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refactor(logging): 将信息日志级别更改为调试级别以减少输出噪声

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Windpicker-owo
2025-10-03 16:15:32 +08:00
parent 7617f85b92
commit 5c0e94cf4c
1 changed files with 6 additions and 6 deletions
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12
src/chat/memory_system/vector_memory_storage_v2.py
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@@ -648,7 +648,7 @@ class VectorMemoryStorage:
limit=self.config.search_limit * 2, # 粗筛返回更多候选 limit=self.config.search_limit * 2, # 粗筛返回更多候选
flexible_mode=True, # 使用灵活匹配模式 flexible_mode=True, # 使用灵活匹配模式
) )
logger.info(f"[JSON元数据粗筛] 完成，筛选出 {len(candidate_ids)} 个候选ID") logger.debug(f"[JSON元数据粗筛] 完成，筛选出 {len(candidate_ids)} 个候选ID")
# 如果粗筛后没有结果，回退到全部记忆搜索 # 如果粗筛后没有结果，回退到全部记忆搜索
if not candidate_ids: if not candidate_ids:
@@ -678,7 +678,7 @@ class VectorMemoryStorage:
where_conditions["memory_id"] = {"$in": candidate_ids} where_conditions["memory_id"] = {"$in": candidate_ids}
logger.debug(f"[向量精筛] 限制在 {len(candidate_ids)} 个候选ID内搜索") logger.debug(f"[向量精筛] 限制在 {len(candidate_ids)} 个候选ID内搜索")
else: else:
logger.info("[向量精筛] 在全部记忆中搜索（元数据筛选无结果回退）") logger.debug("[向量精筛] 在全部记忆中搜索（元数据筛选无结果回退）")
# 查询Vector DB # 查询Vector DB
logger.debug(f"[向量精筛] 开始，limit={min(limit, self.config.search_limit)}") logger.debug(f"[向量精筛] 开始，limit={min(limit, self.config.search_limit)}")
@@ -698,7 +698,7 @@ class VectorMemoryStorage:
metadatas = results.get("metadatas", [[]])[0] metadatas = results.get("metadatas", [[]])[0]
ids = results.get("ids", [[]])[0] ids = results.get("ids", [[]])[0]
logger.info( logger.debug(
f"向量检索返回原始结果：documents={len(documents)}, ids={len(ids)}, metadatas={len(metadatas)}" f"向量检索返回原始结果：documents={len(documents)}, ids={len(ids)}, metadatas={len(metadatas)}"
) )
for i, (doc, metadata, memory_id) in enumerate(zip(documents, metadatas, ids, strict=False)): for i, (doc, metadata, memory_id) in enumerate(zip(documents, metadatas, ids, strict=False)):
@@ -729,13 +729,13 @@ class VectorMemoryStorage:
) )
except Exception: except Exception:
short_text = "" short_text = ""
logger.info(f"检索结果 - id={memory_id}, similarity={similarity:.4f}, summary={short_text}") logger.debug(f"检索结果 - id={memory_id}, similarity={similarity:.4f}, summary={short_text}")
# 按相似度排序 # 按相似度排序
similar_memories.sort(key=lambda x: x[1], reverse=True) similar_memories.sort(key=lambda x: x[1], reverse=True)
self.stats["total_searches"] += 1 self.stats["total_searches"] += 1
logger.info( logger.debug(
f"搜索相似记忆: query='{query_text[:60]}...', limit={limit}, threshold={threshold}, filters={where_conditions}, 返回数={len(similar_memories)}" f"搜索相似记忆: query='{query_text[:60]}...', limit={limit}, threshold={threshold}, filters={where_conditions}, 返回数={len(similar_memories)}"
) )
logger.debug(f"搜索相似记忆 详细结果数={len(similar_memories)}") logger.debug(f"搜索相似记忆 详细结果数={len(similar_memories)}")
@@ -783,7 +783,7 @@ class VectorMemoryStorage:
metadatas = results.get("metadatas", [{}] * len(documents)) metadatas = results.get("metadatas", [{}] * len(documents))
ids = results.get("ids", []) ids = results.get("ids", [])
logger.info(f"按过滤条件获取返回: docs={len(documents)}, ids={len(ids)}") logger.debug(f"按过滤条件获取返回: docs={len(documents)}, ids={len(ids)}")
for i, (doc, metadata) in enumerate(zip(documents, metadatas, strict=False)): for i, (doc, metadata) in enumerate(zip(documents, metadatas, strict=False)):
memory_id = ids[i] if i < len(ids) else None memory_id = ids[i] if i < len(ids) else None