fix(core): use (type, id) keys in vector search hydration to prevent id collisions

src/basic_memory/repository/search_repository_base.py

@@ -40,6 +40,11 @@
 OVERSIZED_ENTITY_VECTOR_SHARD_SIZE = 256
 _SQLITE_MAX_PREPARE_WINDOW = 8
 
+# Entity, observation, and relation rows in search_index carry ids from independent
+# auto-increment sequences, so a bare id is ambiguous across row types. Every map in
+# the vector/hybrid retrieval path must key rows by (type, id) to avoid collisions.
+type SearchIndexKey = tuple[str, int]
+
 
 @dataclass
 class VectorSyncBatchResult:
@@ -1857,7 +1862,7 @@ async def _dispatch_retrieval_mode(
     # ------------------------------------------------------------------
 
     @staticmethod
-    def _parse_chunk_key(chunk_key: str) -> tuple[str, int]:
+    def _parse_chunk_key(chunk_key: str) -> SearchIndexKey:
         """Parse a chunk_key like 'observation:5:0' into (type, search_index_id)."""
         parts = chunk_key.split(":")
         return parts[0], int(parts[1])
@@ -1932,26 +1937,27 @@ def _log_vector_summary() -> None:
 
         hydrate_start = time.perf_counter()
         # Build per-search_index_row similarity scores from chunk-level results.
-        # Each chunk_key encodes the search_index row type and id.
+        # Each chunk_key encodes the search_index row type and id; keep both as the
+        # key because different row types can share the same numeric id (#982).
         # Track the best similarity per row (for ranking) and all chunks (for context).
-        similarity_by_si_id: dict[int, float] = {}
-        chunks_by_si_id: dict[int, list[tuple[float, str]]] = {}
+        similarity_by_si_key: dict[SearchIndexKey, float] = {}
+        chunks_by_si_key: dict[SearchIndexKey, list[tuple[float, str]]] = {}
         for row in vector_rows:
             chunk_key = row.get("chunk_key", "")
             distance = float(row["best_distance"])
             similarity = self._distance_to_similarity(distance)
             chunk_text = row.get("chunk_text", "")
             try:
-                _, si_id = self._parse_chunk_key(chunk_key)
+                si_key = self._parse_chunk_key(chunk_key)
             except (ValueError, IndexError):
                 # Fallback: group by entity_id for chunks without parseable keys
                 continue
-            current = similarity_by_si_id.get(si_id)
+            current = similarity_by_si_key.get(si_key)
             if current is None or similarity > current:
-                similarity_by_si_id[si_id] = similarity
-            chunks_by_si_id.setdefault(si_id, []).append((similarity, chunk_text))
+                similarity_by_si_key[si_key] = similarity
+            chunks_by_si_key.setdefault(si_key, []).append((similarity, chunk_text))
 
-        if not similarity_by_si_id:
+        if not similarity_by_si_key:
             hydrate_ms = (time.perf_counter() - hydrate_start) * 1000
             _log_vector_summary()
             return []
@@ -1962,16 +1968,17 @@ def _log_vector_summary() -> None:
             min_similarity if min_similarity is not None else self._semantic_min_similarity
         )
         if effective_min_similarity > 0.0:
-            similarity_by_si_id = {
-                k: v for k, v in similarity_by_si_id.items() if v >= effective_min_similarity
+            similarity_by_si_key = {
+                k: v for k, v in similarity_by_si_key.items() if v >= effective_min_similarity
             }
-            if not similarity_by_si_id:
+            if not similarity_by_si_key:
                 hydrate_ms = (time.perf_counter() - hydrate_start) * 1000
                 _log_vector_summary()
                 return []
 
-        # Fetch the actual search_index rows
-        si_ids = list(similarity_by_si_id.keys())
+        # Fetch the actual search_index rows. Colliding (type, id) keys share one
+        # bare id, so deduplicate while preserving first-seen order.
+        si_ids = list(dict.fromkeys(si_id for _, si_id in similarity_by_si_key))
         search_index_rows = await self._fetch_search_index_rows_by_ids(si_ids)
 
