improve search relevance: hybrid scoring + AND-then-OR matching

- Blend BM25/ts_rank relevance with importance instead of sorting by one
  dimension only. Default mode: 40% relevance + 60% importance. Relevance
  mode: 70% relevance + 30% importance.
- Try AND-match first for precise results, fall back to OR-match when too
  few results are found. Prevents single-word matches from flooding results.
- Applied to both SQLite (local) and PostgreSQL (API) search paths.

src/claude_memory/api/app.py

@@ -151,10 +151,13 @@ async def recall_memories(body: MemoryRecall, user: AuthUser = Depends(get_curre
 
     query_text = f"{body.context} {body.expanded_query}".strip()
 
-    order_clause = "ts_rank(search_vector, query) DESC"
+    # Hybrid scoring: blend ts_rank relevance (0-1) with importance (0-1)
+    hybrid_score = "(ts_rank(search_vector, query) * 0.7 + importance * 0.3)"
     if body.sort_by == "importance":
-        order_clause = "importance DESC, ts_rank(search_vector, query) DESC"
-    elif body.sort_by == "recency":
+        hybrid_score = "(ts_rank(search_vector, query) * 0.4 + importance * 0.6)"
+
+    order_clause = f"{hybrid_score} DESC"
+    if body.sort_by == "recency":
         order_clause = "created_at DESC"
 
     category_filter = ""
@@ -164,6 +167,8 @@ async def recall_memories(body: MemoryRecall, user: AuthUser = Depends(get_curre
         params.append(body.category)
 
     async with pool.acquire() as conn:
+        # Try AND-match first (plainto_tsquery ANDs by default), fall back to
+        # OR-match via individual word disjunction for broader results
         rows = await conn.fetch(
             f"""
             SELECT id, content, category, tags, importance, is_sensitive,
@@ -180,6 +185,35 @@ async def recall_memories(body: MemoryRecall, user: AuthUser = Depends(get_curre
             *params,
         )
 
+        # If AND-match returned too few results, broaden to OR-match
+        if len(rows) < body.limit and query_text:
+            words = query_text.split()
+            if len(words) > 1:
+                or_tsquery = " | ".join(w for w in words if w)
+                or_params: list = [user.user_id, or_tsquery, body.limit]
+                or_cat_filter = ""
+                if body.category:
+                    or_cat_filter = "AND category = $4"
+                    or_params.append(body.category)
+                seen_ids = {r["id"] for r in rows}
+                or_rows = await conn.fetch(
+                    f"""
+                    SELECT id, content, category, tags, importance, is_sensitive,
+                           ts_rank(search_vector, query) AS rank,
+                           created_at, updated_at
+                    FROM memories, to_tsquery('english', $2) query
+                    WHERE user_id = $1
+                      AND deleted_at IS NULL
+                      AND search_vector @@ query
+                      {or_cat_filter}
+                    ORDER BY {order_clause}
+                    LIMIT $3
+                    """,
+                    *or_params,
+                )
+                rows = list(rows) + [r for r in or_rows if r["id"] not in seen_ids]
+                rows = rows[:body.limit]
+
     results = []
     for row in rows:
         content = row["content"]

src/claude_memory/mcp_server.py

@@ -435,30 +435,35 @@ class MemoryServer:
         import sqlite3
 
         all_terms = f"{context} {expanded_query}".strip()
-        words = all_terms.split()
-        fts_query = " OR ".join(f'"{w.replace(chr(34), "")}"' for w in words if w)
+        words = [w.replace(chr(34), "") for w in all_terms.split() if w]
+        and_query = " AND ".join(f'"{w}"' for w in words)
+        or_query = " OR ".join(f'"{w}"' for w in words)
+
+        # Hybrid scoring: blend BM25 relevance with importance
+        # bm25() returns negative values (lower = better match), so negate it
         order = (
-            "bm25(memories_fts), m.importance DESC"
+            "(-bm25(memories_fts) * 0.7 + m.importance * 0.3) DESC"
             if sort_by == "relevance"
-            else "m.importance DESC, m.created_at DESC"
+            else "(-bm25(memories_fts) * 0.4 + m.importance * 0.6) DESC"
+        )
+
+        base_select = (
+            f"SELECT m.id, m.content, m.category, m.tags, m.importance, m.created_at "
+            f"FROM memories m JOIN memories_fts fts ON m.id = fts.rowid "
         )
         cursor = self.sqlite_conn.cursor()
         try:
-            if category:
+            # Try AND first for precise matches, fall back to OR for broader results
+            cat_filter = "AND m.category = ?" if category else ""
+            for fts_query in (and_query, or_query):
+                params = [fts_query, category, limit] if category else [fts_query, limit]
                 cursor.execute(
-                    f"SELECT m.id, m.content, m.category, m.tags, m.importance, m.created_at "
-                    f"FROM memories m JOIN memories_fts fts ON m.id = fts.rowid "
-                    f"WHERE memories_fts MATCH ? AND m.category = ? ORDER BY {order} LIMIT ?",
-                    (fts_query, category, limit),
+                    f"{base_select}WHERE memories_fts MATCH ? {cat_filter} ORDER BY {order} LIMIT ?",
+                    tuple(p for p in params if p is not None),
                 )
-            else:
-                cursor.execute(
-                    f"SELECT m.id, m.content, m.category, m.tags, m.importance, m.created_at "
-                    f"FROM memories m JOIN memories_fts fts ON m.id = fts.rowid "
-                    f"WHERE memories_fts MATCH ? ORDER BY {order} LIMIT ?",
-                    (fts_query, limit),
-                )
-            rows = cursor.fetchall()
+                rows = cursor.fetchall()
+                if rows:
+                    break
         except sqlite3.OperationalError:
             like = f"%{context}%"
             if category: