Address round-5 reviewer feedback on PR #747:
- Escape `webhook_uri` in the admin pane HTML template so an operator-
controlled env value (`WEBHOOK_INTERNAL_URL`, `NEXTCLOUD_MCP_SERVER_URL`)
can't inject markup. The sibling `preset_id` and exception messages were
already escaped — this one was the odd one out.
- Convert the eight remaining f-string `logger.warning`/`logger.error`
calls in `api/webhooks.py` to lazy `%s` formatting, matching the style
already adopted by `webhook_receiver.py` and `webhook_routes.py`.
- Document why the 401 from `handle_nextcloud_webhook` deliberately omits
`WWW-Authenticate`: NC's webhook delivery worker has no auth-flow state
machine to negotiate against, the bearer is a static shared secret
configured out-of-band via `WEBHOOK_SECRET`, and a challenge response
wouldn't change client behaviour. The existing warning log already
records the rejection.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Address the two Security findings from PR review:
- webhook_receiver: encode Authorization header and expected bearer to
utf-8 bytes before hmac.compare_digest. Conventional form; doesn't
rely on Python's implicit ASCII encoding.
- webhook_routes: html.escape user-influenced and exception-derived
strings before interpolating into HTMLResponse content. Covers the
preset_id path param echoed in the "Unknown preset" branch and the
str(e) text rendered on handler exceptions.
Adds regression tests verifying compare_digest is invoked on bytes and
that <script> payloads (in preset_id and exception messages) are
emitted as escaped entities, not active markup.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Addresses round-3 review feedback on PR #747:
- webhook_receiver: wrap send_stream.send() in anyio.fail_after(1.0)
and return 503 with reason="queue full" if the queue is saturated.
Avoids pinning the handler until NC's outbound timeout fires; the
503 retry contract is the same as the existing "sync not running"
branch.
- webhook_receiver: revise the compare_digest comment to match what
the function actually guarantees — it avoids the per-character
short-circuit of `==` but is not fully constant-time across length
differences.
- _get_webhook_uri: read WEBHOOK_INTERNAL_URL and
NEXTCLOUD_MCP_SERVER_URL via dynaconf so operators using
settings.toml (rather than env vars) aren't silently routed into
the docker/localhost fallback. Adds webhook_internal_url to
Settings/_DEFAULTS/_field_map; nextcloud_mcp_server_url already
existed. Docker-detection markers stay on os.getenv since they're
container-runtime signals, not user-facing config.
- webhook_routes: sweep remaining f-string logger calls to lazy %s
formatting per CLAUDE.md.
- client/webhooks: modernise full file's type hints to
dict / list / | None per CLAUDE.md.
Tests:
- New test_returns_503_when_queue_is_full exercises the timeout
branch with a saturated buffer and a shortened deadline.
- test_webhook_uri tests now patch get_settings (matching the
auth-pair tests in the same file) instead of monkeypatching env
vars directly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- webhook_receiver: always run hmac.compare_digest (drop the
`not provided or` short-circuit) so the constant-time path is
taken regardless of whether the Authorization header is present.
- client/webhooks: modernise the new `auth_data` type hint to
`dict[str, str] | None` per CLAUDE.md.
- tests/client: rename `test_create_webhook_with_auth_headers` →
`test_create_webhook_with_static_headers` and use
`auth_method="header"` (NC's webhook_listeners only supports
"none" and "header"; the previous "bearer" value was invalid).
- auth/webhook_routes: extract `_register_preset_webhooks` from
`enable_webhook_preset` so the auth-threading behaviour is
testable without standing up a Starlette app + auth middleware.
- tests/unit: new test_webhook_routes_register covering the helper
with secret set / unset, and verifying ids round-trip in order.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds optional shared-secret authentication for /webhooks/nextcloud,
addressing the security follow-up flagged in #747.
Behavior:
- WEBHOOK_SECRET set: registrations pass authMethod="header" with
authData={"Authorization": "Bearer <secret>"} (encrypted at-rest in
Nextcloud's DB and forwarded on every delivery). The receiver
validates the same header with hmac.compare_digest before parsing
any payload; missing/invalid → 401.
