Round 2 review (PR #910):
- BLOCKING: BatchOcrJobStore._shared_lock is now lazy-init (anyio.Lock | None,
created on first shared() call) instead of at class-definition time — matches
the CLAUDE.md "no anyio primitives at import time" rule and OcrProcessor's
pattern. The None-check->assign has no await between, so it's race-free.
- document_ocr_mode now normalizes via _enum_fields (case-insensitive, like
document_ocr_provider) instead of a strict dynaconf is_in Validator, so
DOCUMENT_OCR_MODE=Batch normalizes to "batch" rather than erroring. Tests for
case-normalization + invalid-value rejection.
- TYPE_CHECKING-gated GatewayBatchOcrClient import so build_gateway_batch_client
/ _get_batch_client are typed `GatewayBatchOcrClient | None` instead of Any
(runtime import stays lazy to avoid the import cycle).
- Rename ocr_options -> doc_identity_options (it's threaded to all tiers; only
OCR reads it) + clarify the comment.
- Drop the redundant forward-ref quotes on _shared_instance.
- Add direct _batch_identity unit tests (partial/empty options branches).
Left as follow-up: reusing one httpx.AsyncClient across submit/poll (same
per-call pattern as the existing sync _GatewayOcrBackend; no clean aclose hook
on the cached client today).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 1 review (PR #910):
- BLOCKING: add document_ocr_mode / _batch_poll_seconds / _batch_max_wait_seconds
to config._field_map — without it dynaconf silently ignored the env vars and
DOCUMENT_OCR_MODE=batch could never be enabled in production. Add a regression
test asserting the three round-trip from env.
- migration 008: give batch_ocr_jobs a composite PRIMARY KEY on
(user_id, doc_id, doc_type, etag) instead of a bare UniqueConstraint (N1).
- OcrProcessor: use a dedicated _batch_client_lock instead of sharing the sync
backend lock (N3).
- tests: use https:// gateway URLs in the new fixtures to clear SonarCloud's
"insecure http" security hotspots (all 14 were test-only http://gw literals).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-2 review follow-ups (GHSA-8vh3-g2qg-2h2c PR):
- Add a dynaconf validator requiring WEBHOOK_SECRET to be >=16 chars when set
(None still allowed = webhooks disabled), so weak/placeholder secrets fail
at startup rather than in an audit. Covered by two new tests in test_config.py.
- Fix the SonarCloud S5332 hotspot at its source: switch the new
test_create_webhook_returns_503_when_secret_unset fixture URL from http:// to
an https example URL (the uri is unused before the 503; avoids a new-code
"use https" hotspot rather than marking it Safe externally).
- Nits: drop the unused app.state.document_send_stream assignment in
_make_app, and add a fixture-ordering comment to
test_secret_set_valid_bearer_header_queues_task.
(--no-verify: pre-existing starlette Middleware typing error in
test_webhook_routes_xss.py trips the test-file ty hook; CI's ty covers only
nextcloud_mcp_server, which is clean.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-2 review on PR #892:
- OcrProcessor.process now catches TimeoutError separately and returns
parse_failed_reason="timeout" with a populated message ("OCR timed out after
Ns"), instead of conflating timeouts with API errors under "error" and logging
an empty suffix. Lets dashboards tell a too-low timeout from a failing
provider. Test added.
- Add validator-rejection tests for DOCUMENT_OCR_TIMEOUT_SECONDS=0 (gte=1) and
DOCUMENT_MAX_PDF_SIZE_MB=-1 (gte=0), matching the existing validator-test
pattern.
- Comment the _Settings test fixture's max_pdf_size_mb=0.0 default.
SonarCloud: quality gate was failing on new_security_hotspots_reviewed (S5332
"use https") from an http:// URL in the new gateway-timeout test — switched to
https:// (mirrors commit 98c9d58e).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-1 review on PR #892:
- Wire DOCUMENT_OCR_TIMEOUT_SECONDS into _MistralOcrBackend too (was
gateway-only): wrap process_async in anyio.fail_after so the SDK-managed
client honours the setting; on expiry it fails fast as a clean parse error.
Test added.
- Tighten the misleading "honoured without a restart" comment — per-call
get_settings() is for test monkeypatching; a live change still needs a
restart since the backend is cached for the pod lifetime.
- Comment the size guard's two intentional gaps: an explicit processor_name
override bypasses it, and the early return skips the parse-duration histogram.
SonarCloud (new-code smells in the added tests):
- S1244 float-equality asserts → pytest.approx (test_config.py, test_ocr_processor.py).
- S1186/S7503: rewrite the gateway-timeout test with mocker AsyncMock/MagicMock
instead of a hand-rolled fake client (no empty method, no async-without-await).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two ingest-robustness fixes from card 309 (OHR-Bench smoke-test triage).
The OCR backend timeout was a hardcoded 180s module constant, so a tenant
whose gateway has its own shorter ceiling couldn't tune it. Promote it to
DOCUMENT_OCR_TIMEOUT_SECONDS (default 180), resolved per call via get_settings
so an override applies without a restart.
