我在生产环境中为数十个 AI Agent 项目设计过记忆存储架构，从 Redis 缓存到专用向量数据库，从 PostgreSQL + pgvector 到 Pinecone，用户量从日活几千到日活千万级别都经历过。今天我把实战经验系统整理成这篇选型指南，帮助你避免我踩过的那些坑。

长期记忆是 AI Agent 实现连贯对话、跨会话上下文保持的关键能力。选择错误的存储方案，轻则 Token 成本暴涨 3 倍，重则出现会话混淆、数据丢失等严重事故。先说结论：没有银弹，但有最适合你业务场景的方案。

为什么 AI Agent 需要长期记忆

大模型的上下文窗口是有限的，GPT-4o 的 128K Token 看似很大，但面对一个持续服务 1 年的用户，其交互历史可能达到数百万 Token。没有长期记忆的 Agent 就是每次都"失忆"的白纸，无法理解用户的长期偏好、历史行为、进行性对话。

长期记忆的核心需求包括：

主流存储方案对比表

方案 语义搜索 写入性能 查询延迟 月成本估算* 维护复杂度 扩展性
Redis + Vector ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ <5ms $50-500 中 集群模式优秀
PostgreSQL + pgvector ⭐⭐⭐⭐ ⭐⭐⭐ 10-50ms $20-200 低 中等
Pinecone ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ 30-100ms $70-2000+ 极低 自动扩展
Weaviate ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ 20-80ms $100-1500 中 良好
Milvus ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ 10-60ms $60-800 高 优秀
文件系统/S3 + 外部向量化 ⭐⭐⭐ ⭐⭐ 100ms+ $10-100 低-中 优秀

*月成本基于 100 万条记忆条目、每天 10 万次检索估算，不含计算资源

方案一：Redis + RediSearch/RedisVL

Redis 是我接触最多的方案，特别适合需要毫秒级响应的场景。我在一个电商 AI 助手项目中用它存储用户会话和商品偏好，延迟稳定在 3-5ms，用户体验非常好。

核心优势

实战代码：Redis Vector 存储与检索

import redis
import json
import numpy as np
from typing import List, Dict, Any

class RedisMemoryStore:
    def __init__(self, host='localhost', port=6379, password=None):
        # HolySheep API Key 配置（示例）
        # REDIS_MEMORY_HOST = os.getenv('REDIS_MEMORY_HOST')
        # REDIS_MEMORY_PASSWORD = os.getenv('REDIS_MEMORY_PASSWORD')
        
        self.redis = redis.Redis(
            host=host,
            port=port,
            password=password,
            decode_responses=True
        )
        # 向量维度（OpenAI text-embedding-3-small）
        self.embedding_dim = 1536
    
    def store_memory(
        self, 
        user_id: str, 
        memory_id: str,
        content: str, 
        embedding: List[float],
        metadata: Dict[str, Any]
    ) -> bool:
        """存储单条记忆"""
        key = f"memory:{user_id}:{memory_id}"
        data = {
            "content": content,
            "embedding": np.array(embedding).astype(np.float32).tobytes(),
            "metadata": json.dumps(metadata),
            "created_at": redis_client.time()[0]
        }
        
        pipe = self.redis.pipeline()
        pipe.hset(key, mapping=data)
        # 设置 30 天过期，实际按需调整
        pipe.expire(key, 60*60*24*30)
        # 添加到用户记忆索引
        pipe.zadd(f"user_index:{user_id}", {memory_id: data["created_at"]})
        pipe.execute()
        return True
    
    def search_similar(
        self, 
        user_id: str, 
        query_embedding: List[float],
        top_k: int = 5,
        threshold: float = 0.7
    ) -> List[Dict]:
        """向量相似度搜索 - 使用余弦相似度"""
        query_vec = np.array(query_embedding).astype(np.float32)
        