- WEBHOOK_SECRET unset: registrations stay on authMethod="none" and
the receiver accepts unauthenticated POSTs (logging a one-time
startup warning). Backward compatible — operators can roll out at
their own pace.
Implementation notes:
- WebhooksClient.create_webhook gains an `auth_data` parameter mapped
to NC's `authData` body field; this is distinct from the existing
`headers` parameter (`headers` is plaintext static request headers,
`authData` is encrypted at-rest in NC and only emitted when
authMethod="header"). The previous `auth_method="bearer"` mention in
the docstring was incorrect — NC supports only "none" and "header".
- A small `webhook_auth_pair()` helper in auth/webhook_routes.py
centralises the secret→(auth_method, auth_data) resolution so the
preset flow and the Astrolabe-facing /api/v1/webhooks endpoint stay
in sync.
Also addresses the smaller review points from #747:
- f-string → lazy %s formatting in webhook_receiver.py and
webhook_routes.py.
- Move `int(time)` inside webhook_parser's try/except so a malformed
`time` field returns None instead of raising ValueError.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The /webhooks/nextcloud endpoint was a no-op stub that logged the
payload and returned 200 OK; webhook deletions never reached Qdrant.
Compounding that, _get_webhook_uri() registered the docker-compose
internal hostname (http://mcp:8000) with Nextcloud whenever
/.dockerenv existed — including ECS Fargate — so cloud deployments
were registering a URL NC could not resolve.
- New vector/webhook_parser.py extracts a DocumentTask from
NodeCreatedEvent / NodeWrittenEvent / BeforeNodeDeletedEvent
payloads scoped to */files/Notes/*.md (matching the registered
preset filters).
- New vector/webhook_receiver.py pushes that task onto the same
send-stream the scanner uses (app.state.document_send_stream),
with 503 when sync is not running so NC retries delivery.
- _get_webhook_uri() now prefers NEXTCLOUD_MCP_SERVER_URL over the
/.dockerenv branch, so the explicit public URL set on cloud tasks
wins; docker-compose dev still falls back to the internal name when
no public URL is configured.
Calendar / Tables event parsing is intentionally out of scope here.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
caldav 3.x lists niquests as a mandatory dependency and prefers it over
httpx. Passing httpx.BasicAuth via the auth= argument breaks under the
niquests backend with "Unexpected non-callable authentication" — see #731.
Switch CalendarClient.__init__ from auth=Auth|None to keyword-only
password/token, and forward them to AsyncDAVClient as password= plus an
explicit auth_type ("basic" or "bearer"). caldav then builds whichever
auth object its active backend needs (niquests.auth.HTTPBasicAuth or
httpx.BasicAuth), so we stay backend-agnostic.
Threaded raw credentials through NextcloudClient — added keyword-only
password/token to its __init__, and updated from_env, from_token, and
the four call sites that build NextcloudClient (context.py basic-auth
and Login Flow paths, auth/userinfo_routes.py, vector/oauth_sync.py).
Four new unit tests pin the construction wiring so the niquests
regression can't recur silently — basic, bearer, no-creds, and
password-precedence cases.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Many identity providers (AWS Cognito, Okta, Azure AD) reject or mishandle
colons in OAuth scope names. This migrates all custom scopes from
`resource:action` to `resource.action` format (e.g., `notes:read` →
`notes.read`), which is universally accepted and aligns with industry
conventions (Microsoft, Google).
Includes Alembic migration 004 for stored scope strings and ADR-024
documenting the rationale and RFC references.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Login Flow v2 is a deployment-wide mode where all users authenticate
with app passwords (not OAuth refresh tokens). Set use_basic_auth=True
when enable_login_flow is true so the background sync user manager
queries the app_passwords table and scanners use app password
authentication for Nextcloud API calls.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When enable_login_flow is true, also check the app_passwords table
for provisioned users. Previously, OAuth mode only queried the
refresh_tokens table, missing users who were provisioned via
Login Flow v2 (which stores app passwords, not refresh tokens).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Enable ruff PLC0415 rule for all source files (tests excluded via
per-file-ignores). Move 136 inline imports to top-level across 33 files.
