---
title: embeddinggemma-300m
description: EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

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#  embeddinggemma-300m 

Text Embeddings • Google 

`@cf/google/embeddinggemma-300m` 

EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.

## Usage

* [  TypeScript ](#tab-panel-5261)
* [  Python ](#tab-panel-5262)
* [  curl ](#tab-panel-5263)

```ts
export interface Env {
  AI: Ai;
}


export default {
  async fetch(request, env): Promise<Response> {


    // Can be a string or array of strings]
    const stories = [
      "This is a story about an orange cloud",
      "This is a story about a llama",
      "This is a story about a hugging emoji",
    ];


    const embeddings = await env.AI.run(
      "@cf/google/embeddinggemma-300m",
      {
        text: stories,
      }
    );


    return Response.json(embeddings);
  },
} satisfies ExportedHandler<Env>;
```

```py
import os
import requests


ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")


stories = [
  'This is a story about an orange cloud',
  'This is a story about a llama',
  'This is a story about a hugging emoji'
]


response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/google/embeddinggemma-300m",
  headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
  json={"text": stories}
)


print(response.json())
```

```sh
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/google/embeddinggemma-300m  \
  -X POST  \
  -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN"  \
  -d '{ "text": ["This is a story about an orange cloud", "This is a story about a llama", "This is a story about a hugging emoji"] }'
```

OpenAI compatible endpoints 

Workers AI also supports OpenAI compatible API endpoints for `/v1/chat/completions` and `/v1/embeddings`. For more details, refer to [Configurations ](https://edgetunnel-b2h.pages.dev/workers-ai/configuration/open-ai-compatibility/). 

## Parameters

* [ Input ](#tab-panel-5264)
* [ Output ](#tab-panel-5265)

▶text

`one of`required

▶shape\[\]

`array`

▶data\[\]

`array`Embeddings of the requested text values

## API Schemas (Raw)

Input [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/embeddinggemma-300m/schema-input.json "Open") [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/embeddinggemma-300m/schema-input.json "Download") 

Output [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/embeddinggemma-300m/schema-output.json "Open") [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/embeddinggemma-300m/schema-output.json "Download")

```json
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```