Skip to content
Cloudflare Docs

Use browser rendering with AI

The ability to browse websites can be crucial when building workflows with AI. Here, we provide an example where we use Browser Rendering to visit https://labs.apnic.net/ and then, using a machine learning model available in Workers AI, extract the first post as JSON with a specified schema.

Prerequisites

  1. Use the create-cloudflare CLI to generate a new Hello World Cloudflare Worker script:
Terminal window
npm create cloudflare@latest -- browser-worker
  1. Install @cloudflare/puppeteer, which allows you to control the Browser Rendering instance:
Terminal window
npm i @cloudflare/puppeteer
  1. Install zod so we can define our output format and zod-to-json-schema so we can convert it into a JSON schema format:
Terminal window
npm i zod
npm i zod-to-json-schema
  1. Activate the nodejs compatibility flag and add your Browser Rendering binding to your new Wrangler configuration:
{
  "compatibility_flags": [
    "nodejs_compat"
  ]
}
 
{
  "browser": {
    "binding": "MY_BROWSER"
  }
}
  1. In order to use Workers AI, you need to get your Account ID and API token. Once you have those, create a .dev.vars file and set them there:
ACCOUNT_ID=
API_TOKEN=

We use .dev.vars here since it's only for local development, otherwise you'd use Secrets.

Load the page using Browser Rendering

In the code below, we launch a browser using await puppeteer.launch(env.MY_BROWSER), extract the rendered text and close the browser. Then, with the user prompt, the desired output schema and the rendered text, prepare a prompt to send to the LLM.

Replace the contents of src/index.ts with the following skeleton script:

import { z } from "zod";
import puppeteer from "@cloudflare/puppeteer";
import zodToJsonSchema from "zod-to-json-schema";


export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    if (url.pathname != "/") {
      return new Response("Not found");
    }


    // Your prompt and site to scrape
    const userPrompt = "Extract the first post only.";
    const targetUrl = "https://labs.apnic.net/";


    // Launch browser
    const browser = await puppeteer.launch(env.MY_BROWSER);
    const page = await browser.newPage();
    await page.goto(targetUrl);


    // Get website text
    const renderedText = await page.evaluate(() => {
      // @ts-ignore js code to run in the browser context
      const body = document.querySelector("body");
      return body ? body.innerText : "";
    });
    // Close browser since we no longer need it
    await browser.close();


    // define your desired json schema
    const outputSchema = zodToJsonSchema(
      z.object({ title: z.string(), url: z.string(), date: z.string() })
    );


    // Example prompt
    const prompt = `
    You are a sophisticated web scraper. You are given the user data extraction goal and the JSON schema for the output data format.
    Your task is to extract the requested information from the text and output it in the specified JSON schema format:


        ${JSON.stringify(outputSchema)}


    DO NOT include anything else besides the JSON output, no markdown, no plaintext, just JSON.


    User Data Extraction Goal: ${userPrompt}


    Text extracted from the webpage: ${renderedText}`;


    // TODO call llm
    //const result = await getLLMResult(env, prompt, outputSchema);
    //return Response.json(result);
  }


} satisfies ExportedHandler<Env>;

Call an LLM

Having the webpage text, the user's goal and output schema, we can now use an LLM to transform it to JSON according to the user's request. The example below uses @hf/thebloke/deepseek-coder-6.7b-instruct-awq but other models, or services like OpenAI, could be used with minimal changes:

async getLLMResult(env, prompt: string, schema?: any) {
    const model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"
    const requestBody = {
        messages: [{
            role: "user",
            content: prompt
        }
        ],
    };
    const aiUrl = `https://api.cloudflare.com/client/v4/accounts/${env.ACCOUNT_ID}/ai/run/${model}`


    const response = await fetch(aiUrl, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${env.API_TOKEN}`,
      },
      body: JSON.stringify(requestBody),
    });
    if (!response.ok) {
      console.log(JSON.stringify(await response.text(), null, 2));
      throw new Error(`LLM call failed ${aiUrl} ${response.status}`);
    }


    // process response
    const data = await response.json();
    const text = data.result.response || '';
    const value = (text.match(/```(?:json)?\s*([\s\S]*?)\s*```/) || [null, text])[1];
    try {
      return JSON.parse(value);
    } catch(e) {
      console.error(`${e} . Response: ${value}`)
    }
  }

If you want to use Browser Rendering with OpenAI instead you'd just need to change the aiUrl endpoint and requestBody (or check out the llm-scraper-worker ↗ package).

Conclusion

The full Worker script now looks as follows:

import { z } from "zod";
import puppeteer from "@cloudflare/puppeteer";
import zodToJsonSchema from "zod-to-json-schema";


export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    if (url.pathname != "/") {
      return new Response("Not found");
    }


    // Your prompt and site to scrape
    const userPrompt = "Extract the first post only.";
    const targetUrl = "https://labs.apnic.net/";


    // Launch browser
    const browser = await puppeteer.launch(env.MY_BROWSER);
    const page = await browser.newPage();
    await page.goto(targetUrl);


    // Get website text
    const renderedText = await page.evaluate(() => {
      // @ts-ignore js code to run in the browser context
      const body = document.querySelector("body");
      return body ? body.innerText : "";
    });
    // Close browser since we no longer need it
    await browser.close();


    // define your desired json schema
    const outputSchema = zodToJsonSchema(
      z.object({ title: z.string(), url: z.string(), date: z.string() })
    );


    // Example prompt
    const prompt = `
    You are a sophisticated web scraper. You are given the user data extraction goal and the JSON schema for the output data format.
    Your task is to extract the requested information from the text and output it in the specified JSON schema format:


        ${JSON.stringify(outputSchema)}


    DO NOT include anything else besides the JSON output, no markdown, no plaintext, just JSON.


    User Data Extraction Goal: ${userPrompt}


    Text extracted from the webpage: ${renderedText}`;


    // call llm
    const result = await getLLMResult(env, prompt, outputSchema);
    return Response.json(result);
  }


} satisfies ExportedHandler<Env>;




async function getLLMResult(env, prompt: string, schema?: any) {
    const model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"
    const requestBody = {
        messages: [{
            role: "user",
            content: prompt
        }
        ],
    };
    const aiUrl = `https://api.cloudflare.com/client/v4/accounts/${env.ACCOUNT_ID}/ai/run/${model}`


    const response = await fetch(aiUrl, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${env.API_TOKEN}`,
      },
      body: JSON.stringify(requestBody),
    });
    if (!response.ok) {
      console.log(JSON.stringify(await response.text(), null, 2));
      throw new Error(`LLM call failed ${aiUrl} ${response.status}`);
    }


    // process response
    const data = await response.json() as { result: { response: string }};
    const text = data.result.response || '';
    const value = (text.match(/```(?:json)?\s*([\s\S]*?)\s*```/) || [null, text])[1];
    try {
      return JSON.parse(value);
    } catch(e) {
      console.error(`${e} . Response: ${value}`)
    }
}

You can run this script to test it using Wrangler's --remote flag:

Terminal window
npx wrangler dev --remote

With your script now running, you can go to http://localhost:8787/ and should see something like the following:

{
  "title": "IP Addresses in 2024",
  "url": "http://example.com/ip-addresses-in-2024",
  "date": "11 Jan 2025"
}

For more complex websites or prompts, you might need a better model. Check out the latest models in Workers AI.