8 imports suppressed with noqa for legitimate reasons: circular
dependencies (client/__init__.py, context.py), optional dependency
guards (app.py document processors, auth/userinfo_routes.py), and
post-env-setup imports (smithery_main.py).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move httpx import to top-level and use anyio task group for concurrent
validation in cleanup_invalid_app_passwords (storage.py)
- Respect Retry-After header for 429 responses, capped at 300s (oauth_sync.py)
- Soften pre-validation exceptions so transient failures don't crash the
background sync task (oauth_sync.py)
- Replace f-string SQL with blanket DELETE and add returncode checks (conftest.py)
- Extract clear_stale_test_state() helper to deduplicate cleanup logic
in astrolabe background sync tests
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The revoke test failed because it only completed Step 2 (app password) but
not Step 1 (OAuth authorization). In hybrid mode, Astrolabe requires both
steps for $isFullyConfigured=true, which gates the "Revoke Access" button.
Changes:
- Use complete_astrolabe_authorization() in revoke test for full two-step flow
- Add stale state cleanup (app passwords, bruteforce entries, Astrolabe prefs)
to both enablement and revoke tests
- Add startup cleanup of invalid app passwords in BasicAuth mode
- Pre-validate credentials before entering scanner loop to fail fast
- Handle 401/403/429 in scanner with proper backoff and circuit breaking
- Clean up app passwords in test_users_setup fixture teardown
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Previously, the multi-user BasicAuth mode attempted to retrieve app passwords
via OAuth client_credentials grant, which Nextcloud OIDC doesn't support.
This fix implements local storage for app passwords:
- Add app_passwords table via Alembic migration (002)
- Add store/get/delete methods to RefreshTokenStorage
- Add management API endpoints for app password provisioning:
- POST /api/v1/users/{user_id}/app-password
- GET /api/v1/users/{user_id}/app-password
- DELETE /api/v1/users/{user_id}/app-password
- Update oauth_sync.py to read from local storage
- Update Astrolabe to send app passwords to MCP server after validation
- Add app-hook to configure mcp_server_url in Nextcloud
The flow is now:
1. User creates app password in Nextcloud Security settings
2. User enters it in Astrolabe Personal Settings
3. Astrolabe validates against Nextcloud, then sends to MCP server
4. MCP server stores encrypted app password locally
5. Background sync uses locally stored password
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Completely separates multi-user BasicAuth mode from OAuth mode with no
fallback between them. These are now mutually exclusive authentication
strategies based on deployment configuration.
Changes:
- Create separate functions: get_user_client_basic_auth() and
get_user_client_oauth() with clear separation of concerns
- Update get_user_client() to dispatch based on use_basic_auth parameter
- Pass use_basic_auth through all background sync tasks
- Update app.py to determine auth mode at startup
- Rewrite integration tests to verify no OAuth fallback in BasicAuth mode
- Fix test assertions for response field names and duplicate title handling
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement multi-user BasicAuth pass-through mode (ADR-020) where each
request includes BasicAuth credentials that are forwarded to Nextcloud
APIs without persistent storage.
Changes:
- Add _get_client_from_basic_auth() in context.py to extract credentials
from Authorization header (set by BasicAuthMiddleware)
- Add AstrolabeClient for app password provisioning via Astrolabe API
- Update oauth_sync.py with dual credential support (app passwords first,
then refresh tokens as fallback)
- Simplify oauth_tools.py provisioning logic
- Add integration tests for app password provisioning and multi-user BasicAuth
Features:
- Stateless multi-user mode: credentials passed per-request
- Optional background sync via app passwords (stored in Astrolabe)
- Falls back to refresh tokens if app password not available
- Test coverage for provisioning flow and pass-through mode
Related: ADR-019 (Multi-user BasicAuth), ADR-020 (Deployment Modes)
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Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add type casts for Starlette app state access
- Add assertions for cipher, card, board, stack after initialization
- Add None checks for XML element text attributes
- Handle __package__ being None in tracing setup
- Fix TokenBrokerService initialization to use storage credentials
Resolves 42 type warnings from ty-check, enabling CI linting to pass.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add Plotly.js 3D scatter plot showing search results in PCA space
- Create shared visualization.py module to avoid code duplication
- Pass include_pca parameter through API chain to enable coordinates
- Fix OAuth redirects to use /settings/user/astroglobe
The visualization shows document embeddings projected to 3D via PCA,
with the query point highlighted in red. Uses Viridis colorscale
for score visualization, matching the existing vector-viz page.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Addresses reviewer feedback on PR #395 about O(n²) performance issue.
