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fix: 修复代码质量问题 - 更正异常处理和导入语句

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Co-authored-by: Windpicker-owo <221029311+Windpicker-owo@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
2025-11-07 04:39:35 +00:00
parent 3bdcfa3dd4
commit 5caf630623
20 changed files with 893 additions and 910 deletions
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157
scripts/deduplicate_memories.py
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@@ -10,13 +10,13 @@
使用方法：
# 预览模式（不实际删除）
python scripts/deduplicate_memories.py --dry-run
# 执行去重
python scripts/deduplicate_memories.py
# 指定相似度阈值
python scripts/deduplicate_memories.py --threshold 0.9
# 指定数据目录
python scripts/deduplicate_memories.py --data-dir data/memory_graph
"""
@@ -25,27 +25,26 @@ import asyncio
import sys
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple
import numpy as np
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.common.logger import get_logger
from src.memory_graph.manager_singleton import get_memory_manager, initialize_memory_manager, shutdown_memory_manager
from src.memory_graph.manager_singleton import initialize_memory_manager, shutdown_memory_manager
logger = get_logger(__name__)
class MemoryDeduplicator:
"""记忆去重器"""
def __init__(self, data_dir: str = "data/memory_graph", dry_run: bool = False, threshold: float = 0.85):
self.data_dir = data_dir
self.dry_run = dry_run
self.threshold = threshold
self.manager = None
# 统计信息
self.stats = {
"total_memories": 0,
@@ -54,34 +53,34 @@ class MemoryDeduplicator:
"duplicates_removed": 0,
"errors": 0,
}
async def initialize(self):
"""初始化记忆管理器"""
logger.info(f"正在初始化记忆管理器 (data_dir={self.data_dir})...")
self.manager = await initialize_memory_manager(data_dir=self.data_dir)
if not self.manager:
raise RuntimeError("记忆管理器初始化失败")
self.stats["total_memories"] = len(self.manager.graph_store.get_all_memories())
logger.info(f"✅ 记忆管理器初始化成功，共 {self.stats['total_memories']} 条记忆")
async def find_similar_pairs(self) -> List[Tuple[str, str, float]]:
async def find_similar_pairs(self) -> list[tuple[str, str, float]]:
"""
查找所有相似的记忆对（通过向量相似度计算）
Returns:
[(memory_id_1, memory_id_2, similarity), ...]
"""
logger.info("正在扫描相似记忆对...")
similar_pairs = []
seen_pairs = set() # 避免重复
# 获取所有记忆
all_memories = self.manager.graph_store.get_all_memories()
total_memories = len(all_memories)
logger.info(f"开始计算 {total_memories} 条记忆的相似度...")
# 两两比较记忆的相似度
for i, memory_i in enumerate(all_memories):
# 每处理10条记忆让出控制权
@@ -89,115 +88,115 @@ class MemoryDeduplicator:
await asyncio.sleep(0)
if i > 0:
logger.info(f"进度: {i}/{total_memories} ({i*100//total_memories}%)")
# 获取记忆i的向量（从主题节点）
vector_i = None
for node in memory_i.nodes:
if node.embedding is not None:
vector_i = node.embedding
break
if vector_i is None:
continue
# 与后续记忆比较
for j in range(i + 1, total_memories):
memory_j = all_memories[j]
# 获取记忆j的向量
vector_j = None
for node in memory_j.nodes:
if node.embedding is not None:
vector_j = node.embedding
break
if vector_j is None:
continue
# 计算余弦相似度
similarity = self._cosine_similarity(vector_i, vector_j)
# 只保存满足阈值的相似对
if similarity >= self.threshold:
pair_key = tuple(sorted([memory_i.id, memory_j.id]))
if pair_key not in seen_pairs:
seen_pairs.add(pair_key)
similar_pairs.append((memory_i.id, memory_j.id, similarity))
self.stats["similar_pairs"] = len(similar_pairs)
