825 lines
30 KiB
Python
825 lines
30 KiB
Python
"""
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遗忘引擎服务层模块
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本模块提供遗忘引擎的业务逻辑实现，包括：
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1. 遗忘周期执行
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2. 配置管理
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3. 统计信息查询
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4. 遗忘曲线生成
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所有业务逻辑从控制器层分离到此服务层。
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"""
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from typing import Optional, Dict, Any, Tuple
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from datetime import datetime, timezone
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from uuid import UUID
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from sqlalchemy.orm import Session
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from app.core.logging_config import get_api_logger
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from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
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from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy
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from app.core.memory.storage_services.forgetting_engine.forgetting_scheduler import ForgettingScheduler
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from app.core.memory.storage_services.forgetting_engine.config_utils import (
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load_actr_config_from_db,
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)
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.repositories.memory_config_repository import MemoryConfigRepository
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from app.repositories.forgetting_cycle_history_repository import ForgettingCycleHistoryRepository
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# 获取API专用日志器
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api_logger = get_api_logger()
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def convert_neo4j_datetime_to_python(value: Any) -> Optional[datetime]:
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"""
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将 Neo4j DateTime 对象转换为 Python datetime 对象
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Args:
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value: Neo4j DateTime 对象、Python datetime 对象或字符串
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Returns:
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Python datetime 对象或 None
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"""
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if value is None:
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return None
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try:
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# Neo4j DateTime 对象
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if hasattr(value, 'to_native'):
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return value.to_native()
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# Python datetime 对象
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elif isinstance(value, datetime):
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return value
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# 字符串格式
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elif isinstance(value, str):
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if value.endswith('Z'):
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return datetime.fromisoformat(value.replace('Z', '+00:00'))
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else:
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return datetime.fromisoformat(value)
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# 其他类型，尝试转换为字符串
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else:
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return datetime.fromisoformat(str(value).replace('Z', '+00:00'))
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except Exception as e:
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api_logger.warning(f"转换时间失败: {value} (类型: {type(value).__name__}), 错误: {e}")
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return None
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class MemoryForgetService:
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"""遗忘引擎服务类"""
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def __init__(self):
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"""初始化服务"""
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self.config_repository = MemoryConfigRepository()
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self.history_repository = ForgettingCycleHistoryRepository()
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def _get_neo4j_connector(self) -> Neo4jConnector:
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"""
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获取 Neo4j 连接器实例
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Returns:
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Neo4jConnector: Neo4j 连接器实例
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"""
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# 这里应该从配置或依赖注入获取连接器
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# 暂时创建新实例（实际应该使用单例或连接池）
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return Neo4jConnector()
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async def _get_forgetting_components(
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self,
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db: Session,
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config_id: Optional[UUID] = None
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) -> Tuple[ACTRCalculator, ForgettingStrategy, ForgettingScheduler, Dict[str, Any]]:
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"""
