---
title: Workers Binding
description: Send and retrieve batch inference requests using a Workers AI binding.
image: https://developers.cloudflare.com/dev-products-preview.png
---

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# Workers Binding

You can use Workers Bindings to interact with the Batch API.

## Send a Batch request

Send your initial batch inference request by composing a JSON payload containing an array of individual inference requests and the `queueRequest: true` property (which is what controls queueing behavior).

Note

Ensure that the total payload is under 10 MB.

src/index.ts

```
export interface Env {  AI: Ai;}export default {  async fetch(request, env): Promise<Response> {    const embeddings = await env.AI.run(      "@cf/baai/bge-m3",      {        requests: [          {            query: "This is a story about Cloudflare",            contexts: [              {                text: "This is a story about an orange cloud",              },              {                text: "This is a story about a llama",              },              {                text: "This is a story about a hugging emoji",              },            ],          },        ],      },      { queueRequest: true },    );
    return Response.json(embeddings);  },} satisfies ExportedHandler<Env>;
```

```
{  "status": "queued",  "model": "@cf/baai/bge-m3",  "request_id": "000-000-000"}
```

You will get a response with the following values:

* **`status`**: Indicates that your request is queued.
* **`request_id`**: A unique identifier for the batch request.
* **`model`**: The model used for the batch inference.

Of these, the `request_id` is important for when you need to [poll the batch status](#poll-batch-status).

### Poll batch status

Once your batch request is queued, use the `request_id` to poll for its status. During processing, the API returns a status `queued` or `running` indicating that the request is still in the queue or being processed.

src/index.ts

```
export interface Env {  AI: Ai;}
export default {  async fetch(request, env): Promise<Response> {    const status = await env.AI.run("@cf/baai/bge-m3", {      request_id: "000-000-000",    });
    return Response.json(status);  },} satisfies ExportedHandler<Env>;
```

```
{  "responses": [    {      "id": 0,      "result": {        "response": [          { "id": 0, "score": 0.73974609375 },          { "id": 1, "score": 0.642578125 },          { "id": 2, "score": 0.6220703125 }        ]      },      "success": true,      "external_reference": "reference-1"    }  ],  "usage": { "prompt_tokens": 12, "completion_tokens": 0, "total_tokens": 12 }}
```

When the inference is complete, the API returns a final HTTP status code of `200` along with an array of responses. Each response object corresponds to an individual input prompt, identified by an `id` that maps to the index of the prompt in your original request.

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