dd58f5da20
- 定义核心数据模型 (MemoryNode, MemoryEdge, Memory) - 实现配置管理系统 (MemoryGraphConfig) - 实现向量存储层 (VectorStore with ChromaDB) - 实现图存储层 (GraphStore with NetworkX) - 创建设计文档大纲 - 添加基础测试并验证通过 待完成: - 持久化管理 - 节点去重逻辑 - 记忆构建器 - 记忆检索器
146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
"""
|
||
记忆图系统配置管理
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
from dataclasses import dataclass, field
|
||
from pathlib import Path
|
||
from typing import Dict, Optional
|
||
|
||
|
||
@dataclass
|
||
class ConsolidationConfig:
|
||
"""记忆整理配置"""
|
||
|
||
interval_hours: int = 6 # 整理间隔（小时）
|
||
batch_size: int = 100 # 每次处理记忆数量
|
||
enable_auto_discovery: bool = True # 是否启用自动关联发现
|
||
enable_conflict_detection: bool = True # 是否启用冲突检测
|
||
|
||
|
||
@dataclass
|
||
class RetrievalConfig:
|
||
"""记忆检索配置"""
|
||
|
||
default_mode: str = "auto" # auto/fast/deep
|
||
max_expand_depth: int = 2 # 最大图扩展深度
|
||
vector_weight: float = 0.4 # 向量相似度权重
|
||
graph_distance_weight: float = 0.2 # 图距离权重
|
||
importance_weight: float = 0.2 # 重要性权重
|
||
recency_weight: float = 0.2 # 时效性权重
|
||
|
||
def __post_init__(self):
|
||
"""验证权重总和"""
|
||
total = self.vector_weight + self.graph_distance_weight + self.importance_weight + self.recency_weight
|
||
if abs(total - 1.0) > 0.01:
|
||
raise ValueError(f"权重总和必须为1.0，当前为 {total}")
|
||
|
||
|
||
@dataclass
|
||
class NodeMergerConfig:
|
||
"""节点去重配置"""
|
||
|
||
similarity_threshold: float = 0.85 # 相似度阈值
|
||
context_match_required: bool = True # 是否要求上下文匹配
|
||
merge_batch_size: int = 50 # 批量处理大小
|
||
|
||
def __post_init__(self):
|
||
"""验证阈值范围"""
|
||
if not 0.0 <= self.similarity_threshold <= 1.0:
|
||
raise ValueError(f"相似度阈值必须在 [0, 1] 范围内，当前为 {self.similarity_threshold}")
|
||
|
||
|
||
@dataclass
|
||
class StorageConfig:
|
||
"""存储配置"""
|
||
|
||
data_dir: Path = field(default_factory=lambda: Path("data/memory_graph"))
|
||
vector_collection_name: str = "memory_nodes"
|
||
graph_file_name: str = "memory_graph.json"
|
||
enable_persistence: bool = True # 是否启用持久化
|
||
auto_save_interval: int = 300 # 自动保存间隔（秒）
|
||
|
||
|
||
@dataclass
|
||
class MemoryGraphConfig:
|
||
"""记忆图系统总配置"""
|
||
|
||
consolidation: ConsolidationConfig = field(default_factory=ConsolidationConfig)
|
||
retrieval: RetrievalConfig = field(default_factory=RetrievalConfig)
|
||
node_merger: NodeMergerConfig = field(default_factory=NodeMergerConfig)
|
||
storage: StorageConfig = field(default_factory=StorageConfig)
|
||
|
||
# 时间衰减配置
|
||
decay_rates: Dict[str, float] = field(
|
||
default_factory=lambda: {
|
||
"EVENT": 0.05, # 事件衰减较快
|
||
"FACT": 0.01, # 事实衰减慢
|
||
"RELATION": 0.005, # 关系衰减很慢
|
||
"OPINION": 0.03, # 观点中等衰减
|
||
}
|
||
)
|
||
|
||
# 嵌入模型配置
|
||
embedding_model: Optional[str] = None # 如果为None，则使用系统默认
|
||
embedding_dimension: int = 384 # 默认使用 sentence-transformers 的维度
|
||
|
||
# 调试和日志
|
||
enable_debug_logging: bool = False
|
||
enable_visualization: bool = False # 是否启用记忆可视化
|
||
|
||
@classmethod
|
||
def from_dict(cls, config_dict: Dict) -> MemoryGraphConfig:
|
||
"""从字典创建配置"""
|
||
return cls(
|
||
consolidation=ConsolidationConfig(**config_dict.get("consolidation", {})),
|
||
retrieval=RetrievalConfig(**config_dict.get("retrieval", {})),
|
||
node_merger=NodeMergerConfig(**config_dict.get("node_merger", {})),
|
||
storage=StorageConfig(**config_dict.get("storage", {})),
|
||
decay_rates=config_dict.get("decay_rates", cls().decay_rates),
|
||
embedding_model=config_dict.get("embedding_model"),
|
||
embedding_dimension=config_dict.get("embedding_dimension", 384),
|
||
enable_debug_logging=config_dict.get("enable_debug_logging", False),
|
||
enable_visualization=config_dict.get("enable_visualization", False),
|
||
)
|
||
|
||
def to_dict(self) -> Dict:
|
||
"""转换为字典"""
|
||
return {
|
||
"consolidation": {
|
||
"interval_hours": self.consolidation.interval_hours,
|
||
"batch_size": self.consolidation.batch_size,
|
||
"enable_auto_discovery": self.consolidation.enable_auto_discovery,
|
||
"enable_conflict_detection": self.consolidation.enable_conflict_detection,
|
||
},
|
||
"retrieval": {
|
||
"default_mode": self.retrieval.default_mode,
|
||
"max_expand_depth": self.retrieval.max_expand_depth,
|
||
"vector_weight": self.retrieval.vector_weight,
|
||
"graph_distance_weight": self.retrieval.graph_distance_weight,
|
||
"importance_weight": self.retrieval.importance_weight,
|
||
"recency_weight": self.retrieval.recency_weight,
|
||
},
|
||
"node_merger": {
|
||
"similarity_threshold": self.node_merger.similarity_threshold,
|
||
"context_match_required": self.node_merger.context_match_required,
|
||
"merge_batch_size": self.node_merger.merge_batch_size,
|
||
},
|
||
"storage": {
|
||
"data_dir": str(self.storage.data_dir),
|
||
"vector_collection_name": self.storage.vector_collection_name,
|
||
"graph_file_name": self.storage.graph_file_name,
|
||
"enable_persistence": self.storage.enable_persistence,
|
||
"auto_save_interval": self.storage.auto_save_interval,
|
||
},
|
||
"decay_rates": self.decay_rates,
|
||
"embedding_model": self.embedding_model,
|
||
"embedding_dimension": self.embedding_dimension,
|
||
"enable_debug_logging": self.enable_debug_logging,
|
||
"enable_visualization": self.enable_visualization,
|
||
}
|
||
|
||
|
||
# 默认配置实例
|
||
DEFAULT_CONFIG = MemoryGraphConfig()
|