separator: "\n"

This index uses GoogleGenAIDocumentEmbedder 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

  GoogleGenAIDocumentEmbedder:
    type: haystack_integrations.components.embedders.google_genai.document_embedder.GoogleGenAIDocumentEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
          - GOOGLE_API_KEY
          - GEMINI_API_KEY
        strict: false
      api: gemini
      vertex_ai_project:
      vertex_ai_location:
      model: text-embedding-004
      prefix: ""
      suffix: ""
      batch_size: 32
      progress_bar: true
      meta_fields_to_embed:
      embedding_separator: "\n"
      config:

  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: 'google-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: GoogleGenAIDocumentEmbedder.documents
  - sender: GoogleGenAIDocumentEmbedder.documents
    receiver: DocumentWriter.documents

inputs:
  files:
    - TextFileToDocument.sources

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

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

Outputs

Parameter	Type	Description
documents	List[Document]	A list of documents with embeddings.
meta	Dict[str, Any]	Information about the usage of the model.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var(['GOOGLE_API_KEY', 'GEMINI_API_KEY'], strict=False)	Google API key. Not needed if using Vertex AI with Application Default Credentials.
api	Literal['gemini', 'vertex']	gemini	Which API to use. Either "gemini" for the Gemini Developer API or "vertex" for Vertex AI.
vertex_ai_project	Optional[str]	None	Google Cloud project ID for Vertex AI. Required when using Vertex AI with Application Default Credentials.
vertex_ai_location	Optional[str]	None	Google Cloud location for Vertex AI (for example, "us-central1", "europe-west1"). Required when using Vertex AI with Application Default Credentials.
model	str	text-embedding-004	The name of the model to use for calculating embeddings.
prefix	str	""	A string to add at the beginning of each text.
suffix	str	""	A string to add at the end of each text.
batch_size	int	32	Number of documents to embed at once.
progress_bar	bool	True	If True, shows a progress bar when running.
meta_fields_to_embed	Optional[List[str]]	None	List of metadata fields to embed along with the document text.
embedding_separator	str	\n	Separator used to concatenate the metadata fields to the document text.
config	Optional[Dict[str, Any]]	None	A dictionary to configure embedding content configuration. Defaults to {"task_type": "SEMANTIC_SIMILARITY"}. See Google AI Task types.

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

Related Information

• Create Secrets
• Google AI Task types