fix:用户画像修复

service/memory/facade.py

@@ -48,13 +48,15 @@ class MemoryService:
         self.relations = relations or CustomerRelationMemory()
         self.products = products or CustomerProductMemory()
         self.holdings = holdings or CustomerHoldingsMemory()
+        self.long_term = long_term or LongTermMemoryService()
+        # composed 依赖上面三个已初始化的组件：必须先赋值 self.long_term，
+        # 否则默认分支会把 None 传进去（历史 bug）。
         self.composed = composed or ComposedProfileService(
             profile=self.profile,
             long_term=self.long_term,
             holdings=self.holdings,
             redis=self.short_term.redis,
         )
-        self.long_term = long_term or LongTermMemoryService()
         self.archiver = archiver or ConversationArchiver(short_term=self.short_term)
         self.rank_tool = rank_tool or FinalConfidenceRankTool()
         self.interest_tracker = interest_tracker or InterestTopicTracker(self.short_term.redis)
@@ -72,8 +74,24 @@ class MemoryService:
         session_id: str,
         query: str | None = None,
         limit: int = 10,
+        include_long_term: bool = False,
+        include_composed: bool = False,
     ) -> CustomerMemoryContext:
-        """召回并组装客服 Agent 当前请求的全部可用记忆。"""
+        """召回并组装客服 Agent 当前请求的全部可用记忆。
+
+        默认**不含**长期记忆与最终画像：客服 Agent 的职责是知识问答/开户引导/
+        公司信息，合规上不推荐产品，长期记忆（偏好标签、投资目标）对其没有
+        合法用途，却要付出 memory_unit 查询 + Embedding + Milvus 检索的代价。
+
+        长期记忆与最终画像的两个合法消费方各自显式开启：
+
+        - 投顾 Agent：``include_long_term=True``（推荐场景需要偏好与目标）；
+        - 最终画像接口（``/profile/composed``，仅员工）：直接走
+          ``ComposedProfileService.compose_response``，不经本方法。
+
+        写入路径（``save_memory`` / ``record_interest_signal``）不受影响，
+        客服会话仍持续沉淀长期记忆，供投顾与画像使用。
+        """
         if limit < 0:
             raise ValueError("limit 必须是非负整数")
         warnings: list[str] = []
@@ -81,14 +99,15 @@ class MemoryService:
         warnings.extend(self.short_term.last_warnings)
 
         async with self._db() as db:
-            try:
-                refresh_confidence = getattr(self.long_term, "refresh_confidence", None)
-                if refresh_confidence is not None:
-                    await refresh_confidence(
-                        db, customer_id, limit=max(limit, 10)
-                    )
-            except Exception as exc:
-                warnings.append(f"confidence_refresh_failed:{type(exc).__name__}")
+            if include_long_term:
+                try:
+                    refresh_confidence = getattr(self.long_term, "refresh_confidence", None)
+                    if refresh_confidence is not None:
+                        await refresh_confidence(
+                            db, customer_id, limit=max(limit, 10)
+                        )
+                except Exception as exc:
+                    warnings.append(f"confidence_refresh_failed:{type(exc).__name__}")
             profile, profile_warnings = await self.profile.get(db, customer_id)
             warnings.extend(profile_warnings)
             try:
@@ -106,14 +125,45 @@ class MemoryService:
             except Exception as exc:
                 customer_products = []
                 warnings.append(f"customer_product_recall_failed:{type(exc).__name__}")
-            try:
-                memories, memory_warnings = await self.long_term.recall(
-                    db, customer_id, limit=max(limit, 10), query=query
-                )
-                warnings.extend(memory_warnings)
-            except Exception as exc:
-                memories = []
-                warnings.append(f"long_term_recall_failed:{type(exc).__name__}")
+
+            memories: list[MemoryUnitDTO] = []
+            if include_long_term:
+                try:
+                    memories, memory_warnings = await self.long_term.recall(
+                        db, customer_id, limit=max(limit, 10), query=query
+                    )
+                    warnings.extend(memory_warnings)
+                except Exception as exc:
+                    memories = []
+                    warnings.append(f"long_term_recall_failed:{type(exc).__name__}")
+
+            holdings_summary = None
+            composed_payload: dict[str, Any] | None = None
+            if include_composed:
+                try:
+                    holdings_summary, holdings_warnings = await self.holdings.summary(
+                        db, customer_id
+                    )
+                    warnings.extend(holdings_warnings)
+                except Exception as exc:
+                    warnings.append(f"holdings_recall_failed:{type(exc).__name__}")
+
+                try:
+                    # ComposedProfileService 期望 list[dict]（与 _recall_memories
+                    # 的返回一致），这里传已序列化的记忆，避免在块外再触碰会话。
+                    composed_payload, composed_warnings = await self.composed.compose(
+                        db,
+                        customer_id=customer_id,
+                        query=query,
+                        profile=profile,
+                        memories=[
+                            memory.model_dump(mode="json") for memory in memories
+                        ],
+                        holdings_summary=holdings_summary,
+                    )
+                    warnings.extend(composed_warnings)
+                except Exception as exc:
+                    warnings.append(f"composed_profile_failed:{type(exc).__name__}")
 
         ranked = self.rank_tool.rank(
             [memory.model_dump(mode="json") for memory in memories],
@@ -121,28 +171,6 @@ class MemoryService:
         )
         ranked_memories = [MemoryUnitDTO.model_validate(item) for item in ranked]
 
-        holdings_summary = None
-        try:
-            holdings_summary, holdings_warnings = await self.holdings.summary(
-                db, customer_id
-            )
-            warnings.extend(holdings_warnings)
-        except Exception as exc:
-            warnings.append(f"holdings_recall_failed:{type(exc).__name__}")
-        composed_payload: dict[str, Any] | None = None
-        try:
-            composed_payload, composed_warnings = await self.composed.compose(
-                db,
-                customer_id=customer_id,
-                query=query,
-                profile=profile,
-                memories=ranked,
-                holdings_summary=holdings_summary,
-            )
-            warnings.extend(composed_warnings)
-        except Exception as exc:
-            warnings.append(f"composed_profile_failed:{type(exc).__name__}")
-
         return build_customer_memory_context(
             customer_id=customer_id,
             session_id=session_id,