refactor(memory): integrate unified memory service into agent controller
- Replace direct memory agent service calls with unified MemoryService in read endpoint - Update query preprocessor to use new prompt format and return structured queries - Enhance MemorySearchResult model with filtering, merging, and ID tracking capabilities - Add intermediate outputs display for problem split, perceptual retrieval, and search results - Fix parameter alignment and remove unused history parameter in memory agent service
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@@ -15,13 +15,14 @@ from pydantic import BaseModel, Field
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.celery_app import celery_app
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from app.core.agent.agent_middleware import AgentMiddleware
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from app.core.agent.langchain_agent import LangChainAgent
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from app.core.config import settings
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from app.core.error_codes import BizCode
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from app.core.exceptions import BusinessException
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from app.core.logging_config import get_business_logger
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from app.core.memory.enums import SearchStrategy
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from app.core.memory.memory_service import MemoryService
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from app.core.rag.nlp.search import knowledge_retrieval
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from app.db import get_db_context
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from app.models import AgentConfig, ModelConfig
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@@ -29,10 +30,8 @@ from app.repositories.tool_repository import ToolRepository
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from app.schemas.app_schema import FileInput, Citation
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from app.schemas.model_schema import ModelInfo
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from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message
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from app.services import task_service
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from app.services.conversation_service import ConversationService
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from app.services.langchain_tool_server import Search
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from app.services.memory_agent_service import MemoryAgentService
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from app.services.model_parameter_merger import ModelParameterMerger
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from app.services.model_service import ModelApiKeyService
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from app.services.multimodal_service import MultimodalService
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@@ -107,38 +106,41 @@ def create_long_term_memory_tool(
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logger.info(f" 长期记忆工具被调用!question={question}, user={end_user_id}")
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try:
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with get_db_context() as db:
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memory_content = asyncio.run(
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MemoryAgentService().read_memory(
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end_user_id=end_user_id,
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message=question,
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history=[],
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search_switch="2",
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config_id=config_id,
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db=db,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id
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)
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)
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task = celery_app.send_task(
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"app.core.memory.agent.read_message",
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args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id]
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)
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result = task_service.get_task_memory_read_result(task.id)
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status = result.get("status")
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logger.info(f"读取任务状态:{status}")
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if memory_content:
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memory_content = memory_content['answer']
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logger.info(f'用户ID:Agent:{end_user_id}')
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logger.debug("调用长期记忆 API", extra={"question": question, "end_user_id": end_user_id})
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memory_service = MemoryService(db, config_id, end_user_id)
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search_result = asyncio.run(memory_service.read(question, SearchStrategy.QUICK))
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logger.info(
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"长期记忆检索成功",
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extra={
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"end_user_id": end_user_id,
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"content_length": len(str(memory_content))
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}
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)
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return f"检索到以下历史记忆:\n\n{memory_content}"
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# memory_content = asyncio.run(
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# MemoryAgentService().read_memory(
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# end_user_id=end_user_id,
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# message=question,
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# history=[],
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# search_switch="2",
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# config_id=config_id,
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# db=db,
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# storage_type=storage_type,
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# user_rag_memory_id=user_rag_memory_id
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# )
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# )
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# task = celery_app.send_task(
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# "app.core.memory.agent.read_message",
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# args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id]
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# )
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# result = task_service.get_task_memory_read_result(task.id)
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# status = result.get("status")
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# logger.info(f"读取任务状态:{status}")
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# if memory_content:
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# memory_content = memory_content['answer']
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# logger.info(f'用户ID:Agent:{end_user_id}')
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# logger.debug("调用长期记忆 API", extra={"question": question, "end_user_id": end_user_id})
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#
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# logger.info(
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# "长期记忆检索成功",
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# extra={
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# "end_user_id": end_user_id,
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# "content_length": len(str(memory_content))
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# }
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# )
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return f"检索到以下历史记忆:\n\n{search_result.content}"
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except Exception as e:
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logger.error("长期记忆检索失败", extra={"error": str(e), "error_type": type(e).__name__})
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return f"记忆检索失败: {str(e)}"
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@@ -405,7 +405,7 @@ class MemoryAgentService:
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self,
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end_user_id: str,
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message: str,
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history: List[Dict],
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history: List[Dict], # FIXME: unused parameter
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search_switch: str,
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config_id: Optional[uuid.UUID] | int,
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db: Session,
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@@ -505,8 +505,8 @@ class MemoryAgentService:
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initial_state = {
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"messages": [HumanMessage(content=message)],
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"search_switch": search_switch,
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"end_user_id": end_user_id
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, "storage_type": storage_type,
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"end_user_id": end_user_id,
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"storage_type": storage_type,
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"user_rag_memory_id": user_rag_memory_id,
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"memory_config": memory_config}
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# 获取节点更新信息
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@@ -642,6 +642,8 @@ class MemoryAgentService:
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"answer": summary,
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"intermediate_outputs": result
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}
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# TODO: redis search -> answer
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except Exception as e:
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# Ensure proper error handling and logging
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error_msg = f"Read operation failed: {str(e)}"
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@@ -163,7 +163,7 @@ class MemoryConfigService:
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def load_memory_config(
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self,
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config_id: Optional[UUID] = None,
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config_id: UUID | str | int | None = None,
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workspace_id: Optional[UUID] = None,
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service_name: str = "MemoryConfigService",
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) -> MemoryConfig:
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@@ -187,16 +187,6 @@ class MemoryConfigService:
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"""
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start_time = time.time()
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config_logger.info(
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"Starting memory configuration loading",
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extra={
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"operation": "load_memory_config",
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"service": service_name,
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"config_id": str(config_id) if config_id else None,
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"workspace_id": str(workspace_id) if workspace_id else None,
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},
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)
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logger.info(f"Loading memory configuration from database: config_id={config_id}, workspace_id={workspace_id}")
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try:
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@@ -236,11 +226,7 @@ class MemoryConfigService:
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f"Configuration not found: config_id={config_id}, workspace_id={workspace_id}"
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)
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# Get workspace for the config
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db_query_start = time.time()
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result = MemoryConfigRepository.get_config_with_workspace(self.db, memory_config.config_id)
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db_query_time = time.time() - db_query_start
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logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s")
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if not result:
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raise ConfigurationError(
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