
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory init variables	"api_key": The Google API key. Can be set with `GOOGLE_API_KEY` or `GEMINI_API_KEY` env var.
Mandatory run variables	"text": A string
Output variables	"embedding": A list of float numbers "meta": A dictionary of metadata
API reference	Google AI
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai

Overview

GoogleGenAITextEmbedder embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the GoogleGenAIDocumentEmbedder, which enriches the document with the computed embedding, also known as vector.

The component supports the following Google AI models:

text-embedding-004 (default)
text-embedding-004-v2

To start using this integration with Haystack, install it with:

pip install google-genai-haystack

Authentication

Google Gen AI is compatible with both the Gemini Developer API and the Vertex AI API.

To use this component with the Gemini Developer API and get an API key, visit Google AI Studio.
To use this component with the Vertex AI API, visit Google Cloud > Vertex AI.

The component uses a GOOGLE_API_KEY or GEMINI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with a Secret and Secret.from_token static method:

embedder = GoogleGenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))

The following examples show how to use the component with the Gemini Developer API and the Vertex AI API.

Gemini Developer API (API Key Authentication)

from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAITextEmbedder()

Vertex AI (Application Default Credentials)

from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# Using Application Default Credentials (requires gcloud auth setup)
chat_generator = GoogleGenAITextEmbedder(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
)

Vertex AI (API Key Authentication)

from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAITextEmbedder(api="vertex")

Usage

On its own

Here is how you can use the component on its own. You'll need to pass in your Google API key with a Secret or set it as an environment variable called GOOGLE_API_KEY or GEMINI_API_KEY. The examples below assume you've set the environment variable.

from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = GoogleGenAITextEmbedder()

print(text_embedder.run(text_to_embed))
# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
#  'meta': {'model': 'text-embedding-004',
#           'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}

In a pipeline

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

document_embedder = GoogleGenAIDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", GoogleGenAITextEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

# Document(id=..., content: 'My name is Wolfgang and I live in Berlin')