Fix false-positive validation error where DBSF (Distribution-Based Score
Fusion) correctly produces scores > 1.0 but SearchResult validation
incorrectly rejected them.
**Root Cause**: SearchResult.__post_init__() enforced scores in [0.0, 1.0]
range, but DBSF sums normalized scores from multiple retrieval systems
(dense semantic + sparse BM25), resulting in scores like 1.55 when both
systems strongly agree a document is relevant.
**Changes**:
- Relaxed validation to allow any score ≥ 0.0 (algorithms.py:147-157)
- Updated SearchResult and SemanticSearchResult documentation to explain
score ranges for RRF ([0.0, 1.0]) vs DBSF (unbounded)
- Added comprehensive test coverage for both fusion methods
- Added DBSF fusion option to vector visualization UI
- Updated viz routes and vizApp() to support fusion parameter selection
**Testing**: All 157 unit tests pass, type checking passes, ruff passes
Fixes error: "Configuration error: Score must be between 0.0 and 1.0, got 1.1528953"
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Co-Authored-By: Claude <noreply@anthropic.com>
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
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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.
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Co-Authored-By: Claude <noreply@anthropic.com>
Add nc_notes_semantic_search_answer tool that combines semantic search
with MCP sampling to generate natural language answers from retrieved
Nextcloud Notes. This enables Retrieval-Augmented Generation (RAG)
patterns without requiring a server-side LLM.
Key features:
- Client-side LLM generation via ctx.session.create_message()
- Graceful fallback when sampling unavailable
- Proper source citations in generated answers
- No results optimization (skips sampling when no docs found)
- Comprehensive unit and integration tests
Implementation details:
- SamplingSearchResponse model with generated_answer and sources
- Fixed prompt template with document context and citation instructions
- Model preferences hint Claude Sonnet for balanced performance
- Falls back to returning documents without answer on sampling failure
Updates:
- Add ADR-008 documenting sampling architecture decision
- Add MCP sampling pattern guidance to CLAUDE.md
- Update README.md and docs/notes.md (7 → 9 tools)
- Add 4 unit tests and 6 integration tests
🤖 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>
- Import recipes from URLs using schema.org metadata
- Full CRUD operations for recipes
- Search, categorize, and organize recipes
- Manage keywords/tags and categories
- Configure app settings and trigger reindexing
Chris Coutinho