- Add a fixed-UUID sentinel point written after a successful doc_id
backfill so subsequent restarts retrieve it and short-circuit the
O(N) scroll. Sentinel has no user_id/doc_id/doc_type payload so
production search filters never see it.
- Pre-fetch payload_schema in _ensure_keyword_payload_indexes and
silently skip fields that are already indexed; the "Created KEYWORD
payload index" INFO log fires only on actual creation.
- Narrow stale `int | str` doc_id annotations to `str` across
search/verification.py (BatchVerifier return type, per-verifier
accessible sets, by_type / accessible_by_type / inaccessible
collections); drop the now-redundant `type(d).__name__` prefix in
the dropped-docs log.
- Align the backfill log message with the PR description's
"Running doc_id backfill" promise; add a caller cross-reference to
the wait=True comment.
- Fix _get_file_path_from_qdrant docstring (file_id is str, not numeric).
- Convert legacy `id=1` to `id="1"` in test_search_result.py to match
the SearchResult.id: str annotation.
Three new unit tests cover sentinel-found, sentinel-written, and
skip-existing-index branches; existing backfill tests pass dimension
and explicit retrieve.return_value=[] for the no-sentinel path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- _ensure_keyword_payload_indexes: distinguish 400 (schema conflict, warning)
from other status codes (5xx/network, error) so a transient outage doesn't
silently leave the collection unindexed.
- build_search_result_from_point: use .get("doc_id") + return None on missing
instead of KeyError-crashing the search; reverse metadata merge order so
payload-derived chunk_index/total_chunks win over caller-supplied extras.
- docs/configuration.md: restore the OpenAI/Mistral/Bedrock/Simple provider
sections + reference-table rows that were dropped in the rebase. Reword
the "Startup migrations" bullet to describe what the code actually does
(no sampling — full scroll, zero writes when clean). Add operator note
about the SemanticSearchResult.id TypeError path.
- tests: pytest.approx for float equality (Sonar python:S1244); coverage
for non-400 → ERROR, payload={doc_id: None}, and missing doc_id key.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Addresses reviewer feedback on PR #773:
- Backfill set_payload now uses wait=True to avoid a race where
_ensure_keyword_payload_indexes builds the KEYWORD index before
fire-and-forget writes have committed, leaving int payloads
invisible to filters.
- Batch points sharing the same int doc_id into a single set_payload
call (one document → many chunks → one round-trip instead of N).
- Drop _has_int_doc_id_sample short-circuit. The sample's false-negative
window (clean first 256 results, ints further in) is gone; full scroll
is the dominant cost on first run anyway.
- Simplify _ensure_keyword_payload_indexes: the "already exists" 400
branch was dead code (Qdrant returns 200 on identical re-create); any
400 now logs a warning and continues.
- search/context.py: comment the broadened file-type guard. Add explicit
not doc_id.isdigit() checks at the top of note/news_item/deck_card
branches in _fetch_document_text so malformed payloads surface as
warnings instead of being swallowed by the broad except.
Also extracts build_search_result_from_point into search/algorithms.py
to deduplicate the 71-line payload-extraction loop shared by
SemanticSearchAlgorithm and BM25HybridSearchAlgorithm. This fixes
SonarQube's quality-gate failure (4.0% new-code duplication, max 3%).
Test coverage:
- 7 new unit tests for build_search_result_from_point covering missing
payload, note/file/deck_card metadata, int doc_id coercion, and
metadata_extras merging.
- Replace _has_int_doc_id_sample tests with clean-collection no-op and
per-batch grouping tests.
- Update set_payload assertions from wait=False to wait=True.
Co-Authored-By: Claude Opus 4.7 (1M context) <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>
