[AC-AGENT-ENHANCE] feat(mid): 增强 Agent 编排器和工具

- 优化 agent_orchestrator 的系统提示词指导
- 改进 kb_scene 参数的自动注入逻辑
- 增强 kb_search_dynamic_tool 的元数据处理
- 优化 memory_recall_tool 的记忆召回逻辑
- 更新 memory_adapter 的用户记忆模型

ai-service/app/services/mid/agent_orchestrator.py

@@ -35,7 +35,7 @@ from app.models.mid.schemas import (
 from app.services.llm.base import ToolDefinition
 from app.services.mid.tool_guide_registry import ToolGuideRegistry, get_tool_guide_registry
 from app.services.mid.timeout_governor import TimeoutGovernor
-from app.services.mid.tool_converter import convert_tools_to_llm_format, build_tool_result_message
+from app.services.mid.tool_converter import convert_tool_to_llm_format, convert_tools_to_llm_format, build_tool_result_message
 from app.services.prompt.template_service import PromptTemplateService
 from app.services.prompt.variable_resolver import VariableResolver
 
@@ -482,27 +482,35 @@ class AgentOrchestrator:
 
 **步骤3：调用 kb_search_dynamic 进行搜索**
 - 使用步骤1获取的元数据字段构造 context 参数
-- scene 参数必须从元数据字段的 kb_scene 常见值中选择，不要硬编码
+- scene 参数会自动注入到 context.kb_scene，无需手动在 context 中设置 kb_scene
+- scene 参数应从元数据字段的 kb_scene 常见值中选择
+
+**kb_scene 自动注入说明：**
+- 系统会自动将 scene 参数值注入到 context.kb_scene 字段
+- AI 只需在 context 中设置其他过滤字段（如 grade、subject）
+- 不要在 context 中重复设置 kb_scene，系统会自动处理
 
 **示例流程：**
 1. 调用 `list_document_metadata_fields` 获取字段信息
 2. 根据返回结果，发现可用字段：grade（年级）、subject（学科）、kb_scene（场景）
 3. 分析用户问题"三年级语文怎么学"，确定过滤条件：grade="三年级", subject="语文"
 4. 从 kb_scene 的常见值中选择合适的 scene（如"学习方案"）
-5. 调用 `kb_search_dynamic`，传入构造好的 context 和 scene
+5. 调用 `kb_search_dynamic`，传入 scene="学习方案"，context={"grade": "三年级", "subject": "语文"}
+6. 系统自动将 scene 注入到 context.kb_scene
 
 ## 注意事项
 - **严禁**在调用 kb_search_dynamic 之前不调用 list_document_metadata_fields。
+- **不要**在 context 中手动设置 kb_scene，系统会自动从 scene 参数注入。
 """
 
         if not self._template_service or not self._tenant_id:
             return default_prompt
 
         try:
-            from app.core.database import get_session
+            from app.core.database import async_session_maker
             from app.core.prompts import SYSTEM_PROMPT
             
-            async with get_session() as session:
+            async with async_session_maker() as session:
                 template_service = PromptTemplateService(session)
                 
                 base_prompt = await template_service.get_published_template(
@@ -511,6 +519,15 @@ class AgentOrchestrator:
                     resolver=self._variable_resolver,
                 )
                 
+                if not base_prompt or base_prompt == SYSTEM_PROMPT:
+                    base_prompt = await template_service.get_published_template(
+                        tenant_id=self._tenant_id,
+                        scene="agent_react",
+                        resolver=self._variable_resolver,
+                    )
+                    if base_prompt and base_prompt != SYSTEM_PROMPT:
+                        logger.info("[AC-MARH-07] Using agent_react template for Function Calling mode")
+                
                 if not base_prompt or base_prompt == SYSTEM_PROMPT:
                     base_prompt = await template_service.get_published_template(
                         tenant_id=self._tenant_id,
@@ -519,7 +536,7 @@ class AgentOrchestrator:
                     )
                 
                 if not base_prompt or base_prompt == SYSTEM_PROMPT:
-                    logger.info("[AC-MARH-07] No published template found for agent_fc or default, using default prompt")
+                    logger.info("[AC-MARH-07] No published template found for agent_fc/agent_react/default, using default prompt")
                     return default_prompt
 
                 agent_protocol = """
@@ -545,10 +562,15 @@ class AgentOrchestrator:
 
