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)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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>
Changes to make tests work without external qdrant/ollama dependencies:
1. docker-compose.yml (mcp service):
- Switch from QDRANT_URL (network mode) to QDRANT_LOCATION=":memory:"
- Comment out QDRANT_URL and QDRANT_API_KEY (not needed for in-memory)
- Keep OLLAMA_BASE_URL commented out (use SimpleEmbeddingProvider fallback)
2. nextcloud_mcp_server/vector/qdrant_client.py:
- Fix collection creation bug in in-memory mode
- Previously: All ValueError exceptions were re-raised
- Now: Only dimension mismatch ValueError is re-raised
- Allows "Collection not found" ValueError to trigger auto-creation
3. tests/integration/test_sampling.py:
- Update test to handle all sampling unsupported cases
- Check for multiple fallback search_method values
- Skip test gracefully when sampling unavailable
This configuration enables:
- CI testing without external services (qdrant, ollama)
- In-memory vector database (ephemeral but sufficient for tests)
- SimpleEmbeddingProvider for embeddings (feature hashing, 384 dims)
- Automatic collection creation on first use
Test result: test_semantic_search_answer_successful_sampling now passes
(skipped with appropriate message when sampling unsupported)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This PR enables safe switching between embedding models and multi-server
deployments by implementing auto-generated Qdrant collection names based on
deployment ID and model name.
## Problem
Previously, all deployments used a single hardcoded collection name
"nextcloud_content", which caused two critical issues:
1. **Dimension mismatches when switching models**: Changing
OLLAMA_EMBEDDING_MODEL (e.g., nomic-embed-text at 768D → all-minilm at
384D) would cause runtime errors as vectors couldn't be inserted into a
collection with incompatible dimensions.
2. **Collection collisions in multi-server setups**: Multiple MCP servers
sharing a single Qdrant instance would overwrite each other's data,
making horizontal scaling impossible.
## Solution
### Auto-Generated Collection Naming
Collections are now automatically named using the pattern:
\`{deployment-id}-{model-name}\`
**Deployment ID**: Uses \`OTEL_SERVICE_NAME\` if configured (and not default
value), otherwise falls back to \`hostname\` for simple Docker deployments.
**Model Name**: From \`OLLAMA_EMBEDDING_MODEL\` with path separators sanitized.
**Examples**:
- \`my-mcp-server-nomic-embed-text\` (with OTEL_SERVICE_NAME=my-mcp-server)
- \`mcp-container-all-minilm\` (simple Docker, hostname=mcp-container)
**Override**: Users can still set \`QDRANT_COLLECTION\` explicitly to bypass
auto-generation for backward compatibility.
### Dimension Validation
Added startup validation that checks collection dimensions match the
embedding service. If a mismatch is detected, the server fails fast with a
clear error message explaining:
- Expected vs actual dimensions
- Likely cause (model change)
- Solutions (delete collection, use different name, or revert model)
### Improved Sampling Error Handling
Enhanced MCP sampling rejection handling to treat user rejections as normal
behavior rather than errors:
- **User rejections** ("rejected", "denied") → INFO log, no traceback
- **Unsupported clients** → INFO log, no traceback
- **Other MCP errors** → WARNING log, no traceback
- **Unexpected errors** → ERROR log WITH traceback
This aligns with the MCP specification where clients SHOULD prompt users for
approval/denial of sampling requests.
## Changes
### Core Implementation
- **nextcloud_mcp_server/config.py**: Added \`get_collection_name()\` method
with deployment ID detection and model name sanitization
- **nextcloud_mcp_server/vector/qdrant_client.py**: Dimension validation on
collection open with helpful error messages
- **nextcloud_mcp_server/vector/{scanner,processor}.py**: Updated to use
\`get_collection_name()\`
- **nextcloud_mcp_server/auth/userinfo_routes.py**: Vector sync status uses
\`get_collection_name()\`
- **nextcloud_mcp_server/server/semantic.py**:
- Updated semantic search tools to use \`get_collection_name()\`
- Improved sampling rejection error handling (McpError vs Exception)
### Documentation
- **docs/semantic-search-architecture.md**: New comprehensive architecture
document (557 lines) covering background sync, semantic search flow, RAG
implementation, and deployment modes
- **docs/configuration.md**: Added detailed "Qdrant Collection Naming"
section with examples and multi-server deployment guidance
- **docker-compose.yml**: Added comments explaining collection naming behavior
- **README.md**: Updated semantic search descriptions to clarify
experimental status, Notes-only support, and infrastructure requirements
## Migration Guide
**For existing single-server deployments:**
Option 1 (Recommended): Use explicit collection name for continuity
\`\`\`bash
QDRANT_COLLECTION=nextcloud_content # Keep existing collection
\`\`\`
Option 2: Allow auto-generation and re-embed
\`\`\`bash
# Remove QDRANT_COLLECTION override
# New collection will be created based on deployment ID + model
# Requires re-embedding all documents (may take time)
\`\`\`
**For new multi-server deployments:**
Set unique OTEL service names per server:
\`\`\`bash
# Server 1
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"
# Server 2
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-staging-nomic-embed-text"
\`\`\`
## Benefits
✅ **Safe model switching**: Each model gets its own collection, preventing
dimension mismatch errors
✅ **Multi-server support**: Multiple MCP servers can share one Qdrant
instance without conflicts
✅ **Clear ownership**: Collection names show which deployment and model owns
the data
✅ **Better error messages**: Dimension validation provides actionable
guidance
✅ **Backward compatible**: Existing deployments can continue using
\`QDRANT_COLLECTION\` override
## Testing
Validated with:
- Single-server deployments (default hostname-based naming)
- Multi-server deployments (OTEL service name-based naming)
- Model switching scenarios (dimension validation)
- Collection override scenarios (backward compatibility)
Next steps: Testing various Ollama embedding models to investigate optimal
chunk sizes and performance characteristics.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
The vector scanner crashed when encountering notes without a 'modified' field,
causing KeyError and preventing initial sync from completing.
