feature/helm-chart-multi-user-basic-support
18 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Chris Coutinho
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20404cf3f2 |
feat(vector): add Deck card vector search with visualization support
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>
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Chris Coutinho
|
19183ad14a |
fix: address PR review feedback
Address all reviewer comments from PR #387: 1. ✅ Add unit tests for annotations (tests/server/test_annotations.py) - 10 comprehensive test functions validating all annotation patterns - Tests for titles, read-only, destructive, idempotent operations - Validates specific ADR-017 decisions (webdav write, semantic search) - Cross-category consistency checks 2. ✅ Fix nc_webdav_write_file idempotency classification - Changed from idempotentHint=False to idempotentHint=True - Rationale: Uses HTTP PUT without version control - Writing same content to same path = same end state (idempotent) 3. ✅ Fix semantic search openWorldHint inconsistency - Changed from openWorldHint=False to openWorldHint=True - Rationale: Consistent with other Nextcloud tools - Nextcloud is external to MCP server (indexed data is implementation detail) 4. ✅ Update ADR-017 with resolved decisions - Converted Open Questions to Resolved Questions - Added detailed rationale for webdav write and semantic search - Updated status from Proposed to Implemented - Added decision timeline with dates 5. ✅ Add MCP Tool Annotations guidelines to CLAUDE.md - Comprehensive section with code examples for all patterns - Key principles documented (idempotency, destructive, open world) - References ADR-017 for detailed rationale All OAuth tools verified to have proper annotations (oauth_tools.py lines 686-751). |
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Chris Coutinho
|
e1412320a7 |
feat: add MCP tool annotations for enhanced UX
Add ToolAnnotations to all 105+ MCP tools across 13 modules to enable better client-side UX with human-readable titles and behavioral hints. Changes: - Add title and ToolAnnotations to all @mcp.tool() decorators - Apply correct idempotency classification per ADR-017 - Add destructiveHint for delete operations - Set openWorldHint=False for semantic search (internal data only) Modules updated: - OAuth (4 tools): Authentication and provisioning - Notes (7 tools): Note management - WebDAV (11 tools): File operations - Semantic (3 tools): Semantic search and RAG - Calendar (16 tools): Events and todos - Contacts (7 tools): Address book management - Sharing (5 tools): File/folder sharing - Tables (6 tools): Structured data - Deck (25 tools): Kanban board management - Cookbook (13 tools): Recipe management - News (8 tools): RSS feed reader Annotation patterns: - Read operations: readOnlyHint=True, openWorldHint=True - Create operations: idempotentHint=False, openWorldHint=True - Update operations: idempotentHint=False, openWorldHint=True - Delete operations: destructiveHint=True, idempotentHint=True, openWorldHint=True See docs/ADR-017-mcp-tool-annotations.md for rationale and implementation details. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> |
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Chris Coutinho
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5c73b85f65 |
fix: Increase MCP sampling timeout to 5 minutes for slower LLMs
- Increase sampling timeout from 30s to 300s in semantic.py to accommodate slower local LLMs like Ollama - Refactor RAG integration tests to support multiple providers (ollama, openai, anthropic, bedrock) - Remove unnecessary embedding_provider fixture since MCP server handles embeddings internally - Add --provider flag via tests/integration/conftest.py - Add provider_fixtures.py with factory functions for generation providers 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
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03b984d5a7 |
fix(smithery): Enable JSON response format for scanner compatibility
The Smithery scanner was reporting "0 tools" despite the server returning
valid tool definitions. Root cause: the server was returning SSE-formatted
responses (event: message\ndata: {...}) which the scanner couldn't parse.
Changes:
- Add json_response=True to FastMCP for Smithery stateless mode
- Clean up verbose docstring examples in semantic.py and webdav.py
The MCP spec allows both SSE and plain JSON responses for HTTP transport.
