404 lines
13 KiB
Python
404 lines
13 KiB
Python
"""
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记忆去重工具
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功能：
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1. 扫描所有标记为"相似"关系的记忆边
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2. 对相似记忆进行去重（保留重要性高的，删除另一个）
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3. 支持干运行模式（预览不执行）
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4. 提供详细的去重报告
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使用方法：
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# 预览模式（不实际删除）
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python scripts/deduplicate_memories.py --dry-run
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# 执行去重
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python scripts/deduplicate_memories.py
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# 指定相似度阈值
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python scripts/deduplicate_memories.py --threshold 0.9
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# 指定数据目录
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python scripts/deduplicate_memories.py --data-dir data/memory_graph
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"""
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import argparse
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import asyncio
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import sys
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from src.common.logger import get_logger
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from src.memory_graph.manager_singleton import initialize_memory_manager, shutdown_memory_manager
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logger = get_logger(__name__)
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class MemoryDeduplicator:
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"""记忆去重器"""
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def __init__(self, data_dir: str = "data/memory_graph", dry_run: bool = False, threshold: float = 0.85):
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self.data_dir = data_dir
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self.dry_run = dry_run
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self.threshold = threshold
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self.manager = None
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# 统计信息
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self.stats = {
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"total_memories": 0,
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"similar_pairs": 0,
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"duplicates_found": 0,
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"duplicates_removed": 0,
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"errors": 0,
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}
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async def initialize(self):
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"""初始化记忆管理器"""
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logger.info(f"正在初始化记忆管理器 (data_dir={self.data_dir})...")
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self.manager = await initialize_memory_manager(data_dir=self.data_dir)
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if not self.manager:
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raise RuntimeError("记忆管理器初始化失败")
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self.stats["total_memories"] = len(self.manager.graph_store.get_all_memories())
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logger.info(f"✅ 记忆管理器初始化成功，共 {self.stats['total_memories']} 条记忆")
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async def find_similar_pairs(self) -> list[tuple[str, str, float]]:
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"""
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查找所有相似的记忆对（通过向量相似度计算）
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Returns:
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[(memory_id_1, memory_id_2, similarity), ...]
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"""
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logger.info("正在扫描相似记忆对...")
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similar_pairs = []
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seen_pairs = set() # 避免重复
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# 获取所有记忆
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all_memories = self.manager.graph_store.get_all_memories()
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total_memories = len(all_memories)
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logger.info(f"开始计算 {total_memories} 条记忆的相似度...")
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# 两两比较记忆的相似度
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for i, memory_i in enumerate(all_memories):
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# 每处理10条记忆让出控制权
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if i % 10 == 0:
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await asyncio.sleep(0)
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if i > 0:
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logger.info(f"进度: {i}/{total_memories} ({i*100//total_memories}%)")
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# 获取记忆i的向量（从主题节点）
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vector_i = None
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for node in memory_i.nodes:
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if node.embedding is not None:
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vector_i = node.embedding
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break
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if vector_i is None:
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continue
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# 与后续记忆比较
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for j in range(i + 1, total_memories):
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memory_j = all_memories[j]
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# 获取记忆j的向量
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vector_j = None
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for node in memory_j.nodes:
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if node.embedding is not None:
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vector_j = node.embedding
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break
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if vector_j is None:
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continue
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# 计算余弦相似度
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similarity = self._cosine_similarity(vector_i, vector_j)
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# 只保存满足阈值的相似对
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if similarity >= self.threshold:
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pair_key = tuple(sorted([memory_i.id, memory_j.id]))
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if pair_key not in seen_pairs:
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seen_pairs.add(pair_key)
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similar_pairs.append((memory_i.id, memory_j.id, similarity))
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self.stats["similar_pairs"] = len(similar_pairs)
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logger.info(f"找到 {len(similar_pairs)} 对相似记忆（阈值>={self.threshold}）")
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return similar_pairs
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def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:
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"""计算余弦相似度"""
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try:
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vec1_norm = np.linalg.norm(vec1)
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vec2_norm = np.linalg.norm(vec2)
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if vec1_norm == 0 or vec2_norm == 0:
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return 0.0
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similarity = np.dot(vec1, vec2) / (vec1_norm * vec2_norm)
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return float(similarity)