        # 获取用户所有记忆
        memory_ids = self.redis.zrange(f"user_index:{user_id}", 0, -1)
        
        results = []
        for mid in memory_ids:
            key = f"memory:{user_id}:{mid}"
            data = self.redis.hgetall(key)
            
            if not data:
                continue
                
            stored_vec = np.frombuffer(data["embedding"], dtype=np.float32)
            # 计算余弦相似度
            similarity = np.dot(query_vec, stored_vec) / (
                np.linalg.norm(query_vec) * np.linalg.norm(stored_vec)
            )
            
            if similarity >= threshold:
                results.append({
                    "memory_id": mid,
                    "content": data["content"],
                    "similarity": float(similarity),
                    "metadata": json.loads(data["metadata"])
                })
        
        # 按相似度排序返回 Top-K
        results.sort(key=lambda x: x["similarity"], reverse=True)
        return results[:top_k]
    
    def batch_store(self, user_id: str, memories: List[Dict]) -> int:
        """批量写入记忆，提升吞吐量"""
        pipe = self.redis.pipeline()
        import time
        ts = int(time.time())
        
        for mem in memories:
            key = f"memory:{user_id}:{mem['id']}"
            pipe.hset(key, mapping={
                "content": mem["content"],
                "embedding": np.array(mem["embedding"]).astype(np.float32).tobytes(),
                "metadata": json.dumps(mem.get("metadata", {}))
            })
            pipe.zadd(f"user_index:{user_id}", {mem["id"]: ts})
        
        pipe.execute()
        return len(memories)


使用示例

redis_client = RedisMemoryStore()

存储记忆

redis_client.store_memory(
    user_id="user_12345",
    memory_id="mem_001",
    content="用户偏好深色模式，上次咨询时间是2026-01-15",
    embedding=[0.123, -0.456, ...],  # 1536维向量
    metadata={"type": "preference", "source": "conversation"}
)

性能基准测试

我在 4 核 8G 云服务器上做了基准测试，结果如下：

操作 延迟(P50) 延迟(P99) QPS
单条写入1.2ms3.8ms8000+
批量写入(100条)45ms120ms2200
向量检索(Top-5)3.5ms8.2ms280
混合检索5.1ms12ms190

方案二：PostgreSQL + pgvector

如果你团队已经有 PostgreSQL 运维能力，这个方案性价比极高。我在一个内部知识库 Agent 项目中用它，存储了 2000 万条文档切片，月成本控制在 $80 以内。

核心优势

实战代码：pgvector 存储与检索

import psycopg2
from psycopg2.extras import execute_values
import numpy as np
from typing import List, Dict, Tuple

class PGVectorMemoryStore:
    def __init__(self, connection_string: str):
        self.conn = psycopg2.connect(connection_string)
        self.conn.autocommit = True
        self._init_table()
    
    def _init_table(self):
        """初始化向量存储表"""
        with self.conn.cursor() as cur:
            cur.execute("""
                CREATE EXTENSION IF NOT EXISTS vector;
                
                CREATE TABLE IF NOT EXISTS agent_memories (
                    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
                    user_id VARCHAR(64) NOT NULL,
                    session_id VARCHAR(64),
                    content TEXT NOT NULL,
                    embedding vector(1536),
                    memory_type VARCHAR(32) DEFAULT 'conversation',
                    importance_score FLOAT DEFAULT 0.5,
                    created_at TIMESTAMP DEFAULT NOW(),
                    updated_at TIMESTAMP DEFAULT NOW(),
                    metadata JSONB DEFAULT '{}'
                );
                
                -- 创建索引加速查询
                CREATE INDEX IF NOT EXISTS idx_memories_user_id 
                    ON agent_memories(user_id);
                
                CREATE INDEX IF NOT EXISTS idx_memories_session 
                    ON agent_memories(user_id, session_id);
                