Changes:
- scanner.py: Add metadata field to DocumentTask with board_id/stack_id
- scanner.py: Populate metadata during deck card scanning (both initial and incremental sync)
- processor.py: Use metadata for O(1) card lookup via get_card() API when available
- processor.py: Fallback to iteration for legacy data without metadata
- context.py: Add _get_deck_metadata_from_qdrant() helper to retrieve metadata from Qdrant
- context.py: Use metadata for fast path lookup in chunk context expansion
- context.py: Add user_id parameter to _fetch_document_text() for metadata retrieval
Performance Impact:
- Before: O(boards × stacks × cards) iteration for each card lookup
- After: O(1) direct API call using stored board_id/stack_id
- Graceful degradation: Falls back to iteration for legacy data
Testing:
- All existing integration tests pass (test_deck_vector_search.py)
- Type checking passes with no new errors
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Adds comprehensive vector search support for Nextcloud Deck cards,
including semantic search indexing, chunk preview in the vector viz UI,
and proper deep linking to cards.
**Vector Search Indexing**
- Add deck_card scanning in scanner.py (scan_deck_cards function)
- Index cards from non-archived, non-deleted boards
- Store metadata: board_id, board_title, stack_id, stack_title, card_type, duedate, owner
- Content structure: title + "\n\n" + description (matches indexing format)
- Incremental sync based on lastModified timestamp
- Deletion tracking with grace period
**Vector Visualization Support**
- Add deck_card handler in context.py for chunk preview expansion
- Include board_id in search result metadata (bm25_hybrid.py, semantic.py)
- Expose metadata in viz_routes.py JSON responses
- Update vector-viz.js to construct proper Deck URLs: /apps/deck/board/{board_id}/card/{card_id}
- Update vector_viz.html filter label from "Deck" to "Deck Cards"
**Bug Fixes**
- Skip soft-deleted boards (deletedAt > 0) to prevent 403 Forbidden errors
- Applies to scanner, processor, and context expansion code paths
- Deck API returns deleted boards but rejects stack access with 403
**Testing**
- Add integration tests in test_deck_vector_search.py:
- test_deck_card_semantic_search: Filtered search with doc_type="deck_card"
- test_deck_card_appears_in_cross_app_search: Cross-app search includes deck cards
- test_deck_card_chunk_context: Chunk context fetching for viz preview
**Documentation**
- Update README.md: Add Deck cards to semantic search feature list
- Update semantic-search-architecture.md: Document deck_card support
- Update nc_semantic_search tool documentation
**Type Safety**
- Fix type narrowing for page_boundaries (could be None) using cast()
- Fix scanner.py payload None check for type safety
Resolves vector search for Deck cards across indexing, search, and visualization.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Fixed 8 type checker errors across the codebase:
- vector/scanner.py: Handle None scroll results with null-safe iteration
- search/{bm25_hybrid,semantic}.py: Add None checks for result.payload
- auth/{unified_verifier,webhook_routes}.py: Assert non-None auth credentials
- client/webdav.py: Add None checks before int() conversions
- providers/openai.py: Assert embedding_model is not None
- search/algorithms.py: Explicitly type doc_types set and cast values
- observability/logging_config.py: Match parent class signature (log_data)
Also fixed test_create_tag_creates_system_tag to match WebDAV implementation
(was testing OCS API endpoint, now tests correct WebDAV endpoint with
Content-Location header).
Type checker: 0 errors (down from 8), 20 warnings (ignored)
Tests: All 192 unit tests passing
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Remove the complex starred+unread filtering logic in scan_news_items().
The News app's auto-purge feature (default: 200 items per feed) already
limits the total number of items, making explicit filtering unnecessary.