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except Exception as e:
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logger.error(f"计算余弦相似度失败: {e}")
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return 0.0
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def decide_which_to_keep(self, mem_id_1: str, mem_id_2: str) -> tuple[str | None, str | None]:
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"""
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决定保留哪个记忆，删除哪个
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优先级：
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1. 重要性更高的
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2. 激活度更高的
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3. 创建时间更早的
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Returns:
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(keep_id, remove_id)
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"""
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mem1 = self.manager.graph_store.get_memory_by_id(mem_id_1)
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mem2 = self.manager.graph_store.get_memory_by_id(mem_id_2)
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if not mem1 or not mem2:
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logger.warning(f"记忆不存在: {mem_id_1} or {mem_id_2}")
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return None, None
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# 比较重要性
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if mem1.importance > mem2.importance:
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return mem_id_1, mem_id_2
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elif mem1.importance < mem2.importance:
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return mem_id_2, mem_id_1
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# 重要性相同，比较激活度
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if mem1.activation > mem2.activation:
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return mem_id_1, mem_id_2
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elif mem1.activation < mem2.activation:
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return mem_id_2, mem_id_1
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# 激活度也相同，保留更早创建的
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if mem1.created_at < mem2.created_at:
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return mem_id_1, mem_id_2
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else:
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return mem_id_2, mem_id_1
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async def deduplicate_pair(self, mem_id_1: str, mem_id_2: str, similarity: float) -> bool:
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"""
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去重一对相似记忆
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Returns:
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是否成功去重
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"""
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keep_id, remove_id = self.decide_which_to_keep(mem_id_1, mem_id_2)
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if not keep_id or not remove_id:
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self.stats["errors"] += 1
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return False
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keep_mem = self.manager.graph_store.get_memory_by_id(keep_id)
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remove_mem = self.manager.graph_store.get_memory_by_id(remove_id)
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logger.info("")
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logger.info(f"{'[预览]' if self.dry_run else '[执行]'} 去重相似记忆对 (相似度={similarity:.3f}):")
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logger.info(f" 保留: {keep_id}")
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logger.info(f" - 主题: {keep_mem.metadata.get('topic', 'N/A')}")
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logger.info(f" - 重要性: {keep_mem.importance:.2f}")
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logger.info(f" - 激活度: {keep_mem.activation:.2f}")
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logger.info(f" - 创建时间: {keep_mem.created_at}")
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logger.info(f" 删除: {remove_id}")
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logger.info(f" - 主题: {remove_mem.metadata.get('topic', 'N/A')}")
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logger.info(f" - 重要性: {remove_mem.importance:.2f}")
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logger.info(f" - 激活度: {remove_mem.activation:.2f}")
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logger.info(f" - 创建时间: {remove_mem.created_at}")
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if self.dry_run:
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logger.info(" [预览模式] 不执行实际删除")
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self.stats["duplicates_found"] += 1
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return True
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try:
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# 增强保留记忆的属性
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keep_mem.importance = min(1.0, keep_mem.importance + 0.05)
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keep_mem.activation = min(1.0, keep_mem.activation + 0.05)
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# 累加访问次数
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if hasattr(keep_mem, "access_count") and hasattr(remove_mem, "access_count"):
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keep_mem.access_count += remove_mem.access_count
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# 删除相似记忆
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await self.manager.delete_memory(remove_id)
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self.stats["duplicates_removed"] += 1
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logger.info(" ✅ 删除成功")
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# 让出控制权
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await asyncio.sleep(0)
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return True
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except Exception as e:
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logger.error(f" ❌ 删除失败: {e}")
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self.stats["errors"] += 1
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return False
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async def run(self):
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"""执行去重"""
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start_time = datetime.now()
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print("="*70)
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print("记忆去重工具")
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print("="*70)
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print(f"数据目录: {self.data_dir}")
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print(f"相似度阈值: {self.threshold}")
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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）'}")
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print("="*70)
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print()
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# 初始化
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await self.initialize()
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# 查找相似对
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similar_pairs = await self.find_similar_pairs()
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if not similar_pairs:
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logger.info("未找到需要去重的相似记忆对")
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print()
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print("="*70)
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print("未找到需要去重的记忆")
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print("="*70)
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return
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# 去重处理
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logger.info(f"开始{'预览' if self.dry_run else '执行'}去重...")
