GoogleGenAIDocumentEmbedder

Computes document embeddings using Google AI models.

Basic Information

• Type: haystack_integrations.components.embedders.google_genai.document_embedder.GoogleGenAIDocumentEmbedder

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. - meta: Information about the usage of the model.
meta	Dict[str, Any]		A dictionary with the following keys: - documents: A list of documents with embeddings. - meta: Information about the usage of the model.

Overview

Work in Progress

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

Computes document embeddings using Google AI models.

Authentication examples

1. Gemini Developer API (API Key Authentication)

from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIDocumentEmbedder(model="text-embedding-004")

**2. Vertex AI (Application Default Credentials)**
```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# Using Application Default Credentials (requires gcloud auth setup)
document_embedder = GoogleGenAIDocumentEmbedder(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
    model="text-embedding-004"
)

3. Vertex AI (API Key Authentication)

from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIDocumentEmbedder(
    api="vertex",
    model="text-embedding-004"
)

Usage Example

components:
  GoogleGenAIDocumentEmbedder:
    type: integrations.google_genai.src.haystack_integrations.components.embedders.google_genai.document_embedder.GoogleGenAIDocumentEmbedder
    init_parameters:

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, defaults to the GOOGLE_API_KEY and GEMINI_API_KEY environment variables. Not needed if using Vertex AI with Application Default Credentials. Go to https://aistudio.google.com/app/apikey for a Gemini API key. Go to https://cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys for a Vertex AI API key.
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 (e.g., "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. The default model is text-embedding-ada-002.
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 of keyword arguments to configure embedding content configuration types.EmbedContentConfig. If not specified, it defaults to {"task_type": "SEMANTIC_SIMILARITY"}. For more information, see the 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	Default	Description
documents	List[Document]		A list of documents to embed.