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Website RAG Knowledge Builder

Pricing

from $20.00 / 1,000 processed knowledge pages

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Website RAG Knowledge Builder

Website RAG Knowledge Builder

Crawl public website pages and build clean RAG-ready knowledge records with page summaries, key facts, FAQs, links, and retrieval chunks.

Pricing

from $20.00 / 1,000 processed knowledge pages

Rating

0.0

(0)

Developer

Ushba Khan

Ushba Khan

Maintained by Community

Actor stats

0

Bookmarked

2

Total users

1

Monthly active users

3 days ago

Last modified

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This actor turns public websites into clean, RAG-ready knowledge records for AI assistants, support bots, search, documentation QA, and content audits.

It crawls public internal pages, removes noisy layout text, extracts summaries, key facts, FAQ candidates, useful internal links, and retrieval chunks.

What it is useful for

  • Use it to prepare website knowledge before embedding, vector indexing, chatbot ingestion, or documentation review.
  • Building cleaner research exports from messy public web surfaces.
  • Prioritizing prospects, pages, communities, or knowledge sources before manual review.
  • Feeding structured records into spreadsheets, CRMs, dashboards, or automation workflows.

Input

Provide one or more values in startUrls.

{
"startUrls": [
"https://docs.apify.com/platform/actors"
],
"maxItems": 8,
"requestTimeoutSecs": 25,
"maxConcurrency": 3
}

Output you get

The dataset is intentionally compact and buyer-facing. Important fields include:

  • sourceWebsite
  • pageUrl
  • pageTitle
  • knowledgeSummary
  • keyFacts
  • faqCandidates
  • internalLinks
  • ragChunks.chunkId
  • ragChunks.text

Practical workflow

  1. Start with a small list of searches or URLs.
  2. Review the first dataset rows and confirm the signals match your market.
  3. Increase the input list for larger research batches.
  4. Export the dataset to CSV, JSON, Google Sheets, or your automation pipeline.

Notes and limitations

  • The actor uses public web data and does not bypass login walls, private content, CAPTCHA, or protected dashboards.
  • Public search snippets and pages can change, so results should be treated as research signals rather than legal, financial, or valuation advice.
  • The output avoids raw HTML and noisy debug fields so buyers can act on the data immediately.