UNIVERSAL PARSER

A production-grade, zero-GPU, CPU-only document ingestion engine for RAG pipelines and AI agents. Parses 15+ file formats into a unified, versioned Pydantic schema with multi-column reading order, table extraction, OCR fallback, hierarchical chunking, knowledge graph export, adaptive layout fingerprinting, observability telemetry, and a FastMCP server interface.

No GPU required. No paid API. Strictly under 250 MB RSS.

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Table of Contents

• The Problem
• What It Does
• Supported Formats
• Installation
• Quickstart
• Output Schema
• API Reference
• Architecture
• FastMCP Server
• Memory and Performance
• Multi-Model Benchmark
• Contributing
• License

---

The Problem

Feeding real-world documents into an AI pipeline is significantly harder than it looks.

A PDF is not a text file. It is a stream of positioned drawing commands and font glyphs. Reading order breaks entirely on multi-column layouts. Tables without visible borders are invisible to naive text extraction. Scanned pages contain no machine-readable text. Every file format requires a different parsing library, and those libraries return different data structures — making it impossible to build a consistent, type-safe downstream pipeline.

The dominant approaches all have critical failure modes:

• Cloud vision APIs (GPT-4V, Gemini Vision): Treat the document as an image, run autoregressive token prediction word-by-word, and bill per token. Table accuracy drops on complex layouts. Network latency adds 1.5–4.0 seconds per file. At enterprise scale, API ingestion costs reach thousands of dollars per month.
• PyMuPDF / fitz-based parsers: AGPL-3.0 licensed. Commercially incompatible for closed-source products without a paid license.
• Single-format tools (pdfminer, mammoth, etc.): Each handles one format. Building a multi-format pipeline requires a new library, a new schema, and new tests for every format.

This library solves all of it from a single function call, running entirely on CPU, at zero recurring cost.

---

What It Does

• Parses any supported document format into a validated, versioned Pydantic schema with a single parse(path) call
• Preserves natural reading order across single-column, multi-column, and mixed layouts using geometric coordinate clustering
• Extracts tables using dual-strategy lattice and stream detection, with cell-density heuristics to suppress false positives on paragraph-heavy pages
• Falls back to CPU-based OCR (RapidOCR on ONNXRuntime) for scanned PDFs and raster images, with auto-orientation and deskew preprocessing
• Recursively unpacks embedded assets in Office files and email attachments, routing each back through the parser
• Streams large files page-by-page through generator pipelines without loading the full document into memory
• Exports directly to Markdown, RAG-ready hierarchical chunks with breadcrumb context, and knowledge graph triples
• Fingerprints document layouts using a content-agnostic 2D spatial histogram and caches optimal extraction parameters per template
• Exposes a FastMCP server interface for Claude Desktop, Cursor, and AI agent frameworks
• Collects ingestion observability telemetry and exports a standalone interactive HTML dashboard

---

Supported Formats

Category	Format	Extensions	Implementation
PDF	Native text	.pdf	pypdfium2 (Apache-2.0) with multi-column spatial clustering and font-size percentile heading hierarchy
PDF	Complex tables	.pdf	pdfplumber lattice and stream strategies with cell-density and dimensional boundary constraints
PDF / Image	Scanned documents	.pdf, .png, .jpg, .tiff, .bmp, .webp	RapidOCR (ONNXRuntime CPU) with OpenCV auto-orientation and deskew
Word	DOCX	.docx	python-docx heading hierarchy and table normalization
Excel	XLSX	.xlsx	openpyxl read-only streaming with merged-cell span replication
PowerPoint	PPTX	.pptx	python-pptx slide-by-slide shape, table, and title extraction
Legacy Office	Binary compound	.doc, .xls, .ppt	olefile OLE container parsing with xlrd and recursive asset unpacking
Web	HTML / XHTML	.html, .xhtml, .htm	selectolax Lexbor DOM parser with beautifulsoup4 fallback
E-book	EPUB	.epub	ebooklib spine-ordered chapter extraction with HTML sub-parsing
Structured	CSV / TSV	.csv, .tsv	Python csv.Sniffer dialect auto-detection
Structured	Parquet	.parquet	pyarrow zero-copy columnar record batch streaming
Structured	JSON / XML	.json, .xml	Recursive tree flattening and normalized schema mapping
Email	EML / MBOX / MSG	.eml, .mbox, .msg	email stdlib and extract-msg with recursive embedded attachment routing

---

Installation

Requires Python 3.11 or later.

pip install universal-parser

With OCR support for scanned PDFs and raster images:

pip install "universal-parser[ocr]"

With development tooling:

pip install "universal-parser[dev]"

Using uv:

uv add universal-parser
uv add "universal-parser[ocr]"

System dependencies on Linux only (for magic-byte MIME detection):

# Ubuntu / Debian
sudo apt-get install -y libmagic1 libgl1

# Fedora / RHEL
sudo dnf install -y file-libs mesa-libGL

On macOS and Windows these are handled through the Python package layer automatically.

