> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Semantic Search with Cloudflare Workers AI Embeddings

> Set up Meilisearch with Cloudflare Workers AI embedding models for semantic search.

Cloudflare Workers AI provides embedding models that run on Cloudflare's edge network. This guide shows you how to configure Meilisearch with Cloudflare Workers AI embeddings using the REST embedder.

## Requirements

* A Meilisearch project
* A [Cloudflare](https://www.cloudflare.com/) account with access to Workers AI
* Your Cloudflare account ID and API key

## Available models

| Model                            | Dimensions | Notes                               |
| -------------------------------- | ---------- | ----------------------------------- |
| `@cf/baai/bge-small-en-v1.5`     | 384        | Fastest, English only               |
| `@cf/baai/bge-base-en-v1.5`      | 768        | Balanced, English only              |
| `@cf/baai/bge-large-en-v1.5`     | 1024       | Highest quality BGE, English only   |
| `@cf/google/embeddinggemma-300m` | 768        | Google's compact embedding model    |
| `@cf/qwen/qwen3-embedding-0.6b`  | 1024       | Qwen3's lightweight embedding model |

## Configure the embedder

Update your index settings with the Cloudflare Workers AI embedder configuration:

```json theme={null}
{
  "cloudflare": {
    "source": "rest",
    "apiKey": "<CLOUDFLARE_API_KEY>",
    "dimensions": 384,
    "documentTemplate": "A product named '{{doc.name}}': {{doc.description}}",
    "url": "https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai/run/@cf/baai/bge-small-en-v1.5",
    "request": {
      "text": ["{{text}}", "{{..}}"]
    },
    "response": {
      "result": {
        "data": ["{{embedding}}", "{{..}}"]
      }
    }
  }
}
```

Replace `<CLOUDFLARE_API_KEY>` with your Cloudflare API key and `<ACCOUNT_ID>` with your Cloudflare account ID. The model name is part of the URL path. Adjust `dimensions` to match the model you choose.

Send this configuration to Meilisearch:

```sh theme={null}
curl \
  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_NAME/settings' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MEILISEARCH_KEY' \
  --data-binary '{
    "embedders": {
      "cloudflare": {
        "source": "rest",
        "apiKey": "<CLOUDFLARE_API_KEY>",
        "dimensions": 384,
        "documentTemplate": "A product named '\''{{doc.name}}'\'': {{doc.description}}",
        "url": "https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai/run/@cf/baai/bge-small-en-v1.5",
        "request": {
          "text": ["{{text}}", "{{..}}"]
        },
        "response": {
          "result": {
            "data": ["{{embedding}}", "{{..}}"]
          }
        }
      }
    }
  }'
```

Meilisearch handles batching and rate limiting automatically. Monitor the [tasks queue](/reference/api/tasks/list-tasks) to track indexing progress.

## Test the search

```json theme={null}
{
  "q": "comfortable shoes for walking",
  "hybrid": {
    "semanticRatio": 0.5,
    "embedder": "cloudflare"
  }
}
```

## Next steps

* [Document template best practices](/capabilities/hybrid_search/advanced/document_template_best_practices) to optimize which fields are embedded
* [Custom hybrid ranking](/capabilities/hybrid_search/advanced/custom_hybrid_ranking) to tune the balance between keyword and semantic results
* [Embedder settings reference](/reference/api/settings/list-all-settings) for all configuration options