Convert documentation pages to Markdown for RAG
Prepare documentation pages for a vector database. The task reads each page and returns its main content as Markdown with headings, tables and code blocks, plus the heading outline for chunking, a content hash that shows when a page changed, and an estimated token count. The example input uses MDN and the Python tutorial.
Input
Output fields
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Start the "Convert documentation pages to Markdown for RAG" task
The URL to Markdown API - Web Pages to Clean Text for LLMs Actor will start running based on the input automatically. You can also change all the input fields, like start urls, output formats, and more, to match your specific use case.
Receive the "Convert documentation pages to Markdown for RAG" 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, site name, 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 "Convert documentation pages to Markdown for RAG" task via API.
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Compare the contentHash field between runs of the documentation to Markdown for RAG task. It is a SHA-256 hash of the main text, so an unchanged hash means unchanged content.
No. The documentation to Markdown for RAG task returns one record per page, with a headings field listing each heading and its level, so you can chunk by section in your own pipeline.
The documentation to Markdown for RAG task asks for Markdown only through outputFormats, and formats left out are null. Add "text" or "html" to outputFormats to get them too.