LLM Scraper

LLM Scraper is a TypeScript library that allows you to convert any webpages into structured data using LLMs.

Tip

Under the hood, it uses function calling to convert pages to structured data. You can find more about this approach here

Features

• Supports Local (GGUF), OpenAI, Groq chat models
• Schemas defined with Zod
• Full type-safety with TypeScript
• Based on Playwright framework
• Streaming when crawling multiple pages
• Supports 4 input modes:
  • html for loading raw HTML
  • markdown for loading markdown
  • text for loading extracted text (using Readability.js)
  • image for loading a screenshot (multi-modal only)

Make sure to give it a star!

Getting started

1. Install the required dependencies from npm:

   npm i zod playwright llm-scraper
   

2. Initialize your LLM:

   OpenAI

   import OpenAI from 'openai'
   const model = new OpenAI()

   Groq

   import OpenAI from 'openai'
   const model = new OpenAI({
    baseURL: 'https://api.groq.com/openai/v1',
    apiKey: process.env.GROQ_API_KEY,
   })
   
   // Note!
   // Tool calls are used so only the models listed here are supported for Groq
   // when invoking any .run(...) call
   // https://console.groq.com/docs/tool-use

   Local

   import { LlamaModel } from 'node-llama-cpp'
   const model = new LlamaModel({ modelPath: 'model.gguf' })

3. Create a new browser instance and attach LLMScraper to it:

   import { chromium } from 'playwright'
   import LLMScraper from 'llm-scraper'
   
   const browser = await chromium.launch()
   const scraper = new LLMScraper(browser, model)

Example

In this example, we're extracting top stories from HackerNews:

import { chromium } from 'playwright'
import { z } from 'zod'
import OpenAI from 'openai'
import LLMScraper from 'llm-scraper'

// Launch a browser instance
const browser = await chromium.launch()

// Initialize LLM provider
const llm = new OpenAI()

// Create a new LLMScraper
const scraper = new LLMScraper(browser, llm)

// Define schema to extract contents into
const schema = z.object({
  top: z
    .array(
      z.object({
        title: z.string(),
        points: z.number(),
        by: z.string(),
        commentsURL: z.string(),
      })
    )
    .length(5)
    .describe('Top 5 stories on Hacker News'),
})

// URLs to scrape
const urls = ['https://news.ycombinator.com']

// Run the scraper
const pages = await scraper.run(urls, {
  model: 'gpt-4-turbo',
  schema,
  mode: 'html',
  closeOnFinish: true,
})

// Stream the result from LLM
for await (const page of pages) {
  console.log(page.data)
}

Contributing

As an open-source project, we welcome contributions from the community. If you are experiencing any bugs or want to add some improvements, please feel free to open an issue or pull request.