大语言模型智能体记忆系统架构与实现

1. 智能体记忆系统概述

在构建基于大语言模型的智能体时，记忆系统是决定其服务连贯性和个性化的核心组件。一个完整的智能体记忆系统需要解决三个关键问题：如何存储信息、如何检索信息、如何在不同时间尺度上管理信息。

1.1 记忆系统的分层架构

智能体记忆系统通常采用分层设计，模仿人类记忆的工作机制：

• 短期记忆(STM)：处理当前会话中的即时信息，类似于人类的工作记忆
• 长期记忆(LTM)：存储跨会话的持久化信息，形成智能体的"知识库"
• 记忆管理模块：负责记忆的写入、检索、更新和淘汰

这种分层设计解决了单一记忆系统面临的几个关键挑战：

1. 上下文窗口限制问题
2. 信息持久化需求
3. 不同时间尺度信息的访问模式差异

1.2 记忆系统的技术实现栈

现代智能体记忆系统通常构建在以下技术栈之上：

2. 短期记忆机制实现

2.1 短期记忆缓冲区设计

短期记忆的核心是维护一个动态的对话上下文窗口。以下是Python实现的STM Buffer类：

python复制from collections import deque
import tiktoken  # OpenAI的token计数库

class STMBuffer:
    def __init__(self, max_tokens=4096):
        self.buffer = deque()
        self.max_tokens = max_tokens
        self.current_tokens = 0
        self.encoder = tiktoken.get_encoding("cl100k_base")  # GPT-4使用的编码器
        
    def add_message(self, role: str, content: str):
        """添加消息到缓冲区"""
        message = f"{role}: {content}"
        tokens = len(self.encoder.encode(message))
        
        # 滑动窗口机制
        while self.current_tokens + tokens > self.max_tokens:
            if not self.buffer:
                break
            removed = self.buffer.popleft()
            self.current_tokens -= len(self.encoder.encode(removed))
            
        self.buffer.append(message)
        self.current_tokens += tokens
        
    def get_context(self) -> str:
        """获取当前对话上下文"""
        return "\n".join(self.buffer)
    
    def clear(self):
        """清空缓冲区"""
        self.buffer.clear()
        self.current_tokens = 0

关键设计考虑：

1. 精确token计数：使用与LLM匹配的tokenizer确保准确性
2. 动态窗口管理：自动移除最早的消息以维持token限制
3. 角色标识：保留发言者信息(用户/系统)以维持对话结构

2.2 短期记忆优化策略

在实际应用中，我们还需要考虑以下优化点：

关键消息保留机制

python复制def add_message(self, role: str, content: str, is_critical=False):
    """添加消息，支持关键消息标记"""
    if is_critical:
        self.critical_messages.append(content)  # 关键消息单独存储
        
    # 正常添加流程...
    
def get_context(self) -> str:
    """获取上下文，确保包含关键消息"""
    base_context = "\n".join(self.buffer)
    critical_context = "\n".join(f"System: {msg}" for msg in self.critical_messages)
    return f"{critical_context}\n{base_context}" if critical_context else base_context

自适应窗口大小策略

python复制def adjust_window_size(self, complexity: float):
    """
    根据对话复杂度调整窗口大小
    complexity: 0-1之间的值，表示对话复杂程度
    """
    min_tokens = 1024
    max_tokens = 16000  # GPT-4-32k的上下文限制
    self.max_tokens = int(min_tokens + (max_tokens - min_tokens) * complexity)

3. 长期记忆存储架构

3.1 记忆数据模型设计

长期记忆需要更丰富的数据结构来支持各种查询需求：

python复制from pydantic import BaseModel
from datetime import datetime
from typing import Optional, List, Dict
from enum import Enum

class MemoryType(str, Enum):
    FACT = "fact"
    PREFERENCE = "preference"
    EVENT = "event"
    RULE = "rule"

class MemoryRecord(BaseModel):
    id: str  # 格式: user_id:timestamp:hash
    content: str
    embedding: List[float]  # 768维向量
    type: MemoryType
    importance: float  # 1-10
    created_at: datetime
    last_accessed: Optional[datetime] = None
    metadata: Dict[str, str] = {}
    relations: List[str] = []  # 关联的其他记忆ID
    
    def update_access_time(self):
        self.last_accessed = datetime.now()
        
    def add_relation(self, related_memory_id: str):
        if related_memory_id not in self.relations:
            self.relations.append(related_memory_id)

