The workspace-level Cargo.lock is checked into version control
for reproducible builds. All Argo build steps enforce --locked
--frozen to ensure dependency versions match exactly.
This commit includes lockfile updates for new dependencies
(lzw, memchr) added during development.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Changes:
- Use pdftract-test-glibc:1.78 image (has aws/b2 CLI preinstalled)
- Use b2-readonly secret instead of armor-secrets
- Update env var names to ARMOR_ACCESS_KEY_ID/ARMOR_SECRET_ACCESS_KEY
- Remove apt-get install step (tools already in image)
The cer-diff tool was already implemented in a previous commit.
This commit fixes the image and secret references per the bead spec.
References pdftract-2t9 acceptance criteria:
- regression-corpus step runs on every PR (✓ already in workflow)
- Uses pdftract-test-glibc:1.78 image (✓ fixed)
- Uses b2-readonly secret (✓ fixed)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implement the document catalog parser (/Root traversal) for PDF documents.
The catalog parser extracts all key entries from the document catalog
including Pages, Outlines, MarkInfo, StructTreeRoot, AcroForm, Names,
Metadata, PageLabels, OCProperties, OpenAction, AA, and Version.
Key structures:
- MarkInfo: parses /MarkInfo dictionary with is_tagged, user_properties, suspects
- PageLabelStyle: enum for all label styles (D, R, r, A, a)
- PageLabel: single page label with style, prefix, and start value
- PageLabelsTree: number tree parser for /PageLabels with /Nums and /Kids support
- OcProperties: stub for OCG implementation (delegated to dedicated bead)
- Catalog: main catalog struct with all required and optional fields
Number tree implementation:
- Parses /Nums arrays (leaf nodes with alternating key-value pairs)
- Supports /Kids arrays (internal nodes for recursive tree traversal)
- Provides get_label_with_start() and get_label() methods for lookup
- Correctly formats roman numerals (uppercase/lowercase) and letter sequences
All 27 tests pass including proptests for fuzzing robustness (INV-8).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Create tests/fixtures/classifier/ with 200 synthetic PDFs:
- 50 invoices with bill-to/ship-to, item tables, totals
- 50 scientific papers with abstracts, sections, references
- 50 contracts with clauses, legal terminology, signatures
- 50 misc documents (8 receipts, 8 forms, 7 bank statements,
7 slide decks, 7 legal filings, 6 book excerpts, 7 magazines)
- Add MANIFEST.tsv mapping each document to its expected type
with source URL and license (all MIT-0 synthetic data)
- Add scripts/generate_test_corpus.py to regenerate the corpus
using reportlab for PDF generation
- Add tests/test_classifier_corpus.rs with validation harness:
- test_corpus_manifest_validity: verifies manifest structure
and file existence (PASSES)
- test_classifier_corpus_accuracy: will validate precision/
recall/F1 when classifier is implemented (SKIP for now)
- test_classifier_reproducibility: will verify deterministic
classification (SKIP for now)
- Add tests/fixtures/classifier/README.md documenting corpus
structure, generation process, and acceptance criteria
Total corpus size: ~0.4 MB (each PDF < 5 KB)
Acceptance criteria (from plan.md Phase 5.6):
- Per-class precision and recall >= 0.85
- Macro-F1 >= 0.88
- Reproducibility: identical output for same document
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
