Implements fire-and-forget eviction (ADR-019 §"Lazy eviction"): the
search response no longer waits on Qdrant deletes, instead spawning
evict() on a long-lived lifespan-owned task group. Falls back to inline
eviction in modes without vector sync and in unit tests.
Also: harden _verify_news_items against non-numeric ids (fail open
instead of crashing the verifier); document the get_file_info None-on-404
contract; add INDEXED_DOC_TYPES single source of truth in vector/scanner.py
referenced by the CI-guard test; write a Verify-on-Read Latency Budget
section in docs/configuration.md covering the unbounded news.get_items
fetch. Closes the two remaining ADR-019 implementation checklist items.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Cap all_results to limit*2 after sort in the per-doc_types branch of
nc_semantic_search to bound over-verification (was unbounded N-types).
- Switch BatchVerifier from (client, doc_ids, user_id) to (client, results,
semaphore). Verifiers now read file paths and deck board/stack ids from
SearchResult.metadata instead of doing fresh Qdrant scrolls — eliminates
one duplicate round-trip per file/deck-card verification.
- Bound per-id verification concurrency with a shared anyio.Semaphore
(default 20, matching server/semantic.py context-expansion convention).
Prevents httpx pool exhaustion / rate limiting on large search pages.
- Propagate stack_id from Qdrant payload to SearchResult.metadata in both
bm25_hybrid.py and semantic.py (board_id was already propagated).
- Drop now-unused _resolve_file_path / _resolve_deck_metadata helpers.
- Drop redundant int(d) in requested predicate from _verify_news_items.
- Rewrite eviction comment to be honest about inline (not background)
execution and the resulting latency coupling.
- ADR-019 status: Proposed -> Accepted.
- Add news property to NextcloudClientProtocol.
- Widen SearchResult.id and SemanticSearchResult.id to int | str to match
BatchVerifier signature and document support for future string-id types.
- Flip openWorldHint to True on nc_semantic_search_answer (it calls into
Nextcloud via nc_semantic_search).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The vector index lags Nextcloud (5-min webhook cron + scanner interval),
producing ghost records for deleted/unshared documents until the next
reconciliation. Verify each unique document against Nextcloud at query
time, drop inaccessible results, and lazily evict the corresponding
Qdrant points.
Per-doc_type batch verifiers: notes/files/deck cards run concurrently
per id; news items use a single fetch + intersect to avoid the per-item
fetch-all amplification. Transient errors fail open (keep result, log
warning) — only definitive 4xx drops. Multiple chunks of the same doc
collapse to one verification call.
Wired into nc_semantic_search before the limit trim and before context
expansion. nc_semantic_search_answer's per-note re-fetch retained as a
sub-second race guard since verification now happens upstream.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Added support for two fusion algorithms (RRF and DBSF) to combine dense
semantic and sparse BM25 search results, with comprehensive documentation
and unit tests.
Changes:
- Added fusion parameter to nc_semantic_search and nc_semantic_search_answer tools
- Updated ADR-014 with detailed comparison of RRF vs DBSF fusion algorithms
- Added unit tests for fusion algorithm initialization and validation
- Updated search_method in responses to include fusion type (e.g., "bm25_hybrid_rrf")
Fusion Algorithms:
- RRF (Reciprocal Rank Fusion): Default, rank-based, general-purpose
- DBSF (Distribution-Based Score Fusion): Score normalization using statistics
RRF is recommended for most use cases due to its robustness and established
track record. DBSF may provide better results when retrieval systems have
very different score distributions.
🤖 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>
