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NexGenData
NexGenData

Posted on • Originally published at thenextgennexus.com

New: News & Announcements to Markdown for RAG — clean, chunked Markdown from news and press releases for LLM pipelines

What it does

This actor converts press releases, corporate announcements, and news articles into clean, chunked Markdown ready to drop into a RAG or LLM pipeline. Give it article URLs or RSS feeds and it strips boilerplate, keeps the substance, and segments the text into retrieval-friendly chunks. No login required.

Who it's for

AI engineers and data teams building retrieval-augmented generation systems, news monitors, or LLM workflows that need clean source text instead of messy HTML.

Sample fields / output

  • Title
  • Cleaned Markdown body
  • Chunked text segments
  • Source URL
  • Publish date
  • Author / source
  • RSS feed item metadata

Example use cases

  • Build a RAG knowledge base from a set of newswire or RSS sources.
  • Feed corporate announcements into an LLM summarizer or classifier.
  • Normalize multi-source news into a single clean Markdown schema.

-> Run News & Announcements to Markdown for RAG on Apify

Related actors

FAQ

What input does it take?

Article URLs or RSS feed URLs.

Why Markdown chunks?

Chunked Markdown drops straight into vector stores and LLM context windows without extra cleanup.

Does it need credentials?

No login -- point it at public article or feed URLs.

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