7870c6c33f
* [fix]Fix the interface for statistics of recent activities and applications * [changes]Modify the code based on the AI review 1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter. 2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls. * [fix]Fix the interface for statistics of recent activities and applications * [changes]Modify the code based on the AI review 1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter. 2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls.
470 lines
19 KiB
Python
470 lines
19 KiB
Python
"""
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记忆反思服务
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处理反思引擎的调用和执行
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"""
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from datetime import datetime
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from typing import Dict, Any, Optional, Set
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from fastapi import Depends
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from sqlalchemy.orm import Session
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from sqlalchemy import text
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from app.db import get_db
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from app.core.logging_config import get_api_logger
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from app.core.memory.storage_services.reflection_engine import ReflectionConfig, ReflectionEngine
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from app.core.memory.storage_services.reflection_engine.self_reflexion import ReflectionRange, ReflectionBaseline
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from app.repositories.data_config_repository import DataConfigRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.models.app_model import App
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from app.models.app_release_model import AppRelease
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from app.models.end_user_model import EndUser
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api_logger = get_api_logger()
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class WorkspaceAppService:
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"""Workplace Application Service Class """
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def __init__(self, db: Session):
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self.db = db
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def get_workspace_apps_detailed(self, workspace_id: str) -> Dict[str, Any]:
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"""
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Get detailed information of all applications in the workspace
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Args:
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Workspace_id: Workspace ID
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Returns:
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Dictionary containing detailed application information
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"""
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apps = self.db.query(App).filter(
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App.workspace_id == workspace_id,
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App.is_active.is_(True)
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).all()
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app_ids = [str(app.id) for app in apps]
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apps_detailed_info = []
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for app in apps:
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app_info = self._build_app_info(app)
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self._process_app_releases(app, app_info)
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self._process_end_users(app, app_info)
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apps_detailed_info.append(app_info)
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return {
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"status": "成功",
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"message": f"成功查询到 {len(app_ids)} 个应用及其详细信息",
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"workspace_id": str(workspace_id),
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"apps_count": len(app_ids),
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"app_ids": app_ids,
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"apps_detailed_info": apps_detailed_info
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}
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def _build_app_info(self, app: App) -> Dict[str, Any]:
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"""base_infomation"""
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return {
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"id": str(app.id),
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"name": app.name,
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"description": app.description,
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"type": app.type,
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"status": app.status,
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"visibility": app.visibility,
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"created_at": app.created_at.isoformat() if app.created_at else None,
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"updated_at": app.updated_at.isoformat() if app.updated_at else None,
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"releases": [],
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"data_configs": [],
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"end_users": []
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}
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def _process_app_releases(self, app: App, app_info: Dict[str, Any]) -> None:
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"""Process the release version and configuration information of the application"""
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app_releases = self.db.query(AppRelease).filter(AppRelease.app_id == app.id).all()
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if not app_releases:
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return
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processed_configs: Set[str] = set()
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for release in app_releases:
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memory_content = self._extract_memory_content(release.config)
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if memory_content and memory_content in processed_configs:
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continue
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release_info = {
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"app_id": str(release.app_id),
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"config": memory_content
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}
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if memory_content:
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processed_configs.add(memory_content)
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data_config_info = self._get_data_config(memory_content)
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if data_config_info:
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if not any(dc["config_id"] == data_config_info["config_id"] for dc in app_info["data_configs"]):
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app_info["data_configs"].append(data_config_info)
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app_info["releases"].append(release_info)
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def _extract_memory_content(self, config: Any) -> str:
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"""Extract memory_comtent from config"""
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if not config or not isinstance(config, dict):
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return None
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memory_obj = config.get('memory')
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if memory_obj and isinstance(memory_obj, dict):
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return memory_obj.get('memory_content')
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return None
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def _get_data_config(self, memory_content: str) -> Dict[str, Any]:
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"""Retrieve data_comfig information based on memory_comtent"""
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try:
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data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content))
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# data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content)
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# data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone()
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# if data_config_result is None:
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# return None
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if data_config_result:
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return {
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"config_id": data_config_result.config_id,
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"enable_self_reflexion": data_config_result.enable_self_reflexion,
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"iteration_period": data_config_result.iteration_period,
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"reflexion_range": data_config_result.reflexion_range,
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"baseline": data_config_result.baseline,
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"reflection_model_id": data_config_result.reflection_model_id,
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"memory_verify": data_config_result.memory_verify,
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"quality_assessment": data_config_result.quality_assessment,
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"user_id": data_config_result.user_id
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}
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except Exception as e:
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api_logger.warning(f"查询data_config失败，memory_content: {memory_content}, 错误: {str(e)}")
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return None
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def _process_end_users(self, app: App, app_info: Dict[str, Any]) -> None:
