feat(memory): complete M.2-M.5 memory upgrade phases with tests

- M.2: ForgettingCurve, MemoryDecay, MemoryReinforcement (selective forgetting)
- M.3: DailyDigestGenerator, ReminderScheduler, ProactiveInformer (proactive reminders)
- M.4: MemoryExtractor with LLM-based memory extraction from conversations
- M.5: MemoryRecallInjector with token budget control for prompt injection
- All phases include comprehensive unit tests (109 tests passing)
- Updated checklist.md to mark all tasks complete

backend/app/services/agent_service.py

@@ -331,6 +331,40 @@ class AgentService:
         async with async_session() as session:
             await memory_service.try_auto_summarize(session, user_id, conversation_id)
 
+    # ———— M.4: 主动记忆提取 ————
+    async def _extract_memories_background(self, user_id: str, conversation_id: str) -> None:
+        """Background task to extract memories from conversation after response."""
+        from app.services.memory.memory_extractor import MemoryExtractor
+        from sqlalchemy import select
+        from app.models.conversation import Message
+
+        try:
+            async with async_session() as db:
+                # Load last 10 messages from conversation
+                result = await db.execute(
+                    select(Message)
+                    .where(Message.conversation_id == conversation_id)
+                    .order_by(Message.created_at.desc())
+                    .limit(10)
+                )
+                messages = list(result.scalars().all())
+
+                if len(messages) < 2:
+                    return
+
+                extractor = MemoryExtractor()
+                new_memories = await extractor.extract_from_conversation(
+                    db, user_id, conversation_id, messages
+                )
+
+                if new_memories:
+                    await extractor.save_memories(db, user_id, conversation_id, new_memories)
+                    logger.info(
+                        f"[MemoryExtractor] Extracted {len(new_memories)} new memories from conversation {conversation_id}"
+                    )
+        except Exception as e:
+            logger.exception(f"[MemoryExtractor] Extraction failed: {e}")
+
     def _build_progress_event(
         self,
         stage: str,
@@ -543,6 +577,13 @@ class AgentService:
             self.db, user_id, conversation_id, message
         )
 
+        # M.5: Inject recall memories into context (before LLM call)
+        from app.services.memory.recall_injector import MemoryRecallInjector
+
+        recall_ctx = await MemoryRecallInjector().build_context(self.db, user_id, message)
+        if recall_ctx:
+            memory_ctx = f"{memory_ctx}\n{recall_ctx}" if memory_ctx else recall_ctx
+
         assistant_msg = Message(
             conversation_id=conversation_id,
             role="assistant",
@@ -735,6 +776,8 @@ class AgentService:
                 except Exception:
                     logger.exception("save_assistant_message_failed")
                 asyncio.create_task(self._try_auto_summarize_background(user_id, conversation_id))
+                # M.4: Extract memories from conversation
+                asyncio.create_task(self._extract_memories_background(user_id, conversation_id))
 
         return conversation_id, assistant_msg.id, run_agent()
 
@@ -807,6 +850,13 @@ class AgentService:
             self.db, user_id, conversation_id, message
         )
 
+        # M.5: Inject recall memories into context (before LLM call)
+        from app.services.memory.recall_injector import MemoryRecallInjector
+
+        recall_ctx = await MemoryRecallInjector().build_context(self.db, user_id, message)
+        if recall_ctx:
+            memory_ctx = f"{memory_ctx}\n{recall_ctx}" if memory_ctx else recall_ctx
+
         set_current_user(user_id)
         try:
             graph = get_agent_graph()