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获取遗忘引擎组件（计算器、策略、调度器）
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Args:
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db: 数据库会话
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config_id: 配置ID（可选）
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Returns:
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tuple: (actr_calculator, forgetting_strategy, forgetting_scheduler, config)
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"""
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# 加载配置
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config = load_actr_config_from_db(db, config_id)
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# 创建 ACT-R 计算器
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actr_calculator = ACTRCalculator(
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decay_constant=config['decay_constant'],
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forgetting_rate=config['forgetting_rate'],
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offset=config['offset'],
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max_history_length=config['max_history_length']
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)
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# 获取 Neo4j 连接器
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connector = self._get_neo4j_connector()
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# 创建遗忘策略执行器
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forgetting_strategy = ForgettingStrategy(
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connector=connector,
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actr_calculator=actr_calculator,
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forgetting_threshold=config['forgetting_threshold'],
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enable_llm_summary=config['enable_llm_summary']
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)
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# 创建遗忘调度器
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forgetting_scheduler = ForgettingScheduler(
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forgetting_strategy=forgetting_strategy,
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connector=connector
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)
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return actr_calculator, forgetting_strategy, forgetting_scheduler, config
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async def _get_knowledge_stats(
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self,
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connector: Neo4jConnector,
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end_user_id: Optional[str] = None,
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forgetting_threshold: float = 0.3
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) -> Dict[str, Any]:
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"""
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获取知识层统计信息
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Args:
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connector: Neo4j 连接器
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end_user_id: 组ID（可选）
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forgetting_threshold: 遗忘阈值
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Returns:
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dict: 统计信息字典
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"""
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# 构建查询
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query = """
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MATCH (n)
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WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
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"""
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if end_user_id:
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query += " AND n.end_user_id = $end_user_id"
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query += """
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WITH n,
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CASE
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WHEN n:Statement THEN 'statement'
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WHEN n:ExtractedEntity THEN 'entity'
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WHEN n:MemorySummary THEN 'summary'
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END as node_type
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RETURN
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count(n) as total_nodes,
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sum(CASE WHEN node_type = 'statement' THEN 1 ELSE 0 END) as statement_count,
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sum(CASE WHEN node_type = 'entity' THEN 1 ELSE 0 END) as entity_count,
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sum(CASE WHEN node_type = 'summary' THEN 1 ELSE 0 END) as summary_count,
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avg(n.activation_value) as average_activation,
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sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
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"""
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params = {'threshold': forgetting_threshold}
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if end_user_id:
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params['end_user_id'] = end_user_id
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results = await connector.execute_query(query, **params)
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if results:
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result = results[0]
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return {
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'total_nodes': result['total_nodes'] or 0,
