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News Article Extractor: Markdown for RAG

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Convert article URLs into clean text, Markdown and structured JSON for RAG pipelines. Extract titles, authors and dates when available, with per-URL error fields. No embeddings or vector database required.

Pricing

from $1.50 / 1,000 results

Rating

0.0

(0)

Developer

Igor Araujo

Actor stats

Bookmarked

Total users

Monthly active users

9 days ago

Last modified

News RAG Converter

An Apify Actor that fetches articles from URLs and extracts clean, structured content suitable for RAG (Retrieval-Augmented Generation) and LLM ingestion. Uses trafilatura for intelligent text extraction with a BeautifulSoup fallback.

How It Works

Fetches each article URL via HTTP
Extracts content using trafilatura (ML-based extraction) with BeautifulSoup fallback
Returns clean markdown + structured JSON (title, author, date, full text)
Optimized for RAG pipelines and LLM context windows

Input

Field	Type	Description	Default	Required
`urls`	array	List of article URLs to process (1-100)	-	Yes
`format`	string	Output format: markdown, json, or both	`both`	No

Sample Input

{
  "urls": [
    "https://example.com/article-1",
    "https://example.com/article-2"
  ],
  "format": "both"
}

Sample Output

Each item in the dataset includes:

{
  "url": "https://example.com/article-1",
  "title": "Breaking News: Major Discovery",
  "author": "Jane Doe",
  "date": "2026-07-03T12:00:00Z",
  "text_length": 4523,
  "error": "",
  "fetched_at": "2026-07-03T15:30:00Z",
  "full_text": "The full article text...",
  "markdown": "# Breaking News: Major Discovery\n\n**Author:** Jane Doe\n\n..."
}

Use Cases

Building RAG knowledge bases from news articles
LLM fine-tuning dataset preparation
Research and content aggregation
Automated news digest generation

Pricing

Pay-per-event at $0.002 per result item.

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