Changes:
- Use dict.get() with fallback value (0) instead of direct key access
- Log warnings for notes missing 'modified' field
- Apply fix to both initial sync and incremental sync code paths
This ensures the scanner continues processing all notes even if some have
missing metadata fields, preventing scanner crashes that could affect
deployment readiness.
Fixes: Notes without 'modified' field causing scanner crash and readiness check failure
🤖 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>
Replace asyncio.Queue with anyio.create_memory_object_stream() throughout
the vector sync system for better library consistency and improved shutdown
semantics.
## Changes Made
**scanner.py**:
- Changed parameter type from `asyncio.Queue` to `MemoryObjectSendStream[DocumentTask]`
- Replaced all `await document_queue.put()` calls with `await send_stream.send()`
- Wrapped scanner loop in `async with send_stream:` context manager for automatic cleanup
- Updated log messages: "Queued" → "Sent"
- Removed `import asyncio` (no longer needed)
**processor.py**:
- Changed parameter type from `asyncio.Queue` to `MemoryObjectReceiveStream[DocumentTask]`
- Replaced `asyncio.wait_for(document_queue.get(), timeout=1.0)` with `anyio.fail_after(1.0)` + `await receive_stream.receive()`
- Removed all `document_queue.task_done()` calls (not needed with streams)
- Added `anyio.EndOfStream` exception handling for graceful shutdown when scanner closes
- Removed `import asyncio` (no longer needed)
**app.py**:
- Removed `import asyncio` from top-level imports
- Added `from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream`
- Updated AppContext dataclass:
- Replaced `document_queue: Optional[asyncio.Queue]` with:
- `document_send_stream: Optional[MemoryObjectSendStream]`
- `document_receive_stream: Optional[MemoryObjectReceiveStream]`
- Updated `app_lifespan_basic()`:
- Replaced `asyncio.Queue(maxsize=...)` with `anyio.create_memory_object_stream(max_buffer_size=...)`
- Pass `send_stream` to scanner_task
- Pass `receive_stream.clone()` to each processor_task (enables multiple consumers)
- Updated AppContext yield to include both streams
- Updated `starlette_lifespan()`:
- Same changes as app_lifespan_basic for streamable-http transport
- Removed `import asyncio as asyncio_module` (no longer needed)
- Updated app.state storage to use send_stream and receive_stream
**semantic.py**:
- Updated `nc_get_vector_sync_status()` tool:
- Access `document_receive_stream` instead of `document_queue` from lifespan context
- Use `stream_stats.current_buffer_used` instead of `queue.qsize()` for pending count
- More reliable metrics (qsize() was not guaranteed accurate)
## Benefits
1. **Library Consistency**: Pure anyio throughout codebase (was mixing asyncio.Queue with anyio.Event and anyio.create_task_group)
2. **Graceful Shutdown**: `async with send_stream:` automatically closes stream on exit, signaling EndOfStream to all processors
3. **Better Timeout Handling**: `anyio.fail_after()` is more idiomatic than `asyncio.wait_for()`
4. **Stream Cloning**: Easy to add multiple consumers via `receive_stream.clone()`
5. **Better Statistics**: `.statistics()` provides accurate buffer metrics (qsize() was unreliable)
6. **Type Safety**: Separate send/receive types prevent accidental misuse
7. **No task_done() tracking**: Streams handle completion automatically
## Testing
- ✅ All 69 unit tests passing
- ✅ All 5 smoke tests passing
- ✅ No regressions in functionality
- ✅ Graceful shutdown behavior improved
## References
- https://anyio.readthedocs.io/en/stable/why.html#queue-fix
- https://anyio.readthedocs.io/en/stable/streams.html#memory-object-streams🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses issues with vector database synchronization that
were causing test failures:
1. **Deletion Grace Period** (scanner.py)
- Fixed premature deletion of documents due to pagination cursor
inconsistencies in Notes API
- Implemented 2-scan verification with 1.5x scan interval grace period
(15 seconds default)
- Documents must be missing for 2 consecutive scans before deletion
- Documents that reappear are removed from deletion tracking
- Prevents false deletions during concurrent note creation/indexing
2. **Vector Sync Status Tool** (server/notes.py, models/notes.py)
- Added nc_notes_get_vector_sync_status MCP tool
- Returns indexed_count, pending_count, status, and enabled fields
- Enables tests and clients to wait for vector sync completion
- Uses lifespan context to access document queue and Qdrant client
3. **Test Improvements** (test_sampling.py, conftest.py)
- Added temporary_note_factory fixture for creating multiple test notes
- Updated all sampling tests to wait for vector sync completion
- Adjusted score_threshold to 0.0 for SimpleEmbeddingProvider
(feature hashing produces low-quality embeddings)
- Fixed CallToolResult extraction (removed ["result"] key access)
- Removed invalid @pytest.mark.asyncio markers (anyio mode)
All integration tests now pass successfully.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Completes the ADR-007 implementation by adding user-facing semantic search
functionality. Previous phases implemented scanner and processor for background
indexing; this adds the query interface.