Setting json_response=True returns Content-Type: application/json with
plain JSON-RPC instead of text/event-stream with SSE format.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Chris Coutinho
|
a62a007c87 |
feat: Add context expansion to semantic search with chunk overlap removal
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
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Chris Coutinho
|
3aa7128f45 |
feat: add chunk position tracking to vector indexing and search
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> |
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Chris Coutinho
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c28fc955ca |
Merge origin/master into feature/bm25
Resolved conflicts: - viz_routes.py: Kept bm25's extract_dense_vector() function for robust vector handling - hybrid.py: Removed (bm25 uses native Qdrant RRF fusion instead) - uv.lock: Regenerated after accepting master's dependencies This merge brings in: - RAG evaluation framework (ADR-013) - Performance optimizations (double-fetch elimination) - Migration from asyncio to anyio - OpenTelemetry tracing improvements - Notes app enhancements 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
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02700a8e2c |
perf: Eliminate double-fetching in semantic search sampling
Performance optimization that removes redundant verification step and makes content fetching parallel in nc_semantic_search_answer tool. Changes: - Remove verification.py module (only had 1 caller) - Refactor nc_semantic_search to do inline deduplication instead of calling verify_search_results() - Migrate verification patterns (anyio task group, semaphore limiting) to nc_semantic_search_answer's content fetching - Change content fetching from sequential loop to parallel execution Performance impact: - Before: 10 API calls (5 parallel verification + 5 sequential content) = ~5.5s overhead - After: 5 API calls (parallel content fetch) = ~0.5s overhead - Result: 50% fewer API calls, ~10x faster for sampling operations Technical details: - Uses anyio.create_task_group() for structured concurrency - Semaphore limiting (max_concurrent=20) prevents connection pool exhaustion - Index-based storage maintains result ordering - Expected failures (deleted notes) logged at debug level - Deduplication handles hybrid search returning same doc from dense + sparse 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
|
6fe5596c13 |
feat: Implement BM25 hybrid search with native Qdrant RRF fusion
Replace custom keyword/fuzzy search algorithms with industry-standard BM25 sparse vectors, combined with dense semantic vectors using Qdrant's native Reciprocal Rank Fusion (RRF). This consolidates search architecture and improves relevance for both semantic and keyword queries. Key changes: - Add fastembed dependency for BM25 sparse vector generation - Update Qdrant collection schema to support named vectors (dense + sparse) - Create BM25SparseEmbeddingProvider using FastEmbed's Qdrant/bm25 model - Implement BM25HybridSearchAlgorithm with native Qdrant RRF prefetch - Update document processor to generate both dense and sparse embeddings - Simplify nc_semantic_search() tool to use BM25 hybrid only - Remove legacy keyword.py, fuzzy.py, and custom hybrid.py (736 lines) - Update ADR-014 with implementation notes and test results Benefits: - Consolidated architecture (single Qdrant database) - Native database-level RRF fusion (more efficient) - Industry-standard BM25 (replaces brittle custom keyword search) - Better relevance across semantic and keyword queries - Simplified codebase (-285 net lines) Tests: All 125 tests passing (118 unit, 7 integration) Implements ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
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42376483ab |
refactor: Optimize Nextcloud access verification with centralized filtering
