Files
MemoryBear/api/app/controllers/memory_reflection_controller.py
Ke Sun 283c64a358 refactor(memory): restructure memory system and improve configuration management
- Remove deprecated main.py entry point from memory module
- Reorganize imports across controllers and services for consistency
- Update emotion controller to pass db session instead of config_id to services
- Enhance memory agent controller with db session parameter for status_type and user_profile endpoints
- Refactor memory agent service to accept db parameter in classify_message_type method
- Improve configuration handling in celery_app by removing automatic database reload
- Update all memory-related services to use centralized config management
- Standardize import ordering and remove unused imports across 50+ files
- Add pilot_run_service for new pilot execution workflow
- Refactor extraction engine, reflection engine, and search services for better modularity
- Update LLM utilities and embedder configuration for improved flexibility
- Enhance type classifier and verification tools with better error handling
- Improve memory evaluation modules (LOCOMO, LongMemEval, MemSciQA) with consistent patterns
2025-12-23 17:17:04 +08:00

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import asyncio
import time
from app.core.logging_config import get_api_logger
from app.core.memory.storage_services.reflection_engine.self_reflexion import (
ReflectionConfig,
ReflectionEngine,
)
from app.core.response_utils import success
from app.db import get_db
from app.dependencies import get_current_user
from app.models.user_model import User
from app.repositories.data_config_repository import DataConfigRepository
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
from app.schemas.memory_reflection_schemas import Memory_Reflection
from app.services.memory_reflection_service import (
MemoryReflectionService,
WorkspaceAppService,
)
from app.services.model_service import ModelConfigService
from dotenv import load_dotenv
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy import text
from sqlalchemy.orm import Session
load_dotenv()
api_logger = get_api_logger()
router = APIRouter(
prefix="/memory",
tags=["Memory"],
)
@router.post("/reflection/save")
async def save_reflection_config(
request: Memory_Reflection,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""Save reflection configuration to data_comfig table"""
try:
config_id = request.config_id
if not config_id:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="缺少必需参数: config_id"
)
api_logger.info(f"用户 {current_user.username} 保存反思配置config_id: {config_id}")
update_params = {
"enable_self_reflexion": request.reflection_enabled,
"iteration_period": request.reflection_period_in_hours,
"reflexion_range": request.reflexion_range,
"baseline": request.baseline,
"reflection_model_id": request.reflection_model_id,
"memory_verify": request.memory_verify,
"quality_assessment": request.quality_assessment,
}
query, params = DataConfigRepository.build_update_reflection(config_id, **update_params)
result = db.execute(text(query), params)
if result.rowcount == 0:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"未找到config_id为 {config_id} 的配置"
)
db.commit()
# 查询更新后的配置
select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
result = db.execute(text(select_query), select_params).fetchone()
if not result:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"更新后未找到config_id为 {config_id} 的配置"
)
api_logger.info(f"成功保存反思配置到数据库config_id: {config_id}")
reflection_result={
"config_id": result.config_id,
"enable_self_reflexion": result.enable_self_reflexion,
"iteration_period": result.iteration_period,
"reflexion_range": result.reflexion_range,
"baseline": result.baseline,
"reflection_model_id": result.reflection_model_id,
"memory_verify": result.memory_verify,
"quality_assessment": result.quality_assessment,
"user_id": result.user_id}
return success(data=reflection_result, msg="反思配置成功")
except ValueError as ve:
api_logger.error(f"参数错误: {str(ve)}")
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"参数错误: {str(ve)}"
)
except Exception as e:
api_logger.error(f"反思配置保存失败: {str(e)}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"反思配置保存失败: {str(e)}"
)
@router.post("/reflection")
async def start_workspace_reflection(
config_id: int,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""Activate the reflection function for all matching applications in the workspace"""
workspace_id = current_user.current_workspace_id
reflection_service = MemoryReflectionService(db)
try:
api_logger.info(f"用户 {current_user.username} 启动workspace反思workspace_id: {workspace_id}")
service = WorkspaceAppService(db)
result = service.get_workspace_apps_detailed(workspace_id)
reflection_results = []
for data in result['apps_detailed_info']:
if data['data_configs'] == []:
continue
releases = data['releases']
data_configs = data['data_configs']
end_users = data['end_users']
for base, config, user in zip(releases, data_configs, end_users):
if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']:
# 调用反思服务
api_logger.info(f"为用户 {user['id']} 启动反思config_id: {config['config_id']}")
reflection_result = await reflection_service.start_reflection_from_data(
config_data=config,
end_user_id=user['id']
)
reflection_results.append({
"app_id": base['app_id'],
"config_id": config['config_id'],
"end_user_id": user['id'],
"reflection_result": reflection_result
})
return success(data=reflection_results, msg="反思配置成功")
except Exception as e:
api_logger.error(f"启动workspace反思失败: {str(e)}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"启动workspace反思失败: {str(e)}"
)
@router.get("/reflection/configs")
async def start_reflection_configs(
config_id: int,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""通过config_id查询data_config表中的反思配置信息"""
try:
api_logger.info(f"用户 {current_user.username} 查询反思配置config_id: {config_id}")
# 使用DataConfigRepository查询反思配置
select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
result = db.execute(text(select_query), select_params).fetchone()
if not result:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"未找到config_id为 {config_id} 的配置"
)
# 构建返回数据
reflection_config = {
"config_id": result.config_id,
"reflection_enabled": result.enable_self_reflexion,
"reflection_period_in_hours": result.iteration_period,
"reflexion_range": result.reflexion_range,
"baseline": result.baseline,
"reflection_model_id": result.reflection_model_id,
"memory_verify": result.memory_verify,
"quality_assessment": result.quality_assessment,
"user_id": result.user_id
}
api_logger.info(f"成功查询反思配置config_id: {config_id}")
return success(data=reflection_config, msg="反思配置查询成功")
except HTTPException:
# 重新抛出HTTP异常
raise
except Exception as e:
api_logger.error(f"查询反思配置失败: {str(e)}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"查询反思配置失败: {str(e)}"
)
@router.get("/reflection/run")
async def reflection_run(
config_id: int,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""Activate the reflection function for all matching applications in the workspace"""
api_logger.info(f"用户 {current_user.username} 查询反思配置config_id: {config_id}")
# 使用DataConfigRepository查询反思配置
select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
result = db.execute(text(select_query), select_params).fetchone()
if not result:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"未找到config_id为 {config_id} 的配置"
)
api_logger.info(f"成功查询反思配置config_id: {config_id}")
# 验证模型ID是否存在
model_id = result.reflection_model_id
if model_id:
try:
ModelConfigService.get_model_by_id(db=db, model_id=model_id)
api_logger.info(f"模型ID验证成功: {model_id}")
except Exception as e:
api_logger.warning(f"模型ID '{model_id}' 不存在,将使用默认模型: {str(e)}")
# 可以设置为None让反思引擎使用默认模型
model_id = None
config = ReflectionConfig(
enabled=result.enable_self_reflexion,
iteration_period=result.iteration_period,
reflexion_range=result.reflexion_range,
baseline=result.baseline,
output_example='',
memory_verify=result.memory_verify,
quality_assessment=result.quality_assessment,
violation_handling_strategy="block",
model_id=model_id
)
connector = Neo4jConnector()
engine = ReflectionEngine(
config=config,
neo4j_connector=connector,
llm_client=model_id # 传入验证后的 model_id
)
result=await (engine.reflection_run())
return success(data=result, msg="反思试运行")