3.2 存储介质选型指南

根据不同的应用场景，存储方案的选择需要考虑多个维度：

混合存储架构示例：

python复制class HybridMemoryStore:
    def __init__(self):
        self.vector_db = PineconeClient()  # 语义检索
        self.graph_db = Neo4jClient()  # 关系网络
        self.doc_db = MongoDBClient()  # 完整记录存储
        
    async def store(self, memory: MemoryRecord):
        """分布式存储记忆"""
        # 向量存储用于检索
        await self.vector_db.upsert(
            id=memory.id,
            vector=memory.embedding,
            metadata={
                "type": memory.type,
                "importance": memory.importance
            }
        )
        
        # 图数据库存储关系
        if memory.relations:
            await self.graph_db.create_edges(
                source=memory.id,
                targets=memory.relations,
                relationship="RELATED"
            )
            
        # 完整记录存储
        await self.doc_db.insert(memory.dict())

4. 向量数据库核心技术

4.1 相似性度量算法详解

4.1.1 余弦相似度优化实现

python复制import numpy as np
from numba import njit  # 使用JIT编译器加速

@njit(fastmath=True)
def cosine_similarity(v1: np.ndarray, v2: np.ndarray) -> float:
    """优化版余弦相似度计算"""
    dot_product = np.dot(v1, v2)
    norm_v1 = np.sqrt(np.sum(v1**2))
    norm_v2 = np.sqrt(np.sum(v2**2))
    
    # 处理零向量情况
    if norm_v1 == 0 or norm_v2 == 0:
        return 0.0
        
    similarity = dot_product / (norm_v1 * norm_v2)
    return max(min(similarity, 1.0), -1.0)  # 确保在[-1, 1]范围内

性能对比：

4.1.2 混合相似度算法

在实际应用中，我们常常需要结合多种相似度度量：

python复制def hybrid_similarity(query_vec: np.ndarray, 
                     target_vec: np.ndarray,
                     target_metadata: dict,
                     weights: dict = None) -> float:
    """
    混合相似度计算
    weights: {
        "cosine": 0.6,
        "euclidean": 0.2,
        "recency": 0.1,
        "importance": 0.1
    }
    """
    default_weights = {
        "cosine": 0.6,
        "euclidean": 0.2,
        "recency": 0.1,
        "importance": 0.1
    }
    weights = weights or default_weights
    
    # 计算基础相似度
    cosine_sim = cosine_similarity(query_vec, target_vec)
    euclidean_sim = 1 / (1 + np.linalg.norm(query_vec - target_vec))
    
    # 计算元数据得分
    recency_score = calculate_recency(target_metadata.get("last_accessed"))
    importance_score = target_metadata.get("importance", 5) / 10
    
    # 加权综合
    total_score = (
        weights["cosine"] * cosine_sim +
        weights["euclidean"] * euclidean_sim +
        weights["recency"] * recency_score +
        weights["importance"] * importance_score
    )
    
    return total_score

4.2 主流向量数据库对比

4.2.1 Pinecone深度集成

Pinecone作为全托管服务，提供了最佳的生产环境体验：

python复制class PineconeMemoryStore:
    def __init__(self, api_key: str, index_name: str = "agent-memories"):
        pinecone.init(api_key=api_key, environment="us-west1-gcp")
        