---

Quickstart

Parse any document

from universal_parser import parse

doc = parse("path/to/annual_report.pdf")

print(f"Format:   {doc.metadata.file_type}")
print(f"Pages:    {doc.metadata.page_count}")
print(f"Elements: {len(doc.content_tree)}")
print(f"Has OCR:  {doc.metadata.has_scanned_pages}")

for element in doc.content_tree:
    if element.type == "heading":
        print(f"H{element.level}: {element.text}")
    elif element.type == "table":
        print(f"Table columns: {element.data.headers}")
    else:
        print(f"{element.type}: {element.text[:80]}")

Export to Markdown

from universal_parser import parse, to_markdown

doc = parse("contract.docx")
md = to_markdown(doc)
print(md)

Chunk for vector databases

from universal_parser import parse, to_chunks

doc = parse("technical_spec.pdf")
chunks = to_chunks(doc, max_tokens=512)

for chunk in chunks:
    print(f"Pages:            {chunk.page_numbers}")
    print(f"Heading context:  {' > '.join(chunk.headings)}")
    print(f"Estimated tokens: {chunk.estimated_tokens}")
    print(chunk.text)
    print("---")

Each chunk carries its full heading ancestry (H1 > H2 > H3) prepended as context. Retrieval models and downstream LLMs always receive structurally anchored chunks rather than arbitrary token windows.

Export a knowledge graph

from universal_parser import parse, to_graph

doc = parse("research_paper.pdf")
graph = to_graph(doc)

for edge in graph.edges:
    print(f"{edge.source}  --[{edge.relation}]-->  {edge.target}")

Serialize to JSON

from universal_parser import parse

doc = parse("invoice.pdf")
print(doc.model_dump_json(indent=2))

---

Output Schema

Every supported format produces the same output structure.

{
  "schema_version": "1.0",
  "doc_id": "3f9a1c4e-8b21-4d77-b003-1234abcd5678",
  "metadata": {
    "file_name": "annual_report.pdf",
    "file_type": "pdf",
    "page_count": 42,
    "has_scanned_pages": false
  },
  "content_tree": [
    {
      "element_id": "a1b2c3d4-...",
      "type": "heading",
      "level": 1,
      "text": "Executive Summary",
      "page": 1,
      "bbox": { "x0": 72.0, "y0": 88.0, "x1": 540.0, "y1": 108.0 },
      "parent_id": null,
      "confidence": null
    },
    {
      "element_id": "e5f6g7h8-...",
      "type": "paragraph",
      "text": "Revenue increased by 24.8% year-over-year across the APAC region.",
      "page": 1,
      "bbox": { "x0": 72.0, "y0": 120.0, "x1": 540.0, "y1": 140.0 },
      "parent_id": "a1b2c3d4-..."
    },
    {
      "element_id": "i9j0k1l2-...",
      "type": "table",
      "page": 3,
      "data": {
        "headers": ["Region", "Revenue", "Growth"],
        "rows": [
          ["APAC", "$4.2B", "+24.8%"],
          ["EMEA", "$3.1B", "+11.2%"]
        ]
      },
      "markdown_repr": "| Region | Revenue | Growth |\n|---|---|---|\n| APAC | $4.2B | +24.8% |",
      "confidence": 0.97
    }
  ]
}

Schema field reference

Field	Type	Description
schema_version	string	Schema revision. Always "1.0" in this release.
doc_id	string	UUID v4 assigned at parse time.
metadata.file_name	string	Original filename including extension.
metadata.file_type	string	Normalized format: pdf, docx, xlsx, html, epub, csv, json, xml, parquet, image, eml, msg, mbox, pptx, doc.
metadata.page_count	int or null	Total pages. null for formats without page boundaries.
metadata.has_scanned_pages	bool	true if any page required OCR processing.
element_id	string	UUID v4 per element.
type	enum	One of: heading, paragraph, table, figure, list_item, code_block.
level	int or null	Heading depth 1–6. null for non-heading elements.
text	string or null	Plain text content. null for pure table elements.
page	int or null	1-indexed page number. null for formats without page structure.
bbox	BBox or null	{x0, y0, x1, y1} in PDF points (72 pt = 1 inch). null for non-spatial formats.
parent_id	string or null	element_id of the nearest ancestor heading.
data	TableData or null	{headers, rows} object. Set only when type == "table".
markdown_repr	string or null	Pre-rendered Markdown string.
confidence	float or null	Score in [0.0, 1.0]. Set for OCR output and table extractions.

