59081848e2
- 移除整个传统内存系统，包括内存系统模块及所有相关组件 - 删除弃用的内存组件：增强型内存激活器、海马体采样器、内存构建器、内存块、内存遗忘引擎、内存格式器、内存融合器、内存管理器、内存元数据索引、内存查询规划器、内存系统、消息集合处理器、消息集合存储、向量内存存储_v2 - 更新内存图配置，采用增强型检索设置 - 优化内存管理器查询功能，以分析完整对话上下文 - 更新机器人配置模板版本至7.6.1，新增内存图表检索参数 重大变更：旧版内存系统已被完全移除。所有内存功能现依赖于内存图系统。请更新配置以包含新的内存图检索参数。
273 lines
13 KiB
Python
273 lines
13 KiB
Python
"""
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记忆图系统配置管理
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Dict, Optional
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@dataclass
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class ConsolidationConfig:
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"""记忆整理配置"""
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interval_hours: int = 6 # 整理间隔（小时）
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batch_size: int = 100 # 每次处理记忆数量
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enable_auto_discovery: bool = True # 是否启用自动关联发现
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enable_conflict_detection: bool = True # 是否启用冲突检测
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@dataclass
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class RetrievalConfig:
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"""记忆检索配置"""
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default_mode: str = "auto" # auto/fast/deep
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max_expand_depth: int = 2 # 最大图扩展深度
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vector_weight: float = 0.4 # 向量相似度权重
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graph_distance_weight: float = 0.2 # 图距离权重
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importance_weight: float = 0.2 # 重要性权重
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recency_weight: float = 0.2 # 时效性权重
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def __post_init__(self):
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"""验证权重总和"""
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total = self.vector_weight + self.graph_distance_weight + self.importance_weight + self.recency_weight
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if abs(total - 1.0) > 0.01:
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raise ValueError(f"权重总和必须为1.0，当前为 {total}")
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@dataclass
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class NodeMergerConfig:
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"""节点去重配置"""
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similarity_threshold: float = 0.85 # 相似度阈值
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context_match_required: bool = True # 是否要求上下文匹配
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merge_batch_size: int = 50 # 批量处理大小
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def __post_init__(self):
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"""验证阈值范围"""
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if not 0.0 <= self.similarity_threshold <= 1.0:
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raise ValueError(f"相似度阈值必须在 [0, 1] 范围内，当前为 {self.similarity_threshold}")
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@dataclass
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class StorageConfig:
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"""存储配置"""
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data_dir: Path = field(default_factory=lambda: Path("data/memory_graph"))
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vector_collection_name: str = "memory_nodes"
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graph_file_name: str = "memory_graph.json"
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enable_persistence: bool = True # 是否启用持久化
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auto_save_interval: int = 300 # 自动保存间隔（秒）
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@dataclass
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class MemoryGraphConfig:
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"""记忆图系统总配置"""
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# 基础配置
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enable: bool = True # 是否启用记忆图系统
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data_dir: Path = field(default_factory=lambda: Path("data/memory_graph"))
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# 向量存储配置
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vector_collection_name: str = "memory_nodes"
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vector_db_path: Path = field(default_factory=lambda: Path("data/memory_graph/chroma_db"))
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# 检索配置
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search_top_k: int = 10
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search_min_importance: float = 0.3
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search_similarity_threshold: float = 0.5
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enable_query_optimization: bool = True
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# 整合配置
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consolidation_enabled: bool = True
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consolidation_interval_hours: float = 1.0
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consolidation_similarity_threshold: float = 0.85
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consolidation_time_window_hours: int = 24
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# 自动关联配置
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auto_link_enabled: bool = True # 是否启用自动关联
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auto_link_max_candidates: int = 5 # 每个记忆最多关联候选数
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auto_link_min_confidence: float = 0.7 # 最低置信度阈值
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# 遗忘配置
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forgetting_enabled: bool = True
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forgetting_activation_threshold: float = 0.1
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forgetting_min_importance: float = 0.8
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# 激活配置
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activation_decay_rate: float = 0.9
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activation_propagation_strength: float = 0.5
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activation_propagation_depth: int = 1
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# 性能配置
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max_memory_nodes_per_memory: int = 10
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max_related_memories: int = 5
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# 旧配置（向后兼容）
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consolidation: ConsolidationConfig = field(default_factory=ConsolidationConfig)
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retrieval: RetrievalConfig = field(default_factory=RetrievalConfig)
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node_merger: NodeMergerConfig = field(default_factory=NodeMergerConfig)
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storage: StorageConfig = field(default_factory=StorageConfig)
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# 时间衰减配置
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decay_rates: Dict[str, float] = field(
