fix(search): address code review — vec_sync in delete_observations, docstring, constants

- Add vec_sync after fts_sync in delete_observations (stale embedding bug)
- Update search_nodes docstring to reflect hybrid search behavior
- Extract MAX_OBS_FOR_EMBEDDING constant (was magic number 20)
- Log vec_remove_entity failures instead of silently swallowing

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: RMANOV

server.py

@@ -356,6 +356,11 @@ def delete_observations(deletions: list[dict[str, Any]]) -> str:
                 )
                 deleted += cur.rowcount
             _fts_sync(conn, eid)
+            if _VEC_AVAILABLE:
+                try:
+                    _vec_sync(conn, eid)
+                except Exception:
+                    pass
 
     logger.info("delete_observations: %d deleted", deleted)
     return json.dumps({"deleted": deleted})
@@ -414,11 +419,12 @@ def read_graph() -> str:
 
 @mcp.tool()
 def search_nodes(query: str, project: str | None = None) -> str:
-    """Search the knowledge graph using FTS5 BM25-ranked full-text search.
+    """Search the knowledge graph using hybrid BM25 + semantic search.
 
-    Returns matching entities with their observations, ranked by relevance.
-    Applies multi-signal re-ranking (recency, project affinity, graph proximity,
-    observation richness, canonical facts, session context).
+    When sqlite-vec is installed, combines FTS5 keyword matching with vector
+    cosine similarity via Reciprocal Rank Fusion. Falls back to FTS5-only
+    otherwise. Results are re-ranked with 6 contextual signals (recency,
+    project affinity, graph proximity, richness, canonical facts, session).
     """
     fts_q = _fts_query(query)
     with _get_conn() as conn:

vec_search.py

@@ -46,6 +46,7 @@
 _model: SentenceTransformer | None = None
 _MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
 EMBEDDING_DIM = 384
+MAX_OBS_FOR_EMBEDDING = 20  # MiniLM-L6-v2 has 256-token limit; cap observations to fit
 
 
 def _get_model() -> SentenceTransformer:
@@ -108,7 +109,7 @@ def init_vec_table(conn: sqlite3.Connection) -> bool:
 
 def _entity_text(name: str, entity_type: str, observations: list[str]) -> str:
     """Compose the text to embed for an entity."""
-    obs_str = ". ".join(observations[:20])
+    obs_str = ". ".join(observations[:MAX_OBS_FOR_EMBEDDING])
     return f"{name} ({entity_type}): {obs_str}"
 
 
@@ -159,8 +160,8 @@ def vec_remove_entity(conn: sqlite3.Connection, entity_id: int) -> None:
     try:
         if load_vec(conn):
             conn.execute("DELETE FROM entity_embeddings WHERE rowid = ?", (entity_id,))
-    except Exception:
-        pass
+    except Exception as e:
+        logger.debug("vec_remove_entity(%d) failed: %s", entity_id, e)
 
 
 # ── Vector search ──────────────────────────────────────────────────────