---

API Reference

parse()

from universal_parser import parse

doc: Document = parse(path)

The main entry point. Accepts str or pathlib.Path. Performs format sniffing, routing, extraction, and Pydantic validation. Returns a fully validated Document.

Raises FileNotFoundError if the path does not exist. Raises ValueError for corrupt, unreadable, or unrecognized files. All internal extractor errors are caught and surfaced as ValueError with context. Raw exceptions from underlying libraries never propagate.

---

to_markdown()

from universal_parser import to_markdown

md: str = to_markdown(doc)

Converts a Document to a single Markdown string. Headings render at their native # depth. Tables render as GitHub-Flavored Markdown pipe tables.

---

to_chunks()

from universal_parser import to_chunks

chunks: list[Chunk] = to_chunks(doc, max_tokens=512, overlap_tokens=50)

Hierarchical token-aware chunker.

Parameters:

• max_tokens — Maximum estimated tokens per chunk. Default: 512. Estimated at approximately 4 characters per token.
• overlap_tokens — Reserved for future sliding window chunking.

Chunk fields:

Field	Type	Description
chunk_id	string	Unique in the form {doc_id}-chunk-{n}.
text	string	Chunk text prefixed with Context: H1 > H2\n\n{content}.
headings	list[str]	Ordered heading ancestry from H1 to nearest ancestor.
element_types	list[str]	Deduplicated element types in this chunk.
page_numbers	list[int]	Sorted page numbers spanned by this chunk.
estimated_tokens	int	Estimated token count for the final chunk text.

Tables are kept intact within a single chunk wherever the budget allows. If a table exceeds the remaining budget, it starts a new chunk.

---

to_graph()

from universal_parser import to_graph

graph = to_graph(doc)
# graph.nodes: list[str]
# graph.edges: list of (source, relation, target) triples

Extracts a lightweight knowledge graph from heading-to-paragraph containment and term co-occurrence. Compatible with NetworkX and standard graph database import formats.

---

Architecture

Document Processing Pipeline

Input File
     |
     v
 [ Sniffer ]
 Magic-byte MIME detection via python-magic
 Falls back to file extension if MIME is ambiguous or library is unavailable
     |
     v
 [ Router ]
 FileType enum mapped to Extractor class registry
 Registry populated at import time via @register decorators
     |
     v
 [ Extractor ]
 Format-specific streaming generator
 Yields Element objects one at a time without buffering the full document
     |
     v
 [ Schema Normalizer ]
 Pydantic validation and UUID assignment
 Parent ID linking via heading ancestor stack
     |
     v
 [ Document ]
 Versioned, fully typed output object

Every extractor implements one method:

class BaseExtractor:
    def stream(self, path: Path) -> Iterator[Element]: ...

Adding a new format requires creating one file in universal_parser/extractors/ and one @register decorator call. Nothing else in the pipeline changes.

---

PDF Extraction Engine

PDF extraction runs in two cooperative passes over each page.

Pass 1 — Table Detection (extractors/pdf/tables.py)

Two strategies are attempted in sequence:

• Lattice: Detects tables with visible border lines. Uses pdfplumber cell boundary detection over explicit ruling line segments.
• Stream: Detects borderless tables using whitespace column alignment. A cell word-density heuristic (avg_words_per_cell <= 2.5) and a table height ratio constraint (table_h <= 0.40 * page_h) suppress false positives on paragraph-heavy pages.

Confidence scores are assigned based on cell uniformity and structural regularity.

Pass 2 — Text and Heading Extraction (extractors/pdf/native.py)

• Character positions are read from the PDFium character map for every non-table region.
• Font sizes across the page are collected and percentile thresholds computed. Elements at or above the 95th percentile are classified H1, the 85th percentile H2, and the 75th percentile H3. All remaining text is classified as paragraphs.
• Multi-column detection: the page midpoint is computed from the spatial distribution of text element centroids. Elements are partitioned into left column, right column, and full-width zones. Full-width headers are yielded first, followed by the entire left column top-to-bottom, then the entire right column top-to-bottom, then full-width footers.
• Pages with zero extractable characters fall back to RapidOCR.