        # 检查并创建索引
        if index_name not in pinecone.list_indexes():
            pinecone.create_index(
                name=index_name,
                dimension=1536,  # OpenAI text-embedding-3-large
                metric="cosine",
                pods=1,
                pod_type="p1.x1"
            )
            
        self.index = pinecone.Index(index_name)
        
    async def upsert_memory(self, memory: MemoryRecord):
        """异步插入记忆"""
        await self.index.upsert(
            vectors=[(
                memory.id,
                memory.embedding,
                {
                    "type": memory.type,
                    "importance": memory.importance,
                    "created_at": memory.created_at.isoformat(),
                    "content": memory.content[:100]  # 存储前100字符
                }
            )],
            async_req=True
        )
        
    async def search_memories(self, query_vec: list, top_k: int = 5, filters: dict = None):
        """带过滤条件的语义搜索"""
        return await self.index.query(
            vector=query_vec,
            top_k=top_k,
            filter=filters,
            include_metadata=True,
            async_req=True
        )

4.2.2 Weaviate混合检索

Weaviate的突出优势在于支持混合检索模式：

python复制class WeaviateMemoryStore:
    def __init__(self, endpoint: str = "http://localhost:8080"):
        self.client = weaviate.Client(endpoint)
        
        # 定义记忆对象的schema
        memory_schema = {
            "class": "Memory",
            "properties": [
                {
                    "name": "content",
                    "dataType": ["text"]
                },
                # 其他属性...
            ],
            "vectorizer": "none"  # 我们使用预计算的向量
        }
        
        # 确保schema存在
        if not self.client.schema.contains(memory_schema):
            self.client.schema.create_class(memory_schema)
            
    def hybrid_search(self, query: str, query_vec: list, user_id: str, alpha: float = 0.5):
        """
        混合检索
        alpha: 0=纯关键词, 1=纯向量
        """
        return self.client.query.get(
            "Memory",
            ["content", "importance", "type"]
        ).with_hybrid(
            query=query,
            vector=query_vec,
            alpha=alpha,
            properties=["content^2", "type"]  # 内容字段权重加倍
        ).with_where({
            "path": ["user_id"],
            "operator": "Equal",
            "valueString": user_id
        }).do()

5. 知识图谱集成实践

5.1 知识图谱建模

5.1.1 实体关系模型设计

python复制from py2neo import Graph, Node, Relationship

class KnowledgeGraph:
    def __init__(self, uri: str, user: str, password: str):
        self.graph = Graph(uri, auth=(user, password))
        
    def create_entity(self, memory_id: str, memory_type: str, properties: dict):
        """创建知识图谱实体"""
        labels = ["Memory", memory_type.capitalize()]
        node = Node(*labels, 
                   id=memory_id,
                   **properties)
        self.graph.create(node)
        return node
        
    def create_relation(self, source_id: str, target_id: str, rel_type: str):
        """创建实体关系"""
        query = """
        MATCH (a {id: $source_id}), (b {id: $target_id})
        MERGE (a)-[r:%s]->(b)
        RETURN r
        """ % rel_type
        
        return self.graph.run(query, 
                            source_id=source_id,
                            target_id=target_id).data()

5.1.2 时序知识图谱扩展

python复制class TemporalKnowledgeGraph(KnowledgeGraph):
    def add_temporal_relation(self, source_id: str, target_id: str, 
                            rel_type: str, valid_from: datetime, 
                            valid_to: datetime = None):
        """添加带时间有效期的关系"""
        rel_properties = {
            "valid_from": valid_from.isoformat(),
            "valid_to": valid_to.isoformat() if valid_to else None
        }
        
        query = """
        MATCH (a {id: $source_id}), (b {id: $target_id})
        MERGE (a)-[r:%s]->(b)
        SET r += $properties
        RETURN r
        """ % rel_type
        
        return self.graph.run(query,
                            source_id=source_id,
                            target_id=target_id,
                            properties=rel_properties).data()