Changes:
- Replace two API calls (starred + unread) with single all-items call
- Remove deduplication logic that merged both lists
- Update docstring to explain the simpler approach
This reduces code complexity while maintaining the same effective coverage.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add full integration for the Nextcloud News (RSS/Atom reader) app:
- Add NewsClient with complete CRUD operations for folders, feeds, and items
- Add 8 read-only MCP tools for listing/getting folders, feeds, items
- Add Pydantic models for News entities with camelCase alias support
- Add vector sync support for starred + unread items
- Add HTML to Markdown converter using markdownify for better embeddings
- Add Docker post-install hook to enable News app
- Add 25 unit tests for NewsClient API methods
Vector sync indexes starred and unread items, providing a balanced approach
that captures important (starred) and current (unread) content without
indexing the entire article history.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <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>
Previously, pymupdf4llm.to_markdown() was called twice - once in
PyMuPDFProcessor during indexing and again in PDFHighlighter during
visualization. Different image path lengths caused different character
offsets, leading to highlighted pages not matching their chunks.
Also fixed issue where all chunks on the same page showed all highlights
instead of just their own highlight. Now restores original page contents
between chunks using xref stream caching.
Changes:
- Add PDFHighlighter class requiring pre-computed page_boundaries and
full_text from document processor (no fallback extraction)
- Pass pre-computed data from processor to highlighter
- Extract page-relative portion of chunk text for cross-page chunks
- Add bounding box highlighting using text anchor search
- Run highlight generation in parallel with embedding/BM25
- Cache and restore page contents to isolate highlights per chunk
Results: Highlighting success rate improved from 51% to 95% (121/128).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements optional context expansion for semantic search results that
fetches adjacent chunks (N-1 and N+1) from Qdrant to provide before/after
context. Removes configurable chunk overlap (default 200 chars) to avoid
duplicate text appearing in both context and excerpt.
Key changes:
- Add include_context and context_chars parameters to nc_semantic_search
and nc_semantic_search_answer tools
- Implement Qdrant cache fast path for chunk retrieval (avoids re-fetching
and re-parsing documents, especially important for PDFs)
- Add _get_chunk_by_index_from_qdrant() to fetch adjacent chunks
- Remove chunk overlap from before_context (last N chars) and after_context
(first N chars) to prevent duplicate text
- Fetch context in parallel with anyio.Semaphore (max 20 concurrent)
- Pass through page_number from SearchResult to SemanticSearchResult
- Remove document-level deduplication (keep chunk-level dedup from algorithm)
Context expansion is opt-in via include_context=true parameter. When enabled:
- Populates has_context_expansion, marked_text, before_context, after_context
- Adds truncation flags when context exceeds context_chars limit
- Falls back to document fetch for legacy data with truncated excerpts
Related: nextcloud_mcp_server/search/context.py:87-382,
nextcloud_mcp_server/server/semantic.py:161-255
The processor was not setting is_placeholder field when writing real
document chunks to Qdrant. This caused the placeholder filter to exclude
all documents (since None != False), resulting in 0 search results.
Now explicitly sets is_placeholder: False in payload when writing real
indexed chunks, allowing search filters to correctly distinguish between
placeholders and real documents.
- Changed from 2x (120s) to 5x (300s) scan interval
- Large PDFs take 3-4 minutes to process, need longer threshold
- Prevents premature requeuing of in-flight documents
- Only requeue documents if placeholder is older than 2x scan interval (120s default)
- Prevents scanner from immediately requeuing in-flight documents
- Fixes issue where PDFs were being reprocessed every 60 seconds
- Staleness check applied to both notes and files scanning logic
Qdrant validation rejects None for sparse vectors in named vector dicts.
Use models.SparseVector(indices=[], values=[]) instead to create valid
empty sparse vectors for placeholder points.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Introduces a placeholder-based state tracking system to prevent duplicate
document processing during the gap between scanner queuing and processor
completion.
