Commit Graph

5 Commits

Author SHA1 Message Date
Chris Coutinho c8d9cc24e0 refactor: migrate asyncio to anyio for consistent structured concurrency
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>
2025-11-16 03:51:45 +01:00
Chris Coutinho eaeb8eae28 feat: Normalize hybrid search RRF scores to 0-1 range
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>
2025-11-15 06:48:58 +01:00
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 b5b03bfd78 feat: Add multi-document Protocol with cross-app search support
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>
2025-11-15 01:19:29 +01:00
Chris Coutinho 11e620f2d1 feat: Implement unified search algorithm module
Creates shared search module with four algorithms implementing ADR-012:
- Semantic search (vector similarity via Qdrant)
- Keyword search (token-based matching from ADR-001)
- Fuzzy search (character overlap matching)
- Hybrid search (RRF fusion from ADR-003)

Architecture:
- Base SearchAlgorithm interface for consistent API
- SearchResult dataclass for unified result format
- All algorithms async and independently testable
- Proper logging and error handling throughout

Semantic Search (search/semantic.py):
- Extracted from server/semantic.py
- Vector similarity using Qdrant query_points
- Dual-phase authorization (vector filter + API verification)
- Deduplication of document chunks
- Configurable score threshold (default: 0.7)

Keyword Search (search/keyword.py):
- Implements ADR-001 token-based matching
- Title matches weighted 3x higher than content
- Case-insensitive token matching
- Relevance scoring with normalization
- Excerpt extraction with context

Fuzzy Search (search/fuzzy.py):
- Simple character overlap calculation
- Configurable threshold (default: 70%)
- Typo-tolerant matching
- Fast and dependency-free

Hybrid Search (search/hybrid.py):
- Reciprocal Rank Fusion (RRF) from ADR-003
- Parallel execution of sub-algorithms
- Configurable weights per algorithm
- RRF constant k=60 (standard value)
- Weight validation (must sum ≤1.0)

All algorithms:
- Share NextcloudClient for document access
- Support user_id filtering (multi-tenant)
- Support doc_type filtering (currently notes only)
- Return consistent SearchResult objects
- Properly formatted with ruff and type-checked

Next steps: Update MCP tool to use these algorithms

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 00:10:19 +01:00