5.2 图神经网络应用

5.2.1 记忆嵌入传播

python复制import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GATConv

class MemoryGNN(nn.Module):
    """基于图注意力网络的记忆编码器"""
    def __init__(self, input_dim=768, hidden_dim=256, heads=4):
        super().__init__()
        self.conv1 = GATConv(input_dim, hidden_dim, heads=heads)
        self.conv2 = GATConv(hidden_dim*heads, hidden_dim, heads=1)
        self.dropout = nn.Dropout(0.2)
        
    def forward(self, x, edge_index):
        """前向传播"""
        x = self.dropout(x)
        x = self.conv1(x, edge_index)
        x = F.elu(x)
        x = self.dropout(x)
        x = self.conv2(x, edge_index)
        return x
    
    def update_embeddings(self, memory_ids, new_embeddings):
        """更新节点嵌入"""
        # 实现嵌入传播逻辑...

5.2.2 关系推理应用

python复制class RelationPredictor(nn.Module):
    """基于GNN的关系预测模型"""
    def __init__(self, node_dim=256, rel_dim=128):
        super().__init__()
        self.encoder = MemoryGNN(input_dim=node_dim)
        self.rel_mlp = nn.Sequential(
            nn.Linear(node_dim*2, rel_dim),
            nn.ReLU(),
            nn.Linear(rel_dim, rel_dim)
        )
        
    def predict_relation(self, node1, node2):
        """预测两个节点间可能的关系"""
        h1 = self.encoder(node1)
        h2 = self.encoder(node2)
        pair_embedding = torch.cat([h1, h2], dim=-1)
        return self.rel_mlp(pair_embedding)

6. 混合记忆系统实战

6.1 记忆写入流程优化

6.1.1 重要性评估模型

python复制from transformers import pipeline

class MemoryImportanceEvaluator:
    def __init__(self, model_name="facebook/bart-large-mnli"):
        self.classifier = pipeline(
            "zero-shot-classification",
            model=model_name,
            device="cuda" if torch.cuda.is_available() else "cpu"
        )
        
    def evaluate(self, text: str) -> float:
        """评估记忆重要性(0-1)"""
        candidate_labels = [
            "casual conversation",
            "personal preference", 
            "important fact",
            "critical information"
        ]
        
        result = self.classifier(
            text,
            candidate_labels,
            multi_label=False
        )
        
        # 根据标签类型分配重要性分数
        label_scores = {
            "casual conversation": 0.3,
            "personal preference": 0.6,
            "important fact": 0.8,
            "critical information": 1.0
        }
        
        top_label = result['labels'][0]
        confidence = result['scores'][0]
        return label_scores[top_label] * confidence

6.1.2 记忆摘要生成

python复制class MemorySummarizer:
    def __init__(self, model_name="t5-small"):
        self.summarizer = pipeline(
            "summarization",
            model=model_name,
            device="cuda" if torch.cuda.is_available() else "cpu"
        )
        
    def generate_summary(self, text: str, max_length=64) -> str:
        """生成记忆摘要"""
        summary = self.summarizer(
            text,
            max_length=max_length,
            min_length=8,
            do_sample=False
        )
        return summary[0]['summary_text']

6.2 记忆检索与融合

6.2.1 多阶段检索流程

python复制class MemoryRetriever:
    def __init__(self, vector_store, graph_store, cache_size=10000):
        self.vector_store = vector_store
        self.graph_store = graph_store
        self.cache = LRUCache(maxsize=cache_size)
        
    async def retrieve(self, query: str, query_vec: list, user_id: str) -> list:
        """多阶段记忆检索"""
        cache_key = f"{user_id}:{query}"
        
        # 检查缓存
        if cached := self.cache.get(cache_key):
            return cached
            
        # 阶段1：向量相似度检索
        vector_results = await self.vector_store.search(
            query_vec,
            top_k=20,
            filters={"user_id": user_id}
        )
        
        # 阶段2：知识图谱扩展
        memory_ids = [res['id'] for res in vector_results]
        graph_results = await self.graph_store.expand_entities(
            memory_ids,
            hops=2
        )
        
        # 阶段3：结果融合与重排序
        combined = self._combine_results(vector_results, graph_results)
        ranked = self._rerank(combined, query_vec)
        