Changes:
- Add nc_notes_semantic_search MCP tool for natural language queries
- Fix Qdrant point IDs to use UUIDs instead of strings (was causing 400 errors)
- Reduce scan interval default from 1 hour to 5 minutes for faster updates
- Add SemanticSearchResult and SemanticSearchNotesResponse models
- Implement dual-phase authorization (Qdrant filter + Nextcloud API verification)
The semantic search enables finding notes by meaning rather than exact keywords,
using vector embeddings to understand query intent. Point ID fix resolves
critical bug where all document indexing failed with "invalid point ID" errors.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixes background task startup for streamable-http transport by integrating
vector sync initialization into the Starlette lifespan context manager.
Starlette Lifespan Integration:
- Moved background task startup from FastMCP lifespan to Starlette lifespan
- FastMCP lifespan only triggers on MCP session establishment
- Starlette lifespan runs on server startup (correct timing)
- Fixed module scoping issues with local imports (anyio_module, asyncio_module)
- Added conditional startup based on oauth_enabled flag
Scanner Fixes:
- Fixed NotesClient method: list_notes() → get_all_notes()
- Properly handle AsyncIterator with list comprehension
- Collects all notes before processing
Verified Working:
- Background tasks start successfully on server startup
- Scanner fetches notes from Nextcloud API
- Processor pool (3 workers) ready for document processing
- Health endpoint reports Qdrant status
- No startup errors
Phase 3 Complete:
- BasicAuth mode with vector sync fully functional
- Background tasks integrate cleanly with streamable-http transport
- Graceful shutdown with coordinated task cancellation
Related: ADR-007 Background Vector Database Synchronization
🤖 Generated with Claude Code (https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements background vector database synchronization using anyio
TaskGroups for BasicAuth mode with single-user credentials.
Scanner Implementation:
- Periodic document discovery (hourly, configurable)
- Timestamp-based change detection (Nextcloud vs Qdrant)
- Wake event for immediate scanning on-demand
- Supports both initial sync (all docs) and incremental sync (changes only)
- Detects deleted documents and queues for removal
Processor Implementation:
- Concurrent document processing pool (3 workers default)
- I/O-bound embedding generation via Ollama API
- Retry logic with exponential backoff (3 retries)
- Document chunking (512 words, 50-word overlap)
- Handles both index and delete operations
- Upserts vectors to Qdrant with rich metadata
App Lifespan Integration:
- Extended AppContext with background task state
- Modified app_lifespan_basic() to start tasks via anyio TaskGroups
- Graceful shutdown with coordinated task cancellation
- Only activates when VECTOR_SYNC_ENABLED=true
Embedding Service:
- OllamaEmbeddingProvider with TLS support
- Singleton pattern for shared client instances
- Batch embedding support for efficiency
- Auto-detects embedding dimension (768 for nomic-embed-text)
Qdrant Client:
- Async client wrapper with singleton pattern
- Auto-creates collection on first use
- COSINE distance metric for semantic similarity
- Integrates with embedding service for dimension detection
Health Check Enhancement:
- Added Qdrant status check to /health/ready endpoint
- Only checks when VECTOR_SYNC_ENABLED=true
- 2-second timeout for health probe
- Reports connection errors with details
Configuration:
- VECTOR_SYNC_ENABLED: Enable background sync
- VECTOR_SYNC_SCAN_INTERVAL: Scanner frequency (3600s default)
- VECTOR_SYNC_PROCESSOR_WORKERS: Concurrent processors (3 default)
- QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION: Vector DB config
- OLLAMA_BASE_URL, OLLAMA_EMBEDDING_MODEL: Embedding service config
Dependencies Added:
- qdrant-client>=1.7.0: Vector database client
Docker Compose:
- Added Qdrant service with health check
- Exposed ports 6333 (REST) and 6334 (gRPC)
- Configured MCP service with vector sync environment
- Added qdrant-data volume for persistence
Known Issue:
- FastMCP lifespan not triggering for streamable-http transport
- Background tasks will start once lifespan integration is complete
- Lifespan triggers on MCP session establishment, not server startup
Related: ADR-007 Background Vector Database Synchronization
🤖 Generated with Claude Code (https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Chris Coutinho