Implement the conformance test runner pattern that every SDK will
implement to validate against the shared test suite.
- Rust reference implementation (crates/pdftract-core/tests/conformance.rs)
* Full test suite loader and executor
* Comparison engine with min/max, string constraints, tolerances
* Skip logic for unsupported features and schema versions
* Report generation in JSON format
- CLI compare subcommand (crates/pdftract-cli/src/main.rs)
* pdftract compare - Compare actual vs expected with tolerances
* Cross-language comparison tool to avoid reimplementations
- Documentation (docs/conformance/sdk-contract.md)
* Complete pattern specification with pseudocode
* Per-language runner locations
* CI integration requirements
- Python reference stub (tests/python-conformance/test_conformance.py)
* Full pytest-based implementation following the pattern
Closes: pdftract-5omc
Add tests/sdk-conformance/ containing the shared, language-neutral test
specification for all pdftract SDKs. The suite includes 32 cases covering
all 9 contract methods (extract, extract_text, extract_markdown,
extract_stream, search, get_metadata, hash, classify, verify_receipt)
across vector, scanned, encrypted, fillable-form, mixed, large, broken,
and remote PDFs.
- cases.json: 32 test cases with id, fixture, method, options, expected,
tolerances, feature tags, and min_schema_version
- schema.json: JSON Schema v7 draft for validating test case structure
- validate_suite.py: Validation script that checks structure and fixture
existence
- fixtures/: Test PDFs organized by category (symlinks to classifier
fixtures for shared files)
See notes/pdftract-1527.md for verification details.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add pre-commit hook that runs check-provenance.sh before each commit
to ensure fixture files always have valid provenance entries. Update
PROVENANCE.md with validation section documenting the hook usage.
Acceptance criteria:
- PROVENANCE.md exists with one row per fixture file ✓
- Every fixture file enumerated; no orphans ✓
- License column populated; only approved licenses ✓
- SHA256 column populated; matches actual content ✓
- check-provenance.sh validates manifest; CI gate green ✓
- Synthetic fixtures point at generation scripts ✓
Refs: pdftract-5z5d8
Co-Authored-By: Claude Code <noreply@anthropic.com>
Fixed scripts/check-provenance.sh to properly validate PROVENANCE.md
against actual fixture files. The script was failing silently due to
subshell EXIT trap removing temp files before parent could read them,
and arithmetic expansion returning exit code 1 on zero value.
Changes:
- Replaced subshell pipes with process substitution
- Moved temp file cleanup to after reading
- Added validated variable initialization
- Added || true to prevent exit on zero arithmetic
All 200 classifier corpus fixtures have valid provenance entries
with matching SHA256 hashes. PROVENANCE.md already existed with
complete documentation.
Refs: pdftract-5z5d8
Co-Authored-By: Claude Code <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>