        # 缓存结果
        self.cache[cache_key] = ranked[:10]  # 缓存前10个结果
        return ranked[:10]

6.2.2 上下文融合策略

python复制class ContextFuser:
    def __init__(self, tokenizer, max_tokens=8000):
        self.tokenizer = tokenizer
        self.max_tokens = max_tokens
        
    def fuse(self, stm_messages: list, ltm_memories: list) -> str:
        """融合短期记忆和长期记忆"""
        # 构建记忆时间线
        timeline = self._build_timeline(ltm_memories)
        
        # 构建当前对话上下文
        dialogue = self._format_dialogue(stm_messages)
        
        # 组合最终上下文
        context = f"""## 相关背景知识\n{timeline}\n\n## 当前对话\n{dialogue}"""
        
        # 确保不超过token限制
        return self._truncate_to_fit(context)
    
    def _build_timeline(self, memories: list) -> str:
        """将记忆组织为时间线格式"""
        # 按时间排序
        sorted_mem = sorted(memories, key=lambda x: x['timestamp'])
        
        timeline = []
        for mem in sorted_mem:
            date = mem['timestamp'].strftime("%Y-%m-%d")
            timeline.append(f"{date}: {mem['summary']}")
            
        return "\n".join(timeline)

7. 性能优化与监控

7.1 缓存策略实现

python复制from datetime import datetime, timedelta

class MemoryCache:
    """智能记忆缓存系统"""
    def __init__(self, ttl=timedelta(hours=1), max_size=10000):
        self.cache = {}
        self.ttl = ttl
        self.max_size = max_size
        self.hits = 0
        self.misses = 0
        
    def get(self, key: str) -> Optional[list]:
        """获取缓存项"""
        if key not in self.cache:
            self.misses += 1
            return None
            
        entry = self.cache[key]
        if datetime.now() - entry['timestamp'] > self.ttl:
            del self.cache[key]
            self.misses += 1
            return None
            
        self.hits += 1
        return entry['value']
        
    def set(self, key: str, value: list):
        """设置缓存项"""
        if len(self.cache) >= self.max_size:
            self._evict_oldest()
            
        self.cache[key] = {
            'value': value,
            'timestamp': datetime.now()
        }
        
    def _evict_oldest(self):
        """淘汰最旧的缓存项"""
        oldest_key = min(self.cache.keys(), 
                        key=lambda k: self.cache[k]['timestamp'])
        del self.cache[oldest_key]
        
    @property
    def hit_rate(self) -> float:
        """计算缓存命中率"""
        total = self.hits + self.misses
        return self.hits / total if total > 0 else 0

7.2 监控指标设计

python复制from prometheus_client import Counter, Gauge, Histogram

class MemoryMetrics:
    """记忆系统监控指标"""
    def __init__(self):
        # 写入指标
        self.writes_total = Counter(
            'memory_writes_total',
            'Total memory writes',
            ['type', 'importance']
        )
        
        # 检索指标
        self.retrieval_latency = Histogram(
            'memory_retrieval_latency_seconds',
            'Memory retrieval latency',
            buckets=[0.01, 0.05, 0.1, 0.5, 1.0]
        )
        
        # 缓存指标
        self.cache_hits = Counter(
            'memory_cache_hits_total',
            'Total cache hits'
        )
        
        self.cache_misses = Counter(
            'memory_cache_misses_total',
            'Total cache misses'
        )
        
    def record_write(self, memory_type: str, importance: int):
        """记录写入操作"""
        self.writes_total.labels(
            type=memory_type,
            importance=str(importance)
        ).inc()
        
    def record_retrieval(self, duration: float):
        """记录检索延迟"""
        self.retrieval_latency.observe(duration)
        
    def record_cache_hit(self):
        """记录缓存命中"""
        self.cache_hits.inc()
        
    def record_cache_miss(self):
        """记录缓存未命中"""
        self.cache_misses.inc()

