---
title: Google GenAI Embeddings
 | Developer Documentation
---

Using Google’s `google-genai` package, LlamaIndex provides a `GoogleGenAIEmbedding` class that allows you to embed text using Google’s GenAI models from both the Gemini and Vertex AI APIs using the latest `gemini-embedding-2-preview` model.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-google-genai
```

```
import os


os.environ["GOOGLE_API_KEY"] = "..."
```

## Setup

`GoogleGenAIEmbedding` is a wrapper around the `google-genai` package, which means it supports both Gemini and Vertex AI APIs out of the box.

You can pass in the `api_key` directly, or pass in a `vertexai_config` to use the Vertex AI API.

Other options include `embed_batch_size`, `model_name`, and `embedding_config`.

```
from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
from google.genai.types import EmbedContentConfig


embed_model = GoogleGenAIEmbedding(
    model_name="gemini-embedding-2-preview",
    embed_batch_size=100,
    # can pass in the api key directly
    # api_key="...",
    # or pass in a vertexai_config
    # vertexai_config={
    #     "project": "...",
    #     "location": "...",
    # }
    # can also pass in an embedding_config
    # embedding_config=EmbedContentConfig(...)
)
```

## Usage

### Sync

```
embeddings = embed_model.get_text_embedding("Google Gemini Embeddings.")
print(embeddings[:5])
print(f"Dimension of embeddings: {len(embeddings)}")
```

```
[0.031099992, 0.02192731, -0.06523498, 0.016788177, 0.0392835]
Dimension of embeddings: 768
```

```
embeddings = embed_model.get_query_embedding("Query Google Gemini Embeddings.")
print(embeddings[:5])
print(f"Dimension of embeddings: {len(embeddings)}")
```

```
[0.022199392, 0.03671178, -0.06874573, 0.02195774, 0.05475164]
Dimension of embeddings: 768
```

```
embeddings = embed_model.get_text_embedding_batch(
    [
        "Google Gemini Embeddings.",
        "Google is awesome.",
        "Llamaindex is awesome.",
    ]
)
print(f"Got {len(embeddings)} embeddings")
print(f"Dimension of embeddings: {len(embeddings[0])}")
```

```
Got 3 embeddings
Dimension of embeddings: 768
```

### Async

```
embeddings = await embed_model.aget_text_embedding("Google Gemini Embeddings.")
print(embeddings[:5])
print(f"Dimension of embeddings: {len(embeddings)}")
```

```
[0.031099992, 0.02192731, -0.06523498, 0.016788177, 0.0392835]
Dimension of embeddings: 768
```

```
embeddings = await embed_model.aget_query_embedding(
    "Query Google Gemini Embeddings."
)
print(embeddings[:5])
print(f"Dimension of embeddings: {len(embeddings)}")
```

```
[0.022199392, 0.03671178, -0.06874573, 0.02195774, 0.05475164]
Dimension of embeddings: 768
```

```
embeddings = await embed_model.aget_text_embedding_batch(
    [
        "Google Gemini Embeddings.",
        "Google is awesome.",
        "Llamaindex is awesome.",
    ]
)
print(f"Got {len(embeddings)} embeddings")
print(f"Dimension of embeddings: {len(embeddings[0])}")
```

```
Got 3 embeddings
Dimension of embeddings: 768
```