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What is web scraping for RAG systems?
TL;DR
RAG systems need clean text to index and retrieve. Scraping for RAG produces noise-free content optimized for embedding.
What is RAG scraping?
RAG retrieves documents and passes them to LLMs as context. Content quality directly impacts response accuracy. No boilerplate, just meaningful text.
Requirements
- Clean text: No navigation, ads, or boilerplate
- Proper chunking: Split at logical boundaries
- Metadata: Source URLs for citation
- Consistent format: Markdown that embeds predictably
Why quality matters
Noisy content pollutes your index. Navigation menus get retrieved alongside actual content, degrading LLM responses.
Firecrawl's markdown output strips boilerplate automatically. Built-in LangChain and LlamaIndex integrations simplify RAG pipelines.
Key Takeaways
RAG scraping requires clean, chunked content. Quality at scraping determines retrieval accuracy and LLM response quality.
FOOTER
The easiest way to extract
data from the web
data from the web
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