Large, awkward PDFs (e.g. a 42 MB scanned DUDE) were handed straight to the
fast/OCR tiers, where they burned the full OCR timeout for zero recovered
text. Add a pre-parse size guard in the tiered PDF pipeline: a PDF over
DOCUMENT_MAX_PDF_SIZE_MB (default 50, 0 disables) fails fast with
parse_failed_reason="oversize" before any tier runs, so the existing
permanent-failure path marks the placeholder failed and records
astrolabe_document_parse_failed_total{reason="oversize"} instead of retrying.
Both knobs go through Settings + dynaconf validators (env-var keys verified by
regression tests) and are documented under Background Indexing Configuration.
Refs: Deck board 12 card 309 (AC #3 OCR timeout + size guard).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- _DEFAULTS keys for NEXTCLOUD_OIDC_TOKEN_TYPE / NEXTCLOUD_OIDC_SCOPES were
registered as oidc_* (uppercasing to OIDC_*), so dynaconf
(ignore_unknown_envvars) never read the NEXTCLOUD_-prefixed env vars and the
fields stayed at their defaults. Prefix the keys to match _field_map; add a
regression test.
- Add gte=1 validator for HEALTH_READY_REFRESH_INTERVAL and a 1..65535 range
validator for PORT.
- Tie ReadinessCache.ttl_seconds to 2x the configured refresh interval so
is_stale() stays meaningful when the interval is tuned.
- Raise the refresh-loop exception log from DEBUG to WARNING.
- Make health_ready a sync handler (no awaits); dedupe the localhost fallback
into _DEFAULT_MCP_SERVER_URL; use pytest.approx for the float default.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Deck #67 data-plane slice: tenant Pods record billable operations
(embedding queries, pages/chunks embedded) into an app-DB usage_events
table that the control plane later pulls read-only into the billing
ledger and syncs to Stripe Meter Events.
- migration 007: usage_events table (Postgres TIMESTAMPTZ/JSONB/UUID
with portable SQLite fallbacks), indexed (occurred_at, metric) for the
CP rollup's per-day range scan + GROUP BY metric.
- UsageEventStore: best-effort, flag-gated writer reusing the shared
RefreshTokenStorage engine; ON CONFLICT (event_id) DO NOTHING for
idempotent retries; dialect-branched occurred_at bind. All work
(incl. metadata JSON encode) is swallowed so a metering failure never
surfaces to the user op.
- USAGE_METERING_ENABLED flag (default off) wired through Settings +
env map; off-path touches no storage, so OSS self-hosters get an empty
table and zero write overhead.
- two recording hooks: embeddings_queries (per nc_semantic_search, which
nc_semantic_search_answer reuses) and pages_chunks (after dense
embedding succeeds, covering both in-process and procrastinate paths).
- storage.acquire()/.dialect public seams so the sibling store doesn't
reach into the underscored internal.
- tests parametrized over SQLite + Postgres: flag-off no-op, roundtrip,
ON CONFLICT dedup, JSON/NULL metadata, and the best-effort swallow of
both DB errors and unserializable metadata.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add PageAwareChunker, which splits paginated documents (PDFs) on page
boundaries first and only character-splits pages larger than chunk_size.
No chunk spans a page boundary, so page_number is always exact and stored
excerpts never lead with a neighbouring page's text. When chunk_size is at
least the largest page, this yields exactly one chunk per page: a
predictable vector count (== page count), a flat per-page embedding cost,
and zero cross-page overlap duplication.
Gated by DOCUMENT_CHUNK_PAGE_AWARE (default true). When false, the legacy
char-based DocumentChunker + post-hoc assign_page_numbers path runs
unchanged. Only doc_type="file" with page_boundaries (PDFs) takes the
page-aware path; notes/deck/news are unaffected.
Measured on a 15-page record (query "leadership award louis", target =
top-half of page 15): char-based degraded the target to dense-rank 10 at
cs=2048 (OCR) and mislabeled its page; page-aware restored rank 1 across
every fusion/modality and chunk size, with correct page labels and clean
snippets.
BREAKING CHANGE: PDFs are re-chunked page-aware by default. Existing
deployments will re-index PDF content on the next vector sync (different
chunk counts and page_number labels). Set DOCUMENT_CHUNK_PAGE_AWARE=false
to retain the previous char-based behaviour.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #863 review:
- MIN_TEXT_QUALITY 0.45 -> 0.5 so the module/diagnostic default matches the
DOCUMENT_OCR_MIN_TEXT_QUALITY setting (registry always passes the setting; this
keeps classify_pdf and the test/default path on the production threshold).
- image_coverage_per_page is bounded to MAX_SAMPLED_PAGES (the image pass is the
costly part, so a 200-page scan isn't fully rasterised on the hot path); pages
beyond the cap fall back to the text-quality signal, and page_fraction still
gates over every page.
- Extracted _page_image_coverage(page) helper, shared by classify_pdf and
image_coverage_per_page (DRY + keeps the tiling-double-count note in one place).
- Scan-detection failure logs at WARNING (not DEBUG) so a systematic failure on
an OCR-enabled tenant is visible at LOG_LEVEL=INFO.