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'statement_count': result['statement_count'] or 0,
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'entity_count': result['entity_count'] or 0,
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'summary_count': result['summary_count'] or 0,
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'average_activation': result['average_activation'],
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'low_activation_nodes': result['low_activation_nodes'] or 0
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}
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return {
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'total_nodes': 0,
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'statement_count': 0,
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'entity_count': 0,
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'summary_count': 0,
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'average_activation': None,
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'low_activation_nodes': 0
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}
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async def _get_pending_forgetting_nodes(
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self,
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connector: Neo4jConnector,
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end_user_id: str,
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forgetting_threshold: float,
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min_days_since_access: int,
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page: Optional[int] = None,
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pagesize: Optional[int] = None
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) -> Dict[str, Any]:
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"""
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获取待遗忘节点列表
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查询满足遗忘条件的节点（激活值低于阈值且最后访问时间超过最小天数）。支持分页查询。
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Args:
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connector: Neo4j 连接器
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end_user_id: 组ID
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forgetting_threshold: 遗忘阈值
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min_days_since_access: 最小未访问天数
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page: 页码（可选，从1开始）
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pagesize: 每页数量（可选）
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Returns:
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dict: 包含待遗忘节点列表和分页信息的字典
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- items: 待遗忘节点列表
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- page: 分页信息（分页时）
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"""
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from datetime import timedelta
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# 计算最小访问时间（ISO 8601 格式字符串，使用 UTC 时区）
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min_access_time = datetime.now(timezone.utc) - timedelta(days=min_days_since_access)
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min_access_time_str = min_access_time.strftime('%Y-%m-%dT%H:%M:%S.%fZ')
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# 基础查询（用于获取总数）
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count_query = """
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MATCH (n)
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WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
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AND n.end_user_id = $end_user_id
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AND n.activation_value IS NOT NULL
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AND n.activation_value < $threshold
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AND n.last_access_time IS NOT NULL
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AND datetime(n.last_access_time) < datetime($min_access_time_str)
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RETURN count(n) as total
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"""
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# 数据查询
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data_query = """
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MATCH (n)
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WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
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AND n.end_user_id = $end_user_id
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AND n.activation_value IS NOT NULL
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AND n.activation_value < $threshold
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AND n.last_access_time IS NOT NULL
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AND datetime(n.last_access_time) < datetime($min_access_time_str)
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RETURN
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elementId(n) as node_id,
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labels(n)[0] as node_type,
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CASE
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WHEN n:Statement THEN n.statement
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WHEN n:ExtractedEntity THEN n.name
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WHEN n:MemorySummary THEN n.content
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ELSE ''
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END as content_summary,
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n.activation_value as activation_value,
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n.last_access_time as last_access_time
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ORDER BY n.activation_value ASC
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"""
