axiomdoc 1.0.1

Open-source document intelligence for extraction, structure preservation, XML export, and RAG indexing.

AXIOMDoc

AXIOM stands for Any-document eXtraction, Indexing, and Ontology Mapping.

AXIOMDoc is an open-source Python library for document intelligence in RAG pipelines. It is being built to ingest heterogeneous documents, preserve structure, export canonical XML and Markdown, and generate retrieval-ready indexing artifacts with provenance.

What AXIOMDoc is for

• Converting PDFs, XML, DOCX, DOC, XLSX, HTML, and related formats into one canonical document model.
• Preserving headings, reading order, page anchors, metadata, and layout evidence.
• Falling back to OCR for image-only PDFs when text extraction is unavailable.
• Exporting clean XML and Markdown representations for downstream processing.
• Building chunk, section, and field-level artifacts for retrieval and context mapping.

Core requirements

1. Any-document ingestion across common enterprise and knowledge-document formats.
2. Structure fidelity so headings are not missed and body text is not promoted into headings.
3. Canonical export into XML, Markdown, JSON, and retrieval artifacts from one internal schema.
4. RAG-first indexing with chunk provenance, section paths, and page references.
5. XML-safe serialization that strips characters invalid under XML 1.0.

Architecture

AXIOMDoc follows a canonical-document-model approach:

• Parser backends normalize source files into one schema.
• Exporters transform that schema into XML, Markdown, and other artifacts.
• Index builders create retrieval-ready records with explicit provenance.
• Enrichment passes can later add headings, entities, forms, tables, and citation anchors.

This keeps parsing separate from retrieval and avoids binding the project to one vendor model or one OCR stack.

Current package layout

src/axiomdoc/
  cli.py
  pipeline.py
  models.py
  indexing.py
  exporters/
    xml.py
    markdown.py
  parsers/
    base.py
    docx.py
    registry.py
    pdf.py
    xlsx.py
    xml.py
tests/
  test_exporters.py
  test_parsers.py
.github/
  workflows/tests.yml
docs/
  architecture.md
assets/
  axiomdoc-logo.svg

Install

python3 -m pip install -e .

Full parser and test dependencies:

python3 -m pip install -e ".[full,dev]"

Example

axiomdoc parse ./sample.pdf --xml-out ./sample.xml --markdown-out ./sample.md --index-out ./sample.index.json

Evaluation

The comparison above is now populated from a runnable benchmark harness in benchmarks/run_benchmarks.py. This is still an operational benchmark, not a full scientific benchmark with human labels, so the metrics are limited to things we can measure honestly and reproduce today.

Comparison set used in this run:

• AXIOMDoc
• Docling
• PyMuPDF raw extraction baseline
• pdfplumber

Public PDF corpus used in this run:

• attention-is-all-you-need.pdf
• orimi-test.pdf
• w3c-dummy.pdf

Measured results from the current run:

Library	Success Rate	Median Sec/Page	XML Well-Formed Rate	Median Heading Count	Median Markdown Chars	Median Chunk Count
AXIOMDoc	1.00	0.0112	1.00	1	386	3
Docling	1.00	1.6584	1.00	1	386	0
PyMuPDF raw	1.00	0.0015	1.00	0	390	0
pdfplumber	1.00	0.0156	1.00	0	365	0

Interpretation:

• AXIOMDoc is substantially slower than raw PyMuPDF because it does structural classification and builds XML, Markdown, and chunk manifests.
• AXIOMDoc is much faster than Docling on this small corpus while still emitting RAG-ready chunks.
• Docling and AXIOMDoc both recovered markdown headings on the median document in this dataset.
• All evaluated libraries produced well-formed XML in this benchmark because the wrapper export path enforced XML-safe serialization.
• AXIOMDoc is the only library in this comparison currently producing a non-zero chunk manifest because the benchmark used each tool's default or near-default extraction path.

Benchmark files:

• benchmarks/results/latest.json
• benchmarks/results/docling.json

Benchmark command:

.venv/bin/python benchmarks/run_benchmarks.py --dataset-dir benchmarks/datasets/pdfs --libraries axiomdoc pymupdf_raw pdfplumber --output benchmarks/results/latest.json
.venv/bin/python benchmarks/run_benchmarks.py --dataset-dir benchmarks/datasets/pdfs --libraries docling --output benchmarks/results/docling.json

Limits of this benchmark:

• This is PDF-only right now. DOCX, XLSX, and XML are not included yet.
• Heading recovery here is markdown heading count, not labeled precision/recall.
• Markdown character count is a yield proxy, not a semantic quality score.
• The dataset is small and should be expanded before making stronger claims.

Labeled fixture evaluation is now available in benchmarks/labeled_eval.py and exercised in tests/test_hardening.py. That scorer currently measures expected heading recovery and table recovery against explicit JSON labels.

XML safety

XML does not allow certain control and surrogate characters. AXIOMDoc now sanitizes invalid XML 1.0 characters before serialization in src/axiomdoc/exporters/xml.py, so malformed text content does not break XML generation.

Release readiness

The repo now includes:

• production PDF, DOCX, XML, and XLSX parsers
• OCR fallback for image-only PDFs through the local tesseract binary
• structured table preservation in XML, Markdown, and chunk manifests
• pytest coverage for exporters, parser resolution, and PDF smoke behavior
• labeled evaluation fixtures for heading and table recovery
• a GitHub Actions test workflow at .github/workflows/tests.yml
• an MIT LICENSE

Status

The project is in late release-prep. PDF, DOCX, XLSX, and XML baseline parsing are implemented, OCR fallback exists for image-only PDFs, and labeled evaluation now covers heading/table recovery on fixtures. The remaining gaps before a strict 1.0.0 are broader labeled datasets, richer scanned-document accuracy validation, and more advanced form/table semantics. The roadmap remains in docs/architecture.md.