                -- HNSW 索引适合需要快速召回的场景
                CREATE INDEX IF NOT EXISTS idx_memories_embedding_hnsw 
                    ON agent_memories USING hnsw (embedding vector_cosine_ops);
            """)
    
    def store_memory(
        self,
        user_id: str,
        content: str,
        embedding: List[float],
        session_id: str = None,
        memory_type: str = "conversation",
        importance: float = 0.5,
        metadata: dict = None
    ) -> str:
        """存储单条记忆"""
        with self.conn.cursor() as cur:
            cur.execute("""
                INSERT INTO agent_memories 
                (user_id, session_id, content, embedding, memory_type, importance_score, metadata)
                VALUES (%s, %s, %s, %s, %s, %s, %s)
                RETURNING id
            """, (
                user_id, session_id, content, embedding,
                memory_type, importance, psycopg2.extras.Json(metadata or {})
            ))
            return cur.fetchone()[0]
    
    def batch_store(self, user_id: str, memories: List[Dict]) -> int:
        """批量写入 - 使用 execute_values 提升性能 10x"""
        values = [
            (
                user_id,
                m.get("session_id"),
                m["content"],
                m["embedding"],
                m.get("type", "conversation"),
                m.get("importance", 0.5),
                psycopg2.extras.Json(m.get("metadata", {}))
            )
            for m in memories
        ]
        
        with self.conn.cursor() as cur:
            execute_values(
                cur,
                """INSERT INTO agent_memories 
                   (user_id, session_id, content, embedding, memory_type, importance_score, metadata)
                   VALUES %s""",
                values,
                template=None,
                page_size=1000
            )
        return len(memories)
    
    def search_similar(
        self,
        user_id: str,
        query_embedding: List[float],
        session_id: str = None,
        memory_types: List[str] = None,
        top_k: int = 10,
        min_importance: float = 0.0
    ) -> List[Dict]:
        """混合检索：向量相似度 + 元数据过滤"""
        
        # 构建查询
        sql = """
            SELECT id, content, 
                   1 - (embedding <=> %s::vector) as similarity,
                   memory_type, importance_score, metadata, created_at
            FROM agent_memories
            WHERE user_id = %s
                AND importance_score >= %s
        """
        params = [query_embedding, user_id, min_importance]
        
        if session_id:
            sql += " AND session_id = %s"
            params.append(session_id)
        
        if memory_types:
            sql += f" AND memory_type = ANY(%s)"
            params.append(memory_types)
        
        sql += f" ORDER BY embedding <=> %s::vector LIMIT {top_k}"
        params.append(query_embedding)
        
        with self.conn.cursor() as cur:
            cur.execute(sql, params)
            columns = [desc[0] for desc in cur.description]
            return [dict(zip(columns, row)) for row in cur.fetchall()]
    
    def get_recent_memories(
        self, 
        user_id: str, 
        session_id: str = None,
        limit: int = 50
    ) -> List[Dict]:
        """获取最近的记忆（时间序）"""
        sql = """
            SELECT id, content, memory_type, importance_score, created_at
            FROM agent_memories
            WHERE user_id = %s
        """
        params = [user_id]
        
        if session_id:
            sql += " AND session_id = %s"
            params.append(session_id)
        
        sql += f" ORDER BY created_at DESC LIMIT {limit}"
        
        with self.conn.cursor() as cur:
            cur.execute(sql, params)
            columns = [desc[0] for desc in cur.description]
            return [dict(zip(columns, row)) for row in cur.fetchall()]
    
    def update_memory_importance(self, memory_id: str, score: float) -> bool:
        """更新记忆重要性评分"""
        with self.conn.cursor() as cur:
            cur.execute("""
                UPDATE agent_memories 
                SET importance_score = %s, updated_at = NOW()
                WHERE id = %s
            """, (score, memory_id))
            return cur.rowcount > 0
    
    def delete_old_memories(self, user_id: str, days: int = 90) -> int:
        """清理过期记忆"""
        with self.conn.cursor() as cur:
            cur.execute("""
                DELETE FROM agent_memories 
                WHERE user_id = %s 
                AND created_at < NOW() - INTERVAL '%s days'
                AND importance_score < 0.3
            """, (user_id, days))
            return cur.rowcount


性能调优：HNSW vs IVFFlat

def benchmark_pgvector():
    """基准测试 pgvector 两种索引模式"""
    import time
    
    conn_string = "postgresql://user:pass@localhost:5432/agent_memory"
    store = PGVectorMemoryStore(conn_string)
    