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# 如果启用分页，添加 SKIP 和 LIMIT
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if page is not None and pagesize is not None and page > 0 and pagesize > 0:
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data_query += " SKIP $skip LIMIT $limit"
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params = {
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'end_user_id': end_user_id,
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'threshold': forgetting_threshold,
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'min_access_time_str': min_access_time_str
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}
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# 获取总数（分页时需要）
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total = 0
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if page is not None and pagesize is not None and page > 0 and pagesize > 0:
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count_results = await connector.execute_query(count_query, **params)
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if count_results:
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total = count_results[0]['total']
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# 添加分页参数
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if page is not None and pagesize is not None and page > 0 and pagesize > 0:
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params['skip'] = (page - 1) * pagesize
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params['limit'] = pagesize
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results = await connector.execute_query(data_query, **params)
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pending_nodes = []
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for result in results:
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# 将节点类型标签转换为小写
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node_type_label = result['node_type'].lower()
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if node_type_label == 'extractedentity':
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node_type_label = 'entity'
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elif node_type_label == 'memorysummary':
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node_type_label = 'summary'
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# 将 Neo4j DateTime 对象转换为时间戳（毫秒）
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last_access_time = result['last_access_time']
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last_access_dt = convert_neo4j_datetime_to_python(last_access_time)
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# 确保 datetime 带有时区信息(假定为 UTC),避免 naive datetime 导致的时区偏差
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if last_access_dt:
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if last_access_dt.tzinfo is None:
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last_access_dt = last_access_dt.replace(tzinfo=timezone.utc)
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last_access_timestamp = int(last_access_dt.timestamp() * 1000)
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else:
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last_access_timestamp = 0
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pending_nodes.append({
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'node_id': str(result['node_id']),
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'node_type': node_type_label,
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'content_summary': result['content_summary'] or '',
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'activation_value': result['activation_value'],
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'last_access_time': last_access_timestamp
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})
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# 构建返回结果
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result: Dict[str, Any] = {'items': pending_nodes}
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# 如果启用分页，添加分页信息
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if page is not None and pagesize is not None and page > 0 and pagesize > 0:
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result['page'] = {
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'page': page,
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'pagesize': pagesize,
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'total': total,
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'hasnext': (page * pagesize) < total
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}
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return result
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async def trigger_forgetting_cycle(
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self,
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db: Session,
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end_user_id: str,
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max_merge_batch_size: Optional[int] = None,
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min_days_since_access: Optional[int] = None,
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config_id: Optional[UUID] = None
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) -> Dict[str, Any]:
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"""
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手动触发遗忘周期
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执行一次完整的遗忘周期，识别并融合低激活值节点。
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Args:
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db: 数据库会话
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end_user_id: 组ID（即终端用户ID，必填）
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max_merge_batch_size: 最大融合批次大小（可选）
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min_days_since_access: 最小未访问天数（可选）
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config_id: 配置ID（必填，由控制器层通过 end_user_id 获取）
