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feat(memory_manager): 增强配置日志记录，添加搜索权重和阈值过滤支持

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Windpicker-owo
2025-11-11 19:47:31 +08:00
parent e2236f5bc1
commit 19cfa37097
2 changed files with 111 additions and 28 deletions
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36
src/memory_graph/manager.py
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@@ -141,7 +141,29 @@ class MemoryManager:
# 检查配置值
expand_depth = self.config.search_max_expand_depth
expand_semantic_threshold = self.config.search_expand_semantic_threshold
logger.info(f"📊 配置检查: search_max_expand_depth={expand_depth}, search_expand_semantic_threshold={expand_semantic_threshold}")
search_top_k = self.config.search_top_k
# 读取权重配置
search_vector_weight = self.config.search_vector_weight
search_importance_weight = self.config.search_importance_weight
search_recency_weight = self.config.search_recency_weight
# 读取阈值过滤配置
search_min_importance = self.config.search_min_importance
search_similarity_threshold = self.config.search_similarity_threshold
logger.info(
f"📊 配置检查: search_max_expand_depth={expand_depth}, "
f"search_expand_semantic_threshold={expand_semantic_threshold}, "
f"search_top_k={search_top_k}"
)
logger.info(
f"📊 权重配置: vector={search_vector_weight}, "
f"importance={search_importance_weight}, "
f"recency={search_recency_weight}"
)
logger.info(
f"📊 阈值过滤: min_importance={search_min_importance}, "
f"similarity_threshold={search_similarity_threshold}"
)
self.tools = MemoryTools(
vector_store=self.vector_store,
@@ -150,6 +172,12 @@ class MemoryManager:
embedding_generator=self.embedding_generator,
max_expand_depth=expand_depth, # 从配置读取图扩展深度
expand_semantic_threshold=expand_semantic_threshold, # 从配置读取图扩展语义阈值
search_top_k=search_top_k, # 从配置读取默认 top_k
search_vector_weight=search_vector_weight, # 从配置读取向量权重
search_importance_weight=search_importance_weight, # 从配置读取重要性权重
search_recency_weight=search_recency_weight, # 从配置读取时效性权重
search_min_importance=search_min_importance, # 从配置读取最小重要性阈值
search_similarity_threshold=search_similarity_threshold, # 从配置读取相似度阈值
)
self._initialized = True
@@ -348,7 +376,7 @@ class MemoryManager:
async def search_memories(
self,
query: str,
top_k: int = 10,
top_k: int | None = None,
memory_types: list[str] | None = None,
time_range: tuple[datetime, datetime] | None = None,
min_importance: float = 0.0,
@@ -384,6 +412,10 @@ class MemoryManager:
await self.initialize()
try:
# 使用配置的默认值
if top_k is None:
top_k = getattr(self.config, "search_top_k", 10)
# 准备搜索参数
params = {
"query": query,

