app/services/diagnose/suppressor.py

@@ -80,8 +80,9 @@ def _fetch_resolved_incidents(db_path: Path) -> list[str]:
     except sqlite3.OperationalError as exc:
         logger.warning("Could not query resolved incidents (%s) — treating as empty corpus", exc)
         return []
-    except Exception as exc:
-        logger.warning("Unexpected error fetching resolved incidents (%s) — treating as empty corpus", exc)
+    except sqlite3.Error as exc:
+        # Catches all remaining SQLite-family errors (IntegrityError, DatabaseError, etc.)
+        logger.warning("Unexpected SQLite error fetching resolved incidents (%s) — treating as empty corpus", exc)
         return []
 
     texts: list[str] = []
@@ -116,6 +117,7 @@ class FalsePositiveSuppressor:
     ) -> None:
         self._model_id = model_id
         self._device = device
+        # _device stored for future use when get_embedder() supports device selection
         self._suppress_threshold = suppress_threshold
 
     def suppress(
@@ -158,38 +160,11 @@ class FalsePositiveSuppressor:
         # Embed corpus (with caching).
         corpus_embeddings = self._get_corpus_embeddings(embedder, corpus_texts, db_path)
 
-        # Rank hypotheses using novelty score.
-        ranked: list[RankedHypothesis] = []
-        for h in hypotheses:
-            h_text = f"{h.title}. {h.description}"
-            try:
-                h_emb = embedder.embed(h_text)
-                # Convert numpy array to plain Python list for _cosine_similarities
-                h_emb_list: list[float] = h_emb.tolist() if hasattr(h_emb, "tolist") else list(h_emb)
-                sims = _cosine_similarities(h_emb_list, corpus_embeddings)
-                max_sim = max(sims) if sims else 0.0
-            except Exception as exc:
-                logger.warning("Embedding failed for hypothesis %r: %s — treating as novel", h.title, exc)
-                max_sim = 0.0
-
-            max_sim = float(max_sim)
-            novelty_score = 1.0 - max_sim
-            suppress = bool(novelty_score < self._suppress_threshold)
-            suppression_reason = (
-                f"Similar to resolved incident (similarity {max_sim:.2f})"
-                if suppress
-                else None
-            )
-            ranked.append(
-                RankedHypothesis(
-                    hypothesis=h,
-                    novelty_score=novelty_score,
-                    similarity_to_known=max_sim,
-                    suppress=suppress,
-                    suppression_reason=suppression_reason,
-                )
-            )
-
+        # Score each hypothesis and sort by novelty * confidence descending.
+        ranked = [
+            self._score_hypothesis(h, embedder, corpus_embeddings)
+            for h in hypotheses
+        ]
         ranked.sort(key=lambda rh: rh.novelty_score * rh.hypothesis.confidence, reverse=True)
         return ranked
 
@@ -197,12 +172,55 @@ class FalsePositiveSuppressor:
     # Private helpers
     # ------------------------------------------------------------------
 
+    def _score_hypothesis(
+        self,
+        hypothesis: Hypothesis,
+        embedder: Any,
+        corpus_embeddings: list[list[float]],
+    ) -> RankedHypothesis:
+        """Score a single hypothesis against the resolved incident corpus."""
+        try:
+            query_text = f"{hypothesis.title}. {hypothesis.description}"
+            h_emb = embedder.embed(query_text)
+            # Convert numpy array to plain Python list for _cosine_similarities
+            h_emb_list: list[float] = h_emb.tolist() if hasattr(h_emb, "tolist") else list(h_emb)
+            sims = _cosine_similarities(h_emb_list, corpus_embeddings)
+            max_sim = float(max(sims)) if sims else 0.0
+        except Exception as exc:
+            # Broad catch is intentional: catches unknown embedder runtime errors
+            # (e.g. CUDA OOM, backend crashes) so one bad hypothesis never halts the pipeline.
+            logger.warning("Embedding failed for hypothesis %r: %s — treating as novel", hypothesis.title, exc)
+            return RankedHypothesis(
+                hypothesis=hypothesis,
+                novelty_score=1.0,
+                similarity_to_known=0.0,
+                suppress=False,
+                suppression_reason=None,
+            )
+
+        novelty_score = 1.0 - max_sim
+        suppress = bool(novelty_score < self._suppress_threshold)
+        suppression_reason = (
+            f"Similar to resolved incident (similarity {max_sim:.2f})"
+            if suppress
+            else None
+        )
+        return RankedHypothesis(
+            hypothesis=hypothesis,
+            novelty_score=novelty_score,
+            similarity_to_known=max_sim,
+            suppress=suppress,
+            suppression_reason=suppression_reason,
+        )
+
     def _load_embedder(self) -> Any | None:
         """Load the embedding service. Returns None if unavailable."""
         try:
             from app.services.embeddings import get_embedder
             return get_embedder()
         except Exception as exc:
+            # Broad catch is intentional: get_embedder() may raise on import or
+            # backend init failures from any number of third-party libraries.
             logger.warning("Failed to import/initialise embedding service: %s", exc)
             return None
 