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"""Processing end-user information for applications"""
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end_users = self.db.query(EndUser).filter(EndUser.app_id == app.id).all()
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for end_user in end_users:
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end_user_info = {
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"id": str(end_user.id),
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"app_id": str(end_user.app_id)
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}
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app_info["end_users"].append(end_user_info)
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def get_end_user_reflection_time(self, end_user_id: str) -> Optional[Any]:
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"""
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Read the reflection time of end users
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Args:
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End_user_id: End User ID
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Returns:
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Reflection time or None
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"""
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try:
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end_user = self.db.query(EndUser).filter(EndUser.id == end_user_id).first()
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if end_user:
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return end_user.reflection_time
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return None
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except Exception as e:
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api_logger.error(f"读取用户反思时间失败，end_user_id: {end_user_id}, 错误: {str(e)}")
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return None
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def update_end_user_reflection_time(self, end_user_id: str) -> bool:
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"""
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Update the reflection time of end users to the current time
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Args:
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End_user_id: End User ID
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Returns:
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Is the update successful
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"""
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try:
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from datetime import datetime
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end_user = self.db.query(EndUser).filter(EndUser.id == end_user_id).first()
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if end_user:
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end_user.reflection_time = datetime.now()
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self.db.commit()
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api_logger.info(f"成功更新用户反思时间，end_user_id: {end_user_id}")
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return True
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else:
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api_logger.warning(f"未找到用户，end_user_id: {end_user_id}")
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return False
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except Exception as e:
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api_logger.error(f"更新用户反思时间失败，end_user_id: {end_user_id}, 错误: {str(e)}")
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self.db.rollback()
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return False
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class MemoryReflectionService:
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"""Memory reflection service category"""
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def __init__(self,db: Session = Depends(get_db)):
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self.db=db
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async def start_text_reflection(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]:
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try:
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config_id = config_data.get("config_id")
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api_logger.info(f"从配置数据启动反思，config_id: {config_id}, end_user_id: {end_user_id}")
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if not config_data.get("enable_self_reflexion", False):
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return {
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"status": "跳过",
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"message": "反思引擎未启用",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data
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}
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config_data_id = config_data['config_id']
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reflection_config = WorkspaceAppService(self.db)._get_data_config(config_data_id)
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if reflection_config is not None and reflection_config['enable_self_reflexion']:
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reflection_config = self._create_reflection_config_from_data(reflection_config)
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# 3. 执行反思引擎
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reflection_results = await self._execute_reflection_engine(
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reflection_config, end_user_id
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)
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return {
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"status": "完成",
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"message": "反思引擎执行完成",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data,
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"reflection_results": reflection_results
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}
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except Exception as e:
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config_id = config_data.get("config_id", "unknown")
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api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}")
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return {
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"status": "错误",
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"message": f"启动反思失败: {str(e)}",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data
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}
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async def start_reflection_from_data(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]:
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"""
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Starting Reflection from Configuration Data
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Args:
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config_data: Configure data dictionary, including reflective configuration information
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end_user_id: end_user_id
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Returns:
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Reflect on the execution results
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"""
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try:
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config_id = config_data.get("config_id")
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api_logger.info(f"从配置数据启动反思，config_id: {config_id}, end_user_id: {end_user_id}")
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if not config_data.get("enable_self_reflexion", False):
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return {
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"status": "跳过",
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"message": "反思引擎未启用",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data
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}
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config_data_id=config_data['config_id']
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reflection_config=WorkspaceAppService(self.db)._get_data_config(config_data_id)
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if reflection_config is not None and reflection_config['enable_self_reflexion']:
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reflection_config= self._create_reflection_config_from_data(reflection_config)
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iteration_period = int(reflection_config.iteration_period)
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workspace_service = WorkspaceAppService(self.db)
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current_reflection_time = workspace_service.get_end_user_reflection_time(end_user_id)
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# 检查是否需要执行反思
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should_execute = False
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hours_diff = 0
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if current_reflection_time is None:
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# 首次执行反思
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should_execute = True
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api_logger.info(f"首次执行反思，end_user_id: {end_user_id}")
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else:
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# 计算时间差
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try:
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if isinstance(current_reflection_time, str):
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reflection_time = datetime.fromisoformat(current_reflection_time)
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else:
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reflection_time = current_reflection_time
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current_time = datetime.now()
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time_diff = current_time - reflection_time
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hours_diff = int(time_diff.total_seconds() / 3600)
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# 检查是否达到反思周期
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if hours_diff >= iteration_period:
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should_execute = True
