- Get container dimensions before creating Plotly layout to render at correct size immediately
- Add init() method with window resize listener for responsive plot sizing
- Remove post-render resize call (no longer needed with explicit dimensions)
- Improve colorbar positioning and scene domain configuration
This eliminates the visual "jump" during initial render and ensures the plot resizes smoothly when the browser window changes size.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Two fixes for the vector visualization page:
1. **CSS Loading Fix**: Moved CSS <link> from vector_viz.html fragment
to user_info.html <head> block. HTMX fragments don't process <link>
tags in <head>, causing unstyled page. Now CSS loads correctly.
2. **Camera Preservation**: Modified renderPlot() to preserve camera
position when toggling query point visibility. Previously, toggling
the "Show Query Point" checkbox would reset zoom/rotation to default.
Now reads existing camera settings from plot before updating.
Related: nextcloud_mcp_server/auth/static/vector-viz.js:123-130
Related: nextcloud_mcp_server/auth/templates/user_info.html:12
🤖 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>
This commit updates the web interface to better align with Nextcloud's
design system and improve the Vector Viz layout.
Changes:
- Replace emoji icons with Material Design SVG icons for better
consistency with Nextcloud apps
- Simplify navigation styling with minimal padding and subtle active
states (250px width)
- Update CSS variables to match Nextcloud design system
- Restructure Vector Viz from two-column to single-column vertical
layout for better plot visibility
- Move search controls to compact horizontal grid at top
- Make navigation toggle always visible (not just on mobile)
- Fix plot container sizing with overflow:visible to prevent colorbar
clipping
- Remove heavy shadows and custom card styling for cleaner aesthetic
- Add error and success page templates with consistent styling
Technical details:
- Preserve Alpine.js for reactive functionality
- Use CSS Grid for responsive horizontal controls layout
- Add smooth transitions for navigation collapse/expand
- Maintain HTMX for dynamic content loading
🤖 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>
This commit enhances the vector visualization interface with better score
transparency and improved UX:
**Dual-Score Display:**
- Store original algorithm scores before normalization (viz_routes.py:203)
- Display both raw and normalized scores: "Raw Score: 0.842 (89% relative)"
- Update plot hover text with dual scores (userinfo_routes.py:740)
- Fixes issue where all queries showed at least one 100% match regardless
of actual relevance (normalization artifact)
**UI Improvements:**
1. Fusion Method dropdown: Changed from x-show to :disabled
- Prevents jarring layout shift when switching algorithms
- Dropdown stays visible but grayed out when Semantic is selected
- Better UX with opacity: 0.5 and cursor: not-allowed
2. Score Threshold: Changed step from 0.1 to "any"
- Allows arbitrary float precision (0.7, 0.85, 0.123)
- Users can now fine-tune threshold values
3. Document Types: Converted multi-select to checkbox grid
- Replaced clunky Ctrl/Cmd multi-select listbox
- Checkbox grid with cleaner layout
- Positioned left of Score Threshold and Result Limit inputs
- More intuitive UX
**Technical Details:**
- Raw score ranges vary by algorithm:
- Semantic: 0.0-1.0 (cosine similarity)
- BM25 RRF: ~0.001-0.033 (Reciprocal Rank Fusion)
- BM25 DBSF: Can exceed 1.0 (Distribution-Based Score Fusion)
- Normalized scores (0-1) used for visual encoding (marker size, color)
- Original scores preserved in API response via getattr fallback
Files modified:
- nextcloud_mcp_server/auth/viz_routes.py (store original_score)
- nextcloud_mcp_server/auth/templates/vector_viz.html (UI controls)
- nextcloud_mcp_server/auth/userinfo_routes.py (plot hover text)
🤖 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>
Extracted vector visualization HTML template to separate file to resolve
syntax conflicts between Jinja2, Alpine.js, and CSS. Added chunk context
endpoint for fetching matched chunks with surrounding text.
Changes:
- Moved vector_viz.html to templates/ directory (separates Jinja2/Alpine.js/CSS)
- Added /app/chunk-context endpoint for retrieving chunk text with context
- Updated .dockerignore to include HTML files in Docker builds
- Moved anthropic and boto3 to main dependencies (needed for production features)
- Added jinja2 dependency for template rendering
Fixes Jinja2 TemplateSyntaxError caused by CSS colons being parsed as
Jinja2 syntax when template was inline in Python code.
🤖 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>
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"
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Refactored LLM provider infrastructure to support sustainable additions of new providers with both embedding and text generation capabilities.
