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v1.0.0 - Production Release: Multi-Format Engine, Adaptive Fingerprinting, FastMCP, and PyPI Distribution

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@Edge-Explorer Edge-Explorer released this 08 Sep 13:42
· 46 commits to main since this release
v1.0.0
0633fdd

Overview

universal-doc-parser v1.0.0 is the first production release of the zero-GPU, CPU-only document ingestion engine built specifically for RAG pipelines and autonomous AI agents. It provides deterministic layout preservation, sub-500ms processing latencies, strictly bounded memory consumption (< 250MB RSS), and 100% commercially permissive licensing across 15+ file formats.

What is in v1.0.0

Core Extraction Architecture

  • Unified, versioned Pydantic schema across 15+ formats (PDF, DOCX, XLSX, PPTX, HTML, EPUB, CSV, TSV, Parquet, JSON, XML, EML, MSG, MBOX, and legacy OLE binary documents).
  • Deterministic multi-column reading order preservation via geometric coordinate clustering.
  • Font-size percentile hierarchy computation (P95, P85, P75) for structured heading trees.
  • Dual-strategy table extraction (Lattice and Stream) with cell word-density filtering and table height ratio heuristics to prevent side-by-side text block merging.
  • Built-in CPU-based OCR fallback via RapidOCR and OpenCV for scanned pages and images with automatic deskew and orientation correction.

Downstream RAG and Knowledge Integration

  • Hierarchical chunking (to_chunks) with heading ancestry context breadcrumbs injected into every chunk to eliminate retrieval hallucinations.
  • Clean GitHub-Flavored Markdown generation (to_markdown).
  • Entity and relationship Knowledge Graph extraction (to_graph).

Adaptive Layout Engine

  • 2D spatial histogram discretization (10x10 grid) for content-agnostic document layout fingerprinting.
  • Thread-safe LRU template configuration cache with exact SHA-256 and fuzzy Cosine-Jaccard similarity lookups.
  • Coordinate-descent heuristic auto-tuner for optimizing extraction parameters on repeating document layouts.

FastMCP Server Integration

  • Built-in Model Context Protocol (MCP) server over standard I/O for direct tool integration with Claude Desktop, Cursor, and AI agents (parse_document, list_supported_formats).

Observability and Telemetry

  • Real-time ingestion telemetry metrics tracking duration, page volume, element counts, and anomaly signals.
  • Standalone self-contained HTML telemetry and drift dashboard export.

Performance and Multi-Model Benchmarks

  • Verified < 250MB RSS memory ceiling across 1,000+ page streaming document tests.
  • Comprehensive multi-model benchmark suite profiling local CPU parsing against 15 frontier and open-weight LLMs (Google Gemini, OpenAI GPT-4o, Anthropic Claude, DeepSeek, Alibaba Qwen, Meta Llama, Mistral AI, Moonshot Kimi, Zhipu GLM, and Cohere).

Installation

Via PyPI

pip install universal-doc-parser
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