 **步骤3：调用 kb_search_dynamic 进行搜索**
 - 使用步骤1获取的元数据字段构造 context 参数
-- scene 参数必须从元数据字段的 kb_scene 常见值中选择，不要硬编码
+- scene 参数会自动注入到 context.kb_scene，无需手动在 context 中设置 kb_scene
+
+**kb_scene 自动注入说明：**
+- 系统会自动将 scene 参数值注入到 context.kb_scene 字段
+- AI 只需在 context 中设置其他过滤字段（如 grade、subject）
 
 ## 注意事项
 - **严禁**在调用 kb_search_dynamic 之前不调用 list_document_metadata_fields。
+- **不要**在 context 中手动设置 kb_scene，系统会自动从 scene 参数注入。
 """
                 
                 final_prompt = base_prompt + agent_protocol

ai-service/app/services/mid/kb_search_dynamic_tool.py

@@ -127,6 +127,8 @@ class KbSearchDynamicTool:
             "知识库动态检索工具。"
             "根据租户配置的元数据字段定义，动态构建检索过滤器。"
             "支持必填字段检测和可观测降级。"
+            "重要：context 参数中应包含 kb_scene 字段用于场景过滤，"
+            "系统会自动从外部请求的 scene 参数注入到 context.kb_scene。"
         )
 
     def get_tool_schema(self) -> dict[str, Any]:
@@ -146,7 +148,7 @@ class KbSearchDynamicTool:
                     },
                     "scene": {
                         "type": "string",
-                        "description": "场景标识，如 'open_consult', 'intent_match'",
+                        "description": "场景标识（如 'open_consult', 'intent_match'），系统会自动将其注入到 context.kb_scene 作为过滤条件",
                     },
                     "top_k": {
                         "type": "integer",
@@ -155,7 +157,7 @@ class KbSearchDynamicTool:
                     },
                     "context": {
                         "type": "object",
-                        "description": "上下文信息，包含动态过滤字段值",
+                        "description": "上下文信息，包含动态过滤字段值。重要字段：kb_scene（场景过滤，由系统自动从 scene 参数注入）、grade（年级）、subject（学科）等",
                     },
                 },
                 "required": ["query"],
@@ -299,13 +301,14 @@ class KbSearchDynamicTool:
         [AC-MARH-05] 执行 KB 动态检索。
         [AC-MRS-SLOT-META-02] 支持槽位状态聚合和过滤构建优先级
         [Step-KB-Binding] 支持步骤级别的知识库约束
+        [KB-SCENE-INJECT] 自动将 scene 参数注入到 context.kb_scene
 
         Args:
             query: 检索查询
             tenant_id: 租户 ID
-            scene: 场景标识（默认值，会被 context 中的 scene 覆盖）
+            scene: 场景标识（会自动注入到 context.kb_scene）
             top_k: 返回数量
-            context: 上下文（包含动态过滤值，包括 scene）
+            context: 上下文（包含动态过滤值）
             slot_state: 预聚合的槽位状态（可选，优先使用）
             step_kb_config: 步骤级别的知识库配置（可选）
             slot_policy: 槽位策略（flow_strict=流程严格模式，agent_relaxed=通用问答宽松模式）
@@ -326,6 +329,25 @@ class KbSearchDynamicTool:
         effective_context = dict(context) if context else {}
         effective_scene = effective_context.get("scene", scene)
         