         # Apply optional filters if requested
@@ -2003,16 +2010,14 @@ def _log_vector_summary() -> None:
                 limit=VECTOR_FILTER_SCAN_LIMIT,
                 offset=0,
             )
-            # Use (id, type) tuples to avoid collisions between different
+            # Use (type, id) tuples to avoid collisions between different
             # search_index row types that share the same auto-increment id.
-            allowed_keys = {(row.id, row.type) for row in filtered_rows if row.id is not None}
-            search_index_rows = {
-                k: v for k, v in search_index_rows.items() if (v.id, v.type) in allowed_keys
-            }
+            allowed_keys = {(row.type, row.id) for row in filtered_rows if row.id is not None}
+            search_index_rows = {k: v for k, v in search_index_rows.items() if k in allowed_keys}
 
         ranked_rows: list[SearchIndexRow] = []
-        for si_id, similarity in similarity_by_si_id.items():
-            row = search_index_rows.get(si_id)
+        for si_key, similarity in similarity_by_si_key.items():
+            row = search_index_rows.get(si_key)
             if row is None:
                 continue
 
@@ -2022,7 +2027,7 @@ def _log_vector_summary() -> None:
             if content_snippet and len(content_snippet) <= SMALL_NOTE_CONTENT_LIMIT:
                 matched_chunk_text = content_snippet
             else:
-                si_chunks = chunks_by_si_id.get(si_id, [])
+                si_chunks = chunks_by_si_key.get(si_key, [])
                 si_chunks.sort(key=lambda c: c[0], reverse=True)
                 top_texts = [text for _, text in si_chunks[:TOP_CHUNKS_PER_RESULT]]
                 matched_chunk_text = "\n---\n".join(top_texts) if top_texts else None
@@ -2088,8 +2093,12 @@ async def _fetch_entity_rows_by_ids(self, entity_ids: list[int]) -> dict[int, Se
 
     async def _fetch_search_index_rows_by_ids(
         self, row_ids: list[int]
-    ) -> dict[int, SearchIndexRow]:
-        """Fetch search_index rows by their primary key (id), any type."""
+    ) -> dict[SearchIndexKey, SearchIndexRow]:
+        """Fetch search_index rows by id, keyed by (type, id) to disambiguate types.
+
+        A bare id can match one row per type (independent id sequences), so the
+        result must carry every matching row rather than letting one clobber another.
+        """
         if not row_ids:
             return {}
         placeholders = ",".join(f":id_{idx}" for idx in range(len(row_ids)))
@@ -2106,11 +2115,11 @@ async def _fetch_search_index_rows_by_ids(
             WHERE project_id = :project_id
               AND id IN ({placeholders})
         """
-        result: dict[int, SearchIndexRow] = {}
+        result: dict[SearchIndexKey, SearchIndexRow] = {}
         async with db.scoped_session(self.session_maker) as session:
             row_result = await session.execute(text(sql), params)
             for row in row_result.fetchall():
-                result[row.id] = SearchIndexRow(
+                result[(row.type, row.id)] = SearchIndexRow(
                     project_id=self.project_id,
                     id=row.id,
                     title=row.title,
@@ -2156,7 +2165,7 @@ async def _search_hybrid(
     ) -> List[SearchIndexRow]:
         """Fuse FTS and vector results using score-based fusion.
 
-        Uses search_index row id as the fusion key. The formula
+        Uses the search_index (type, id) pair as the fusion key. The formula
         ``max(vec, fts) + FUSION_BONUS * min(vec, fts)`` preserves
         the dominant signal and rewards dual-source agreement.
         """
@@ -2199,50 +2208,52 @@ async def _search_hybrid(
         vector_ms = (time.perf_counter() - vector_start) * 1000
         fusion_start = time.perf_counter()
 
-        # --- Score-based fusion keyed on search_index row id ---
+        # --- Score-based fusion keyed on (type, id) ---
+        # A bare row id collides across row types (independent id sequences), so
+        # fusion must key on (type, id) or distinct rows would merge (#982).
         # FTS scores are normalized to [0, 1] (BM25 is unbounded).
         # Vector scores are used raw — already calibrated [0, 1] by _distance_to_similarity().
-        rows_by_id: dict[int, SearchIndexRow] = {}
+        rows_by_key: dict[SearchIndexKey, SearchIndexRow] = {}
 
         # Normalize FTS scores to [0, 1] — handles both SQLite (negative bm25)
         # and Postgres (positive ts_rank) by using absolute values
         fts_abs = [abs(row.score or 0.0) for row in fts_results]
         fts_max = max(fts_abs) if fts_abs else 1.0
 