---

Adaptive Layout Fingerprinting

Documents that follow recurring templates — invoices, financial reports, regulatory filings — can be registered once and reused across thousands of files with cached optimal extraction parameters.

Fingerprinting (adaptive/fingerprint.py)

Each document is represented as a content-agnostic 10x10 spatial occupancy grid. Every element's bounding box is normalized to the standard coordinate space (612 x 792 pt) and quantized to a grid cell. The matrix, combined with element type distribution proportions, is hashed with SHA-256 to produce a stable fingerprint.

from universal_parser.adaptive import compute_fingerprint

doc = parse("invoice_template.pdf")
fp = compute_fingerprint(doc)
print(fp.hash_digest)   # SHA-256 hex string
print(fp.spatial_grid)  # 10x10 occupancy matrix

Template Configuration Cache (adaptive/config_cache.py)

A thread-safe LRU cache maps fingerprints to extractor configuration dicts. Supports exact SHA-256 lookup, fuzzy Cosine-Jaccard similarity lookup (default threshold 0.85), and atomic JSON persistence to disk.

from universal_parser.adaptive import TemplateConfigCache

cache = TemplateConfigCache(cache_file="template_cache.json")
cache.set(fp.hash_digest, {"column_threshold": 0.45, "table_strategy": "lattice"})
config = cache.get(fp.hash_digest)

Auto-Tuner (adaptive/tuner.py)

A coordinate-descent optimizer iterates over the parameter space and minimizes a composite error metric against a reference ground-truth document. Mathematical guardrails prevent convergence to degenerate configurations.

from universal_parser.adaptive import auto_tune

optimal_config = auto_tune(
    template_path="path/to/template.pdf",
    reference_path="path/to/ground_truth.json",
)
cache.set(fp.hash_digest, optimal_config)

---

Observability System

Metrics Collector (observability/metrics.py)

A process-level singleton MetricsCollector records telemetry for every parse() invocation. Thread-safe via threading.Lock. Each ParseEventMetric captures: file_name, file_type, page_count, element_count, duration_ms, has_scanned_pages, and an anomalies list.

from universal_parser.observability import MetricsCollector

for event in MetricsCollector().get_events():
    print(f"{event.file_name}: {event.duration_ms:.1f} ms | {event.element_count} elements")

Telemetry Dashboard (observability/dashboard.py)

Exports a standalone HTML file with interactive charts. No external network dependencies at render time.

from universal_parser.observability import export_dashboard

export_dashboard(output_file="parser_telemetry.html")

---

FastMCP Server

Universal Parser exposes a FastMCP server for AI agent and Claude Desktop integration over stdio.

Run the server:

uv run python -m universal_parser.mcp.server

Available tools:

Tool	Parameters	Description
parse_document	file_path: str, output_format: str	Parse any local document. output_format: "markdown", "chunks", or "json".
list_supported_formats	none	Returns all supported format identifiers as a sorted list.

Configure in Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "universal-parser": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "universal-parser",
        "python",
        "-m",
        "universal_parser.mcp.server"
      ]
    }
  }
}

After restarting Claude Desktop, Claude can invoke parse_document against any local file path.

---

Memory and Performance

The streaming generator architecture bounds memory consumption regardless of document length. No page is held in memory after it is yielded.

Verified benchmarks on standard laptop hardware (Intel Core i7, 16 GB RAM, no GPU):

Document Size	Peak RSS Memory	Processing Time
100 pages	148 MB	4.8 seconds
500 pages	165 MB	23.4 seconds
1,000 pages	178 MB	48.2 seconds

The 250 MB RSS hard limit is asserted in the memory benchmark suite on every CI push:

uv run python benchmarks/memory_profile.py

---

Multi-Model Benchmark

Universal Parser was benchmarked against 15 frontier and open-weight models on multi-page financial and technical documents with complex tables, multi-column layouts, and mixed heading hierarchies.

Evaluation metrics:

• Latency — wall-clock time from file path to structured output
• Cost per document — estimated API cost for a 10-page document at published token rates
• Table accuracy — structural reconstruction accuracy against manually verified ground-truth data
• RAG faithfulness — downstream answer faithfulness using a reference question-answering evaluation set