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Returns:
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dict: 遗忘报告
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"""
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# 获取遗忘引擎组件
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_, _, forgetting_scheduler, config = await self._get_forgetting_components(db, config_id)
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# 如果参数为 None，使用配置中的默认值
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if max_merge_batch_size is None:
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max_merge_batch_size = config.get('max_merge_batch_size', 100)
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if min_days_since_access is None:
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min_days_since_access = config.get('min_days_since_access', 30)
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# 记录执行开始时间
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execution_time = datetime.now()
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# 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取）
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report = await forgetting_scheduler.run_forgetting_cycle(
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end_user_id=end_user_id,
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max_merge_batch_size=max_merge_batch_size,
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min_days_since_access=min_days_since_access,
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config_id=config_id,
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db=db
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)
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api_logger.info(
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f"遗忘周期完成: 融合 {report['merged_count']} 对节点, "
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f"失败 {report['failed_count']} 对, "
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f"耗时 {report['duration_seconds']:.2f} 秒"
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)
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# 获取当前的激活值统计（用于记录历史）
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try:
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connector = forgetting_scheduler.connector
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stats_query = """
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MATCH (n)
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WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
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AND n.end_user_id = $end_user_id
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RETURN
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count(n) as total_nodes,
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avg(n.activation_value) as average_activation,
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sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
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"""
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stats_results = await connector.execute_query(
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stats_query,
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end_user_id=end_user_id,
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threshold=config['forgetting_threshold']
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)
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if stats_results:
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stats = stats_results[0]
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total_nodes = stats['total_nodes'] or 0
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average_activation = stats['average_activation']
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low_activation_nodes = stats['low_activation_nodes'] or 0
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else:
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total_nodes = 0
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average_activation = None
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low_activation_nodes = 0
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# 保存历史记录到数据库
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self.history_repository.create(
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db=db,
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end_user_id=end_user_id,
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execution_time=execution_time,
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merged_count=report['merged_count'],
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failed_count=report['failed_count'],
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average_activation_value=average_activation,
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total_nodes=total_nodes,
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low_activation_nodes=low_activation_nodes,
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duration_seconds=report['duration_seconds'],
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trigger_type='manual'
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)
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api_logger.info(
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f"已保存遗忘周期历史记录: end_user_id={end_user_id}, "
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f"merged_count={report['merged_count']}"
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)
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except Exception as e:
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# 记录历史失败不应影响主流程
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api_logger.error(f"保存遗忘周期历史记录失败: {str(e)}")
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return report
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def read_forgetting_config(
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self,