**Key Changes:**
1. **Placeholder Helper Functions** (`vector/placeholder.py`):
- `write_placeholder_point()` - Creates zero-vector placeholder when queuing
- `query_document_metadata()` - Queries for existing entry (placeholder or real)
- `delete_placeholder_point()` - Removes placeholder before writing real vectors
- `get_placeholder_filter()` - Filters placeholders from user-facing queries
2. **Scanner Updates** (`vector/scanner.py`):
- Replace `indexed_at` comparison with `modified_at` comparison
- Write placeholder before queuing each document
- Query per-document metadata instead of bulk-querying indexed_at
- Fixes bug where files were resubmitted every scan cycle
3. **Processor Updates** (`vector/processor.py`):
- Delete placeholder before upserting real vectors
- Ensures no duplicate points in Qdrant
4. **Query Filters** (all search files):
- Add `get_placeholder_filter()` to all user-facing queries
- Ensures placeholders never appear in search results or visualizations
- Applied to: bm25_hybrid.py, semantic.py, viz_routes.py, algorithms.py
**Architecture:**
- Placeholders use zero vectors with dimension from embedding service
- Payload includes `is_placeholder: True` flag for filtering
- Status field tracks: "pending", "processing", "completed", "failed"
- Deterministic UUIDs using uuid5 for consistent point IDs
**Impact:**
- Eliminates duplicate processing of same documents
- Fixes race condition where long-running documents get queued multiple times
- Prevents scanner from resubmitting files every scan cycle
- Maintains clean separation between in-flight and indexed documents
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit fixes two critical issues with PDF processing:
1. **Text extraction mismatch (context expansion bug)**:
- Indexing used pymupdf4llm.to_markdown() producing markdown text
- Context expansion used page.get_text() producing plain text
- Different text formats caused character offset misalignment
- Search would find correct chunk, but expansion showed wrong section
- Fixed by making context.py use pymupdf4llm.to_markdown() consistently
2. **Diagnostic logging for page number assignment**:
- Added logging to verify page_boundaries exist in metadata
- Added logging to verify assign_page_numbers() assigns values
- Helps diagnose why page numbers show as null in search results
3. **mime_type storage bug**:
- Fixed incorrect field reference in processor.py:405
- Was using file_metadata.get("content_type", "")
- Should use content_type from WebDAV response
Changes:
- nextcloud_mcp_server/search/context.py: Use pymupdf4llm.to_markdown()
for PDF text extraction to match indexing method
- nextcloud_mcp_server/vector/processor.py: Add diagnostic logging for
page boundaries and assignment, fix mime_type storage
- tests/unit/client/test_webdav.py: Fix import sorting
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- scanner.py: Use file_info['id'] as doc_id instead of file_path
- scanner.py: Pass file_path in DocumentTask for content retrieval
- processor.py: Store file_path in Qdrant payload for later lookup
- context.py: Add _get_file_path_from_qdrant() to resolve file_id → file_path
- context.py: Update get_chunk_with_context() to handle file ID resolution
This makes the system resilient to file renames since file IDs are stable
identifiers in Nextcloud, while file paths can change.
This commit addresses multiple issues with async operations, PDF metadata
extraction, and type safety in document processing and search.
## Async/Await Fixes
- processor.py:259 - Added await for chunker.chunk_text(content)
- processor.py:270 - Added await for bm25_service.encode_batch(chunk_texts)
- tests/unit/test_document_chunker.py - Converted all 12 test methods to async
## PDF Metadata Enhancement
- pymupdf.py:143 - Added file_size metadata extraction
- pymupdf.py:145-206 - Refactored to extract text page-by-page
- Manually loop through pages instead of using page_chunks=True
- Generate page_boundaries metadata for precise page tracking
- Works around pymupdf.layout.activate() breaking page_chunks=True
- processor.py:32-66 - Added assign_page_numbers() helper function
- Assigns page numbers to chunks based on overlap with page boundaries
- Handles chunks spanning multiple pages
- processor.py:298-300 - Call assign_page_numbers() for PDF files
## Type Safety Fixes
- bm25_hybrid.py:184 - Removed int() conversion of doc_id
- semantic.py:131 - Removed int() conversion of doc_id
- viz_routes.py:275 - Removed int() conversion of doc_id
- Added comments documenting that doc_id can be int (notes) or str (file paths)
## Testing
- All 18 tests passing (12 unit + 6 integration)
- No type errors in modified files
- Container logs show successful processing
- Vector viz searches working correctly
🤖 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>
Migrates from custom word-based chunking to LangChain's MarkdownTextSplitter
for better semantic search quality. This implements the chunking portion of
ADR-011.