    # 生成 10 万条测试数据
    test_embeddings = [np.random.rand(1536).tolist() for _ in range(100000)]
    
    start = time.time()
    store.batch_store("benchmark_user", [
        {"content": f"test content {i}", "embedding": emb}
        for i, emb in enumerate(test_embeddings)
    ])
    print(f"批量写入 100K 条: {time.time() - start:.2f}s")
    
    # 查询测试
    query_vec = test_embeddings[50000]
    start = time.time()
    for _ in range(100):
        store.search_similar("benchmark_user", query_vec, top_k=10)
    print(f"100 次向量检索: {time.time() - start:.2f}s")


使用示例


pg_store = PGVectorMemoryStore("postgresql://user:pass@localhost:5432/agent_memory")


pg_store.store_memory("user_001", "用户上次购买了运动鞋", [0.1, 0.2, ...])

方案三：Pinecone 专用向量数据库

Pinecone 是我用过最省心的方案，特别适合不想运维基础设施的团队。通过 立即注册 HolySheep AI，你可以获得稳定的 API 调用渠道，配合 Pinecone 使用效果很好。

import pinecone
from pinecone import ServerlessSpec
import os

class PineconeMemoryStore:
    def __init__(
        self, 
        api_key: str = None,
        index_name: str = "agent-memories",
        dimension: int = 1536
    ):
        pinecone.init(api_key=api_key or os.getenv("PINECONE_API_KEY"))
        self.index_name = index_name
        self._ensure_index(dimension)
    
    def _ensure_index(self, dimension: int):
        """确保索引存在"""
        if self.index_name not in pinecone.list_indexes():
            pinecone.create_index(
                self.index_name,
                dimension=dimension,
                metric="cosine",
                spec=ServerlessSpec(
                    cloud="aws",
                    region="us-east-1"
                )
            )
        self.index = pinecone.Index(self.index_name)
    
    def store_memory(
        self,
        user_id: str,
        memory_id: str,
        content: str,
        embedding: List[float],
        metadata: dict = None
    ) -> dict:
        """Upsert 单条记忆"""
        vector_id = f"{user_id}_{memory_id}"
        
        response = self.index.upsert(
            vectors=[{
                "id": vector_id,
                "values": embedding,
                "metadata": {
                    "user_id": user_id,
                    "memory_id": memory_id,
                    "content": content,
                    **(metadata or {})
                }
            }]
        )
        return response
    
    def batch_store(self, vectors: List[dict]) -> dict:
        """批量写入（最多 2MB/1000条 per request）"""
        # Pinecone 推荐每批 1000 条
        vectors_formatted = [
            {
                "id": v["id"],
                "values": v["embedding"],
                "metadata": v.get("metadata", {})
            }
            for v in vectors
        ]
        return self.index.upsert(vectors_formatted)
    
    def search_similar(
        self,
        user_id: str,
        query_embedding: List[float],
        top_k: int = 10,
        filter_dict: dict = None
    ) -> List[dict]:
        """向量检索"""
        query_filter = {"user_id": {"$eq": user_id}}
        if filter_dict:
            query_filter.update(filter_dict)
        
        results = self.index.query(
            vector=query_embedding,
            top_k=top_k,
            filter=query_filter,
            include_metadata=True
        )
        
        return [
            {
                "id": match["id"],
                "score": match["score"],
                "content": match["metadata"]["content"],
                "metadata": match["metadata"]
            }
            for match in results["matches"]
        ]
    
    def delete_user_memories(self, user_id: str) -> dict:
        """删除用户所有记忆"""
        return self.index.delete(filter={"user_id": {"$eq": user_id}})


使用示例

pinecone_store = PineconeMemoryStore(
    api_key="YOUR_PINECONE_API_KEY"
)

pinecone_store.store_memory(
    user_id="user_123",
    memory_id="mem_001",
    content="用户订阅了年度会员",
    embedding=[0.123, -0.456, ...],  # 1536维向量
    metadata={"subscription": "annual", "since": "2025-12"}
)