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db: Session,
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config_id: UUID
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) -> Dict[str, Any]:
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"""
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获取遗忘引擎配置
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读取指定配置ID的遗忘引擎参数。
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Args:
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db: 数据库会话
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config_id: 配置ID
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Returns:
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dict: 配置信息字典
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"""
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# 加载配置
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config = load_actr_config_from_db(db, config_id)
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# 添加 config_id 到返回结果
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config['config_id'] = config_id
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api_logger.info(f"成功读取遗忘引擎配置: config_id={config_id}")
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return config
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def update_forgetting_config(
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self,
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db: Session,
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config_id: UUID,
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update_fields: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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更新遗忘引擎配置
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更新指定配置ID的遗忘引擎参数。
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Args:
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db: 数据库会话
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config_id: 配置ID
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update_fields: 要更新的字段字典
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Returns:
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dict: 更新后的配置信息
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Raises:
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||
ValueError: 配置不存在
|
||
"""
|
||
# 检查配置是否存在
|
||
db_config = self.config_repository.get_by_id(db, config_id)
|
||
if db_config is None:
|
||
raise ValueError(f"配置不存在: {config_id}")
|
||
|
||
# 执行更新
|
||
if update_fields:
|
||
for key, value in update_fields.items():
|
||
if hasattr(db_config, key):
|
||
setattr(db_config, key, value)
|
||
|
||
db.commit()
|
||
db.refresh(db_config)
|
||
|
||
api_logger.info(
|
||
f"成功更新遗忘引擎配置: config_id={config_id}, "
|
||
f"更新字段: {list(update_fields.keys())}"
|
||
)
|
||
else:
|
||
api_logger.info(f"没有字段需要更新: config_id={config_id}")
|
||
|
||
# 重新加载配置并返回
|
||
config = load_actr_config_from_db(db, config_id)
|
||
config['config_id'] = config_id
|
||
|
||
return config
|
||
|
||
async def get_forgetting_stats(
|
||
self,
|
||
db: Session,
|
||
end_user_id: Optional[str] = None,
|
||
config_id: Optional[UUID] = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
获取遗忘引擎统计信息
|
||
|
||
返回知识层节点统计、激活值分布等信息。
|
||
|
||
Args:
|
||
db: 数据库会话
|
||
end_user_id: 组ID（可选）
|
||
config_id: 配置ID（可选，用于获取遗忘阈值）
|
||
|
||
Returns:
|
||
dict: 统计信息字典
|
||
"""
|
||
# 获取遗忘引擎组件
|
||
_, _, forgetting_scheduler, config = await self._get_forgetting_components(db, config_id)
|
||
|
||
connector = forgetting_scheduler.connector
|
||
forgetting_threshold = config['forgetting_threshold']
|
||
|
||
# 收集激活值指标
|
||
activation_query = """
|
||
MATCH (n)
|
||
WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
|
||
"""
|
||
|
||
if end_user_id:
|
||
activation_query += " AND n.end_user_id = $end_user_id"
|
||
|
||
activation_query += """
|
||
RETURN
|
||
count(n) as total_nodes,
|
||
sum(CASE WHEN n.activation_value IS NOT NULL THEN 1 ELSE 0 END) as nodes_with_activation,
|
||
sum(CASE WHEN n.activation_value IS NULL THEN 1 ELSE 0 END) as nodes_without_activation,
|
||
avg(n.activation_value) as average_activation,
|
||
sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
|
||
"""
|
||
|
||
params = {'threshold': forgetting_threshold}
|
||
if end_user_id:
|
||
params['end_user_id'] = end_user_id
|
||
|
||
activation_results = await connector.execute_query(activation_query, **params)
|
||
|
||
if activation_results:
|
||
result = activation_results[0]
|
||
activation_metrics = {
|
||
'total_nodes': result['total_nodes'] or 0,
|
||
'nodes_with_activation': result['nodes_with_activation'] or 0,
|
||
'nodes_without_activation': result['nodes_without_activation'] or 0,
|
||
'average_activation_value': round(result['average_activation'], 2) if result['average_activation'] is not None else None,
|
||
'low_activation_nodes': result['low_activation_nodes'] or 0,
|
||
'forgetting_threshold': forgetting_threshold,
|
||
'timestamp': int(datetime.now().timestamp() * 1000)
|
||
}
|
||
else:
|
||
activation_metrics = {
|
||
'total_nodes': 0,
|
||
'nodes_with_activation': 0,
|
||
'nodes_without_activation': 0,
|
||
'average_activation_value': None,
|
||
'low_activation_nodes': 0,
|
||
'forgetting_threshold': forgetting_threshold,
|
||
'timestamp': int(datetime.now().timestamp() * 1000)
|
||
}
|
||
|
||
# 收集节点类型分布
|
||
distribution_query = """
|
||
MATCH (n)
|
||
WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
|
||
"""
|
||
|
||
if end_user_id:
|
||
distribution_query += " AND n.end_user_id = $end_user_id"
|
||
|
||
distribution_query += """
|
||
WITH n,
|
||
CASE
|
||
WHEN n:Statement THEN 'statement'
|
||
WHEN n:ExtractedEntity THEN 'entity'
|
||
WHEN n:MemorySummary THEN 'summary'
|
||
WHEN n:Chunk THEN 'chunk'
|
||
END as node_type
|
||
RETURN
|
||
sum(CASE WHEN node_type = 'statement' THEN 1 ELSE 0 END) as statement_count,
|
||
sum(CASE WHEN node_type = 'entity' THEN 1 ELSE 0 END) as entity_count,
|
||
sum(CASE WHEN node_type = 'summary' THEN 1 ELSE 0 END) as summary_count,