## Major Changes
### Unified Provider Architecture (ADR-015)
- Created `nextcloud_mcp_server/providers/` with unified Provider ABC
- Providers now support optional capabilities (embeddings and/or generation)
- Auto-detection registry with priority: Bedrock → Ollama → Simple
- Backward compatible - existing code continues to work
### New Providers
- **BedrockProvider**: Full Amazon Bedrock integration
- Embeddings: Titan Embed, Cohere Embed models
- Generation: Claude, Llama, Titan Text, Mistral models
- Model-specific request/response handling
- AWS credential chain integration
- **OllamaProvider**: Migrated with both capabilities support
- **AnthropicProvider**: Moved from test code to production providers
- **SimpleProvider**: Migrated in-memory fallback provider
### Breaking Changes
None - full backward compatibility maintained:
- `embedding.get_embedding_service()` still works
- RAG evaluation tests updated to use unified providers
- All existing tests pass (127 unit tests)
### Testing
- Added 9 comprehensive Bedrock unit tests with mocked boto3
- All existing unit tests pass
- Type checking (ty) and linting (ruff) pass
- Verified backward compatibility
### Documentation
- `docs/ADR-015-unified-provider-architecture.md`: Comprehensive ADR
- `docs/bedrock-setup.md`: AWS setup guide with IAM permissions
- `CLAUDE.md`: Updated with provider architecture section
### Dependencies
- Added `boto3>=1.35.0` to dev dependencies (optional)
## Environment Variables
### Bedrock
- `AWS_REGION`: AWS region (e.g., "us-east-1")
- `BEDROCK_EMBEDDING_MODEL`: Model ID for embeddings
- `BEDROCK_GENERATION_MODEL`: Model ID for generation
- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`: Optional credentials
### Ollama
- `OLLAMA_BASE_URL`: API URL
- `OLLAMA_EMBEDDING_MODEL`: Embedding model (default: "nomic-embed-text")
- `OLLAMA_GENERATION_MODEL`: Generation model
## AWS Bedrock Permissions Required
Minimal IAM policy:
```json
{
"Effect": "Allow",
"Action": ["bedrock:InvokeModel"],
"Resource": ["arn:aws:bedrock:*::foundation-model/*"]
}
```
See `docs/bedrock-setup.md` for detailed setup instructions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- viz_routes.py: Extract "dense" vector from named vector dict
- semantic.py: Specify using="dense" for BM25 hybrid collections
- Fixes "X must be 2D array" error in hybrid search
- Fixes "Dense vector is not found" error in semantic search
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
The visualization UI was still using the old 'hybrid' algorithm name and
weight parameters that were replaced by the BM25 hybrid search refactor.
This caused "Unknown algorithm: hybrid" errors when using the search
& visualize feature.
Changes:
- Update default algorithm from 'hybrid' to 'bm25_hybrid'
- Update default scoreThreshold from 0.7 to 0.0 to match backend
- Remove deprecated semanticWeight, keywordWeight, fuzzyWeight parameters
- Remove weight parameters from search request
Fixes the visualization search functionality after BM25 hybrid refactor.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove obsolete search algorithm imports (Fuzzy, Keyword, Hybrid)
- Update UI to only show Semantic and BM25 Hybrid algorithms
- Replace manual weight controls with RRF fusion info message
- Update default algorithm from "hybrid" to "bm25_hybrid"
- Remove weight parameters (semantic_weight, keyword_weight, fuzzy_weight)
- Update score_threshold default from 0.7 to 0.0 for RRF scoring
- Document ty type checker in CLAUDE.md
Fixes unresolved-import type errors after BM25 refactor.
🤖 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>
Improve user comprehension by scaling RRF scores to match the intuitive
0-1 range used by other search algorithms.
## Problem
RRF (Reciprocal Rank Fusion) scores had a drastically different scale
than semantic/keyword/fuzzy scores:
- Semantic similarity: 0.0 to 1.0 (typical: 0.5-0.9)
- RRF scores: 0.0 to ~0.016 (typical: 0.005-0.015)
This caused user confusion - a score of 0.0078 looked terrible but was
actually excellent (near theoretical maximum).
## Solution
Normalize RRF scores using the formula:
`normalized_score = rrf_score * (rrf_k + 1) / total_weight`
Where:
- rrf_k = 60 (RRF constant)
- total_weight = sum of algorithm weights (default: 1.0)
**Example transformation:**
- Before: 0.0078 (confusing)
- After: 0.477 (intuitive)
## Changes
**nextcloud_mcp_server/search/hybrid.py:**
- Store total_weight as instance variable (line 63)
- Calculate normalization factor in _reciprocal_rank_fusion() (line 209)
- Apply normalization to all RRF scores (line 217)
- Preserve raw RRF score in metadata for debugging (line 222)
## Impact
**User Experience:**
- Hybrid search scores now comparable with semantic/keyword/fuzzy
- Score of 0.5 indicates good match across all algorithms
- Consistent scale improves score threshold usability
**Backward Compatibility:**
- Raw RRF scores preserved in metadata["rrf_score_raw"]
- Result ordering unchanged (normalization is linear transformation)
- Breaking change: Existing score thresholds need adjustment
**Performance:**
- Negligible overhead (single multiplication per result)
## Testing
Verified with nc_semantic_search and nc_semantic_search_answer:
- Hybrid scores now 0.47-0.7 range (was 0.003-0.011)
- Semantic scores unchanged (0.75)
- Result ordering preserved
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
Vector Visualization Improvements:
- Add interactive vector viz tab with Alpine.js and Plotly.js to user info page
- Refactor viz route CSS for better scoping and maintainability
- Remove unused nextcloud_host variable
Performance Optimizations:
- Parallelize access verification in fuzzy and keyword search algorithms
- Use asyncio.gather() to verify multiple documents concurrently
- Add exception handling with return_exceptions=True for resilience
Dependencies:
- Update third_party/oidc submodule to include RFC 9728 resource_url support
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Skip tracing for /app/vector-sync/status to reduce noise from HTMX polling.