Changes:
- Replace custom regex word chunker with MarkdownTextSplitter
- Optimized for Markdown content (headers, code blocks, lists)
- Convert from word-based (512 words) to character-based (2048 chars) chunking
- Maintain backward-compatible ChunkWithPosition interface
- Update configuration defaults and validation
- Update all unit tests (12/12 passing)
Benefits:
- Respects markdown structure boundaries
- Never breaks code blocks or headers mid-chunk
- Preserves semantic coherence within chunks
- Expected 20-30% improvement in recall quality
- Industry-standard approach (used by production RAG systems)
Note: Full reindex required to apply new chunking to existing documents.
Current vector database still contains old word-based chunks.
Related: ADR-011 (Improving Semantic Search Quality)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Track character offsets (start_offset, end_offset) for each chunk in vector
database metadata, enabling precise chunk highlighting in visualization pane.
Changes:
- processor.py: Store chunk_start_offset and chunk_end_offset in Qdrant metadata
- processor.py: Added metadata_version=2 to indicate position tracking support
- search/semantic.py: Return chunk positions from search results
- server/semantic.py: Expose chunk positions in API responses (SemanticSearchResult)
Enables viz pane to:
1. Display exact matched chunk with surrounding context
2. Highlight the precise portion of text that matched the query
3. Build user trust by showing what the RAG system actually retrieved
Position tracking uses ChunkWithPosition dataclass from document_chunker.py
which provides character-accurate offsets in the original document.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed a critical infinite loop bug in document_chunker.py that occurred
when the overlap parameter caused the chunker to not make forward progress.
Changes:
- Added ChunkWithPosition dataclass to track character positions
- Refactored chunk_text() to use regex word matching for accurate position tracking
- Added safety check to ensure forward progress (next_start_idx > start_idx)
- Changed return type from list[str] to list[ChunkWithPosition]
The bug manifested when:
1. end_idx reached len(word_matches) (processing last chunk)
2. next_start_idx = end_idx - overlap would not advance past start_idx
3. Loop would continue indefinitely without making progress
Fix ensures chunker always terminates by breaking when not advancing.
All 9 unit tests now pass in 1.66s (previously timing out at 180s).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses critical performance issues with vector visualization
search (reducing time from 40s to ~2s) and improves result visualization
through better visual encoding.
## Performance Fixes
### 1. Fix blocking sleep in retry decorator (base.py:51)
- Changed `time.sleep(5)` to `await anyio.sleep(5)` in @retry_on_429
- Prevents entire event loop from freezing during rate limit retries
- Impact: Reduced search time from 22s to 16s initially
### 2. Add concurrency limiting for verification (verification.py:77-93)
- Added `anyio.Semaphore(20)` to limit concurrent HTTP requests
- Prevents connection pool exhaustion (RequestError) from 90+ simultaneous requests
- Fixes false filtering (was filtering 77/90 results incorrectly)
- Note: Semaphore still in code but verification removed from viz endpoint
### 3. Remove unnecessary verification from viz endpoint (viz_routes.py:483-486)
- Visualization only needs Qdrant metadata (title, excerpt), not full content
- Verification only required for sampling (LLM needs full note content)
- Impact: Reduced search time from 43.7s to ~2s (final fix)
### 4. Restore streaming scanner pattern (scanner.py)
- Process notes one-at-a-time using async generator
- Avoids loading all notes into memory
## Visualization Improvements
### 5. Result-relative score normalization (viz_routes.py:489-504)
- Normalize scores within result set: best=1.0, worst=0.0
- Removes arbitrary RRF normalization (theoretical max didn't make sense)
- Makes visual encoding meaningful regardless of algorithm scores
### 6. Power scaling for marker sizes (userinfo_routes.py:743)
- Changed from linear `8 + (score * 12)` to power `6 + (score² * 14)`
- Creates dramatic visual contrast: 0.0→6px, 0.5→9.5px, 1.0→20px
- Combined with opacity (0.2-1.0) for clear visual hierarchy
### 7. Multi-channel visual encoding (userinfo_routes.py:740-745)
- Size: Exponentially scaled with score²
- Opacity: Linear 0.2-1.0 (keeps all points visible)
- Color: Viridis gradient (blue→yellow)
- Effect: Top results are large/bright/opaque, context results small/dim/transparent
## Result
- Search time: 40s → ~2s (20x faster)
- Visual contrast: Subtle → dramatic (clear result hierarchy)
- No arbitrary cutoffs: All results visible, best naturally highlighted
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace asyncio primitives with anyio equivalents throughout the codebase
to establish a single async pattern. This provides better structured
concurrency with automatic cancellation on errors and aligns with the
pytest anyio configuration.