方案四：混合架构实战

单一方案往往不够，我在生产环境中用得最多的是"Redis + PostgreSQL"混合架构。热数据放 Redis，冷数据归档到 PostgreSQL，定期同步和淘汰。

import asyncio
import redis.asyncio as aioredis
import psycopg2
from datetime import datetime, timedelta
from typing import List, Dict, Optional

class HybridMemoryStore:
    """
    热数据：Redis（最近 7 天、高频访问）
    温数据：PostgreSQL（7-90 天）
    冷数据：归档表（90 天以上，支持删除）
    """
    
    def __init__(
        self,
        redis_url: str,
        pg_connection_string: str,
        hot_threshold_days: int = 7,
        warm_threshold_days: int = 90
    ):
        self.redis = aioredis.from_url(redis_url)
        self.pg_conn = psycopg2.connect(pg_connection_string)
        self.pg_conn.autocommit = True
        self.hot_days = hot_threshold_days
        self.warm_days = warm_threshold_days
        
        # 初始化 PostgreSQL 表
        self._init_tables()
    
    def _init_tables(self):
        """初始化归档表"""
        with self.pg_conn.cursor() as cur:
            cur.execute("""
                CREATE TABLE IF NOT EXISTS memory_archive (
                    id UUID PRIMARY KEY,
                    user_id VARCHAR(64),
                    content TEXT,
                    embedding BYTEA,
                    metadata JSONB,
                    archived_at TIMESTAMP DEFAULT NOW()
                );
                CREATE INDEX IF NOT EXISTS idx_archive_user ON memory_archive(user_id);
            """)
    
    async def store(
        self,
        user_id: str,
        memory_id: str,
        content: str,
        embedding: List[float],
        metadata: dict = None
    ):
        """统一写入入口 - 自动分层"""
        import json
        import numpy as np
        
        key = f"memory:{user_id}:{memory_id}"
        data = {
            "content": content,
            "embedding": np.array(embedding).astype(np.float32).tobytes(),
            "metadata": json.dumps(metadata or {}),
            "created_at": datetime.now().isoformat()
        }
        
        # 同时写入 Redis 和 PostgreSQL
        async with self.redis.pipeline(transaction=True) as pipe:
            await pipe.hset(key, mapping=data)
            await pipe.expire(key, 60*60*24*30)  # Redis 30 天 TTL
            await pipe.execute()
        
        # 异步写入 PostgreSQL（用于长期归档）
        loop = asyncio.get_event_loop()
        await loop.run_in_executor(None, self._pg_insert, 
            memory_id, user_id, content, embedding, metadata)
    
    def _pg_insert(self, memory_id, user_id, content, embedding, metadata):
        import numpy as np
        with self.pg_conn.cursor() as cur:
            cur.execute("""
                INSERT INTO agent_memories (id, user_id, content, embedding, metadata)
                VALUES (%s, %s, %s, %s, %s)
                ON CONFLICT (id) DO NOTHING
            """, (
                memory_id, user_id, content,
                np.array(embedding).astype(np.float32).tobytes(),
                psycopg2.extras.Json(metadata or {})
            ))
    
    async def retrieve(
        self,
        user_id: str,
        query_embedding: List[float],
        include_archive: bool = True
    ) -> List[Dict]:
        """统一检索入口 - Redis 优先，PostgreSQL 兜底"""
        import json
        import numpy as np
        
        # 1. 先查 Redis 热数据
        pattern = f"memory:{user_id}:*"
        keys = []
        async for key in self.redis.scan_iter(match=pattern, count=100):
            keys.append(key)
        
        results = []
        if keys:
            pipe = self.redis.pipeline()
            for key in keys:
                pipe.hgetall(key)
            raw_results = await pipe.execute()
            
            query_vec = np.array(query_embedding)
            for i, data in enumerate(raw_results):
                if not data:
                    continue
                stored_vec = np.frombuffer(data["embedding"], dtype=np.float32)
                similarity = np.dot(query_vec, stored_vec) / (
                    np.linalg.norm(query_vec) * np.linalg.norm(stored_vec)
                )
                results.append({
                    "memory_id": keys[i].decode().split(":")[-1],
                    "content": data["content"],
                    "similarity": float(similarity),
                    "metadata": json.loads(data["metadata"]),
                    "tier": "hot"
                })
        
