
Most common position in a pipeline	Before a DocumentWriter in an indexing 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	"documents": A list of documents to be embedded
Output variables	"documents": A list of documents (enriched with embeddings) "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

GoogleGenAIDocumentEmbedder enriches the metadata of documents with an embedding of their content. To embed a string, you should use the GoogleGenAITextEmbedder.

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

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

To get a Google AI API key, head over to https://ai.google.dev/.

Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this by using the Document Embedder:

from haystack import Document
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

doc = Document(content="some text", meta={"title": "relevant title", "page number": 18})

embedder = GoogleGenAIDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

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 via 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 import Document
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = GoogleGenAIDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)
# [0.017020374536514282, -0.023255806416273117, ...]

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.writers import DocumentWriter
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")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", GoogleGenAIDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

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')