fix(memory): address PR review — numpy/UPSERT soft deps + BM25 floor + BLOB dim

- numpy soft dependency: try/except import + _HAS_NUMPY flag; _encode_embedding
  and _decode_embedding fall back to struct.pack/unpack; search_vector falls back
  to pure-Python cosine loop — startup never fails without numpy reinstalled
- SQLite UPSERT guard: _HAS_UPSERT = sqlite_version_info >= (3,24,0); save_chunk
  and save_chunks_batch fall back to INSERT OR REPLACE on SQLite < 3.24 with a
  one-time startup warning about potential FTS rowid drift
- _bm25_rank_to_score floor: 0.3 + 0.69*(|rank|/(1+|rank|)) → always in [0.3, 0.99),
  prevents small-corpus matches scoring 0.0 and being filtered by min_score
- detect_index_dim BLOB-aware: check isinstance(raw, bytes) first and return
  len(raw)//4 before json.loads, so /memory status works after embedding format switch
- Comment: "CJK single-char" → "CJK tokens shorter than 3 characters"

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

agent/memory/embedding/state.py

@@ -31,9 +31,13 @@ def detect_index_dim(storage) -> Optional[int]:
     if not row or not row["embedding"]:
         return None
     try:
-        emb = json.loads(row["embedding"])
+        raw = row["embedding"]
+        if isinstance(raw, (bytes, bytearray)):
+            # New BLOB format: 4 bytes per float32
+            return len(raw) // 4
+        emb = json.loads(raw)
         return len(emb) if isinstance(emb, list) else None
-    except (json.JSONDecodeError, TypeError):
+    except (json.JSONDecodeError, TypeError, Exception):
         return None
 
 

agent/memory/storage.py

@@ -13,7 +13,17 @@ import threading
 from typing import List, Dict, Optional, Any
 from pathlib import Path
 from dataclasses import dataclass
-import numpy as np
+try:
+    import numpy as np
+    _HAS_NUMPY = True
+except ImportError:
+    _HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+# UPSERT (INSERT … ON CONFLICT DO UPDATE) requires SQLite ≥ 3.24.0 (2018).
+# Older systems (e.g. CentOS 7 ships SQLite 3.7) fall back to INSERT OR REPLACE,
+# which risks FTS5 rowid drift on chunk updates (see save_chunk docstring).
+_HAS_UPSERT = sqlite3.sqlite_version_info >= (3, 24, 0)
 
 # ---------------------------------------------------------------------------
 # CJK character ranges, compiled once at module load.
@@ -93,6 +103,14 @@ class MemoryStorage:
             
             # Check FTS5 support
             self.fts5_available = self._check_fts5_support()
+            if not _HAS_UPSERT:
+                from common.log import logger
+                logger.warning(
+                    "[MemoryStorage] SQLite %s < 3.24 — UPSERT unavailable. "
+                    "Falling back to INSERT OR REPLACE; FTS5 rowid may drift on "
+                    "chunk updates (rebuild index periodically to recover).",
+                    sqlite3.sqlite_version,
+                )
             if not self.fts5_available:
                 from common.log import logger
                 logger.debug("[MemoryStorage] FTS5 not available, using LIKE-based keyword search")
@@ -403,6 +421,7 @@ class MemoryStorage:
         ON CONFLICT DO UPDATE fires the AFTER UPDATE trigger (chunks_au /
         chunks_trigram_au) and keeps the original rowid intact.
         """
+        if _HAS_UPSERT:
             _SQL = """
                 INSERT INTO chunks
                 (id, user_id, scope, source, path, start_line, end_line,
@@ -421,6 +440,13 @@ class MemoryStorage:
                     metadata    = excluded.metadata,
                     updated_at  = strftime('%s', 'now')
             """
+        else:
+            _SQL = """
+                INSERT OR REPLACE INTO chunks
+                (id, user_id, scope, source, path, start_line, end_line,
+                 text, embedding, hash, metadata, updated_at)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, strftime('%s', 'now'))
+            """
         params = (
             chunk.id, chunk.user_id, chunk.scope, chunk.source, chunk.path,
             chunk.start_line, chunk.end_line, chunk.text,
@@ -437,6 +463,7 @@ class MemoryStorage:
 