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api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时，达到周期 {iteration_period} 小时")
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else:
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api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时，未达到周期 {iteration_period} 小时")
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except (ValueError, TypeError) as e:
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api_logger.warning(f"解析反思时间失败: {e}，将执行反思")
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should_execute = True
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if should_execute:
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api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时")
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# 3. 执行反思引擎
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reflection_results = await self._execute_reflection_engine(
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reflection_config, end_user_id
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)
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# 更新反思时间为当前时间
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update_success = workspace_service.update_end_user_reflection_time(end_user_id)
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if update_success:
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api_logger.info(f"成功更新用户 {end_user_id} 的反思时间")
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else:
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api_logger.error(f"更新用户 {end_user_id} 的反思时间失败")
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return {
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"status": "完成",
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"message": "反思引擎执行完成",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data,
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"reflection_results": reflection_results
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}
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else:
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return {
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"status": "等待中",
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"message": f"反思引擎未开始执行，距离下次执行还需 {iteration_period - hours_diff} 小时",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data,
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"hours_since_last_reflection": hours_diff,
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"next_reflection_in_hours": iteration_period - hours_diff
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}
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except Exception as e:
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config_id = config_data.get("config_id", "unknown")
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api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}")
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return {
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"status": "错误",
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"message": f"启动反思失败: {str(e)}",
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"config_id": config_id,
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"end_user_id": end_user_id,
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"config_data": config_data
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}
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def _create_reflection_config_from_data(self, config_data: Dict[str, Any]) -> ReflectionConfig:
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"""Create reflective configuration objects from configuration data"""
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reflexion_range_value = config_data.get("reflexion_range")
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if reflexion_range_value is None or reflexion_range_value == "":
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reflexion_range_value = "partial"
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reflexion_range = ReflectionRange(reflexion_range_value)
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baseline_value = config_data.get("baseline")
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if baseline_value is None or baseline_value == "":
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baseline_value = "TIME"
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baseline = ReflectionBaseline(baseline_value)
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# iteration_period =
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iteration_period = config_data.get("iteration_period", 24)
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if isinstance(iteration_period, str):
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try:
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iteration_period = int(iteration_period)
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except (ValueError, TypeError):
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iteration_period = 24 # 默认24小时
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return ReflectionConfig(
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enabled=config_data.get("enable_self_reflexion", False),
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iteration_period=str(iteration_period), # ReflectionConfig期望字符串
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reflexion_range=reflexion_range,
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baseline=baseline,
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memory_verify=config_data.get("memory_verify", False),
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quality_assessment=config_data.get("quality_assessment", False),
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model_id=config_data.get("reflection_model_id", "")
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)
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async def _execute_reflection_engine(
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self,
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reflection_config: ReflectionConfig,
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user_id: str
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) -> Dict[str, Any]:
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"""Execute Reflection Engine"""
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try:
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# 创建Neo4j连接器
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connector = Neo4jConnector()
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# 创建反思引擎
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engine = ReflectionEngine(
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config=reflection_config,
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neo4j_connector=connector,
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llm_client=reflection_config.model_id
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)
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# 执行反思
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reflection_result = await engine.execute_reflection(user_id)
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return {
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"success": reflection_result.success,
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"message": reflection_result.message,
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"conflicts_found": reflection_result.conflicts_found,
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"conflicts_resolved": reflection_result.conflicts_resolved,
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"memories_updated": reflection_result.memories_updated,
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"execution_time": reflection_result.execution_time,
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"details": reflection_result.details
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}
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except Exception as e:
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api_logger.error(f"反思引擎执行失败: {str(e)}")
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return {
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"success": False,
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"message": f"反思引擎执行失败: {str(e)}",
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"conflicts_found": 0,
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"conflicts_resolved": 0,
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"memories_updated": 0,
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"execution_time": 0.0
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}
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class Memory_Reflection_Service:
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"""Memory Reflection Service - Used for calling the/reflection interface"""
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def __init__(self, db: Session):
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self.db = db
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self.reflection_service = MemoryReflectionService(db)
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async def start_reflection(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]:
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"""
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Activate the reflection function
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Args:
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config_data: 配置数据，格式如下：
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{
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"config_id": 26,
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"enable_self_reflexion": true,
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"iteration_period": "6",
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"reflexion_range": "partial",
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"baseline": "TIME",
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"reflection_model_id": "ea405fa6-c387-4d78-80ab-826d692301b3",
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"memory_verify": true,
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"quality_assessment": false,
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"user_id": null
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}
|
||
end_user_id: end_user_id，example "12a8b235-6eb1-4481-a53c-b77933b5c949"
|
||
|
||
Returns:
|
||
"""
|
||
api_logger.info(f"Memory_Reflection_Service启动反思，config_id: {config_data.get('config_id')}, end_user_id: {end_user_id}")
|
||
|
||
# 调用核心反思服务
|
||
result = await self.reflection_service.start_reflection_from_data(config_data, end_user_id)
|
||
|
||
return result |