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Build a chatbot knowledge base from a help center

Crawl a help center or FAQ site into chunked Markdown ready for a support chatbot or RAG knowledge base.

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Web Crawler: Website to Markdown for LLM & RAG
Web Crawler: Website to Markdown for LLM & RAGscrape-lads/website-content-crawler-rag
URL
Title
Description
Language
+6 fields
Text
Number
Boolean
List
Object

Input

Start URLs(required)
url:https://help.apify.com/en/
Max pages:100
Max crawl depth:5
Crawl scope:path
Include URLs (globs)
Exclude URLs (globs)
Crawler type:auto
Output formats:markdown
Remove boilerplate:true
Chunk size (characters):1000
Chunk overlap (characters):200
Discover pages from sitemaps:false
Respect robots.txt:true
Max concurrency:10

Output fields

URL
Title
Description
Language
Words
Links
Depth
HTTP status
Rendered with
Crawled at

How it works

Sign up on Apify or log in

The Web Crawler: Website to Markdown for LLM & RAG Actor is ready to run with the inputs already set for "Build a chatbot knowledge base from a help center". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Build a chatbot knowledge base from a help center" task

The Web Crawler: Website to Markdown for LLM & RAG Actor will start running based on the input automatically. You can also change all the input fields, like start urls, max pages, and more, to match your specific use case.

Receive the "Build a chatbot knowledge base from a help center" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as URL, title, description, and more.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Build a chatbot knowledge base from a help center" task via API.

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Integrate "Build a chatbot knowledge base from a help center" into your workflow

Connect "Build a chatbot knowledge base from a help center" with 100+ integrations, or run it via API or MCP.

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