+        logger.info(
+            f"[KB-DEBUG] execute() called with: scene='{scene}', context={context}, "
+            f"effective_context_keys={list(effective_context.keys())}"
+        )
+        
+        # [KB-SCENE-INJECT] 自动将 scene 参数注入到 context.kb_scene
+        # 优先级：context.kb_scene > context.scene > scene 参数
+        if "kb_scene" not in effective_context and scene:
+            effective_context["kb_scene"] = scene
+            logger.info(
+                f"[KB-SCENE-INJECT] Injected scene='{scene}' into context.kb_scene, "
+                f"effective_context now={effective_context}"
+            )
+        else:
+            logger.info(
+                f"[KB-SCENE-INJECT] Skipped injection: kb_scene in context={('kb_scene' in effective_context)}, "
+                f"scene is empty={not scene}"
+            )
+
         # [Step-KB-Binding] 记录步骤知识库约束
         step_kb_binding_info: dict[str, Any] = {}
         if step_kb_config:
@@ -445,8 +467,8 @@ class KbSearchDynamicTool:
                 status=ToolCallStatus.OK,
                 args_digest=f"query={query[:50]}, scene={effective_scene}",
                 result_digest=f"hits={len(hits)}",
-                arguments={"query": query, "scene": effective_scene, "context": context},
-                result={"hits_count": len(hits), "kb_hit": kb_hit},
+                arguments={"query": query, "scene": effective_scene, "context": effective_context},
+                result={"hits_count": len(hits), "kb_hit": kb_hit, "applied_filter": metadata_filter},
             )
 
             logger.info(
@@ -482,7 +504,7 @@ class KbSearchDynamicTool:
                 duration_ms=duration_ms,
                 status=ToolCallStatus.TIMEOUT,
                 error_code="KB_TIMEOUT",
-                arguments={"query": query, "scene": effective_scene, "context": context},
+                arguments={"query": query, "scene": effective_scene, "context": effective_context},
             )
 
             return KbSearchDynamicResult(
@@ -509,7 +531,7 @@ class KbSearchDynamicTool:
                 duration_ms=duration_ms,
                 status=ToolCallStatus.ERROR,
                 error_code="KB_ERROR",
-                arguments={"query": query, "scene": effective_scene, "context": context},
+                arguments={"query": query, "scene": effective_scene, "context": effective_context},
             )
 
             return KbSearchDynamicResult(
@@ -905,7 +927,7 @@ def register_kb_search_dynamic_tool(
 
     registry.register(
         name=KB_SEARCH_DYNAMIC_TOOL_NAME,
-        description="知识库动态检索工具，支持元数据驱动过滤",
+        description="知识库动态检索工具，支持元数据驱动过滤。系统会自动将 scene 参数注入到 context.kb_scene 进行场景过滤。",
         handler=handler,
         tool_type=RegistryToolType.INTERNAL,
         version="1.0.0",
@@ -922,9 +944,12 @@ def register_kb_search_dynamic_tool(
                 "properties": {
                     "query": {"type": "string", "description": "检索查询文本"},
                     "tenant_id": {"type": "string", "description": "租户 ID"},
-                    "scene": {"type": "string", "description": "场景标识，如 open_consult"},
+                    "scene": {"type": "string", "description": "场景标识，系统自动注入到 context.kb_scene"},
                     "top_k": {"type": "integer", "description": "返回条数"},
-                    "context": {"type": "object", "description": "上下文，用于动态过滤字段"}
+                    "context": {
+                        "type": "object",
+                        "description": "过滤条件上下文。kb_scene 由系统自动注入，其他字段如 grade、subject 根据用户意图填写"
+                    }
                 },
                 "required": ["query", "tenant_id"]
             },
@@ -933,9 +958,10 @@ def register_kb_search_dynamic_tool(
                 "tenant_id": "default",
                 "scene": "open_consult",
                 "top_k": 5,
-                "context": {"product_line": "vip_course", "region": "beijing"}
+                "context": {"grade": "初二", "subject": "数学"}
             },
-            "result_interpretation": "success=true 且 hits 非空表示命中知识；missing_required_slots 非空时应先向用户补采信息。"
+            "result_interpretation": "success=true 且 hits 非空表示命中知识；missing_required_slots 非空时应先向用户补采信息。",
+            "kb_scene_injection": "系统会自动将 scene 参数值注入到 context.kb_scene 字段，用于知识库场景过滤。AI 无需手动在 context 中设置 kb_scene。"
         },
     )
 

ai-service/app/services/mid/memory_adapter.py

@@ -9,6 +9,8 @@ Reference:
 """
 
 import asyncio
+import inspect
+import json
 import logging
 from dataclasses import dataclass, field
 from datetime import datetime
@@ -17,6 +19,7 @@ from typing import Any, Callable
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from app.models.entities import UserMemory as UserMemoryEntity
 from app.models.mid.memory import (
     MemoryFact,
     MemoryProfile,
@@ -93,14 +96,6 @@ class MemoryAdapter:
 