@@ -230,6 +248,9 @@ class FalsePositiveSuppressor:
                 for e in raw_embeddings
             ]
         except Exception as exc:
+            # Broad catch is intentional: embed_batch() may raise from any backend
+            # (network timeout, CUDA error, etc.) — treat as empty corpus so the
+            # pipeline can continue without suppression.
             logger.warning("Corpus embedding failed: %s — treating as empty corpus", exc)
             return []
 

tests/test_diagnose_suppressor.py

@@ -4,8 +4,8 @@ All tests use mocking; no real model downloads are made.
 """
 from __future__ import annotations
 
+import math
 import sqlite3
-import tempfile
 from pathlib import Path
 from unittest.mock import MagicMock, patch
 
@@ -37,10 +37,8 @@ def _make_hypothesis(
     )
 
 
-def _make_db_with_incidents(incidents: list[tuple[str, str]]) -> Path:
+def _make_db_with_incidents(incidents: list[tuple[str, str]], db_path: Path) -> Path:
     """Create a temporary SQLite database with resolved incidents. Returns the db path."""
-    tmp = tempfile.mktemp(suffix=".db")
-    db_path = Path(tmp)
     with sqlite3.connect(str(db_path)) as conn:
         conn.execute(
             "CREATE TABLE incidents "
@@ -55,10 +53,8 @@ def _make_db_with_incidents(incidents: list[tuple[str, str]]) -> Path:
     return db_path
 
 
-def _make_empty_db() -> Path:
+def _make_empty_db(db_path: Path) -> Path:
     """Create a temporary SQLite DB with no incidents table."""
-    tmp = tempfile.mktemp(suffix=".db")
-    db_path = Path(tmp)
     with sqlite3.connect(str(db_path)) as conn:
         conn.execute("CREATE TABLE unrelated (id INTEGER PRIMARY KEY)")
         conn.commit()
@@ -104,13 +100,13 @@ def reset_suppressor_cache():
 # Test 1: No model configured — passthrough, ranked by confidence
 # ---------------------------------------------------------------------------
 
-def test_no_model_passthrough_ranked_by_confidence():
+def test_no_model_passthrough_ranked_by_confidence(tmp_path):
     """model_id='' → all novelty_score=1.0, suppress=False, ranked by confidence desc."""
     h_low = _make_hypothesis(title="Low", confidence=0.3)
     h_high = _make_hypothesis(title="High", confidence=0.9)
     h_mid = _make_hypothesis(title="Mid", confidence=0.6)
 
-    db_path = Path(tempfile.mktemp(suffix=".db"))
+    db_path = tmp_path / "turnstone.db"
     suppressor = FalsePositiveSuppressor(model_id="")
     results = suppressor.suppress([h_low, h_high, h_mid], db_path)
 
@@ -129,7 +125,7 @@ def test_no_model_passthrough_ranked_by_confidence():
 # Test 2: High similarity → suppressed
 # ---------------------------------------------------------------------------
 
-def test_high_similarity_suppresses_hypothesis():
+def test_high_similarity_suppresses_hypothesis(tmp_path):
     """Hypothesis with embedding nearly identical to corpus → suppress=True."""
     identical_vec = [1.0] + [0.0] * 383
     corpus_vec = [1.0] + [0.0] * 383  # cosine similarity = 1.0
@@ -139,7 +135,10 @@ def test_high_similarity_suppresses_hypothesis():
         embed_batch_return=[corpus_vec],
     )
 
-    db_path = _make_db_with_incidents([("OOM killer", "Memory pressure caused OOM kill")])
+    db_path = _make_db_with_incidents(
+        [("OOM killer", "Memory pressure caused OOM kill")],
+        tmp_path / "turnstone.db",
+    )
     suppressor = FalsePositiveSuppressor(model_id="test-model", suppress_threshold=0.85)
 
     with patch.object(suppressor, "_load_embedder", return_value=mock_embedder):
@@ -158,7 +157,7 @@ def test_high_similarity_suppresses_hypothesis():
 # Test 3: Low similarity → not suppressed
 # ---------------------------------------------------------------------------
 
-def test_low_similarity_does_not_suppress():
+def test_low_similarity_does_not_suppress(tmp_path):
     """Hypothesis with embedding orthogonal to corpus → suppress=False."""
     hypothesis_vec = [1.0] + [0.0] * 383
     corpus_vec = [0.0, 1.0] + [0.0] * 382  # orthogonal → similarity = 0.0
@@ -168,7 +167,10 @@ def test_low_similarity_does_not_suppress():
         embed_batch_return=[corpus_vec],
     )
 
-    db_path = _make_db_with_incidents([("Disk I/O", "Storage saturation caused latency")])
+    db_path = _make_db_with_incidents(
+        [("Disk I/O", "Storage saturation caused latency")],
+        tmp_path / "turnstone.db",
+    )
     suppressor = FalsePositiveSuppressor(model_id="test-model", suppress_threshold=0.85)
 
     with patch.object(suppressor, "_load_embedder", return_value=mock_embedder):
@@ -186,9 +188,9 @@ def test_low_similarity_does_not_suppress():
 # Test 4: Empty hypotheses list returns []
 # ---------------------------------------------------------------------------
 