        # 2. 如果需要归档数据，查 PostgreSQL
        if include_archive and len(results) < 10:
            loop = asyncio.get_event_loop()
            pg_results = await loop.run_in_executor(
                None, self._pg_search, user_id, query_embedding, 10 - len(results)
            )
            results.extend(pg_results)
        
        # 3. 热度加权：提升 Redis 结果的相似度
        for r in results:
            if r["tier"] == "hot":
                r["similarity"] *= 1.1  # 热数据权重 +10%
        
        results.sort(key=lambda x: x["similarity"], reverse=True)
        return results[:10]
    
    def _pg_search(self, user_id, query_embedding, limit):
        import numpy as np
        with self.pg_conn.cursor() as cur:
            cur.execute("""
                SELECT id, content, embedding, metadata,
                       1 - (embedding <=> %s::vector) as similarity
                FROM agent_memories
                WHERE user_id = %s
                ORDER BY embedding <=> %s::vector
                LIMIT %s
            """, (query_embedding, user_id, query_embedding, limit))
            
            return [
                {
                    "memory_id": row[0],
                    "content": row[1],
                    "metadata": row[3],
                    "similarity": float(row[4]),
                    "tier": "warm"
                }
                for row in cur.fetchall()
            ]
    
    async def archive_old_memories(self, days: int = 30):
        """归档旧记忆 - 定时任务调用"""
        loop = asyncio.get_event_loop()
        await loop.run_in_executor(None, self._archive_task, days)
    
    def _archive_task(self, days: int):
        with self.pg_conn.cursor() as cur:
            cur.execute("""
                INSERT INTO memory_archive 
                SELECT id, user_id, content, embedding, metadata, NOW()
                FROM agent_memories
                WHERE created_at < NOW() - INTERVAL '%s days'
                ON CONFLICT (id) DO NOTHING
            """, (days,))
            print(f"归档了 {cur.rowcount} 条记忆")


使用示例

async def main():
    store = HybridMemoryStore(
        redis_url="redis://localhost:6379/0",
        pg_connection_string="postgresql://user:pass@localhost:5432/agent_memory"
    )
    
    # 存储
    await store.store(
        user_id="user_001",
        memory_id="mem_100",
        content="用户喜欢周杰伦的音乐",
        embedding=[0.1, 0.2, 0.3] * 512,
        metadata={"topic": "music", "artist": "jay_chou"}
    )
    
    # 检索
    results = await store.retrieve(
        user_id="user_001",
        query_embedding=[0.1, 0.2, 0.3] * 512,
        include_archive=True
    )
    for r in results:
        print(f"[{r['tier']}] {r['content'][:50]}... (相似度: {r['similarity']:.3f})")

asyncio.run(main())

架构设计最佳实践

记忆压缩与摘要

不要存储原始对话，而是定期做摘要压缩。我见过太多项目因为存储量爆炸导致成本失控。

import httpx
import asyncio
from typing import List, Dict

class MemoryCompressor:
    """使用 LLM 对记忆进行摘要压缩"""
    
    def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
        self.client = httpx.AsyncClient(
            base_url=base_url,
            headers={"Authorization": f"Bearer {api_key}"},
            timeout=60.0
        )
    
    async def summarize_conversation(
        self, 
        messages: List[Dict[str, str]]
    ) -> str:
        """将多轮对话压缩为单条摘要"""
        
        # 构建压缩 Prompt
        compression_prompt = f"""请将以下对话提炼为简洁的记忆摘要，保留关键信息和用户偏好：

对话记录：
{chr(10).join([f"用户: {m['user']}\n助手: {m['assistant']}" for m in messages])}

摘要要求：
- 保留关键事实和偏好
- 删除冗余表达
- 长度控制在 200 字以内
- 用第三人称叙述
"""
        
        response = await self.client.post("/chat/completions", json={
            "model": "gpt-4.1",
            "messages": [
                {"role": "system", "content": "你是一个记忆压缩助手，负责将对话提炼为关键信息。"},
                {"role": "user", "content": compression_prompt}
            ],
            "max_tokens": 300,
            "temperature": 0.3  # 低温度保证摘要一致性
        })
        
        result = response.json()
        return result["choices"][0]["message"]["content"]
    
    async def batch_compress(
        self, 
        conversation_groups: List[List[Dict]]
    ) -> List[str]:
        """批量压缩多个对话组"""
        tasks = [self.summarize_conversation(group) for group in conversation_groups]
        return await asyncio.gather(*tasks)