Metrics collection continues for this endpoint.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Move Webhooks tab to the right (User Info | Vector Sync | Vector Viz | Webhooks)
- Use request.user.display_name instead of session for viz routes
- Fixes session middleware error when accessing via iframe
- Add /app/vector-viz endpoint for interactive search testing
- Implement server-side PCA dimensionality reduction (768-dim → 2D)
- Support multi-select document type filter for cross-app search
- Support all search algorithms: semantic, keyword, fuzzy, hybrid
- Display 2D scatter plot of vector embeddings using Plotly
- Show search results with scores and document types
- Register viz routes in app.py
- 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
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
Implements NextcloudClientProtocol for multi-document type search following
user requirement that document types are not 1:1 with apps (e.g., Notes app
specializes in markdown, while Files/WebDAV handles multiple file types).
Key Changes:
- NextcloudClientProtocol: Generic protocol with app-specific client properties
- get_indexed_doc_types(): Query Qdrant for actually-indexed document types
- Document dispatch: All algorithms check Qdrant before attempting access
- Cross-type deduplication: Use (doc_id, doc_type) tuples in hybrid RRF
Search Algorithm Updates:
- Semantic: Added _verify_document_access() with dispatch to appropriate client
- Deduplication by (doc_id, doc_type) tuple
- Only "note" verification implemented, others return None with info log
- Keyword: Added _fetch_documents() dispatch method
- Queries Qdrant for available types before fetching
- Supports cross-app search when doc_type=None
- Fuzzy: Same pattern as keyword search
- Hybrid: Already uses (doc_id, doc_type) for deduplication (no changes needed)
Future-Proof Design:
- File/calendar verification stubs in place
- Clear logging when unsupported types found
- Easy to extend when processor indexes new document types
Currently Supported:
- "note" documents fully implemented and tested
- Other types gracefully handled (logged but skipped)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
Previously, an empty query string to nc_notes_search_notes would return
zero results due to an early return when no query tokens were present.
This was counterintuitive - users expect an empty query to list all
notes, not return nothing.
Changes:
- Modified NotesSearchController.search_notes() to return all notes
when query is empty
- Added documentation to clarify this behavior
- Empty query results have _score: None (no relevance scoring)
- Non-empty query results continue to have relevance scores
Fixes behavior where listing all notes was impossible via the search tool.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixes#296
The application code was looking for OIDC_CLIENT_ID and OIDC_CLIENT_SECRET
(without NEXTCLOUD_ prefix), but the Helm chart, documentation, and CLI
all use NEXTCLOUD_OIDC_CLIENT_ID and NEXTCLOUD_OIDC_CLIENT_SECRET.
This mismatch caused OAuth deployments via Helm to fail with crashloops
because the credentials weren't being found.
Changes:
- app.py: Use NEXTCLOUD_OIDC_CLIENT_ID/SECRET in setup_oauth_config()
- config.py: Use NEXTCLOUD_OIDC_CLIENT_ID/SECRET in get_settings()
- Updated documentation comments and error messages
This aligns with the documented naming convention where all Nextcloud-related
environment variables use the NEXTCLOUD_ prefix.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Created @instrument_tool decorator for automatic MCP tool metrics collection.
Applied to all 7 tools in notes.py.
Changes:
- observability/metrics.py:
* New instrument_tool() decorator for automatic timing and error tracking
* Compatible with @mcp.tool() and @require_scopes() decorators
* Records tool_name, duration, and success/error status
- server/notes.py:
* Applied @instrument_tool to all 7 tool functions
* nc_notes_create_note, nc_notes_update_note, nc_notes_append_content
* nc_notes_search_notes, nc_notes_get_note, nc_notes_get_attachment
* nc_notes_delete_note
These metrics will populate the MCP Tool Calls dashboard panels.
Part of PR #295 - Complete metrics instrumentation (Phase 5)
Remaining: 86 tools across 8 server files
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>
Chris Coutinho