8. 电商客服Agent案例实现

8.1 系统架构设计

mermaid复制graph TD
    A[用户请求] --> B[API网关]
    B --> C{请求类型?}
    C -->|查询类| D[记忆检索模块]
    C -->|操作类| E[工具调用模块]
    D --> F[向量数据库]
    D --> G[知识图谱]
    F --> H[结果融合]
    G --> H
    H --> I[LLM生成响应]
    E --> J[订单系统]
    E --> K[库存系统]
    J --> I
    K --> I
    I --> L[响应返回]
    L --> M[记忆更新队列]
    M --> N[异步写入存储]

8.2 关键业务逻辑实现

8.2.1 用户偏好处理

python复制class PreferenceManager:
    def __init__(self, memory_store):
        self.store = memory_store
        
    async def extract_preferences(self, user_id: str, conversation: list):
        """从对话中提取用户偏好"""
        # 使用LLM分析对话中的偏好信息
        extracted = await self._analyze_with_llm(conversation)
        
        # 存储到记忆系统
        for pref in extracted:
            memory = MemoryRecord(
                id=f"pref_{user_id}_{hash(pref['type'])}",
                content=f"用户偏好: {pref['value']}",
                type=MemoryType.PREFERENCE,
                importance=8,  # 偏好通常比较重要
                metadata={
                    "user_id": user_id,
                    "preference_type": pref['type'],
                    "preference_value": pref['value']
                }
            )
            await self.store.upsert(memory)
            
    async def get_recommendations(self, user_id: str, product_type: str):
        """基于用户偏好获取推荐"""
        # 检索相关偏好
        prefs = await self.store.search(
            filters={
                "user_id": user_id,
                "type": "preference",
                "preference_type": {"$in": ["color", "size", "brand"]}
            },
            limit=5
        )
        
        # 构建推荐查询
        query = self._build_recommendation_query(product_type, prefs)
        return await self._query_products(query)

8.2.2 订单历史集成

python复制class OrderHistoryManager:
    def __init__(self, memory_store, order_db):
        self.store = memory_store
        self.db = order_db
        
    async def sync_order_history(self, user_id: str):
        """同步订单历史到记忆系统"""
        orders = await self.db.get_orders(user_id)
        
        for order in orders:
            memory = MemoryRecord(
                id=f"order_{order['id']}",
                content=f"订单 {order['id']}: 购买了 {order['items']}",
                type=MemoryType.EVENT,
                importance=7,
                metadata={
                    "user_id": user_id,
                    "order_id": order['id'],
                    "order_date": order['date'],
                    "items": order['items'],
                    "total_amount": order['amount']
                }
            )
            
            # 存储订单基本信息
            await self.store.upsert(memory)
            
            # 为每个商品创建关联记忆
            for item in order['items']:
                item_memory = MemoryRecord(
                    id=f"item_{order['id']}_{item['sku']}",
                    content=f"购买商品: {item['name']}",
                    type=MemoryType.FACT,
                    importance=6,
                    metadata={
                        "user_id": user_id,
                        "sku": item['sku'],
                        "category": item['category'],
                        "purchase_date": order['date']
                    },
                    relations=[memory.id]  # 关联到主订单
                )
                await self.store.upsert(item_memory)

8.3 性能优化实践

8.3.1 批量写入优化

python复制class BatchMemoryWriter:
    def __init__(self, memory_store, batch_size=100):
        self.store = memory_store
        self.batch_size = batch_size
        self.buffer = []
        self.lock = asyncio.Lock()
        
    async def add_memory(self, memory: MemoryRecord):
        """添加记忆到批量缓冲区"""
        async with self.lock:
            self.buffer.append(memory)
            if len(self.buffer) >= self.batch_size:
                await self._flush()
                
    async def _flush(self):
        """执行批量写入"""
        if not self.buffer:
            return
            
        # 分批处理避免过大请求
        chunks = [self.buffer[i:i+self.batch_size] 
                 for i in range(0, len(self.buffer), self.batch_size)]
        
        for chunk in chunks:
            await self.store.batch_upsert(chunk)
            
        self.buffer.clear()
        
    async def __aenter__(self):
        return self
        
    async def __aexit__(self, exc_type, exc, tb):
        await self._flush()

