Commit Graph

3 Commits

Author SHA1 Message Date
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.

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 05:19:35 +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)

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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

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