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default_factory=lambda: {
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"EVENT": 0.05, # 事件衰减较快
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"FACT": 0.01, # 事实衰减慢
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"RELATION": 0.005, # 关系衰减很慢
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"OPINION": 0.03, # 观点中等衰减
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}
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)
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# 嵌入模型配置
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embedding_model: Optional[str] = None # 如果为None，则使用系统默认
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embedding_dimension: int = 384 # 默认使用 sentence-transformers 的维度
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# 调试和日志
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enable_debug_logging: bool = False
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enable_visualization: bool = False # 是否启用记忆可视化
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@classmethod
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def from_bot_config(cls, bot_config) -> MemoryGraphConfig:
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"""从bot_config加载配置"""
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try:
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# 尝试获取配置（优先使用memory，兼容memory_graph）
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if hasattr(bot_config, 'memory') and bot_config.memory is not None:
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mg_config = bot_config.memory
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elif hasattr(bot_config, 'memory_graph'):
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mg_config = bot_config.memory_graph
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config = cls(
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enable=getattr(mg_config, 'enable', True),
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data_dir=Path(getattr(mg_config, 'data_dir', 'data/memory_graph')),
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vector_collection_name=getattr(mg_config, 'vector_collection_name', 'memory_nodes'),
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vector_db_path=Path(getattr(mg_config, 'vector_db_path', 'data/memory_graph/chroma_db')),
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search_top_k=getattr(mg_config, 'search_top_k', 10),
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search_min_importance=getattr(mg_config, 'search_min_importance', 0.3),
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search_similarity_threshold=getattr(mg_config, 'search_similarity_threshold', 0.5),
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enable_query_optimization=getattr(mg_config, 'enable_query_optimization', True),
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consolidation_enabled=getattr(mg_config, 'consolidation_enabled', True),
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consolidation_interval_hours=getattr(mg_config, 'consolidation_interval_hours', 1.0),
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consolidation_similarity_threshold=getattr(mg_config, 'consolidation_similarity_threshold', 0.85),
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consolidation_time_window_hours=getattr(mg_config, 'consolidation_time_window_hours', 24),
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auto_link_enabled=getattr(mg_config, 'auto_link_enabled', True),
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auto_link_max_candidates=getattr(mg_config, 'auto_link_max_candidates', 5),
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auto_link_min_confidence=getattr(mg_config, 'auto_link_min_confidence', 0.7),
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forgetting_enabled=getattr(mg_config, 'forgetting_enabled', True),
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forgetting_activation_threshold=getattr(mg_config, 'forgetting_activation_threshold', 0.1),
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forgetting_min_importance=getattr(mg_config, 'forgetting_min_importance', 0.8),
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activation_decay_rate=getattr(mg_config, 'activation_decay_rate', 0.9),
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activation_propagation_strength=getattr(mg_config, 'activation_propagation_strength', 0.5),
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activation_propagation_depth=getattr(mg_config, 'activation_propagation_depth', 1),
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max_memory_nodes_per_memory=getattr(mg_config, 'max_memory_nodes_per_memory', 10),
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max_related_memories=getattr(mg_config, 'max_related_memories', 5),
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# 检索配置
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retrieval=RetrievalConfig(
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max_expand_depth=getattr(mg_config, 'search_max_expand_depth', 2),
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vector_weight=getattr(mg_config, 'search_vector_weight', 0.4),
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graph_distance_weight=getattr(mg_config, 'search_graph_distance_weight', 0.2),
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importance_weight=getattr(mg_config, 'search_importance_weight', 0.2),
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recency_weight=getattr(mg_config, 'search_recency_weight', 0.2),
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),
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)
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return config
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else:
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# 没有找到memory_graph配置，使用默认值
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return cls()
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except Exception as e:
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import logging
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logger = logging.getLogger(__name__)
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logger.warning(f"从bot_config加载memory_graph配置失败，使用默认配置: {e}")
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return cls()
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@classmethod
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def from_dict(cls, config_dict: Dict) -> MemoryGraphConfig:
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"""从字典创建配置"""
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return cls(
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# 新配置字段
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enable=config_dict.get("enable", True),
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data_dir=Path(config_dict.get("data_dir", "data/memory_graph")),
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vector_collection_name=config_dict.get("vector_collection_name", "memory_nodes"),
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vector_db_path=Path(config_dict.get("vector_db_path", "data/memory_graph/chroma_db")),
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search_top_k=config_dict.get("search_top_k", 10),
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search_min_importance=config_dict.get("search_min_importance", 0.3),