         在响应前执行，注入基础属性、事实记忆与偏好记忆。
         失败时返回空记忆，不阻断主链路。
-        
-        Args:
-            user_id: 用户ID
-            session_id: 会话ID
-            tenant_id: 租户ID（可选）
-            
-        Returns:
-            RecallResponse: 包含 profile/facts/preferences 的响应
         """
         try:
             return await asyncio.wait_for(
@@ -126,9 +121,6 @@ class MemoryAdapter:
         session_id: str,
         tenant_id: str | None,
     ) -> RecallResponse:
-        """
-        内部召回实现
-        """
         profile = await self._recall_profile(user_id, tenant_id)
         facts = await self._recall_facts(user_id, tenant_id)
         preferences = await self._recall_preferences(user_id, tenant_id)
@@ -152,7 +144,6 @@ class MemoryAdapter:
         user_id: str,
         tenant_id: str | None,
     ) -> MemoryProfile | None:
-        """召回用户基础属性"""
         return MemoryProfile(
             grade="初一",
             region="北京",
@@ -165,7 +156,6 @@ class MemoryAdapter:
         user_id: str,
         tenant_id: str | None,
     ) -> list[MemoryFact]:
-        """召回用户事实记忆"""
         return [
             MemoryFact(content="已购课程：数学思维训练营", source="order", confidence=1.0),
             MemoryFact(content="学习目标：提高数学成绩", source="profile", confidence=0.9),
@@ -177,7 +167,6 @@ class MemoryAdapter:
         user_id: str,
         tenant_id: str | None,
     ) -> MemoryPreferences | None:
-        """召回用户偏好"""
         return MemoryPreferences(
             tone="friendly",
             focus_subjects=["数学", "物理"],
@@ -189,8 +178,16 @@ class MemoryAdapter:
         user_id: str,
         tenant_id: str | None,
     ) -> str | None:
-        """召回最近会话摘要"""
-        return "上次讨论了数学学习计划，用户对课程安排比较满意"
+        if not tenant_id:
+            return None
+
+        stmt = select(UserMemoryEntity).where(
+            UserMemoryEntity.tenant_id == tenant_id,
+            UserMemoryEntity.user_id == user_id,
+        )
+        result = await self._session.execute(stmt)
+        record = result.scalar_one_or_none()
+        return record.summary if record else None
 
     async def update(
         self,
@@ -200,22 +197,6 @@ class MemoryAdapter:
         summary: str | None = None,
         tenant_id: str | None = None,
     ) -> bool:
-        """
-        [AC-IDMP-14] 异步更新用户记忆
-        
-        在对话完成后异步执行，不阻塞主响应。
-        包含会话摘要的回写。
-        
-        Args:
-            user_id: 用户ID
-            session_id: 会话ID
-            messages: 本轮对话消息
-            summary: 会话摘要（可选）
-            tenant_id: 租户ID
-            
-        Returns:
-            bool: 是否成功提交更新任务
-        """
         request = UpdateRequest(
             user_id=user_id,
             session_id=session_id,
@@ -242,9 +223,6 @@ class MemoryAdapter:
         request: UpdateRequest,
         tenant_id: str | None,
     ) -> None:
-        """
-        内部更新实现
-        """
         try:
             await asyncio.wait_for(
                 self._do_update(request, tenant_id),
@@ -270,10 +248,18 @@ class MemoryAdapter:
         request: UpdateRequest,
         tenant_id: str | None,
     ) -> None:
-        """
-        执行实际的记忆更新
-        """
         if request.summary:
+            summary_payload = self._parse_summary_payload(request.summary)
+            if summary_payload:
+                await self._save_summary(
+                    request.user_id,
+                    summary_payload.get("summary", ""),
+                    tenant_id,
+                    facts=summary_payload.get("facts"),
+                    preferences=summary_payload.get("preferences"),
+                    open_issues=summary_payload.get("open_issues"),
+                )
+            else:
                 await self._save_summary(request.user_id, request.summary, tenant_id)
 