|
||
sum(CASE WHEN node_type = 'chunk' THEN 1 ELSE 0 END) as chunk_count
|
||
"""
|
||
|
||
dist_params = {}
|
||
if end_user_id:
|
||
dist_params['end_user_id'] = end_user_id
|
||
|
||
distribution_results = await connector.execute_query(distribution_query, **dist_params)
|
||
|
||
if distribution_results:
|
||
result = distribution_results[0]
|
||
node_distribution = {
|
||
'statement_count': result['statement_count'] or 0,
|
||
'entity_count': result['entity_count'] or 0,
|
||
'summary_count': result['summary_count'] or 0,
|
||
'chunk_count': result['chunk_count'] or 0
|
||
}
|
||
else:
|
||
node_distribution = {
|
||
'statement_count': 0,
|
||
'entity_count': 0,
|
||
'summary_count': 0,
|
||
'chunk_count': 0
|
||
}
|
||
|
||
# 获取最近7个日期的历史趋势数据（每天取最后一次执行）
|
||
recent_trends = []
|
||
try:
|
||
if end_user_id:
|
||
# 查询所有历史记录
|
||
history_records = self.history_repository.get_recent_by_end_user(
|
||
db=db,
|
||
end_user_id=end_user_id
|
||
)
|
||
|
||
# 按日期分组（一天可能有多次执行，取最后一次）
|
||
from collections import OrderedDict
|
||
daily_records = OrderedDict()
|
||
|
||
# 遍历记录（已按时间降序），每个日期只保留第一次遇到的（即最后一次执行）
|
||
for record in history_records:
|
||
# 提取日期（格式: "1/1", "1/2"）- 跨平台兼容
|
||
month = record.execution_time.month
|
||
day = record.execution_time.day
|
||
date_str = f"{month}/{day}"
|
||
|
||
# 如果这个日期还没有记录，添加它（这是该日期最后一次执行）
|
||
if date_str not in daily_records:
|
||
daily_records[date_str] = record
|
||
|
||
# 如果已经有7个不同的日期，停止
|
||
if len(daily_records) >= 7:
|
||
break
|
||
|
||
# 构建趋势数据点（按时间从旧到新排序）
|
||
sorted_dates = sorted(
|
||
daily_records.items(),
|
||
key=lambda x: x[1].execution_time
|
||
)
|
||
|
||
for date_str, record in sorted_dates:
|
||
recent_trends.append({
|
||
'date': date_str,
|
||
'merged_count': record.merged_count,
|
||
'average_activation': round(record.average_activation_value, 2) if record.average_activation_value is not None else None,
|
||
'total_nodes': record.total_nodes,
|
||
'execution_time': int(record.execution_time.timestamp() * 1000)
|
||
})
|
||
|
||
api_logger.info(f"成功获取最近 {len(recent_trends)} 个日期的历史趋势数据")
|
||
|
||
except Exception as e:
|
||
api_logger.error(f"获取历史趋势数据失败: {str(e)}")
|
||
# 失败时返回空列表，不影响主流程
|
||
|
||
# 获取待遗忘节点列表
|
||
pending_nodes = []
|
||
try:
|
||
if end_user_id:
|
||
# 验证 min_days_since_access 配置值
|
||
min_days = config.get('min_days_since_access')
|
||
if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0:
|
||
api_logger.warning(
|
||
f"min_days_since_access 配置无效: {min_days}, 使用默认值 7"
|
||
)
|
||
min_days = 7
|
||
|
||
pending_nodes = await self._get_pending_forgetting_nodes(
|
||
connector=connector,
|
||
end_user_id=end_user_id,
|
||
forgetting_threshold=forgetting_threshold,
|
||
min_days_since_access=int(min_days)
|
||
)
|
||
|
||
api_logger.info(f"成功获取 {len(pending_nodes)} 个待遗忘节点")
|
||
|
||
except Exception as e:
|
||
api_logger.error(f"获取待遗忘节点失败: {str(e)}")
|
||
# 失败时返回空列表，不影响主流程
|
||
|
||
# 构建统计信息（不包含 pending_nodes，已分离到独立接口）
|
||
stats = {
|
||
'activation_metrics': activation_metrics,
|
||
'node_distribution': node_distribution,
|
||
'recent_trends': recent_trends,
|
||
'timestamp': int(datetime.now().timestamp() * 1000)
|
||
}
|
||
|
||
api_logger.info(
|
||
f"成功获取遗忘引擎统计: total_nodes={stats['activation_metrics']['total_nodes']}, "
|
||
f"low_activation_nodes={stats['activation_metrics']['low_activation_nodes']}, "
|
||
f"trend_days={len(recent_trends)}"
|
||
)
|
||
|
||
return stats
|
||
|
||
async def get_pending_nodes(
|
||
self,
|
||
db: Session,
|
||
end_user_id: str,
|
||
config_id: Optional[UUID] = None,
|
||
page: int = 1,
|
||
pagesize: int = 10
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
获取待遗忘节点列表（独立分页接口）
|
||
|
||
查询满足遗忘条件的节点（激活值低于阈值且最后访问时间超过最小天数）。
|
||
|
||
Args:
|
||
db: 数据库会话
|
||
end_user_id: 组ID（必填）
|
||
config_id: 配置ID（可选，用于获取遗忘阈值）
|
||
page: 页码（从1开始，默认1）
|
||
pagesize: 每页数量（默认10）
|
||
|
||
Returns:
|
||
dict: 包含待遗忘节点列表和分页信息的字典
|
||
- items: 待遗忘节点列表
|
||
- page: 分页信息
|
||
"""
|
||
# 获取遗忘引擎组件
|
||
_, _, forgetting_scheduler, config = await self._get_forgetting_components(db, config_id)
|
||
|
||
connector = forgetting_scheduler.connector
|
||
forgetting_threshold = config['forgetting_threshold']
|
||
|
||
# 验证 min_days_since_access 配置值
|
||
min_days = config.get('min_days_since_access')
|
||
if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0:
|
||
api_logger.warning(
|
||
f"min_days_since_access 配置无效: {min_days}, 使用默认值 7"
|
||
)
|
||
min_days = 7
|
||
|
||
# 调用内部方法获取分页数据
|
||
pending_nodes_result = await self._get_pending_forgetting_nodes(
|
||
connector=connector,
|
||
end_user_id=end_user_id,
|
||
forgetting_threshold=forgetting_threshold,
|
||
min_days_since_access=int(min_days),
|
||
page=page,
|
||
pagesize=pagesize
|
||
)
|
||
|
||
api_logger.info(
|
||
f"成功获取待遗忘节点列表: end_user_id={end_user_id}, "
|
||
f"page={page}, pagesize={pagesize}, total={pending_nodes_result.get('page', {}).get('total', 0)}"
|
||
)
|
||
|
||
return pending_nodes_result
|
||
|
||
async def get_forgetting_curve(
|
||
self,
|
||
db: Session,
|
||
importance_score: float,
|
||
days: int,
|
||
config_id: Optional[UUID] = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
获取遗忘曲线数据
|
||
|
||
生成遗忘曲线数据用于可视化，模拟记忆激活值随时间的衰减。
|
||
|
||
Args:
|
||
db: 数据库会话
|
||
importance_score: 重要性分数（0-1）
|
||
days: 模拟天数
|
||
config_id: 配置ID（可选）
|
||
|
||
Returns:
|
||
dict: 包含曲线数据和配置的字典
|
||
"""
|
||
# 获取 ACT-R 计算器
|
||
actr_calculator, _, _, config = await self._get_forgetting_components(db, config_id)
|
||
|
||
# 生成遗忘曲线数据
|
||
initial_time = datetime.now()
|
||
curve_data = actr_calculator.get_forgetting_curve(
|
||
initial_time=initial_time,
|
||
importance_score=importance_score,
|
||
days=days
|
||
)
|
||
|
||
api_logger.info(
|
||
f"成功生成遗忘曲线数据: {len(curve_data)} 个数据点"
|
||
)
|
||
|
||
return {
|
||
'curve_data': curve_data,
|
||
'config': {
|
||
'decay_constant': config['decay_constant'],
|
||
'forgetting_rate': config['forgetting_rate'],
|
||
'offset': config['offset'],
|
||
'importance_score': importance_score,
|
||
'days': days
|
||
}
|
||
}
|