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search_similarity_threshold=config_dict.get("search_similarity_threshold", 0.5),
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enable_query_optimization=config_dict.get("enable_query_optimization", True),
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consolidation_enabled=config_dict.get("consolidation_enabled", True),
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consolidation_interval_hours=config_dict.get("consolidation_interval_hours", 1.0),
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consolidation_similarity_threshold=config_dict.get("consolidation_similarity_threshold", 0.85),
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consolidation_time_window_hours=config_dict.get("consolidation_time_window_hours", 24),
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auto_link_enabled=config_dict.get("auto_link_enabled", True),
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auto_link_max_candidates=config_dict.get("auto_link_max_candidates", 5),
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auto_link_min_confidence=config_dict.get("auto_link_min_confidence", 0.7),
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forgetting_enabled=config_dict.get("forgetting_enabled", True),
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forgetting_activation_threshold=config_dict.get("forgetting_activation_threshold", 0.1),
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forgetting_min_importance=config_dict.get("forgetting_min_importance", 0.8),
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activation_decay_rate=config_dict.get("activation_decay_rate", 0.9),
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activation_propagation_strength=config_dict.get("activation_propagation_strength", 0.5),
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activation_propagation_depth=config_dict.get("activation_propagation_depth", 1),
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max_memory_nodes_per_memory=config_dict.get("max_memory_nodes_per_memory", 10),
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max_related_memories=config_dict.get("max_related_memories", 5),
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# 旧配置字段（向后兼容）
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consolidation=ConsolidationConfig(**config_dict.get("consolidation", {})),
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retrieval=RetrievalConfig(
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max_expand_depth=config_dict.get("search_max_expand_depth", 2),
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vector_weight=config_dict.get("search_vector_weight", 0.4),
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graph_distance_weight=config_dict.get("search_graph_distance_weight", 0.2),
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importance_weight=config_dict.get("search_importance_weight", 0.2),
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recency_weight=config_dict.get("search_recency_weight", 0.2),
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**config_dict.get("retrieval", {})
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),
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node_merger=NodeMergerConfig(**config_dict.get("node_merger", {})),
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storage=StorageConfig(**config_dict.get("storage", {})),
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decay_rates=config_dict.get("decay_rates", cls().decay_rates),
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embedding_model=config_dict.get("embedding_model"),
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embedding_dimension=config_dict.get("embedding_dimension", 384),
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enable_debug_logging=config_dict.get("enable_debug_logging", False),
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enable_visualization=config_dict.get("enable_visualization", False),
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)
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def to_dict(self) -> Dict:
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"""转换为字典"""
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return {
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"consolidation": {
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"interval_hours": self.consolidation.interval_hours,
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"batch_size": self.consolidation.batch_size,
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"enable_auto_discovery": self.consolidation.enable_auto_discovery,
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"enable_conflict_detection": self.consolidation.enable_conflict_detection,
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},
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"retrieval": {
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"default_mode": self.retrieval.default_mode,
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"max_expand_depth": self.retrieval.max_expand_depth,
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"vector_weight": self.retrieval.vector_weight,
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"graph_distance_weight": self.retrieval.graph_distance_weight,
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"importance_weight": self.retrieval.importance_weight,
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"recency_weight": self.retrieval.recency_weight,
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},
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"node_merger": {
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"similarity_threshold": self.node_merger.similarity_threshold,
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"context_match_required": self.node_merger.context_match_required,
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"merge_batch_size": self.node_merger.merge_batch_size,
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},
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"storage": {
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"data_dir": str(self.storage.data_dir),
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"vector_collection_name": self.storage.vector_collection_name,
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"graph_file_name": self.storage.graph_file_name,
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"enable_persistence": self.storage.enable_persistence,
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"auto_save_interval": self.storage.auto_save_interval,
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},
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"decay_rates": self.decay_rates,
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"embedding_model": self.embedding_model,
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"embedding_dimension": self.embedding_dimension,
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"enable_debug_logging": self.enable_debug_logging,
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"enable_visualization": self.enable_visualization,
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}
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# 默认配置实例
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DEFAULT_CONFIG = MemoryGraphConfig()
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