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

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

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# Bring your own generation model

When using AI Search, AI Search uses a Workers AI model to generate the response. If you want to use a model outside of Workers AI, you can use AI Search for `search` while using a different model to generate responses.

This example uses an OpenAI model to generate responses from AI Search results. It uses the [Workers binding](https://developers.cloudflare.com/ai-search/api/search/workers-binding/).

Note

AI Search supports [bringing your own models natively](https://developers.cloudflare.com/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.

* [  JavaScript ](#tab-panel-6931)
* [  TypeScript ](#tab-panel-6932)

JavaScript

```
import { openai } 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 "${userQuery}"` });    }
    // Join all document chunks into a single string    const chunks = searchResult.chunks      .map((chunk) => {        return `<file name="${chunk.item.key}">${chunk.text}</file>`;      })      .join("\n\n");
    // Send the user query + matched documents to OpenAI for answer    const generateResult = await generateText({      model: openai("gpt-4o-mini"),      messages: [        {          role: "system",          content:            "You are a helpful assistant and your task is to answer the user question using the provided files.",        },        { role: "user", content: chunks },        { role: "user", content: userQuery },      ],    });
    return Response.json({ text: generateResult.text });  },};
```

TypeScript

```
import { openai } 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 "${userQuery}"` });    }
    // Join all document chunks into a single string    const chunks = searchResult.chunks      .map((chunk) => {        return `<file name="${chunk.item.key}">${chunk.text}</file>`;      })      .join("\n\n");
    // Send the user query + matched documents to OpenAI for answer    const generateResult = await generateText({      model: openai("gpt-4o-mini"),      messages: [        {          role: "system",          content:            "You are a helpful assistant and your task is to 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>;
```

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