-def test_empty_hypotheses_returns_empty():
+def test_empty_hypotheses_returns_empty(tmp_path):
     """suppress([]) → [] regardless of model or db state."""
-    db_path = Path(tempfile.mktemp(suffix=".db"))
+    db_path = tmp_path / "turnstone.db"
     suppressor = FalsePositiveSuppressor(model_id="test-model")
     results = suppressor.suppress([], db_path)
     assert results == []
@@ -198,7 +200,7 @@ def test_empty_hypotheses_returns_empty():
 # Test 5: Ranking by novelty_score * confidence
 # ---------------------------------------------------------------------------
 
-def test_ranking_by_novelty_times_confidence():
+def test_ranking_by_novelty_times_confidence(tmp_path):
     """Results are sorted by novelty_score * confidence descending."""
     # Hypothesis A: novelty=0.9, confidence=0.5 → score=0.45
     # Hypothesis B: novelty=0.5, confidence=0.9 → score=0.45 (tie, order stable-ish)
@@ -218,7 +220,6 @@ def test_ranking_by_novelty_times_confidence():
 
     # Simpler approach: create 3 hypotheses and verify output is sorted correctly
     # by providing distinct embeddings that produce known similarities.
-    import math
 
     # Corpus: single vector [1, 0, 0, ...]
     corpus_vec = [1.0] + [0.0] * 383
@@ -254,7 +255,10 @@ def test_ranking_by_novelty_times_confidence():
     mock_embedder.embed_batch.return_value = [batch_m]
     mock_embedder.embed.side_effect = side_effect_embed
 
-    db_path = _make_db_with_incidents([("OOM", "Memory exhaustion")])
+    db_path = _make_db_with_incidents(
+        [("OOM", "Memory exhaustion")],
+        tmp_path / "turnstone.db",
+    )
     suppressor = FalsePositiveSuppressor(model_id="test-model", suppress_threshold=0.85)
 
     with patch.object(suppressor, "_load_embedder", return_value=mock_embedder):
@@ -273,9 +277,9 @@ def test_ranking_by_novelty_times_confidence():
 # Test 6: DB with no resolved incidents → novelty_score=1.0
 # ---------------------------------------------------------------------------
 
-def test_no_resolved_incidents_in_db_passthrough():
+def test_no_resolved_incidents_in_db_passthrough(tmp_path):
     """When incidents table is empty, all hypotheses get novelty_score=1.0."""
-    db_path = _make_db_with_incidents([])  # table exists but zero rows
+    db_path = _make_db_with_incidents([], tmp_path / "turnstone.db")  # table exists but zero rows
     mock_embedder = _make_mock_embedder()
     suppressor = FalsePositiveSuppressor(model_id="test-model")
 
@@ -293,9 +297,9 @@ def test_no_resolved_incidents_in_db_passthrough():
 # Test 7: DB query failure → graceful fallback, no crash
 # ---------------------------------------------------------------------------
 
-def test_db_query_failure_graceful_fallback():
+def test_db_query_failure_graceful_fallback(tmp_path):
     """When the incidents table is missing, suppress() returns passthrough without raising."""
-    db_path = _make_empty_db()  # no 'incidents' table
+    db_path = _make_empty_db(tmp_path / "turnstone.db")  # no 'incidents' table
     mock_embedder = _make_mock_embedder()
     suppressor = FalsePositiveSuppressor(model_id="test-model")
 
@@ -311,9 +315,12 @@ def test_db_query_failure_graceful_fallback():
 # Test 8: Embedding service unavailable (returns None) → graceful fallback
 # ---------------------------------------------------------------------------
 
-def test_embedding_service_unavailable_passthrough():
+def test_embedding_service_unavailable_passthrough(tmp_path):
     """When get_embedder() returns None, suppress() falls back without crashing."""
-    db_path = _make_db_with_incidents([("OOM", "Memory pressure")])
+    db_path = _make_db_with_incidents(
+        [("OOM", "Memory pressure")],
+        tmp_path / "turnstone.db",
+    )
     suppressor = FalsePositiveSuppressor(model_id="test-model")
 
     with patch.object(suppressor, "_load_embedder", return_value=None):
@@ -329,10 +336,13 @@ def test_embedding_service_unavailable_passthrough():
 # Test 9: Corpus cache invalidated when corpus changes
 # ---------------------------------------------------------------------------
 
-def test_corpus_cache_invalidated_on_corpus_change():
+def test_corpus_cache_invalidated_on_corpus_change(tmp_path):
     """When the corpus changes between calls, embed_batch is called again."""
     # First DB: one incident
-    db_path = _make_db_with_incidents([("OOM", "Memory pressure")])
+    db_path = _make_db_with_incidents(
+        [("OOM", "Memory pressure")],
+        tmp_path / "turnstone.db",
+    )
 
     corpus_vec_1 = [1.0] + [0.0] * 383
     corpus_vec_2 = [0.0, 1.0] + [0.0] * 382