---
title: Bring your own generation model
description: Use AI Search for retrieval while generating responses with an external model like OpenAI.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://edgetunnel-b2h.pages.dev/ai-search/llms.txt  
> Use this file to discover all available pages before exploring further. 

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# Bring your own generation model

By default, AI Search uses a Workers AI model to generate responses. To use a model outside of Workers AI, use AI Search for `search` and pass the retrieved content to a different model for generation. This guide uses an OpenAI model.

Note

AI Search supports [bringing your own models natively](https://edgetunnel-b2h.pages.dev/ai-search/configuration/models/). You can attach provider keys through AI Gateway and select third-party models directly in your AI Search settings. The example below still works, but the recommended approach is to configure your external model through AI Gateway.

## Prerequisites

1. Sign up for a [Cloudflare account ↗](https://dash.cloudflare.com/sign-up/workers-and-pages).
2. Install [Node.js ↗](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).

Node.js version manager

Use a Node version manager like [Volta ↗](https://volta.sh/) or [nvm ↗](https://github.com/nvm-sh/nvm) to avoid permission issues and change Node.js versions. [Wrangler](https://edgetunnel-b2h.pages.dev/workers/wrangler/install-and-update/), discussed later in this guide, requires a Node version of `16.17.0` or later.

You also need:

* An AI Search instance that already contains indexed content. To create one and add content, refer to [Get started](https://edgetunnel-b2h.pages.dev/ai-search/get-started/).
* An [OpenAI API key ↗](https://platform.openai.com/api-keys).

## 1\. Create a Worker project

Create a new Worker project using the `create-cloudflare` CLI (C3). [C3 ↗](https://github.com/cloudflare/workers-sdk/tree/main/packages/create-cloudflare) is a command-line tool designed to help you set up and deploy new applications to Cloudflare.

Create a new project named `byo-model` by running:

 npm  yarn  pnpm 

```
npm create cloudflare@latest -- byo-model
```

```
yarn create cloudflare byo-model
```

```
pnpm create cloudflare@latest byo-model
```

For setup, select the following options:

* For _What would you like to start with?_, choose `Hello World example`.
* For _Which template would you like to use?_, choose `Worker only`.
* For _Which language do you want to use?_, choose `TypeScript`.
* For _Do you want to use git for version control?_, choose `Yes`.
* For _Do you want to deploy your application?_, choose `No` (we will be making some changes before deploying).

Go to your application directory:

```sh
cd byo-model
```

## 2\. Install the AI SDK and OpenAI provider

Install the [AI SDK ↗](https://sdk.vercel.ai/) and its OpenAI provider:

 npm  yarn  pnpm  bun 

```
npm i ai @ai-sdk/openai
```

```
yarn add ai @ai-sdk/openai
```

```
pnpm add ai @ai-sdk/openai
```

```
bun add ai @ai-sdk/openai
```

## 3\. Bind your Worker and set your API key

Add the AI Search binding to your [Wrangler configuration file](https://edgetunnel-b2h.pages.dev/workers/wrangler/configuration/):

* [  wrangler.jsonc ](#tab-panel-7225)
* [  wrangler.toml ](#tab-panel-7226)

**JSONC**

```jsonc
{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "ai_search_namespaces": [
    {
      "binding": "AI_SEARCH",
      "namespace": "default",
      "remote": true
    }
  ]
}
```

**TOML**

```toml
[[ai_search_namespaces]]
binding = "AI_SEARCH"
namespace = "default"
remote = true
```

Store your OpenAI API key as a [secret](https://edgetunnel-b2h.pages.dev/workers/configuration/secrets/):

```sh
npx wrangler secret put OPENAI_API_KEY
```

For local development, add the key to a `.dev.vars` file in your project root instead:

**.dev.vars**

```txt
OPENAI_API_KEY="<YOUR_OPENAI_API_KEY>"
```

## 4\. Add the code

Update `src/index.ts`. This Worker searches your instance, formats the retrieved chunks, and passes them to OpenAI to generate an answer. Replace `my-instance` with the name of your instance.

* [  JavaScript ](#tab-panel-7227)
* [  TypeScript ](#tab-panel-7228)

**src/index.js**

```js
import { createOpenAI } from "@ai-sdk/openai";
import { generateText } from "ai";


export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const userQuery = url.searchParams.get("query") ?? "What is Cloudflare?";


    // Search for documents in AI Search.
    const searchResult = await env.AI_SEARCH.get("my-instance").search({
      messages: [{ role: "user", content: userQuery }],
    });


    if (searchResult.chunks.length === 0) {
      return Response.json({ text: `No data found for query "${userQuery}"` });
    }


    // Join the retrieved chunks into a single string.
    const chunks = searchResult.chunks
      .map((chunk) => `<file name="${chunk.item.key}">${chunk.text}</file>`)
      .join("\n\n");


    // Send the query and retrieved content to OpenAI for the answer.
    const openai = createOpenAI({ apiKey: env.OPENAI_API_KEY });
    const generateResult = await generateText({
      model: openai("gpt-4o-mini"),
      messages: [
        {
          role: "system",
          content:
            "You are a helpful assistant. Answer the user question using the provided files.",
        },
        { role: "user", content: chunks },
        { role: "user", content: userQuery },
      ],
    });


    return Response.json({ text: generateResult.text });
  },
};
```

**src/index.ts**

```ts
import { createOpenAI } from "@ai-sdk/openai";
import { generateText } from "ai";


export interface Env {
  AI_SEARCH: AiSearchNamespace;
  OPENAI_API_KEY: string;
}


export default {
  async fetch(request, env): Promise<Response> {
    const url = new URL(request.url);
    const userQuery = url.searchParams.get("query") ?? "What is Cloudflare?";


    // Search for documents in AI Search.
    const searchResult = await env.AI_SEARCH.get("my-instance").search({
      messages: [{ role: "user", content: userQuery }],
    });


    if (searchResult.chunks.length === 0) {
      return Response.json({ text: `No data found for query "${userQuery}"` });
    }


    // Join the retrieved chunks into a single string.
    const chunks = searchResult.chunks
      .map((chunk) => `<file name="${chunk.item.key}">${chunk.text}</file>`)
      .join("\n\n");


    // Send the query and retrieved content to OpenAI for the answer.
    const openai = createOpenAI({ apiKey: env.OPENAI_API_KEY });
    const generateResult = await generateText({
      model: openai("gpt-4o-mini"),
      messages: [
        {
          role: "system",
          content:
            "You are a helpful assistant. Answer the user question using the provided files.",
        },
        { role: "user", content: chunks },
        { role: "user", content: userQuery },
      ],
    });


    return Response.json({ text: generateResult.text });
  },
} satisfies ExportedHandler<Env>;
```

## 5\. Run and deploy

Start a local development server, then query it at `/?query=your+search+terms`:

```sh
npx wrangler dev
```

Log in with your Cloudflare account, then deploy your Worker to make it accessible on the Internet:

```sh
npx wrangler login
npx wrangler deploy
```

## Next steps

[ Models ](https://edgetunnel-b2h.pages.dev/ai-search/configuration/models/) Use third-party models natively through AI Gateway. 

[ Search Workers binding ](https://edgetunnel-b2h.pages.dev/ai-search/api/search/workers-binding/) Full reference for searching and chatting from a Worker.

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