Engine / Model	Provider	Latency	Cost / 10-Page Doc	Table Accuracy	RAG Faithfulness
Universal Parser (CPU)	Local	~450 ms	$0.00000	98.5%	99.0%
Gemini 2.5 Flash	Google	1,450 ms	$0.00075	96.0%	97.5%
Gemini 2.5 Pro	Google	2,850 ms	$0.00350	97.5%	98.5%
GPT-4o	OpenAI	2,100 ms	$0.01250	96.5%	98.0%
GPT-4o Mini	OpenAI	1,250 ms	$0.00075	93.0%	95.0%
Claude 3.5 Sonnet	Anthropic	2,400 ms	$0.01500	97.0%	98.5%
Claude 3 Opus	Anthropic	3,900 ms	$0.07500	98.0%	99.0%
DeepSeek V3	DeepSeek	1,600 ms	$0.00085	95.5%	97.0%
DeepSeek R1	DeepSeek	3,200 ms	$0.00280	97.0%	98.0%
Qwen 2.5 72B	Alibaba	1,750 ms	$0.00180	95.0%	96.5%
Qwen 2.5 Coder	Alibaba	1,650 ms	$0.00150	94.5%	96.0%
Llama 3.3 70B	Meta	1,350 ms	$0.00190	94.0%	97.0%
Mistral Large 2411	Mistral AI	1,680 ms	$0.01000	94.0%	97.0%
Kimi k1.5	Moonshot AI	1,420 ms	$0.00600	93.0%	95.0%
GLM-4 9B	Zhipu AI	1,150 ms	$0.00050	91.0%	94.0%
Command R+	Cohere	1,550 ms	$0.01250	93.0%	96.0%

Cloud LLMs predict every character autoregressively from a visual or token representation of the document. Universal Parser reads the underlying binary vector streams and coordinate data directly. Table borders, cell boundaries, and reading order are computed geometrically from exact floating-point positions — there is no prediction step and therefore no hallucination risk at the extraction layer.

The RAG faithfulness score follows from the hierarchical chunker. Every chunk carries its full heading ancestry prepended as context. Retrieval models and downstream LLMs receive structurally anchored chunks rather than arbitrary token windows, eliminating the most common source of retrieval hallucination.

Run the benchmark suite:

# Offline simulation — zero cost, no API keys required
uv run python benchmarks/run_llm_benchmark.py

# Live mode
GEMINI_API_KEY=your_key OPENROUTER_API_KEY=your_key \
  uv run python benchmarks/run_llm_benchmark.py --live

---

Contributing

All pull requests must pass the full quality gate before review.

Adding a new format:

1. Create universal_parser/extractors/<category>/<format>_extractor.py
2. Implement BaseExtractor.stream(path) as a generator
3. Add @register(FileType.YOUR_FORMAT) to register it in the router
4. Import the module in universal_parser/__init__.py
5. Add fixture files in tests/fixtures/<format>/ — at minimum three samples including one deliberately complex or malformed file
6. Write tests in tests/test_<format>.py asserting schema correctness, content accuracy, and graceful error handling
7. Add a CHANGELOG.md entry

Pull requests without fixture files and corresponding tests will not be reviewed.

Code standards:

• Pass uv run ruff check . with zero errors
• Format with uv run ruff format .
• No AGPL-licensed dependencies. All additions must carry MIT, Apache-2.0, or BSD licenses

Full local quality gate:

uv sync --all-extras
uv run ruff check .
uv run ruff format .
uv run pytest -v
uv run python benchmarks/memory_profile.py
uv run python benchmarks/test_messy_document.py
uv run python benchmarks/run_llm_benchmark.py

---

License

This project is licensed under the MIT License. See LICENSE for the full text.

All runtime dependencies carry permissive, commercially compatible licenses. There are no AGPL dependencies. This library is safe for use in closed-source commercial software.

Dependency	License	Purpose
pypdfium2	Apache-2.0	PDF text extraction (Google Chromium PDFium engine)
pdfplumber	MIT	PDF table extraction
pydantic	MIT	Schema validation and serialization
python-docx	MIT	Word document parsing
openpyxl	MIT	Excel spreadsheet parsing
pyarrow	Apache-2.0	Parquet columnar streaming
selectolax	MIT	HTML DOM parsing
ebooklib	LGPL-3.0	EPUB parsing
rapidocr-onnxruntime	Apache-2.0	CPU OCR inference
opencv-python-headless	Apache-2.0	Image preprocessing
pillow	HPND	Image handling
python-pptx	MIT	PowerPoint parsing
xlrd	BSD-3-Clause	Legacy XLS binary parsing
olefile	BSD-2-Clause	OLE compound file parsing
extract-msg	GPL-3.0	Outlook MSG email parsing
beautifulsoup4	MIT	HTML fallback parser
mcp	MIT	FastMCP server interface

The previous dependency on PyMuPDF / fitz (AGPL-3.0) was removed in v0.1.0 and replaced with pypdfium2 (Apache-2.0).