Changes:
- hybrid.py: Replace asyncio.gather() with anyio task groups
- token_broker.py: Replace asyncio.Lock() with anyio.Lock()
- storage.py: Replace asyncio.run() with anyio.run()
- app.py: Replace tg.start_soon() with await tg.start() for task status
- processor.py: Add task_status parameter for structured startup
- scanner.py: Add task_status parameter for structured startup
- CLAUDE.md: Update async/await patterns guidance
The change from start_soon() to await tg.start() enables proper task
initialization signaling, ensuring background tasks are ready before
proceeding. This follows anyio best practices for structured concurrency.
All 118 unit tests pass with the new implementation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add custom PCA implementation using numpy eigendecomposition
- Replace sklearn.decomposition.PCA with custom implementation
- Maintains same API (fit, transform, fit_transform)
- Supports explained_variance_ratio_ for variance analysis
- Removes scikit-learn dependency from project
- Add type hints and assertion for type safety
This fixes dimension mismatch errors when using embedding models with
non-standard dimensions (e.g., qwen3-embedding:4b produces 2560-dim
vectors instead of the hardcoded 768).
Changes:
- OllamaEmbeddingProvider: Detect dimensions dynamically by generating
test embedding instead of hardcoding to 768
- qdrant_client: Call dimension detection before collection creation
- app.py: Initialize Qdrant collection before starting background tasks
in streamable-http transport path
- tests: Fix integration tests to properly mock EmbeddingService wrapper
Fixes dimension mismatch error:
"could not broadcast input array from shape (2560,) into shape (768,)"
All integration tests passing (6/6).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Simplifies the OpenTelemetry tracing setup by removing the redundant
OTEL_ENABLED flag and using the presence of OTEL_EXPORTER_OTLP_ENDPOINT
to determine if tracing should be enabled. This follows the standard
OpenTelemetry environment variable conventions more closely.
Changes:
- Remove OTEL_ENABLED/tracing_enabled flag in favor of checking if
OTEL_EXPORTER_OTLP_ENDPOINT is set
- Add OTEL_EXPORTER_VERIFY_SSL configuration option for OTLP endpoints
with self-signed certificates (defaults to false for development)
- Move HTTPXClientInstrumentor initialization to module level to ensure
httpx calls are traced across all Nextcloud API requests
- Add tracing spans to vector sync operations (scan_user_documents)
- Fix authorization header logging to only warn about missing headers
in OAuth mode (BasicAuth mode doesn't use Authorization headers)
- Update observability documentation to reflect simplified configuration
- Refactor Dockerfile to use --no-editable flag for uv sync
Breaking changes:
- OTEL_ENABLED environment variable is removed
- Tracing is now automatically enabled when OTEL_EXPORTER_OTLP_ENDPOINT
is set
Migration guide:
- Remove OTEL_ENABLED=true from environment configuration
- Tracing will be enabled automatically if OTEL_EXPORTER_OTLP_ENDPOINT
is configured
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
The Nextcloud Notes API intentionally returns all note IDs (with only 'id'
field) in the last chunk to enable deletion detection. Without using the
pruneBefore parameter, this causes duplicates - all notes appear with full
data in chunks, then again with minimal data in the last chunk.
This commit implements proper pruneBefore support:
- NotesClient.get_all_notes() now accepts prune_before timestamp parameter
- Scanner calculates max(indexed_at) from Qdrant to use as prune threshold
- Only notes modified after this timestamp are sent with full data
- Deduplication logic handles the API's deletion detection pattern
- Significantly reduces data transfer for incremental syncs
The behavior is documented in Notes API v1 spec - this is not an API bug,
but a feature we weren't utilizing correctly.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
