---
title: "GoogleGenAIDocumentEmbedder"
id: googlegenaidocumentembedder
slug: "/googlegenaidocumentembedder"
description: "The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents."
---

# GoogleGenAIDocumentEmbedder

The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [DocumentWriter](../writers/documentwriter.mdx) 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)  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Google AI](/reference/integrations-google-genai) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai |

</div>

## Overview

`GoogleGenAIDocumentEmbedder` enriches the metadata of documents with an embedding of their content. To embed a string, you should use the [`GoogleGenAITextEmbedder`](googlegenaitextembedder.mdx).

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:

```shell
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](https://aistudio.google.com/).
To use this component with the Vertex AI API, visit [Google Cloud > Vertex AI](https://cloud.google.com/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](../../concepts/secret-management.mdx) and `Secret.from_token` static method:

```python
embedder = GoogleGenAIDocumentEmbedder(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)

```python
from haystack_integrations.components.embedders.google_genai import (
    GoogleGenAIDocumentEmbedder,
)

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

#### Vertex AI (Application Default Credentials)

```python
from haystack_integrations.components.embedders.google_genai import (
    GoogleGenAIDocumentEmbedder,
)

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

#### Vertex AI (API Key Authentication)

```python
from haystack_integrations.components.embedders.google_genai import (
    GoogleGenAIDocumentEmbedder,
)

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

## Usage

### 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:

```python
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.

```python
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

```python
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')
```