         await self._extract_and_save_facts(
@@ -285,9 +271,41 @@ class MemoryAdapter:
         user_id: str,
         summary: str,
         tenant_id: str | None,
+        facts: list[str] | None = None,
+        preferences: dict[str, Any] | None = None,
+        open_issues: list[str] | None = None,
     ) -> None:
-        """保存会话摘要"""
-        pass
+        if not tenant_id:
+            logger.warning("[AC-IDMP-14] Missing tenant_id when saving summary")
+            return
+
+        stmt = select(UserMemoryEntity).where(
+            UserMemoryEntity.tenant_id == tenant_id,
+            UserMemoryEntity.user_id == user_id,
+        )
+        result = await self._session.execute(stmt)
+        record = result.scalar_one_or_none()
+
+        if record:
+            record.summary = summary
+            record.facts = facts or record.facts
+            record.preferences = preferences or record.preferences
+            record.open_issues = open_issues or record.open_issues
+            record.summary_version = (record.summary_version or 0) + 1
+            record.updated_at = datetime.utcnow()
+        else:
+            record = UserMemoryEntity(
+                tenant_id=tenant_id,
+                user_id=user_id,
+                summary=summary,
+                facts=facts,
+                preferences=preferences,
+                open_issues=open_issues,
+                summary_version=1,
+            )
+            self._session.add(record)
+
+        await self._session.flush()
 
     async def _extract_and_save_facts(
         self,
@@ -295,8 +313,25 @@ class MemoryAdapter:
         messages: list[dict[str, Any]],
         tenant_id: str | None,
     ) -> None:
-        """从消息中提取并保存事实"""
-        pass
+        if not tenant_id:
+            return
+
+        for msg in messages:
+            payload = msg.get("memory_payload") or msg.get("summary_payload")
+            if not payload:
+                continue
+            parsed = self._parse_summary_payload(payload)
+            if not parsed:
+                continue
+            await self._save_summary(
+                user_id=user_id,
+                summary=parsed.get("summary", ""),
+                tenant_id=tenant_id,
+                facts=parsed.get("facts"),
+                preferences=parsed.get("preferences"),
+                open_issues=parsed.get("open_issues"),
+            )
+            break
 
     async def update_with_summary_generation(
         self,
@@ -305,41 +340,92 @@ class MemoryAdapter:
         messages: list[dict[str, Any]],
         tenant_id: str | None = None,
         summary_generator: Callable | None = None,
+        recent_turns: int = 8,
     ) -> bool:
-        """
-        [AC-IDMP-14] 带摘要生成的记忆更新
+        request = UpdateRequest(
+            user_id=user_id,
+            session_id=session_id,
+            messages=messages,
+            summary=None,
+        )
 
-        如果未提供摘要，会尝试生成摘要后回写
-        """
+        task = asyncio.create_task(
+            self._update_with_generation_internal(
+                request,
+                tenant_id,
+                summary_generator,
+                recent_turns,
+            ),
+            name=f"memory_update_gen_{user_id}_{session_id}",
+        )
+        self._pending_updates.append(task)
+        task.add_done_callback(lambda t: self._pending_updates.remove(t))
+
+        logger.info(
+            f"[AC-IDMP-14] Memory update (with summary) scheduled for user={user_id}, "
+            f"session={session_id}, messages_count={len(messages)}"
+        )
+
+        return True
+
+    async def _update_with_generation_internal(
+        self,
+        request: UpdateRequest,
+        tenant_id: str | None,
+        summary_generator: Callable | None,
+        recent_turns: int,
+    ) -> None:
+        try:
+            await asyncio.wait_for(
+                self._do_update_with_generation(
+                    request,
+                    tenant_id,
+                    summary_generator,
+                    recent_turns,
+                ),
+                timeout=self._update_timeout_ms / 1000,
+            )
+            logger.info(
+                f"[AC-IDMP-14] Memory updated (with summary) for user={request.user_id}, "
+                f"session={request.session_id}"
+            )
+        except asyncio.TimeoutError:
+            logger.warning(
+                f"[AC-IDMP-14] Memory update (with summary) timeout for user={request.user_id}, "
+                f"session={request.session_id}"
+            )
+        except Exception as e:
+            logger.error(
+                f"[AC-IDMP-14] Memory update (with summary) failed for user={request.user_id}, "
+                f"session={request.session_id}, error={e}"
+            )
+
+    async def _do_update_with_generation(
+        self,
+        request: UpdateRequest,
+        tenant_id: str | None,
+        summary_generator: Callable | None,
+        recent_turns: int,
+    ) -> None:
         summary = None
         if summary_generator:
             try:
-                summary = await summary_generator(messages)
+                old_summary = await self._load_latest_summary(request.user_id, tenant_id)
+                recent_messages = self._trim_recent_messages(request.messages, recent_turns)
+                summary = await self._call_summary_generator(
+                    summary_generator,
+                    recent_messages,
+                    old_summary,
+                )
             except Exception as e:
                 logger.warning(
                     f"[AC-IDMP-14] Summary generation failed: {e}"
                 )
 
