GoogleGenAITextEmbedder

Embed strings using Google AI models.

Basic Information

• Type: haystack_integrations.components.embedders.google_genai.text_embedder.GoogleGenAITextEmbedder
• Components it can connect with:
  • Input: Receives a query string as input in a query pipeline.
  • Retrievers: Sends the computed embedding to an embedding Retriever.

Inputs

Parameter	Type	Default	Description
text	str		Text to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		The embedding of the input text.
meta	Dict[str, Any]		Information about the usage of the model.

Overview

GoogleGenAITextEmbedder embeds strings using Google AI models. It supports both the Gemini Developer API and Vertex AI.

Use this component in query pipelines to embed the user's query before passing it to a retriever for semantic search. Make sure to use the same embedding model as the one used to embed the documents in the document store.

Compatible Models

You can find the supported models in the Google AI 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

Gemini Developer API: Create a secret with your Google API key. Type GOOGLE_API_KEY or GEMINI_API_KEY as the secret key. Get your API key from Google AI Studio.

Vertex AI: Create secrets for GCP_PROJECT_ID and GCP_DEFAULT_REGION, or use Application Default Credentials.

For detailed instructions on creating secrets, see Create Secrets.

Usage Example

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

components:
  GoogleGenAITextEmbedder:
    type: haystack_integrations.components.embedders.google_genai.text_embedder.GoogleGenAITextEmbedder
    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: ""
      config:

  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: '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:
      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: GoogleGenAITextEmbedder.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:
    - GoogleGenAITextEmbedder.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

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 to embed.
suffix	str	""	A string to add at the end of each text to embed.
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 run() method. You can pass these parameters at query time through the API, in Playground, or when running a job.

Parameter	Type	Default	Description
text	str		Text to embed.