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.
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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
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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.
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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.
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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
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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.
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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
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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
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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
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Skip tracing for /app/vector-sync/status to reduce noise from HTMX polling.
Metrics collection continues for this endpoint.
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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)
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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
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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.
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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.
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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).
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Co-Authored-By: Claude <noreply@anthropic.com>
Fixes layout issues on the webhooks admin tab:
- Add min-height to container to fill viewport consistently
- Use CSS Grid to overlay tab panes without jumpiness
- Add smooth htmx fade transitions for content swaps
- Adjust vector sync polling interval from 3s to 10s
- Add .playwright-mcp/ to gitignore for test screenshots
The CSS Grid approach allows tabs to overlay without absolute positioning,
preventing content cutoff while maintaining smooth transitions without
container resizing jumps.
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Co-Authored-By: Claude <noreply@anthropic.com>
Implement real-time vector sync status updates in the /app UI without
requiring page refreshes. The status (indexed documents, pending
documents, sync state) now updates automatically every 3 seconds.
Changes:
- Add vector_sync_status_fragment() endpoint that returns HTML fragment
with current vector sync status
- Modify user_info_html() to use htmx loading for vector sync section
with hx-trigger="load" on initial render
- Status fragment includes hx-trigger="every 3s" for continuous polling
- Add /app/vector-sync/status route to browser_routes
The implementation uses htmx (already loaded on page) to poll the status
endpoint, providing near real-time updates with minimal overhead. The
endpoint queries Qdrant for indexed count and reads from memory streams
for pending count, returning only the status HTML fragment.
Pattern follows existing webhook management UI which also uses htmx
for dynamic loading.
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Co-Authored-By: Claude <noreply@anthropic.com>
Simplified the webapp routing structure by consolidating the admin UI
to a single clean endpoint.
Changes:
- Moved webapp from /user/page to /app (root of mount)
- Removed /user JSON endpoint (no longer needed)
- Updated mount point from /user to /app in app.py
- Updated all route path checks (3 locations)
- Updated OAuth redirects to point to /app
- Updated all HTMX endpoint references
- Updated documentation (ADR-007, CHANGELOG)
- Added redirect from /app to /app/ for trailing slash handling
New Route Structure:
- /app - Main webapp (HTML UI with tabs)
- /app/revoke - Revoke background access
- /app/webhooks - Webhook management UI
- /app/webhooks/enable/{preset_id} - Enable webhook preset
- /app/webhooks/disable/{preset_id} - Disable webhook preset
Breaking Change: Existing bookmarks to /user or /user/page will no longer work.
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Co-Authored-By: Claude <noreply@anthropic.com>
Refactored the storage system to use a unified SQLite database for both
webhook tracking and OAuth token storage, available in both BasicAuth
and OAuth modes.
Changes:
- Renamed refresh_token_storage.py → storage.py
- Made TOKEN_ENCRYPTION_KEY optional (only required for OAuth token ops)
- Added registered_webhooks table with schema versioning
- Added webhook storage methods (store, get, delete, list, clear)
- Initialize storage in both BasicAuth and OAuth modes
- Updated webhook routes to persist registrations in database
- Database-first pattern for webhook status checks (performance)
- Updated all imports across codebase
Storage Behavior:
- Database created automatically at startup if needed
- Existing databases detected and reused
- Server fails fast if database initialization fails
- No migrations needed (OAuth feature is experimental)
Testing:
- Added 13 comprehensive unit tests for webhook storage
- All 118 unit tests pass
- All 5 smoke tests pass
- Verified fail-fast behavior on initialization errors
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Co-Authored-By: Claude <noreply@anthropic.com>
Manual testing of Nextcloud webhook_listeners app to validate webhook
payloads against ADR-010 expected schemas and document implementation
requirements for webhook-based vector synchronization.
## Changes
- Add test webhook endpoint at /webhooks/nextcloud in app.py
- Captures and logs webhook payloads for analysis
- Returns 200 OK immediately for webhook delivery confirmation
- Create webhook-testing-findings.md with comprehensive test results
- Captured payloads for 5/6 webhook event types
- Critical findings: missing node.id in deletions, type mismatches
- Implementation recommendations with code examples
- Update ADR-010 with Appendix A: Manual Webhook Testing Results
- Document actual vs expected webhook behavior
- Update event mapping table with tested webhook status
- Add 6 specific implementation recommendations
- Include testing implications for future development
## Testing Results
✅ NodeCreatedEvent - fires correctly, includes node.id (integer)
✅ NodeWrittenEvent - fires correctly, includes node.id (integer)
✅ NodeDeletedEvent - fires but missing node.id field (path only)
✅ CalendarObjectCreatedEvent - fires correctly with full iCal
✅ CalendarObjectUpdatedEvent - fires correctly with full iCal
❌ CalendarObjectDeletedEvent - does not fire (potential NC bug)
## Key Findings
1. NodeDeletedEvent missing node.id field - requires path-based fallback
2. node.id returns integer not string - needs casting for consistency
3. Multiple webhooks fire per operation - needs deduplication logic
4. Calendar deletion webhooks don't fire - reported as issue #53497
5. Calendar webhooks include full iCal content - enables rich parsing
## GitHub Issues
- Created issue #56371: NodeDeletedEvent missing node.id field
- Commented on issue #53497: CalendarObjectDeletedEvent not firing
Closes#283
---
_This commit was generated with the help of AI, and reviewed by a Human_
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
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Co-Authored-By: Claude <noreply@anthropic.com>
Health check and metrics endpoints are frequently polled and don't
provide meaningful trace data. This change skips OpenTelemetry span
creation for:
- /health/* (liveness, readiness checks)
- /metrics (Prometheus metrics)
These endpoints still record Prometheus metrics (request count, latency,
in-flight requests) but no longer create trace spans, reducing tracing
noise and storage costs.
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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.
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Chris Coutinho