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print()
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processed_pairs = set() # 避免重复处理
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for mem_id_1, mem_id_2, similarity in similar_pairs:
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# 检查是否已处理（可能一个记忆已被删除）
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pair_key = tuple(sorted([mem_id_1, mem_id_2]))
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if pair_key in processed_pairs:
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continue
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# 检查记忆是否仍存在
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if not self.manager.graph_store.get_memory_by_id(mem_id_1):
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logger.debug(f"记忆 {mem_id_1} 已不存在，跳过")
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continue
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if not self.manager.graph_store.get_memory_by_id(mem_id_2):
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logger.debug(f"记忆 {mem_id_2} 已不存在，跳过")
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continue
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# 执行去重
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success = await self.deduplicate_pair(mem_id_1, mem_id_2, similarity)
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if success:
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processed_pairs.add(pair_key)
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# 保存数据（如果不是干运行）
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if not self.dry_run:
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logger.info("正在保存数据...")
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await self.manager.persistence.save_graph_store(self.manager.graph_store)
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logger.info("✅ 数据已保存")
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# 统计报告
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elapsed = (datetime.now() - start_time).total_seconds()
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print()
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print("="*70)
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print("去重报告")
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print("="*70)
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print(f"总记忆数: {self.stats['total_memories']}")
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print(f"相似记忆对: {self.stats['similar_pairs']}")
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print(f"发现重复: {self.stats['duplicates_found'] if self.dry_run else self.stats['duplicates_removed']}")
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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']}")
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print(f"错误数: {self.stats['errors']}")
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print(f"耗时: {elapsed:.2f}秒")
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if self.dry_run:
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print()
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print("⚠️ 这是预览模式，未实际删除任何记忆")
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print("💡 要执行实际删除，请运行: python scripts/deduplicate_memories.py")
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else:
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print()
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print("✅ 去重完成！")
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final_count = len(self.manager.graph_store.get_all_memories())
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print(f"📊 最终记忆数: {final_count} (减少 {self.stats['total_memories'] - final_count} 条)")
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print("="*70)
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async def cleanup(self):
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"""清理资源"""
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if self.manager:
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await shutdown_memory_manager()
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async def main():
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"""主函数"""
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parser = argparse.ArgumentParser(
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description="记忆去重工具 - 对标记为相似的记忆进行一键去重",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例：
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# 预览模式（推荐先运行）
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python scripts/deduplicate_memories.py --dry-run
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# 执行去重
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python scripts/deduplicate_memories.py
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# 指定相似度阈值（只处理相似度>=0.9的记忆对）
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python scripts/deduplicate_memories.py --threshold 0.9
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# 指定数据目录
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python scripts/deduplicate_memories.py --data-dir data/memory_graph
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# 组合使用
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python scripts/deduplicate_memories.py --dry-run --threshold 0.95 --data-dir data/test
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"""
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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help="预览模式，不实际删除记忆（推荐先运行此模式）"
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)
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parser.add_argument(
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"--threshold",
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type=float,
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default=0.85,
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help="相似度阈值，只处理相似度>=此值的记忆对（默认: 0.85）"
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)
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parser.add_argument(
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"--data-dir",
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type=str,
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default="data/memory_graph",
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help="记忆数据目录（默认: data/memory_graph）"
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)
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args = parser.parse_args()
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# 创建去重器
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deduplicator = MemoryDeduplicator(
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data_dir=args.data_dir,
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dry_run=args.dry_run,
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threshold=args.threshold
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)
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try:
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# 执行去重
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await deduplicator.run()
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except KeyboardInterrupt:
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print("\n\n⚠️ 用户中断操作")
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except Exception as e:
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logger.error(f"执行失败: {e}")
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print(f"\n❌ 执行失败: {e}")
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return 1
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finally:
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# 清理资源
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await deduplicator.cleanup()
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return 0
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if __name__ == "__main__":
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sys.exit(asyncio.run(main()))
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