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Data Scraping

Data scraping is the process of automatically extracting information from various sources, typically websites, documents, or other digital formats. This technique is essential for gathering large amounts of data that would be impractical to collect manually.

Common Scraping Methods

  1. Web Scraping
  2. HTML parsing
  3. API consumption

  4. Browser automation

  5. Document Scraping

  6. PDF extraction
  7. Image text extraction (OCR)

  8. Document format conversion

  9. Database Scraping

  10. Direct database queries
  11. Export file processing
  12. Log file analysis

Tools and Libraries

General-Purpose Tools

Scrapegraph AI

ScrapeGraphAI is a powerful Python library that leverages LLMs and direct graph logic for web scraping. It can extract information from both websites and local documents (XML, HTML, JSON, Markdown) using natural language prompts. Key features include:

  • Multiple scraping pipelines (single-page, multi-page, search-based)
  • Support for various LLMs (OpenAI, Groq, Azure, Gemini, Ollama)
  • Audio generation from scraped content
  • Python script generation for custom scraping
  • Parallel LLM processing capabilities
  • Built-in browser automation with Playwright

Web Scraping

Document Scraping

  • MinerU
  • Apache Tika
  • Tabula
  • PyMuPDF

MinerU for Document Extraction

MinerU is a powerful open-source tool specifically designed for high-quality PDF extraction. It excels at: - Converting PDFs to machine-readable formats (Markdown, JSON) - Preserving document structure (headings, paragraphs, lists) - Extracting images, tables, and formulas - Supporting multiple languages through OCR - Handling complex layouts and scientific literature

LLM-Specific Tools

Several specialized tools have been developed specifically for gathering and processing data for Large Language Models:

Code Repository Processing

https://github.com/cyclotruc/gitingest

gitingest - Replace 'hub' with 'ingest' in any GitHub URL to get a prompt-friendly extract of a codebase.

https://github.com/yamadashy/repomix

repomix - Packs your entire repository into a single, AI-friendly file

https://github.com/simonw/files-to-prompt

files-to-prompt - Concatenates a directory of files into a single LLM-ready prompt

https://github.com/Doriandarko/RepoToTextForLLMs

RepoToTextForLLMs - Simple Python script for fetching repository content

Web Content Processing

https://github.com/mishushakov/llm-scraper

llm-scraper - Converts webpages into structured data using LLMs

https://github.com/unclecode/crawl4ai

crawl4ai - LLM-friendly web crawler and scraper

https://github.com/jina-ai/reader

reader - Convert any URL to LLM-friendly input using https://r.jina.ai/

https://github.com/mendableai/firecrawl

firecrawl - API to convert websites into LLM-ready markdown or structured data

MCP Server Implementation: firecrawl-mcp-server

Features: - Scraping single URLs with advanced options (formats, content filtering, timeouts) - Batch scraping with parallel processing and rate limiting - Web search with content extraction - Crawling with depth control and link filtering - Structured data extraction using LLMs - Credit usage monitoring and rate limit handling

Configuration options: - Retry behavior with exponential backoff - Credit usage thresholds for warnings - Custom API endpoints for self-hosted instances - Batch processing parameters

Available Tools: - firecrawl_scrape: Single URL scraping - firecrawl_batch_scrape: Multiple URL processing - firecrawl_search: Web search with content extraction - firecrawl_crawl: Deep crawling with controls - firecrawl_extract: Structured data extraction

Integrates with: - Cursor - Claude - Other LLM clients supporting Model Context Protocol (MCP)

https://github.com/mendableai/llmstxt-generator

llmstxt-generator - API to generate llms.txt files from websites

Document Processing

https://github.com/VikParuchuri/marker

marker - Fast PDF to markdown or JSON conversion

https://github.com/adbar/trafilatura

trafilatura - Python & CLI tool for web text and metadata extraction

https://github.com/DS4SD/docling

docling - Simplifies processing and parsing of diverse document formats

Scraping Practices

  1. Respect Rate Limits

    • Implement delays between requests
    • Follow robots.txt guidelines
    • Use appropriate request headers

  2. Data Validation

    • Verify extracted data integrity
    • Handle missing or malformed data
    • Implement error logging

  3. Performance Optimization

    • Use async operations when possible
    • Implement proper caching
    • Consider distributed scraping for large datasets

Additional Resources

For additional resources and datasets specifically focused on post-training, refer to: - llm-datasets - Curated list of datasets and tools for LLM post-training - LLM Data Scrapers Repository - Collection of useful Open Source tools and scrapers for LLMs