Introduce consumer-driven contract testing between nextcloud-mcp-server and the
astrolabe Nextcloud app, published to the homelab Pact Broker and verified in CI.
- pact-python dev dep + `contract` pytest marker
- tests/contract/test_astrolabe_credentials_consumer.py: consumer pact for the
background-sync *status* call (provisioned -> has_background_access:true,
sync_type:"app_password", integer provisioned_at; unprovisioned -> false/null)
- tests/contract/test_mcp_provider_verification.py: env-gated Verifier harness
for this server's /api/v1/* provider role (provider-state handlers stubbed
pending astrolabe's published pacts)
- .github/workflows/pact.yml: join tailnet -> publish pacts -> provider verify
-> can-i-deploy; broker steps skip when PACT_BROKER is unset (forks)
- docs/ADR-029-pact-contract-testing.md
Fix astrolabe_client.get_background_sync_status: it previously read a
non-existent `app_password` field (always reporting no-access). Rewrite it to
read the real status contract (has_background_access / sync_type /
provisioned_at) and drop the unsatisfiable get_user_app_password.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces single-engine pymupdf4llm extraction with a tiered pipeline (Deck #205,
follows the tier-0 classifier #855). pypdfium2 becomes the default and only
hot-path PDF extractor; pymupdf4llm is deprecated to a rollback toggle.
Why: pymupdf4llm's O(n^2) find_tables drove the OOM (#852) and the form-PDF
parse timeouts (#856), carries AGPL/commercial licensing liability, and -- per
the benchmarks -- recovers near-zero usable tables on the real corpus. pypdfium2
(Apache/BSD) extracts the same text far faster (Student 1a.pdf: 120s timeout ->
0.2s) with no table-detection bomb.
- document_processors/pypdfium2_fast.py: tier-1 "fast" processor emitting text +
exact page_boundaries (the pdf_highlighter contract). pymupdf processor is now
tier "structured" (the rollback engine), registered but not default.
- registry: tiered routing in ProcessorRegistry. tier-1 fast extracts, then
classification is DERIVED from that text (classifier.classify_from_text -- no
PDF re-open), records the classification metrics, and escalates scanned /
no-text-layer docs to the "ocr" tier when document_ocr_enabled (default off;
no provider yet, so fast is terminal). Wires record_document_escalation + the
real "escalated" span attribute (was hardcoded False).
- Removes the separate _shadow_classify pass from vector/processor.py -- it
re-opened every PDF and re-extracted text (~0.5-1.3s/doc of pure duplicated
CPU that lowered throughput); classification now rides the tier-1 extraction.
- Settings: document_tier1_engine ("pypdfium2" default | "pymupdf" rollback,
enum-validated), document_ocr_enabled (default false).
Tests: pypdfium2 extractor, registry tiering (fast routing, rollback, classify
recording, OCR escalation on/off), classify_from_text. Full unit suite green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
github-actions[bot]