- Add the missing DOCUMENT_OCR_MIN_PAGE_CHARS range-validator test.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The hot-path classifier escalated to OCR purely on character count, so a
scanned/handwritten PDF with a low-quality embedded text layer (>16 chars/page
but garbled) routed `fast` and indexed the junk -- e.g. Student 147.pdf's
"Little Acoms Primary"/"0110912020", which pollutes the vector and demotes the
doc in search (Deck #207).
- classifier: recalibrate `_text_quality` with a long-token-fraction term that
detects word-merging (dropped inter-word spaces) -- the dominant junk-layer
failure the old whitespace/overlong(>20) terms missed. Measured: the Student
147 scan ~0.42 (60% pages junk) vs >=0.94 for clean digital docs.
- classify_from_text now routes on quality + scan: a page is OCR-worthy if
near-empty OR low text-quality OR (when OCR + scan detection are enabled) it's
mostly a raster image. New `image_coverage_per_page` re-opens the PDF for the
scan signal, so that cost is paid only by OCR-opted-in tenants. Thresholds are
passed in from per-tenant settings (keyword-only).
- config: 4 per-tenant settings -- DOCUMENT_OCR_MIN_TEXT_QUALITY (0.5),
DOCUMENT_OCR_PAGE_FRACTION (0.5), DOCUMENT_OCR_MIN_PAGE_CHARS (16),
DOCUMENT_OCR_DETECT_SCANNED (true) -- with range validators.
- metrics: new astrolabe_document_ocr_page_fraction histogram (the value the
page-fraction threshold acts on) alongside document_text_quality, so operators
can tune the OCR escalation per tenant (quality vs cost).
Escalation gate, OCR backends, and off-by-default behavior unchanged (#858).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds OpenAI provider to the unified provider architecture (ADR-015),
supporting:
- OpenAI API (api.openai.com)
- GitHub Models API (models.github.ai/inference)
- OpenAI-compatible endpoints (Fireworks, Together, etc.)
Features:
- Embedding support with text-embedding-3-small/large models
- Text generation via chat completions API
- Automatic retry with exponential backoff for rate limits
- Provider auto-detection in registry (priority after Bedrock)
Environment variables:
- OPENAI_API_KEY: API key (required)
- OPENAI_BASE_URL: Base URL override (optional)
- OPENAI_EMBEDDING_MODEL: Embedding model (default: text-embedding-3-small)
- OPENAI_GENERATION_MODEL: Generation model (default: gpt-4o-mini)
Also adds:
- Integration tests for RAG pipeline with MCP sampling
- MCP client sampling support for integration tests
- Ground truth Q&A pairs for Nextcloud User Manual
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Extract CSS and JavaScript into separate static files
- Created nextcloud_mcp_server/auth/static/vector-viz.css
- Created nextcloud_mcp_server/auth/static/vector-viz.js
- Updated templates to reference external assets
- Fix vector visualization issues:
- Normalize vectors before PCA to match Qdrant's cosine distance
- Add zero-norm and NaN detection/handling for large datasets
- Enable responsive Plotly sizing (autosize + responsive config)
- Widen plot area to full viewport width with minimized margins
- Improve visualization accuracy:
- Query point now positioned correctly relative to documents
- Handles 200+ points without JSON serialization errors
- Full-width plot maximizes screen space utilization
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Adds flexible Qdrant deployment modes to reduce infrastructure requirements
for local development and smaller deployments:
**Configuration Changes:**
- Add QDRANT_LOCATION environment variable (mutually exclusive with QDRANT_URL)
- Three modes: network (URL), in-memory (:memory:, default), persistent (file path)
- Settings dataclass validation via __post_init__ ensures mutual exclusivity
- API key warning when set in local mode (ignored, only for network mode)
**Client Initialization:**
- Auto-detect mode: network (url + api_key) vs local (:memory: or path=)
- In-memory: AsyncQdrantClient(":memory:") - zero config default
- Persistent: AsyncQdrantClient(path="/app/data/qdrant") - file storage
- Network: AsyncQdrantClient(url, api_key) - production mode
**Docker Compose Updates:**
- Qdrant service moved to optional profile (--profile qdrant)
- MCP service uses QDRANT_LOCATION=:memory: by default
- Added mcp-data volume for persistent storage (/app/data)
- No hard dependency on qdrant service
**Documentation:**
- Comprehensive configuration guide in docs/configuration.md
- All three modes documented with pros/cons
- Docker Compose examples for each mode
- Environment variable reference table
**Tests:**
- 13 new config validation tests (mutual exclusivity, defaults, warnings)
- Persistent mode integration test (create, close, reopen, verify persistence)
- All 82 unit tests + 5 smoke tests pass
**Breaking Change:**
- Default changed from QDRANT_URL=http://qdrant:6333 to QDRANT_LOCATION=:memory:
- Simplifies local development (no external service needed)
- Production deployments: explicitly set QDRANT_URL or QDRANT_LOCATION
Related: ADR-007 background vector sync implementation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
