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NameetP/pdfmux

v1.6.0 Feature

This release adds 12 notable features for engineering teams evaluating rollout.

Published 1mo Developer Productivity
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

ai-agent docling document-parsing llm mcp ocr
+8 more
opendataloader pdf pdf-extraction pdf-to-json pdf-to-markdown python self-healing structured-extraction

Summary

AI summary

New extraction backends, Arabic/RTL support, caching/streaming tools, and DX improvements added.

Full changelog

Highlights

Extraction backends

  • Mistral OCR ($0.002/page) and Marker neural extractor as optional backends
  • Gemma 4 27B IT as a vision provider via GeminiAPI (reuses GEMINI_API_KEY) with native Arabic OCR

Arabic / RTL

  • BiDi post-processing (markdown-aware: preserves headings, lists, code fences)
  • Arabic detection in the classifier; arabic page type wired into the routing matrix

Caching & streaming

  • Smart result cache keyed by file hash + (quality, format, schema). 30d TTL, 1GB LRU.
  • pdfmux stream and extract_streaming MCP tool — NDJSON events as pages complete

DX

  • pdfmux profiles list/show/save/delete and --profile flag (built-ins: invoices, receipts, papers, contracts, bulk-rag)
  • pdfmux watch <dir> — auto-convert on change
  • pdfmux estimate — predict cost before running
  • pdfmux diff a.pdf b.pdf — extraction comparison
  • Better error messages: .user_message, .suggestion, .reproduce_cmd
  • @with_retry (exponential backoff, honors Retry-After) on every LLM provider

Tests: 659 passing (up from 481).

Install: pip install -U pdfmux

View diff on GitHub

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About NameetP/pdfmux

PDF extraction router with built-in MCP server. Classifies each page (digital, scanned, tables) and routes to the best backend (PyMuPDF, Docling, OCR, or optional LLM fallback)

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Related context

Beta — feedback welcome: [email protected]