-        return await self.update(
-            user_id=user_id,
-            session_id=session_id,
-            messages=messages,
-            summary=summary,
-            tenant_id=tenant_id,
-        )
+        request.summary = summary
+        await self._do_update(request, tenant_id)
 
     async def wait_pending_updates(self, timeout: float = 5.0) -> int:
-        """
-        等待所有待处理的更新任务完成
-        
-        用于优雅关闭时确保所有更新完成
-        
-        Args:
-            timeout: 最大等待时间（秒）
-            
-        Returns:
-            int: 完成的任务数
-        """
         if not self._pending_updates:
             return 0
 
@@ -353,3 +439,62 @@ class MemoryAdapter:
         except Exception as e:
             logger.error(f"[AC-IDMP-14] Error waiting for pending updates: {e}")
             return 0
+
+    async def _load_latest_summary(
+        self,
+        user_id: str,
+        tenant_id: str | None,
+    ) -> str | None:
+        if not tenant_id:
+            return None
+
+        stmt = select(UserMemoryEntity).where(
+            UserMemoryEntity.tenant_id == tenant_id,
+            UserMemoryEntity.user_id == user_id,
+        )
+        result = await self._session.execute(stmt)
+        record = result.scalar_one_or_none()
+        return record.summary if record else None
+
+    def _trim_recent_messages(
+        self,
+        messages: list[dict[str, Any]],
+        recent_turns: int,
+    ) -> list[dict[str, Any]]:
+        if recent_turns <= 0:
+            return []
+        return messages[-recent_turns:]
+
+    async def _call_summary_generator(
+        self,
+        summary_generator: Callable,
+        recent_messages: list[dict[str, Any]],
+        old_summary: str | None,
+    ) -> str | None:
+        try:
+            if len(inspect.signature(summary_generator).parameters) >= 2:
+                return await summary_generator(recent_messages, old_summary)
+        except Exception:
+            return await summary_generator(recent_messages)
+
+        return await summary_generator(recent_messages)
+
+    def _parse_summary_payload(
+        self,
+        payload: Any,
+    ) -> dict[str, Any] | None:
+        if not payload:
+            return None
+
+        if isinstance(payload, dict):
+            return payload
+
+        if isinstance(payload, str):
+            try:
+                parsed = json.loads(payload)
+                if isinstance(parsed, dict):
+                    return parsed
+            except Exception:
+                return None
+
+        return None

ai-service/app/services/mid/memory_recall_tool.py

@@ -334,24 +334,20 @@ class MemoryRecallTool:
     ) -> str | None:
         """召回最近会话摘要。"""
         try:
-            from app.models.entities import MidAuditLog
-            from sqlmodel import col
+            from app.models.entities import UserMemory
 
             stmt = (
-                select(MidAuditLog)
+                select(UserMemory)
                 .where(
-                    MidAuditLog.tenant_id == tenant_id,
+                    UserMemory.tenant_id == tenant_id,
+                    UserMemory.user_id == user_id,
                 )
-                .order_by(col(MidAuditLog.created_at).desc())
                 .limit(1)
             )
             result = await self._session.execute(stmt)
-            audit = result.scalar_one_or_none()
+            memory = result.scalar_one_or_none()
 
-            if audit:
-                return f"上次会话模式: {audit.mode}"
-
-            return None
+            return memory.summary if memory else None
 
         except Exception as e:
             logger.warning(f"[AC-IDMP-13] Failed to recall last summary: {e}")