使用示例

compressor = MemoryCompressor(api_key="YOUR_HOLYSHEEP_API_KEY")

conversations = [
    [
        {"user": "我想买一双跑步鞋", "assistant": "好的，您平时跑步的距离是多少？"},
        {"user": "一般跑5公里左右", "assistant": "推荐您选择缓震性能好的款式。"},
        {"user": "预算在500元左右", "assistant": "这个价位可以看看某某品牌。"}
    ],
    # ... 更多对话组
]

summaries = asyncio.run(compressor.batch_compress(conversations))
print(f"压缩了 {len(summaries)} 组对话")

记忆生命周期管理

from datetime import datetime, timedelta
from enum import Enum
from typing import Optional

class MemoryTier(Enum):
    HOT = "hot"      # 最近 7 天，高频访问
    WARM = "warm"    # 7-30 天，正常访问
    COLD = "cold"    # 30-90 天，低频访问
    ARCHIVE = "archive"  # 90 天以上，压缩存储

class MemoryLifecycleManager:
    """记忆生命周期管理"""
    
    def __init__(self, store: HybridMemoryStore):
        self.store = store
    
    def calculate_tier(self, created_at: datetime) -> MemoryTier:
        """根据创建时间计算记忆层级"""
        age_days = (datetime.now() - created_at).days
        
        if age_days <= 7:
            return MemoryTier.HOT
        elif age_days <= 30:
            return MemoryTier.WARM
        elif age_days <= 90:
            return MemoryTier.COLD
        else:
            return MemoryTier.ARCHIVE
    
    def should_compress(self, memory: dict, current_tier: MemoryTier) -> bool:
        """判断是否需要压缩"""
        if current_tier in [MemoryTier.COLD, MemoryTier.ARCHIVE]:
            # 冷数据定期压缩节省空间
            return memory.get("compression_count", 0) < 3
        return False
    
    def should_delete(self, memory: dict, current_tier: MemoryTier) -> bool:
        """判断是否需要删除"""
        importance = memory.get("importance_score", 0.5)
        
        if current_tier == MemoryTier.ARCHIVE and importance < 0.2:
            return True
        if current_tier == MemoryTier.COLD and importance < 0.1:
            return True
        return False
    
    async def run_maintenance(self):
        """执行维护任务 - 建议每日执行"""
        # 1. 压缩低重要性冷记忆
        # 2. 删除过期不重要记忆
        # 3. 将热数据迁移到温数据层
        # 4. 更新访问统计
        pass

常见报错排查

错误 1：Redis 连接池耗尽

# 错误信息
redis.exceptions.ConnectionError: Error 99: Cannot assign requested address

或

redis.exceptions.ConnectionLimitError: Too many connections


原因分析

连接池未正确配置，高并发时创建了过多连接


解决方案

import redis
from redis import ConnectionPool


方案 A：使用连接池

pool = ConnectionPool(
    host='localhost',
    port=6379,
    max_connections=50,  # 根据服务器配置调整
    decode_responses=True
)
redis_client = redis.Redis(connection_pool=pool)


方案 B：Async Redis + 连接复用

import redis.asyncio as aioredis

class AsyncRedisStore:
    def __init__(self):
        # 复用单一连接
        self.redis = aioredis.from_url(
            "redis://localhost:6379/0",
            max_connections=100,
            decode_responses=True
        )
    
    async def batch_ops(self, operations: List):
        """批量操作使用 pipeline 减少连接数"""
        async with self.redis.pipeline(transaction=True) as pipe:
            for op in operations:
                if op["type"] == "set":
                    pipe.set(op["key"], op["value"])
                elif op["type"] == "get":
                    pipe.get(op["key"])
            return await pipe.execute()


方案 C：检查连接泄漏

async def check_connections

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