Was suppressing when novelty_score < 0.85 (i.e. similarity > 0.15), which
would suppress nearly every hypothesis once embeddings are active.
Now suppresses when max_sim >= similarity_threshold (0.85), meaning only
hypotheses that are 85%+ similar to a resolved incident are suppressed.
Also renames suppress_threshold → similarity_threshold for clarity and
adds a borderline boundary test (0.85 suppressed, 0.84 not suppressed).
Closes: #29
- Implements FalsePositiveSuppressor using embedding cosine similarity
- Lazy corpus embedding via get_embedder() with module-level cache keyed by db_path
- Cache invalidated automatically when the resolved incident corpus changes
- Suppresses hypotheses with novelty_score below configurable threshold (default 0.85)
- Full fallback path (novelty=1.0, no suppression) when model_id empty, embedding
service unavailable, or no resolved incidents found in DB
- Graceful handling of missing incidents table and DB query failures
- Numpy bool_ leakage prevented by explicit float()/bool() coercion at assignment
- Pure-Python cosine fallback for environments without numpy
- 9 new tests (all mocked, no real model downloads): passthrough, suppress, no-suppress,
empty list, ranking, empty corpus, DB failure, service unavailable, cache invalidation
- 350 total tests passing (341 pre-existing + 9 new)
Closes: #29
- Add _coerce_float() module-level helper: catches TypeError/ValueError from
non-numeric LLM output (e.g. 'high', 'N/A') and returns a caller-supplied
default instead of raising.
- Replace float(item.get('confidence', 0.5)) with
_coerce_float(item.get('confidence'), 0.5) in _parse_response.
- Guard supporting_cluster_ids: tuple(item.get(...) or []) so a JSON null
from the LLM does not cause TypeError('NoneType is not iterable').
- runbook_refs is hardcoded as () and not sourced from LLM output; no change
needed there.
- Add test_non_numeric_confidence_uses_default (Test 10) to cover the 'high'
string case: asserts no exception and confidence == 0.5.
- 341 tests passing (+1).
Closes: #29
Three-path classification: ML (transformers pipeline, lazy singleton) →
pattern_tags (YAML pattern severity dict) → regex (detect_severity).
- Path A: HF text-classification pipeline loaded lazily on first classify()
call via module-level singleton; shim promotes ERROR+keyword hits to CRITICAL
and demotes low-confidence INFO to DEBUG.
- Path B: maps cluster.pattern_tags through the loaded pattern severity dict;
picks the highest severity across matching tags.
- Path C: falls back to detect_severity() regex scan on representative_text;
defaults to INFO when no keyword matches.
- Pattern file resolved from constructor arg or TURNSTONE_PATTERNS env var
(mirrors app/rest.py convention).
- No crash when transformers is not installed; ImportError on per-cluster ML
inference triggers clean per-cluster fallback to pattern_tags/regex.
- ClassifiedTimeline.classifier_used reflects the primary session path.
Tests (10 new, 328 total, all passing):
- ML ERROR, CRITICAL promotion, DEBUG demotion, WARNING→WARN
- pattern_tags resolution from YAML fixture
- regex ERROR detection and INFO default
- ImportError clean fallback
- empty timeline no-crash
- ClassifiedTimeline FrozenInstanceError on mutation
Closes: #29
- Add gap_significance_seconds constructor param (default 30) to replace hardcoded magic number in gap_count computation
- _parse_iso now returns datetime | None with try/except on ValueError; all callers handle None return by treating malformed timestamps as absent
- Extract reconstruct into four private helpers: _sort_entries, _group_into_raw_clusters, _build_cluster, _dominant_sources_tuple
- Promote _sort_key to module-level function (was nested inside reconstruct)
- Rename old module-level _build_cluster to _make_event_cluster to avoid name collision with new instance method
- Add explanatory comment to type: ignore[arg-type] at _highest_severity call site
- Black-formatted
- Move app/services/diagnose.py verbatim to app/services/diagnose/legacy.py
- Create app/services/diagnose/__init__.py with full implementation so that
patch('app.services.diagnose._HAS_DATEPARSER') targets the correct namespace
and all 303 existing tests continue to pass without modification
- Add app/services/diagnose/models.py with 5 pipeline dataclasses:
EventCluster, TimelineResult, ClassifiedTimeline, Hypothesis, RankedHypothesis
- Add app/services/diagnose/pipeline.py with run_pipeline() stub (Task 6)
- Add MULTI_AGENT_ENABLED feature flag (off by default via env var)
- Zero behavior change; ruff clean
Closes: #29