logger.info(f"找到 {len(similar_pairs)} 对相似记忆（阈值>={self.threshold}）")
return similar_pairs
def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
"""计算余弦相似度"""
try:
vec1_norm = np.linalg.norm(vec1)
vec2_norm = np.linalg.norm(vec2)
if vec1_norm == 0 or vec2_norm == 0:
return 0.0
similarity = np.dot(vec1, vec2) / (vec1_norm * vec2_norm)
return float(similarity)
except Exception as e:
logger.error(f"计算余弦相似度失败: {e}")
return 0.0
def decide_which_to_keep(self, mem_id_1: str, mem_id_2: str) -> Tuple[Optional[str], Optional[str]]:
def decide_which_to_keep(self, mem_id_1: str, mem_id_2: str) -> tuple[str | None, str | None]:
"""
决定保留哪个记忆，删除哪个
优先级：
1. 重要性更高的
2. 激活度更高的
3. 创建时间更早的
Returns:
(keep_id, remove_id)
"""
mem1 = self.manager.graph_store.get_memory_by_id(mem_id_1)
mem2 = self.manager.graph_store.get_memory_by_id(mem_id_2)
if not mem1 or not mem2:
logger.warning(f"记忆不存在: {mem_id_1} or {mem_id_2}")
return None, None
# 比较重要性
if mem1.importance > mem2.importance:
return mem_id_1, mem_id_2
elif mem1.importance < mem2.importance:
return mem_id_2, mem_id_1
# 重要性相同，比较激活度
if mem1.activation > mem2.activation:
return mem_id_1, mem_id_2
elif mem1.activation < mem2.activation:
return mem_id_2, mem_id_1
# 激活度也相同，保留更早创建的
if mem1.created_at < mem2.created_at:
return mem_id_1, mem_id_2
else:
return mem_id_2, mem_id_1
async def deduplicate_pair(self, mem_id_1: str, mem_id_2: str, similarity: float) -> bool:
"""
去重一对相似记忆
Returns:
是否成功去重
"""
keep_id, remove_id = self.decide_which_to_keep(mem_id_1, mem_id_2)
if not keep_id or not remove_id:
self.stats["errors"] += 1
return False
keep_mem = self.manager.graph_store.get_memory_by_id(keep_id)
remove_mem = self.manager.graph_store.get_memory_by_id(remove_id)
logger.info(f"")
logger.info("")
logger.info(f"{'[预览]' if self.dry_run else '[执行]'} 去重相似记忆对 (相似度={similarity:.3f}):")
logger.info(f" 保留: {keep_id}")
logger.info(f" - 主题: {keep_mem.metadata.get('topic', 'N/A')}")
@@ -209,41 +208,41 @@ class MemoryDeduplicator:
logger.info(f" - 重要性: {remove_mem.importance:.2f}")
logger.info(f" - 激活度: {remove_mem.activation:.2f}")
logger.info(f" - 创建时间: {remove_mem.created_at}")
if self.dry_run:
logger.info(" [预览模式] 不执行实际删除")
self.stats["duplicates_found"] += 1
return True
try:
# 增强保留记忆的属性
keep_mem.importance = min(1.0, keep_mem.importance + 0.05)
keep_mem.activation = min(1.0, keep_mem.activation + 0.05)
# 累加访问次数
if hasattr(keep_mem, 'access_count') and hasattr(remove_mem, 'access_count'):
if hasattr(keep_mem, "access_count") and hasattr(remove_mem, "access_count"):
keep_mem.access_count += remove_mem.access_count
# 删除相似记忆
await self.manager.delete_memory(remove_id)
self.stats["duplicates_removed"] += 1
logger.info(f" ✅ 删除成功")
logger.info(" ✅ 删除成功")
# 让出控制权
await asyncio.sleep(0)
return True
except Exception as e:
logger.error(f" ❌ 删除失败: {e}", exc_info=True)
self.stats["errors"] += 1
return False
async def run(self):
"""执行去重"""
start_time = datetime.now()
print("="*70)
print("记忆去重工具")
print("="*70)
@@ -252,13 +251,13 @@ class MemoryDeduplicator:
print(f"模式: {'预览模式（不实际删除）' if self.dry_run else '执行模式（会实际删除）'}")
print("="*70)
print()
# 初始化
await self.initialize()
# 查找相似对
similar_pairs = await self.find_similar_pairs()
if not similar_pairs:
logger.info("未找到需要去重的相似记忆对")
print()
@@ -266,19 +265,19 @@ class MemoryDeduplicator:
print("未找到需要去重的记忆")
print("="*70)
return
# 去重处理
logger.info(f"开始{'预览' if self.dry_run else '执行'}去重...")
