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

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>;

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())

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"] }'

Parameters

* indicates a required field

Input

text one of required
- 0 string min 1
  The text to embed
- 1 array
  Batch of text values to embed
  - items string min 1
    The text to embed

Output

shape array
- items number
data array
Embeddings of the requested text values
- items array
  Floating point embedding representation shaped by the embedding model
  - items number

API Schemas

The following schemas are based on JSON Schema

Input
Output

{
    "type": "object",
    "properties": {
        "text": {
            "oneOf": [
                {
                    "type": "string",
                    "description": "The text to embed",
                    "minLength": 1
                },
                {
                    "type": "array",
                    "description": "Batch of text values to embed",
                    "items": {
                        "type": "string",
                        "description": "The text to embed",
                        "minLength": 1
                    },
                    "maxItems": 100
                }
            ]
        }
    },
    "required": [
        "text"
    ]
}

{
    "type": "object",
    "contentType": "application/json",
    "properties": {
        "shape": {
            "type": "array",
            "items": {
                "type": "number"
            }
        },
        "data": {
            "type": "array",
            "description": "Embeddings of the requested text values",
            "items": {
                "type": "array",
                "description": "Floating point embedding representation shaped by the embedding model",
                "items": {
                    "type": "number"
                }
            }
        }
    }
}