---
title: "GoogleGenAIMultimodalDocumentEmbedder"
id: googlegenaimultimodaldocumentembedder
slug: "/googlegenaimultimodaldocumentembedder"
description: "`GoogleGenAIMultimodalDocumentEmbedder` computes the embeddings of a list of non-textual documents and stores the obtained vectors in the embedding field of each document."
---

# GoogleGenAIMultimodalDocumentEmbedder

`GoogleGenAIMultimodalDocumentEmbedder` computes the embeddings of a list of non-textual documents and stores the obtained vectors in the embedding field of each document.
It uses Google AI multimodal embedding models with the ability to embed text, images, videos, and audio into the same vector space.
<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, with a meta field containing an image file path |
| **Output variables** | `documents`: A list of documents (enriched with embeddings)  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Google GenAI](/reference/integrations-google-genai) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai |
| **Package name** | `google-genai-haystack` |

</div>

## Overview

`GoogleGenAIMultimodalDocumentEmbedder` expects a list of documents containing a file path in a meta field. The meta field can be specified with the `file_path_meta_field` init parameter of this component.

The embedder efficiently loads the files, computes the embeddings using a Google AI model, and stores each of them in the `embedding` field of the document.

`GoogleGenAIMultimodalDocumentEmbedder` is commonly used in indexing pipelines. At retrieval time, you need to use the same model with a `GoogleGenAITextEmbedder` to embed the query, before using an Embedding Retriever.

This component is compatible with Gemini multimodal models: `gemini-embedding-2` and later. For a complete list of supported models, see the [Google AI documentation](https://ai.google.dev/gemini-api/docs/embeddings).

To embed a textual document, you should use the [`GoogleGenAIDocumentEmbedder`](googlegenaidocumentembedder.mdx).
To embed a string, you should use the [`GoogleGenAITextEmbedder`](googlegenaitextembedder.mdx).

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 = GoogleGenAIMultimodalDocumentEmbedder(
    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 (
    GoogleGenAIMultimodalDocumentEmbedder,
)

# set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
embedder = GoogleGenAIMultimodalDocumentEmbedder()
```

#### Vertex AI (Application Default Credentials)

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

# Using Application Default Credentials (requires gcloud auth setup)
embedder = GoogleGenAIMultimodalDocumentEmbedder(
    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 (
    GoogleGenAIMultimodalDocumentEmbedder,
)

# set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
embedder = GoogleGenAIMultimodalDocumentEmbedder(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 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 (
    GoogleGenAIMultimodalDocumentEmbedder,
)

docs = [
    Document(meta={"file_path": "path/to/image.jpg"}),
    Document(meta={"file_path": "path/to/video.mp4"}),
    Document(meta={"file_path": "path/to/pdf.pdf", "page_number": 1}),
    Document(meta={"file_path": "path/to/pdf.pdf", "page_number": 3}),
]

document_embedder = GoogleGenAIMultimodalDocumentEmbedder()

result = document_embedder.run(documents=docs)
print(result["documents"][0].embedding)
# [0.017020374536514282, -0.023255806416273117, ...]
```

### Setting embedding dimensions

Models like `gemini-embedding-2` have a default embedding dimension of 3072, but, thanks to
Matryoshka Representation Learning, it's possible to reduce embedding size while keeping similar performance.

Check the [Google AI documentation](https://ai.google.dev/gemini-api/docs/embeddings#control-embedding-size) for more information.

```python
from haystack import Document

from haystack_integrations.components.embedders.google_genai import (
    GoogleGenAIMultimodalDocumentEmbedder,
)

docs = [Document(meta={"file_path": "path/to/image.jpg"})]

doc_multimodal_embedder = GoogleGenAIMultimodalDocumentEmbedder(
    config={"output_dimensionality": 768},
)
docs_with_embeddings = doc_multimodal_embedder.run(docs)["documents"]
```

### In a pipeline

In the following example, we look for a specific plot in the "Scaling Instruction-Finetuned Language Models" paper (PDF format).

You first need to download the PDF file from https://arxiv.org/pdf/2210.11416.pdf.

```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 (
    GoogleGenAIMultimodalDocumentEmbedder,
)
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

paper_path = "2210.11416.pdf"

documents = [
    Document(meta={"file_path": paper_path, "page_number": i}) for i in range(1, 16)
]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", GoogleGenAIMultimodalDocumentEmbedder())
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 = "plot showing BBH accuracy"

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

print(result["retriever"]["documents"][0].meta)

# {'file_path': '2210.11416.pdf', 'page_number': 9}
```
