Office to Markdown — RAG-Ready Document Extractor

Convert PDF, DOCX, PPTX, XLSX, HTML, images, audio, and 20+ other formats into clean LLM-ready Markdown in one API call. Powered by Microsoft MarkItDown — the highest-fidelity open-source document-to-Markdown engine available.

Optimized for RAG pipelines, embedding ingestion, AI knowledge bases, and document understanding workflows where the quality of upstream chunking determines the quality of downstream retrieval.

v2.0 (2026-05-25) — Upgraded from PDF-only to multi-format. pdfUrl is kept as a legacy alias of fileUrl for backwards compatibility.

Why This Actor

pdf-parse and pdfplumber extract raw text but lose structure: headings collapse, tables stringify, lists flatten. For RAG, that means smaller retrieval precision and more hallucination downstream.

Microsoft MarkItDown preserves:

• Heading hierarchy → #, ##, ### mapped from document outline
• Tables → real Markdown tables, not pipe-broken text
• Lists → bullet and numbered list integrity
• Code blocks → fenced code fences preserved
• Image alt-text → embedded into the flow for context

For DOCX and PPTX, semantic structure (slide titles, footnotes, comments) is preserved. For images and audio, OCR / transcription fallback runs automatically.

Supported Formats

Category	Formats
Documents	PDF, DOCX, PPTX, XLSX, ODT, RTF
Web / Markup	HTML, HTM, XML, MHTML
Data	CSV, JSON, TSV
Plain	TXT, MD
Images (with OCR)	PNG, JPG, JPEG, GIF, BMP, WEBP
Audio (with transcription)	MP3, WAV, M4A
Archives	ZIP (recursive), EPUB
Others	YouTube URLs (transcript), Outlook MSG

Max file size: 100 MB per request.

Use Cases

• RAG ingestion — Convert document libraries into Markdown chunks before embedding with OpenAI / Voyage / Cohere
• AI knowledge bases — Bulk import company wikis, training material, manuals into vector DBs
• Document Q&A — Pre-process source documents for Claude / GPT structured extraction
• Compliance archival — Normalize multi-format historical records to searchable Markdown
• Migration projects — Move from SharePoint / Confluence to modern docs-as-code platforms
• LLM fine-tuning data prep — Clean Markdown corpus from heterogeneous source files

Input

Field	Type	Required	Description
fileUrl	string	✅	Direct HTTPS URL to a supported document (max 100 MB)
pdfUrl	string	—	Legacy alias for fileUrl (v1 compatibility)
includePageBreaks	boolean	—	Insert horizontal-rule between pages (PDF/PPTX). Default false
truncateChars	integer	—	Cap Markdown output at N characters. 0 = no cap (default)

{
  "fileUrl": "https://arxiv.org/pdf/2305.10601",
  "includePageBreaks": true,
  "truncateChars": 0
}

Output

One dataset item per run:

Field	Type	Description
fileUrl	string	Source URL
fileFormat	string	Detected file extension (pdf, docx, ...)
byteSize	integer	Bytes downloaded
charCount	integer	Character length of resulting Markdown
wordCount	integer	Whitespace-tokenized word count
markdown	string	Final cleaned Markdown
disclaimer	string	Conversion accuracy notice
error	string	Populated only on failure

{
  "fileUrl": "https://arxiv.org/pdf/2305.10601",
  "fileFormat": "pdf",
  "byteSize": 1043820,
  "charCount": 48230,
  "wordCount": 7821,
  "markdown": "# Tree of Thoughts: Deliberate Problem Solving with Large Language Models\n\n## Abstract\n\nLanguage models are increasingly being deployed for general problem solving..."
}

Pricing

• $0.05 per document converted (event: pdf-converted)
• Charged only after successful conversion + dataset push
• No charge on download / conversion failures
• Apify platform compute usage is billed separately to users (passOnCosts enabled)

Quick Start

curl

curl -X POST "https://api.apify.com/v2/acts/ntriqpro~pdf-to-markdown/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "fileUrl": "https://arxiv.org/pdf/2305.10601",
    "includePageBreaks": true
  }'

Python (Apify Client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run = client.actor("ntriqpro/pdf-to-markdown").call(run_input={
    "fileUrl": "https://example.com/report.docx"
})
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(items[0]["markdown"])

JavaScript (Apify Client)

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('ntriqpro/pdf-to-markdown').call({
  fileUrl: 'https://example.com/slides.pptx'
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].markdown);

Limitations

Limitation	Detail
Scanned image-only PDFs	OCR is applied but accuracy depends on scan quality
Encrypted / password-protected files	Not supported
Files > 100 MB	Hard-rejected to protect compute cost
Non-Latin scripts (CJK, Arabic, etc.)	Supported but proofreading recommended for production
Streaming sources (S3 signed URLs)	Supported as long as URL is HTTPS-reachable

Always validate critical extractions against the source.

Technology Stack

• Microsoft MarkItDown (MIT) — multi-format → Markdown converter
• httpx (BSD) — Async HTTP client with streaming + size cap
• Apify SDK for Python (Apache 2.0) — Actor runtime

Disclaimer

This Actor is an unofficial open-source wrapper around Microsoft MarkItDown. It is not affiliated with, sponsored by, or endorsed by Microsoft Corporation. Conversion fidelity depends on source-document structure; results are provided for informational and AI ingestion purposes only and are not a substitute for human review of critical or regulated documents.

Changelog

• 2.0 (2026-05-25) — Migrated to Python + Microsoft MarkItDown. Multi-format support (DOCX, PPTX, XLSX, HTML, images, audio, etc.). Output schema enriched with fileFormat / byteSize / charCount. pdfUrl retained as alias of fileUrl.
• 1.0 (2026-04-14) — Initial release with pdf-parse JavaScript backend (PDF only).

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