fix(memory): keep a zero confidence from scoring as the default (#5586)

or 0.5 cannot distinguish an explicit 0.0 from an unset field, so a discounted fact got the default confidence weight in both the FTS5 document and the substring fallback, while the same row indexed through FTS5Retrieval.index_fact kept its zero weight.

backend/packages/harness/deerflow/agents/memory/backends/deermem/deermem/core/retrieval.py

@@ -578,11 +578,13 @@ class FTS5RetrievalAdapter:
         payload = dict(fact)
         payload["scope"] = {"userId": scope.get("userId"), "agentName": scope.get("agentName")}
         source = payload.get("source")
+        confidence = payload.get("confidence")
         return {
             "fact_id": self._document_id(fact_id, scope),
             "content": content,
             "category": str(payload.get("category") or "context"),
-            "confidence": float(payload.get("confidence") or 0.5),
+            # 0.0 is a persisted confidence storage._normalize_fact accepts; only unset/null defaults.
+            "confidence": 0.5 if confidence is None else float(confidence),
             "created_at": payload.get("createdAt") if isinstance(payload.get("createdAt"), str) else None,
             "scope_user": scope_user,
             "scope_agent": scope_agent,

backend/packages/harness/deerflow/agents/memory/backends/deermem/deermem/core/storage.py

@@ -1822,7 +1822,10 @@ class FileMemoryStorage(MemoryStorage):
             for fact in facts:
                 content = fact.get("content")
                 if isinstance(content, str) and query_lower in content.lower():
-                    results.append({"fact": fact, "score": float(fact.get("confidence") or 0.5), "matchType": "substring"})
+                    # 0.0 is a persisted confidence _normalize_fact accepts; only unset/null defaults.
+                    confidence = fact.get("confidence")
+                    score = 0.5 if confidence is None else float(confidence)
+                    results.append({"fact": fact, "score": score, "matchType": "substring"})
         results.sort(key=lambda result: result["score"], reverse=True)
         return results[:top_k]
 

backend/tests/test_memory_retrieval_adapter.py

@@ -287,3 +287,34 @@ def test_deermem_create_and_restart_use_retrieval_adapter(tmp_path: Path) -> Non
 
     restarted = DeerMem(backend_config=config)
     assert any(fact["id"] == fact_id for fact in restarted.search("restart retrieval", user_id="alice"))
+
+
+def test_indexed_zero_confidence_keeps_its_retrieval_weight(tmp_path: Path) -> None:
+    adapter = FTS5RetrievalAdapter(tmp_path / "facts.sqlite3")
+    scope = {"userId": "alice", "agentName": "agent-a"}
+    try:
+        adapter.upsert({**_fact("zero", "confidence weight probe"), "confidence": 0.0}, scope=scope, path="")
+        adapter.upsert({**_fact("half", "confidence weight probe"), "confidence": 0.5}, scope=scope, path="")
+        unset_fact = _fact("unset", "confidence weight probe")
+        del unset_fact["confidence"]
+        adapter.upsert(unset_fact, scope=scope, path="")
+        scores = {item["fact"]["id"]: item["score"] for item in adapter.search("confidence", scopes=[scope], top_k=5, mode="fts5", filters=None)}
+
+        assert scores["zero"] < scores["half"]
+        # A missing confidence still falls back to the default, so only the explicit 0.0 is discounted.
+        assert scores["unset"] == pytest.approx(scores["half"])
+    finally:
+        adapter.close()
+
+
+def test_substring_fallback_keeps_zero_confidence_weight(tmp_path: Path) -> None:
+    storage = FileMemoryStorage(DeerMemConfig(storage_path=str(tmp_path)))
+    scope = {"userId": "alice", "agentName": "agent-a"}
+    try:
+        storage.upsert_fact({**_fact("zero", "substring confidence probe"), "confidence": 0.0}, user_id="alice", agent_name="agent-a")
+        storage.upsert_fact({**_fact("half", "substring confidence probe"), "confidence": 0.5}, user_id="alice", agent_name="agent-a")
+        scores = {item["fact"]["id"]: item["score"] for item in storage.search_facts("substring", scopes=[scope], top_k=5)}
+
+        assert scores == {"zero": 0.0, "half": 0.5}
+    finally:
+        storage.close()