Move access verification from individual search algorithms to final output stage, eliminating redundant API calls and improving performance. ## Changes **New:** - `search/verification.py`: Centralized verification using anyio task groups - Deduplicates results by (doc_id, doc_type) before verification - Verifies all unique documents in parallel using structured concurrency - Filters out inaccessible documents in single pass **Modified Search Algorithms:** - `search/semantic.py`: Removed _deduplicate_and_verify() and _verify_document_access() - `search/keyword.py`: Removed _verify_access() and parallel verification - `search/fuzzy.py`: Removed _verify_access() and parallel verification - `search/hybrid.py`: Removed nextcloud_client parameter passing All algorithms now return unverified results from Qdrant payload. **Modified Output Stages:** - `server/semantic.py`: Added verify_search_results() call after search - `auth/viz_routes.py`: Added verify_search_results() call after search Both endpoints now verify access once at final stage with deduplication. ## Performance Impact **Before:** - Hybrid mode (limit=10): 30 API calls (10 per algorithm × 3 algorithms) - Single algorithm: 10-20 API calls (with verification buffer) **After:** - Hybrid mode (limit=10): 10 API calls (deduplicated verification) - Single algorithm: 10 API calls (deduplicated verification) **Performance Gain:** 3x reduction in API calls for hybrid search ## Architecture Benefits - **Separation of concerns**: Algorithms handle scoring, output stage handles security - **Deduplication**: Each document verified exactly once - **Parallel execution**: All verifications run concurrently via anyio task groups - **Consistency**: Same verification logic across MCP tools and viz endpoints 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
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2a078093ed |
refactor!: Make all search algorithms query Qdrant payload, not Nextcloud
BREAKING CHANGE: Search algorithms now require Qdrant to be populated. Vector sync must be enabled and documents indexed for search to work. - Keyword and fuzzy search now query Qdrant scroll API for title/excerpt - Remove inefficient Nextcloud API fetching pattern - Add optional Nextcloud verification for security - Deduplicate by (doc_id, doc_type) tuple, keeping chunk_index=0 - Align with document processor pattern that already stores text in Qdrant |
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Chris Coutinho
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f3bdb8b885 |
feat: Update nc_semantic_search tool with algorithm selection
Implements ADR-012 by adding multi-algorithm support to the MCP tool. Key changes: - Added algorithm parameter: "semantic"|"keyword"|"fuzzy"|"hybrid" (default: "hybrid") - Added weight parameters for hybrid mode configuration - Replaced direct Qdrant/embedding calls with search module abstractions - Updated docstring to describe all four algorithms - Simplified implementation: ~50 lines vs ~150 lines (67% reduction) - Better error handling for missing vector sync Algorithm selection: - semantic: Pure vector similarity (requires VECTOR_SYNC_ENABLED=true) - keyword: Token-based matching with weighted title/content scoring - fuzzy: Character overlap for typo tolerance - hybrid: RRF fusion with configurable weights (default: 0.5/0.3/0.2) Backward compatibility: - Tool name unchanged (nc_semantic_search) - New parameters have sensible defaults - Existing clients get hybrid search automatically (better than pure semantic) - search_method field in response reflects actual algorithm used Weight validation: - Performed in HybridSearchAlgorithm constructor - Must sum to ≤1.0 and all non-negative - At least one weight must be > 0 - Clear error messages on validation failure Next: Update viz pane to use same algorithms 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Chris Coutinho
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c3023d2cc3 |
feat: Complete Phase 5 - Instrument all 93 MCP tools
Applied @instrument_tool decorator to all 86 remaining tools across 8 server files. Instrumented files: - calendar.py: 16 tools - contacts.py: 7 tools - deck.py: 25 tools - webdav.py: 11 tools - tables.py: 6 tools - sharing.py: 5 tools - cookbook.py: 13 tools - semantic.py: 3 tools Total: 93 tools instrumented (7 in notes.py + 86 in other files) These metrics populate: - MCP Tool Calls panel (by tool name and status) - MCP Tool Duration panel (histogram) - MCP Tool Errors panel (by tool name and error type) This completes PR #295 - All 5 phases of metrics instrumentation done: ✅ Phase 1: Queue size metrics (2 locations) ✅ Phase 2: Health checks (1 location) ✅ Phase 3: Database operations (3 methods) ✅ Phase 4: OAuth token metrics (3 locations) ✅ Phase 5: MCP tool metrics (93 tools) All 34 dashboard panels now have data sources. |
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Chris Coutinho
|
4ea5ed72d4 |
feat: Add Grafana dashboard and vector sync metric instrumentation
Implement comprehensive observability for vector database synchronization
with Grafana dashboard and Prometheus metrics.
## Part 1: Grafana Dashboard
Created all-in-one operations dashboard with 7 rows and 34 panels:
### Dashboard Structure:
- **Overview Row**: Request rate, error rate, P95 latency, active requests
- **HTTP Metrics (RED)**: Request/error rates by endpoint, latency percentiles
- **MCP Tools**: Call volume, error rates, execution duration by tool
- **Nextcloud API**: API calls/latency by app, retry patterns
- **OAuth & Authentication**: Token validations, exchanges, cache hit rate
- **Dependencies & Health**: Status for Nextcloud/Qdrant/Keycloak/Unstructured
- **Vector Sync**: Processing throughput, queue depth, Qdrant operations
### Helm Chart Integration:
- Added dashboard-configmap.yaml template for automatic provisioning
- Configured Grafana sidecar auto-discovery (label: grafana_dashboard="1")
- Added dashboards configuration section in values.yaml (opt-in)
- Updated Chart.yaml with dashboard annotations
- Enhanced NOTES.txt with dashboard deployment instructions
- Comprehensive documentation in dashboards/README.md
Dashboard supports dynamic filtering via variables:
- datasource: Prometheus data source selection
- namespace: Filter by Kubernetes namespace
- pod: Multi-select pod filtering
- interval: Query interval (1m/5m/10m/30m/1h)
## Part 2: Vector Sync Metric Instrumentation
Implemented metric recording throughout vector sync pipeline:
### metrics.py:
Added convenience functions:
- record_vector_sync_scan() - Track documents per scan
- record_vector_sync_processing() - Track processing duration/status
- record_qdrant_operation() - Track database operations
- update_vector_sync_queue_size() - Track queue depth
### scanner.py:
- Record number of documents found in each scan
- Enables monitoring of scan throughput
### processor.py:
- Record processing duration for each document
- Track success/failure status with timing
- Record Qdrant upsert/delete operations
- Handle all code paths (success, deletion, error)
### semantic.py:
- Wrap Qdrant query_points with try/except
- Record search operation success/failure
## Metrics Exposed:
- mcp_vector_sync_documents_scanned_total
- mcp_vector_sync_documents_processed_total{status}
- mcp_vector_sync_processing_duration_seconds (histogram)
- mcp_vector_sync_queue_size (gauge)
- mcp_qdrant_operations_total{operation,status}
This enables monitoring of:
- Scan and processing throughput
- Processing latency (P50/P95/P99)
- Error rates for processing and Qdrant operations
- Queue depth trends
- Complete observability of vector sync pipeline
## Testing:
Verified locally that metrics are recorded correctly:
- 36 documents scanned
- 3 documents processed (avg 7.5s each)
- 3 successful Qdrant upsert operations
- Search operations tracked
## Deployment:
Enable dashboard provisioning in Helm values:
```yaml
dashboards:
enabled: true
grafanaFolder: "Nextcloud MCP"
```
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Chris Coutinho
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e575c8e57b |
feat(vector): Support multiple embedding models with auto-generated collection names
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>
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Chris Coutinho
|
72232f937a |
refactor: migrate vector sync from asyncio.Queue to anyio memory object streams
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>
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Chris Coutinho
|
4b026e9aa0 |
feat: implement ADR-009 - refactor semantic search to use generic semantic:read scope
This implements ADR-009, which documents the decision to use a generic
`semantic:read` OAuth scope instead of requiring all app-specific scopes
for semantic search functionality.
Changes:
- Created new `nextcloud_mcp_server/models/semantic.py` with semantic search models
- SemanticSearchResult (with new doc_type field for multi-app support)
- SemanticSearchResponse
- SamplingSearchResponse
- VectorSyncStatusResponse
- Created new `nextcloud_mcp_server/server/semantic.py` with semantic search tools
- nc_semantic_search (renamed from nc_notes_semantic_search)
- nc_semantic_search_answer (renamed from nc_notes_semantic_search_answer)
- nc_get_vector_sync_status (renamed from nc_notes_get_vector_sync_status)
- All tools now use @require_scopes("semantic:read") instead of "notes:read"
- Updated `nextcloud_mcp_server/server/notes.py`
- Removed semantic search tools (moved to semantic.py)
- Removed semantic search model imports
- Removed unused MCP imports (ModelHint, ModelPreferences, etc.)
- Updated `nextcloud_mcp_server/models/notes.py`
- Removed semantic search models (moved to semantic.py)
- Updated `nextcloud_mcp_server/app.py`
- Import configure_semantic_tools
- Register semantic tools when VECTOR_SYNC_ENABLED=true
- Updated `nextcloud_mcp_server/server/__init__.py`
- Export configure_semantic_tools
- Updated tests
- tests/integration/test_sampling.py: Use new tool names
- tests/unit/test_response_models.py: Import from semantic.py, add doc_type field
Architecture:
- Semantic search is now a cross-app feature, not tied to Notes
- Uses dual-phase authorization: semantic:read scope + per-document verification
- Supports future multi-app indexing (notes, calendar, deck, files, contacts)
Test results:
- All 69 unit tests passing
- All 5 smoke tests passing
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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