8.3.2 预取策略实现

python复制class PrefetchManager:
    def __init__(self, memory_store, predictor):
        self.store = memory_store
        self.predictor = predictor
        
    async def prefetch_for_user(self, user_id: str):
        """预测并预取用户可能需要的记忆"""
        # 预测用户下一步可能的行为
        predicted_actions = await self.predictor.predict(user_id)
        
        # 为每个预测行为预取相关记忆
        for action in predicted_actions:
            related_memories = await self._get_related_memories(user_id, action)
            await self._cache_memories(related_memories)
            
    async def _get_related_memories(self, user_id: str, action: str):
        """获取与特定行为相关的记忆"""
        # 基于行为类型构建查询
        if action['type'] == 'product_view':
            return await self.store.search(
                filters={
                    "user_id": user_id,
                    "type": {"$in": ["preference", "purchase"]},
                    "category": action['category']
                },
                limit=10
            )
        # 其他行为类型的处理...

9. 评估与调优

9.1 关键性能指标

9.2 调优策略

向量索引优化

python复制def optimize_pinecone_index(index_name: str, pod_type: str, replicas: int):
    """调整Pinecone索引配置"""
    pinecone.configure_index(
        name=index_name,
        pod_type=pod_type,  # 如"p1.x2"更大型号
        replicas=replicas   # 增加副本提高读取吞吐
    )

图查询优化

python复制def optimize_graph_queries():
    """知识图谱查询优化策略"""
    return {
        "indexing": [
            "CREATE INDEX ON :Memory(user_id)",
            "CREATE INDEX ON :Memory(type)"
        ],
        "query_hints": [
            "使用PROFILE分析慢查询",
            "避免全图扫描",
            "限制遍历深度"
        ],
        "cache_config": {
            "query_cache_size": "2GB",
            "edge_cache_ratio": 0.3
        }
    }

10. 未来演进方向

10.1 多模态记忆扩展

python复制class MultimodalMemory:
    """支持多模态的记忆表示"""
    def __init__(self):
        self.text_embedder = TextEmbedder()
        self.image_embedder = ImageEmbedder()
        self.audio_embedder = AudioEmbedder()
        
    async def store(self, content: Union[str, bytes], content_type: str):
        """存储多模态记忆"""
        if content_type == "text":
            embedding = await self.text_embedder.embed(content)
        elif content_type == "image":
            embedding = await self.image_embedder.embed(content)
        elif content_type == "audio":
            embedding = await self.audio_embedder.embed(content)
        else:
            raise ValueError(f"Unsupported type: {content_type}")
            
        memory = MemoryRecord(
            content=content[:1000] if isinstance(content, str) else f"[{content_type}]",
            embedding=embedding,
            metadata={"content_type": content_type}
        )
        await self.store.upsert(memory)

10.2 分布式记忆架构

python复制class DistributedMemoryStore:
    """分布式记忆存储系统"""
    def __init__(self, shards: list):
        self.shards = [MemoryStoreShard(url) for url in shards]
        self.router = ConsistentHashingRouter(len(shards))
        
    async def get(self, key: str) -> Optional[MemoryRecord]:
        """分布式获取记忆"""
        shard_index = self.router.get_shard(key)
        return await self.shards[shard_index].get(key)
        
    async def set(self, key: str, value: MemoryRecord):
        """分布式存储记忆"""
        shard_index = self.router.get_shard(key)
        await self.shards[shard_index].set(key, value)
        
    async def search(self, query: str, top_k: int = 5) -> list:
        """分布式向量搜索"""
        # 在所有分片上并行搜索
        results = await asyncio.gather(
            *[shard.search(query, top_k) for shard in self.shards]
        )
        
        # 合并并重排序结果
        all_results = [item for sublist in results for item in sublist]
        return sorted(all_results, key=lambda x: -x['score'])[:top_k]