print()
processed_pairs = set() # 避免重复处理
for mem_id_1, mem_id_2, similarity in similar_pairs:
# 检查是否已处理（可能一个记忆已被删除）
pair_key = tuple(sorted([mem_id_1, mem_id_2]))
if pair_key in processed_pairs:
continue
# 检查记忆是否仍存在
if not self.manager.graph_store.get_memory_by_id(mem_id_1):
logger.debug(f"记忆 {mem_id_1} 已不存在，跳过")
@@ -286,22 +285,22 @@ class MemoryDeduplicator:
if not self.manager.graph_store.get_memory_by_id(mem_id_2):
logger.debug(f"记忆 {mem_id_2} 已不存在，跳过")
continue
# 执行去重
success = await self.deduplicate_pair(mem_id_1, mem_id_2, similarity)
if success:
processed_pairs.add(pair_key)
# 保存数据（如果不是干运行）
if not self.dry_run:
logger.info("正在保存数据...")
await self.manager.persistence.save_graph_store(self.manager.graph_store)
logger.info("✅ 数据已保存")
# 统计报告
elapsed = (datetime.now() - start_time).total_seconds()
print()
print("="*70)
print("去重报告")
@@ -312,7 +311,7 @@ class MemoryDeduplicator:
print(f"{'预览通过' if self.dry_run else '成功删除'}: {self.stats['duplicates_found'] if self.dry_run else self.stats['duplicates_removed']}")
print(f"错误数: {self.stats['errors']}")
print(f"耗时: {elapsed:.2f}秒")
if self.dry_run:
print()
print("⚠️ 这是预览模式，未实际删除任何记忆")
@@ -322,9 +321,9 @@ class MemoryDeduplicator:
print("✅ 去重完成！")
final_count = len(self.manager.graph_store.get_all_memories())
print(f"📊 最终记忆数: {final_count} (减少 {self.stats['total_memories'] - final_count} 条)")
print("="*70)
async def cleanup(self):
"""清理资源"""
if self.manager:
@@ -340,50 +339,50 @@ async def main():
示例：
# 预览模式（推荐先运行）
python scripts/deduplicate_memories.py --dry-run
# 执行去重
python scripts/deduplicate_memories.py
# 指定相似度阈值（只处理相似度>=0.9的记忆对）
python scripts/deduplicate_memories.py --threshold 0.9
# 指定数据目录
python scripts/deduplicate_memories.py --data-dir data/memory_graph
# 组合使用
python scripts/deduplicate_memories.py --dry-run --threshold 0.95 --data-dir data/test
"""
)
parser.add_argument(
"--dry-run",
action="store_true",
help="预览模式，不实际删除记忆（推荐先运行此模式）"
)
parser.add_argument(
"--threshold",
type=float,
default=0.85,
help="相似度阈值，只处理相似度>=此值的记忆对（默认: 0.85）"
)
parser.add_argument(
"--data-dir",
type=str,
default="data/memory_graph",
help="记忆数据目录（默认: data/memory_graph）"
)
args = parser.parse_args()
# 创建去重器
deduplicator = MemoryDeduplicator(
data_dir=args.data_dir,
dry_run=args.dry_run,
threshold=args.threshold
)
try:
# 执行去重
await deduplicator.run()
@@ -396,7 +395,7 @@ async def main():
finally:
# 清理资源
await deduplicator.cleanup()
return 0