-        fts_scores: dict[int, float] = {}
+        fts_scores: dict[SearchIndexKey, float] = {}
         for row in fts_results:
             if row.id is None:
                 continue
             norm = abs(row.score or 0.0) / fts_max if fts_max > 0 else 0.0
             # Gate: FTS scores below threshold contribute zero
             if norm < FTS_GATE_THRESHOLD:
                 norm = 0.0
-            fts_scores[row.id] = norm
-            rows_by_id[row.id] = row
+            fts_scores[(row.type, row.id)] = norm
+            rows_by_key[(row.type, row.id)] = row
 
-        vec_scores: dict[int, float] = {}
+        vec_scores: dict[SearchIndexKey, float] = {}
         for row in vector_results:
             if row.id is None:
                 continue
             # Trigger: no re-normalization by vec_max
             # Why: vector similarity is already calibrated [0, 1]; re-normalizing
             # inflates weak matches when the entire result set is mediocre
-            vec_scores[row.id] = row.score or 0.0
-            rows_by_id[row.id] = row
+            vec_scores[(row.type, row.id)] = row.score or 0.0
+            rows_by_key[(row.type, row.id)] = row
 
         # Fuse: max(v, f) + FUSION_BONUS * min(v, f)
         # Preserves the dominant signal; bonus rewards dual-source agreement.
         # Output range: [0, 1.3] for dual-source, [0, 1.0] for single-source.
-        fused_scores: dict[int, float] = {}
-        for row_id in fts_scores.keys() | vec_scores.keys():
-            v = vec_scores.get(row_id, 0.0)
-            f = fts_scores.get(row_id, 0.0)
-            fused_scores[row_id] = max(v, f) + FUSION_BONUS * min(v, f)
+        fused_scores: dict[SearchIndexKey, float] = {}
+        for row_key in fts_scores.keys() | vec_scores.keys():
+            v = vec_scores.get(row_key, 0.0)
+            f = fts_scores.get(row_key, 0.0)
+            fused_scores[row_key] = max(v, f) + FUSION_BONUS * min(v, f)
 
         ranked = sorted(fused_scores.items(), key=lambda item: item[1], reverse=True)
         output: list[SearchIndexRow] = []
-        for row_id, fused_score in ranked[offset : offset + limit]:
-            row = rows_by_id[row_id]
+        for row_key, fused_score in ranked[offset : offset + limit]:
+            row = rows_by_key[row_key]
             # Trigger: FTS-only results have no matched_chunk_text from vector search.
             # Why: without chunk text, API falls back to truncated content, losing answer text.
             # Outcome: FTS-only results get full content_snippet as matched_chunk.

tests/repository/test_hybrid_fusion.py

@@ -228,6 +228,34 @@ async def test_zero_score_produces_zero_fused():
     assert results[0].score == pytest.approx(0.0, rel=1e-6)
 
 
+@pytest.mark.asyncio
+async def test_cross_type_id_collision_keeps_both_results():
+    """An entity and a relation sharing the same numeric id stay distinct (#982).
+
+    search_index row types have independent id sequences, so fusing on a bare
+    row id merged unrelated rows into one result and dropped the other.
+    """
+    repo = ConcreteSearchRepo()
+
+    fts_results = [FakeRow(id=1, type="entity", score=5.0, title="entity-row")]
+    vector_results = [FakeRow(id=1, type="relation", score=0.8, title="relation-row")]
+
+    with (
+        patch.object(repo, "search", new_callable=AsyncMock, return_value=fts_results),
+        patch.object(
+            repo, "_search_vector_only", new_callable=AsyncMock, return_value=vector_results
+        ),
+    ):
+        results = await repo._search_hybrid(**HYBRID_KWARGS)
+
+    assert {(r.type, r.id) for r in results} == {("entity", 1), ("relation", 1)}
+    # Single-source scores must not earn the dual-source fusion bonus across types.
+    entity_result = next(r for r in results if r.type == "entity")
+    relation_result = next(r for r in results if r.type == "relation")
+    assert entity_result.score == pytest.approx(1.0, rel=1e-6)
+    assert relation_result.score == pytest.approx(0.8, rel=1e-6)
+
+
 @pytest.mark.asyncio
 async def test_fts_only_result_gets_matched_chunk_from_content_snippet():
     """FTS-only results should have matched_chunk_text populated from content_snippet."""

