To help you get started, we’ve organized our docs into clear sections: Setup & Installation Basic instructions to install Crawl4AI via pip or Docker. Quick Start A hands-on introduction showing how to do your first crawl, generate Markdown, and do a simple extraction. ...
arun( url="https://docs.micronaut.io/4.7.6/guide/", config=run_config ) print(len(result.markdown)) print(len(result.fit_markdown)) print(len(result.markdown_v2.fit_markdown)) if __name__ == "__main__": asyncio.run(main()) 🖥️ Executing JavaScript & Extract Structured ...
在 configs/ 目录下创建 YAML 格式的配置文件，示例如下：cw22_root_path: <path_to_clueweb22_a>seed_docs_file: seed.txtoutput_dir: crawl_results/seed_10k_crawl_20m_dclm_fasttextnum_selected_docs_per_iter: 10000num_workers: 16save_state_every: -1max_num_docs: 20000000selection_method: dclm_...
You can check the project structure in the directory https://github.com/unclecode/crawl4ai/docs/examples. Over there, you can find a variety of examples; here, some popular examples are shared. 📝 Heuristic Markdown Generation with Clean and Fit Markdown import asyncio from crawl4ai impor...
site_url: https://docs.crawl4ai.com repo_url: https://github.com/unclecode/crawl4ai repo_name: unclecode/crawl4ai docs_dir: docs/md_v3 nav: - Home: index.md - Tutorials: - "Getting Started": tutorials/getting-started.md - "AsyncWebCrawler Basics": tutorials/async-webcr...
async def get_chunking_strategies(): with open(f"{__location__}/docs/chunking_strategies.json", "r") as file: return JSONResponse(content=file.read()) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8888) 深圳...
各种脚本工具.docx 2025-03-03 08:01:38 积分:1 tech_docs-linux常用命令大全 2025-03-03 04:48:38 积分:1 tmgtoolkit-app 2025-03-01 21:02:31 积分:1 VerilogUARTModule 2025-03-01 20:58:33 积分:1 fifo-player 2025-03-01 20:58:00 积分:1 ...
python fetch_docs.py --input_dir <document_ids_dir> --output_dir <document_texts_dir> --num_workers <num_workers> 5. 预训练与评估 最后,可以利用 DCLM 框架进行 LLM 预训练和性能评估。 资源 GitHub 仓库:https://github.com/cxcscmu/Crawl4LLM ...
python fetch_docs.py --input_dir <document_ids_dir> --output_dir <document_texts_dir> --num_workers <num_workers> 5. 预训练与评估 最后,可以利用 DCLM 框架进行 LLM 预训练和性能评估。 资源 GitHub 仓库:https://github.com/cxcscmu/Crawl4LLM ...
python fetch_docs.py--input_dir<document_ids_dir>--output_dir<document_texts_dir>--num_workers<num_workers> 1. 5. 预训练与评估 最后,可以利用 DCLM 框架进行 LLM 预训练和性能评估。 资源 GitHub 仓库:https://github.com/cxcscmu/Crawl4LLM ...