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It is made by the developers of the <a href=\"https://fetchfox.ai\">FetchFox AI scraper</a>.</p>\n</div>\n\n<div align=\"center\">\n  \n<a href=\"https://twitter.com/FetchFoxAI\"><img src=\"https://img.shields.io/twitter/follow/FetchFoxAI?style=social\"></a> [![GitHub stars](https://img.shields.io/github/stars/fetchfox/fetchfox.svg?style=social&label=Star)](https://github.com/fetchfox/fetchfox) <a href=\"https://badge.fury.io/js/fetchfox\"><img src=\"https://badge.fury.io/js/fetchfox.svg\" alt=\"npm version\" height=\"18\"></a> <a href=\"https://discord.gg/mM54bwdu59\"><img src=\"https://img.shields.io/discord/1180618526436888586?label=discord&logo=discord&logoColor=white&style=flat\"></a>\n\n</div>\n\n# Getting started\n\nInstall the package and playwright:\n\n```bash\nnpm i fetchfox\nnpx playwright install-deps\nnpx playwright install\n```\n\nThen use it. Here is the callback style:\n\n```javascript\nimport { fox } from 'fetchfox';\n\nconst workflow = await fox\n  .init('https://pokemondb.net/pokedex/national')\n  .extract({ name: 'Pokemon name', number: 'Pokemon number' })\n  .limit(3)\n  .plan();\n\nconst results = await workflow\n  .run(null, (delta) => { console.log(delta.item) });\n  \nfor (const item of results.items) {\n  console.log('Item:', item);\n}\n```\n\nIf you prefer, you can use the streaming style:\n\n```javascript\nimport { fox } from 'fetchfox';\n\nconst stream = fox\n  .init('https://pokemondb.net/pokedex/national')\n  .extract({ name: 'Pokemon name', number: 'Pokemon number' })\n  .stream();\n\nfor await (const delta of stream) {\n  console.log(delta.item);\n}\n```\n\n## Following URLs\n\nYou'll often want to scrape over multiple levels. You can do this using the `url` field. If you extract a `url` field, FetchFox will follow that URL on the next step.\n\nFor example, you can get HP and attack on the second page of the Pokedex:\n\n```javascript\nconst workflow = await fox\n  .init('https://pokemondb.net/pokedex/national')\n  .extract({ \n    url: 'URL of pokemon profile', \n    name: 'Pokemon name', \n    number: 'Pokemon number'\n  })\n  .extract({ \n    hp: 'Pokemon HP', \n    attack: 'Pokemon attack power', \n  })\n  .limit(3)\n  .plan();\n\nconst results = await workflow\n  .run(null, (delta) => { console.log(delta.item) });\n  \nfor (const item of results.items) {\n  console.log('Item:', item);\n}\n```\n\nThis scraper will start at https://pokemondb.net/pokedex/national, and then go to detail pages like https://pokemondb.net/pokedex/pikachu to get the HP and attack values.\n\n## Enter your API key\n\nYou'll need to give an API key for the AI provider you are using, such as OpenAI. There are a few ways to do this.\n\nThe easiest option is to set the `OPENAI_API_KEY` environment variable. This will get picked up by the FetchFox library, and all AI calls will go through that key. To use this option, run your code like this:\n\n```bash\nOPENAI_API_KEY=sk-your-key node index.js\n```\n\nAlternatively, you can pass in your API key in code, like this:\n\n```javascript\nimport { fox } from 'fetchfox';\n\nconst results = await fox\n  .config({ ai: ['openai:gpt-4o-mini', { apiKey: 'sk-your-key' }]})\n  .init('https://pokemondb.net/pokedex/national')\n  .extract({ name: 'Pokemon name', number: 'Pokemon number' })\n  .limit(3)\n  .run();\n```\n \nThis will use OpenAI's `gpt-4o-mini` model, and the API key you specify. You can also use OpenRouter to access AI models from other providers:\n\n```javascript\nconst results = await fox\n  .config({ ai: ['openrouter:google/gemini-flash-1.5', { apiKey: 'your-openrouter-key' }]})\n  .init('https://pokemondb.net/pokedex/national')\n  .extract({ name: 'Pokemon name', number: 'Pokemon number' })\n  .limit(3)\n  .run();\n```\n\nChoose the AI model that best suits your needs.\n\nThe following providers are supported\n\n* __OpenAI__: Model strings are `openai:...`, for example `openai:gpt-4o`\n* __Google__: Model strings are `google:...`, for example `google:gemini-1.5-flash`\n* __OpenRouter__: Model strings are `openrouter:...`, for example `openrouter:anthropic/claude-3.5-haiku`\n\n\nBy default, FetchFox uses OpenAI's `gpt-4o-mini` model. We've found this model to provide a good tradeoff between cost, runtime, and accuracy. We have a [public benchmarks dashboard](http://dashboard.fetchfox.ai/) where you can review performance data on recent commits.\n\n","readmeFilename":"README.md"}