tests/repository/test_sqlite_vector_search_repository.py

@@ -76,6 +76,32 @@ def _entity_row(
     )
 
 
+def _relation_row(
+    *,
+    project_id: int,
+    row_id: int,
+    entity_id: int,
+    title: str,
+    permalink: str,
+    relation_type: str,
+) -> SearchIndexRow:
+    now = datetime.now(timezone.utc)
+    return SearchIndexRow(
+        project_id=project_id,
+        id=row_id,
+        type=SearchItemType.RELATION.value,
+        title=title,
+        permalink=permalink,
+        file_path=f"{permalink}.md",
+        metadata=None,
+        entity_id=entity_id,
+        from_id=entity_id,
+        relation_type=relation_type,
+        created_at=now,
+        updated_at=now,
+    )
+
+
 def _enable_semantic(
     search_repository: SQLiteSearchRepository,
     embedding_provider: StubEmbeddingProvider | None = None,
@@ -498,6 +524,71 @@ async def test_sqlite_vector_search_returns_ranked_entities(search_repository):
     assert all(result.type == SearchItemType.ENTITY.value for result in results)
 
 
+@pytest.mark.asyncio
+async def test_sqlite_vector_search_survives_cross_type_id_collision(search_repository):
+    """Entity and relation rows sharing one numeric id must both hydrate (#982).
+
+    Entity, observation, and relation rows carry ids from independent
+    auto-increment sequences, so search_index rows of different types routinely
+    share the same numeric id. Keying vector hydration by bare id collapsed
+    colliding hits into one dict slot and silently dropped the other result.
+    """
+    if not isinstance(search_repository, SQLiteSearchRepository):
+        pytest.skip("sqlite-vec repository behavior is local SQLite-only.")
+
+    _enable_semantic(search_repository)
+    await search_repository.init_search_index()
+    await search_repository.bulk_index_items(
+        [
+            _entity_row(
+                project_id=search_repository.project_id,
+                row_id=7,
+                entity_id=701,
+                title="Auth Token Design",
+                permalink="specs/auth-token-design",
+                content_stems="auth token session login design",
+            ),
+            # Same numeric id as the entity row above, different row type.
+            _relation_row(
+                project_id=search_repository.project_id,
+                row_id=7,
+                entity_id=702,
+                title="login flow relates to auth token design",
+                permalink="specs/login-flow/relates-to/auth-token-design",
+                relation_type="relates_to",
+            ),
+        ]
+    )
+    await search_repository.sync_entity_vectors(701)
+    await search_repository.sync_entity_vectors(702)
+
+    results = await search_repository.search(
+        search_text="session token auth",
+        retrieval_mode=SearchRetrievalMode.VECTOR,
+        limit=5,
+        offset=0,
+    )
+
+    # Both rows match the query; both share id=7 and must survive hydration.
+    assert len(results) == 2
+    assert {result.type for result in results} == {
+        SearchItemType.ENTITY.value,
+        SearchItemType.RELATION.value,
+    }
+    entity_result = next(r for r in results if r.type == SearchItemType.ENTITY.value)
+    assert entity_result.permalink == "specs/auth-token-design"
+
+    # The type filter must keep the entity even though a relation shares its id.
+    filtered = await search_repository.search(
+        search_text="session token auth",
+        search_item_types=[SearchItemType.ENTITY],
+        retrieval_mode=SearchRetrievalMode.VECTOR,
+        limit=5,
+        offset=0,
+    )
+    assert [r.permalink for r in filtered] == ["specs/auth-token-design"]
+
+
 @pytest.mark.asyncio
 async def test_sqlite_hybrid_search_combines_fts_and_vector(search_repository):
     """Hybrid mode fuses FTS and vector results with score-based fusion."""

tests/repository/test_vector_pagination.py

@@ -133,7 +133,7 @@ async def test_page1_scores_gte_page2_scores():
 
     repo._embedding_provider = _EmbeddingProvider()
 
-    fake_index_rows = {i: FakeRow(id=i) for i in range(20)}
+    fake_index_rows = {("entity", i): FakeRow(id=i) for i in range(20)}
 
     async def run_page(offset, limit):
         with (