103
src/memory_graph/tools/memory_tools.py
View File

@@ -37,6 +37,14 @@ class MemoryTools:
embedding_generator: EmbeddingGenerator | None = None,
max_expand_depth: int = 1,
expand_semantic_threshold: float = 0.3,
search_top_k: int = 10,
# 新增：搜索权重配置
search_vector_weight: float = 0.65,
search_importance_weight: float = 0.25,
search_recency_weight: float = 0.10,
# 新增：阈值过滤配置
search_min_importance: float = 0.3,
search_similarity_threshold: float = 0.5,
):
"""
初始化工具集
@@ -48,15 +56,37 @@ class MemoryTools:
embedding_generator: 嵌入生成器（可选）
max_expand_depth: 图扩展深度的默认值（从配置读取）
expand_semantic_threshold: 图扩展时语义相似度阈值（从配置读取）
search_top_k: 默认检索返回数量（从配置读取）
search_vector_weight: 向量相似度权重（从配置读取）
search_importance_weight: 重要性权重（从配置读取）
search_recency_weight: 时效性权重（从配置读取）
search_min_importance: 最小重要性阈值（从配置读取）
search_similarity_threshold: 向量相似度阈值（从配置读取）
"""
self.vector_store = vector_store
self.graph_store = graph_store
self.persistence_manager = persistence_manager
self._initialized = False
self.max_expand_depth = max_expand_depth # 保存配置的默认值
self.expand_semantic_threshold = expand_semantic_threshold # 保存配置的语义阈值
self.max_expand_depth = max_expand_depth
self.expand_semantic_threshold = expand_semantic_threshold
self.search_top_k = search_top_k
# 保存权重配置
self.base_vector_weight = search_vector_weight
self.base_importance_weight = search_importance_weight
self.base_recency_weight = search_recency_weight
# 保存阈值过滤配置
self.search_min_importance = search_min_importance
self.search_similarity_threshold = search_similarity_threshold
logger.info(f"MemoryTools 初始化: max_expand_depth={max_expand_depth}, expand_semantic_threshold={expand_semantic_threshold}")
logger.info(
f"MemoryTools 初始化: max_expand_depth={max_expand_depth}, "
f"expand_semantic_threshold={expand_semantic_threshold}, "
f"search_top_k={search_top_k}, "
f"权重配置: vector={search_vector_weight}, importance={search_importance_weight}, recency={search_recency_weight}, "
f"阈值过滤: min_importance={search_min_importance}, similarity_threshold={search_similarity_threshold}"
)
# 初始化组件
self.extractor = MemoryExtractor()
@@ -465,7 +495,7 @@ class MemoryTools:
"""
try:
query = params.get("query", "")
top_k = params.get("top_k", 10)
top_k = params.get("top_k", self.search_top_k) # 使用配置的默认值
expand_depth = params.get("expand_depth", self.max_expand_depth)
use_multi_query = params.get("use_multi_query", True)
prefer_node_types = params.get("prefer_node_types", []) # 🆕 优先节点类型
@@ -610,7 +640,7 @@ class MemoryTools:
if activation_score == 0.0 and memory.activation > 0.0:
activation_score = memory.activation
# 🆕 动态权重计算：根据记忆类型和节点类型自适应调整
# 🆕 动态权重计算：使用配置的基础权重 + 根据记忆类型微调
memory_type = memory.memory_type.value if hasattr(memory.memory_type, 'value') else str(memory.memory_type)
# 检测记忆的主要节点类型
@@ -621,36 +651,48 @@ class MemoryTools:
dominant_node_type = max(node_types_count.items(), key=lambda x: x[1])[0] if node_types_count else "unknown"
# 根据节点类型动态调整权重
# 根据记忆类型和节点类型计算调整系数（在配置权重基础上微调）
if dominant_node_type in ["ATTRIBUTE", "REFERENCE"] or memory_type == "FACT":
# 事实性记忆（如文档地址、配置信息）：语义相似度最重要
weights = {
"similarity": 0.70, # 语义相似度 70% ⬆️
"importance": 0.25, # 重要性 25% ⬆️
"recency": 0.05, # 时效性 5%（事实不随时间失效）
# 事实性记忆：提升相似度权重，降低时效性权重
type_adjustments = {
"similarity": 1.08, # 相似度提升 8%
"importance": 1.0, # 重要性保持
"recency": 0.5, # 时效性降低 50%（事实不随时间失效）
}
elif memory_type in ["CONVERSATION", "EPISODIC"] or dominant_node_type == "EVENT":
# 对话/事件记忆：时效性更重要
weights = {
"similarity": 0.55, # 语义相似度 55% ⬆️
"importance": 0.20, # 重要性 20% ⬆️
"recency": 0.25, # 时效性 25% ⬆️
# 对话/事件记忆：提升时效性权重
type_adjustments = {
"similarity": 0.85, # 相似度降低 15%
"importance": 0.8, # 重要性降低 20%
"recency": 2.5, # 时效性提升 150%
}
elif dominant_node_type == "ENTITY" or memory_type == "SEMANTIC":
# 实体/语义记忆：平衡各项
weights = {
"similarity": 0.60, # 语义相似度 60% ⬆️
"importance": 0.30, # 重要性 30% ⬆️
"recency": 0.10, # 时效性 10%
# 实体/语义记忆：平衡调整
type_adjustments = {
"similarity": 0.92, # 相似度微降 8%
"importance": 1.2, # 重要性提升 20%
"recency": 1.0, # 时效性保持
}
else:
# 默认权重（保守策略，偏向语义）
weights = {
"similarity": 0.65, # 语义相似度 65% ⬆️
"importance": 0.25, # 重要性 25% ⬆️
"recency": 0.10, # 时效性 10%
# 默认不调整
type_adjustments = {
"similarity": 1.0,
"importance": 1.0,
"recency": 1.0,
}
# 应用调整后的权重（基于配置的基础权重）
weights = {
"similarity": self.base_vector_weight * type_adjustments["similarity"],
"importance": self.base_importance_weight * type_adjustments["importance"],
"recency": self.base_recency_weight * type_adjustments["recency"],
}
# 归一化权重（确保总和为1.0）
total_weight = sum(weights.values())
if total_weight > 0:
weights = {k: v / total_weight for k, v in weights.items()}
# 综合分数计算（🔥 移除激活度影响）
final_score = (
similarity_score * weights["similarity"] +
@@ -658,6 +700,15 @@ class MemoryTools:
recency_score * weights["recency"]
)
# 🆕 阈值过滤：基于配置的最小重要性和相似度阈值
if memory.importance < self.search_min_importance:
logger.debug(f"记忆 {memory.id[:8]} 重要性 {memory.importance:.2f} 低于阈值 {self.search_min_importance}，过滤")
continue
if similarity_score < self.search_similarity_threshold:
logger.debug(f"记忆 {memory.id[:8]} 相似度 {similarity_score:.2f} 低于阈值 {self.search_similarity_threshold}，过滤")
continue
# 🆕 节点类型加权：对REFERENCE/ATTRIBUTE节点额外加分（促进事实性信息召回）
if "REFERENCE" in node_types_count or "ATTRIBUTE" in node_types_count:
final_score *= 1.1 # 10% 加成