Commit Graph

6 Commits

Author SHA1 Message Date
Chris Coutinho 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>
2025-11-15 06:21:06 +01:00
Chris Coutinho ed0825e661 feat: Enhance vector visualization UI and parallelize search verification
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>
2025-11-15 05:39:07 +01:00
Chris Coutinho e3153822f7 perf: Exclude vector-sync status polling from distributed tracing
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>
2025-11-15 05:19:35 +01:00
Chris Coutinho 2b35dd729f fix: Reorder tabs and fix viz pane session access
- 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
2025-11-15 02:41:42 +01:00
Chris Coutinho 916af1c8f3 feat: Add vector visualization pane with multi-select document types
- 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
2025-11-15 02:32:10 +01:00
Chris Coutinho 9a62c8478f feat: Implement custom PCA to remove sklearn dependency
- 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
2025-11-15 02:02:57 +01:00