         See save_chunk for why UPSERT is used instead of INSERT OR REPLACE.
         """
+        if _HAS_UPSERT:
             _SQL = """
                 INSERT INTO chunks
                 (id, user_id, scope, source, path, start_line, end_line,
@@ -455,6 +482,13 @@ class MemoryStorage:
                     metadata    = excluded.metadata,
                     updated_at  = strftime('%s', 'now')
             """
+        else:
+            _SQL = """
+                INSERT OR REPLACE INTO chunks
+                (id, user_id, scope, source, path, start_line, end_line,
+                 text, embedding, hash, metadata, updated_at)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, strftime('%s', 'now'))
+            """
         params_list = [
             (
                 c.id, c.user_id, c.scope, c.source, c.path,
@@ -544,6 +578,7 @@ class MemoryStorage:
         if not vectors:
             return []
 
+        if _HAS_NUMPY:
             matrix = np.array(vectors, dtype=np.float32)        # (N, D)
             q_vec = np.array(query_embedding, dtype=np.float32)  # (D,)
 
@@ -560,7 +595,6 @@ class MemoryStorage:
             top_idx = np.argpartition(sims, -k)[-k:]
             top_idx = top_idx[np.argsort(sims[top_idx])[::-1]]
 
-
             return [
                 SearchResult(
                     path=valid_rows[i]['path'],
@@ -574,6 +608,31 @@ class MemoryStorage:
                 for i in top_idx
                 if sims[i] > 0
             ]
+        else:
+            # Pure-Python cosine similarity fallback (numpy not installed)
+            import math
+            q = query_embedding
+            q_norm = math.sqrt(sum(x * x for x in q)) or 1e-10
+            scored = []
+            for i, vec in enumerate(vectors):
+                dot = sum(a * b for a, b in zip(vec, q))
+                v_norm = math.sqrt(sum(x * x for x in vec)) or 1e-10
+                sim = dot / (v_norm * q_norm)
+                if sim > 0:
+                    scored.append((sim, valid_rows[i]))
+            scored.sort(key=lambda x: x[0], reverse=True)
+            return [
+                SearchResult(
+                    path=row['path'],
+                    start_line=row['start_line'],
+                    end_line=row['end_line'],
+                    score=sim,
+                    snippet=self._truncate_text(row['text'], 500),
+                    source=row['source'],
+                    user_id=row['user_id']
+                )
+                for sim, row in scored[:limit]
+            ]
     
     def search_keyword(
         self,
@@ -621,8 +680,8 @@ class MemoryStorage:
             if trigram_results:
                 return trigram_results
 
-        # Step 3: LIKE fallback — last resort (FTS5 unavailable, or CJK single-char
-        # that trigram cannot match because it requires ≥3-char tokens).
+        # Step 3: LIKE fallback — last resort (FTS5 unavailable, or CJK tokens
+        # shorter than 3 characters that trigram cannot match, e.g. a single-char query).
         if not self.fts5_available or MemoryStorage._contains_cjk(query):
             return self._search_like(query, user_id, scopes, limit)
 
@@ -829,18 +888,27 @@ class MemoryStorage:
 
     @staticmethod
     def _encode_embedding(embedding: Optional[List[float]]) -> Optional[bytes]:
-        """Encode embedding as float32 BLOB bytes (~6x smaller and faster than JSON)."""
+        """Encode embedding as float32 BLOB bytes (~6x smaller and faster than JSON).
+        Falls back to struct.pack when numpy is unavailable."""
         if embedding is None:
             return None
+        if _HAS_NUMPY:
             return np.array(embedding, dtype=np.float32).tobytes()
+        import struct
+        return struct.pack(f'{len(embedding)}f', *embedding)
 
     @staticmethod
     def _decode_embedding(raw) -> Optional[List[float]]:
-        """Decode embedding from BLOB bytes or legacy JSON string."""
+        """Decode embedding from BLOB bytes or legacy JSON string.
+        Handles both numpy and numpy-free environments."""
         if raw is None:
             return None
         if isinstance(raw, (bytes, bytearray)):
+            if _HAS_NUMPY:
                 return np.frombuffer(raw, dtype=np.float32).tolist()
+            import struct
+            n = len(raw) // 4
+            return list(struct.unpack(f'{n}f', raw))
         # Legacy JSON format written by older versions
         return json.loads(raw)
 
@@ -970,7 +1038,10 @@ class MemoryStorage:
         """
         if rank is None:
             return 0.0
-        return abs(rank) / (1.0 + abs(rank))
+        # Add a floor of 0.3 so any FTS5 match always exceeds typical
+        # min_score thresholds (default 0.1).  Small-corpus ranks close to
+        # 0 would otherwise produce score≈0 and be filtered out downstream.
+        return 0.3 + 0.69 * (abs(rank) / (1.0 + abs(rank)))
     
     @staticmethod
     def _truncate_text(text: str, max_chars: int) -> str: