AgentDB Advanced Features

AgentDB 高级功能

此技能的作用

介绍 AgentDB 在分布式系统、多数据库协调、自定义距离度量、混合搜索（向量 + 元数据）、QUIC 同步和生产部署模式方面的高级功能。助力构建复杂的 AI 系统，实现亚毫秒级的跨节点通信和高级搜索能力。

性能：<1ms QUIC 同步、支持过滤的混合搜索、自定义距离度量。

前提条件

• Node.js 18+
• AgentDB v1.0.7+（通过 agentic-flow）
• 了解分布式系统（用于 QUIC 同步）
• 掌握向量搜索基础知识

---

QUIC 同步

什么是 QUIC 同步？

QUIC（Quick UDP Internet Connections）支持 AgentDB 实例跨网络边界实现亚毫秒级延迟同步，并提供自动重试、多路复用和加密功能。

优势：

• 节点间延迟 <1ms
• 多路复用流（同时执行多个操作）
• 内置加密（TLS 1.3）
• 自动重试和恢复
• 基于事件的广播

启用 QUIC 同步

import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';

// Initialize with QUIC synchronization
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/distributed.db',
  enableQUICSync: true,
  syncPort: 4433,
  syncPeers: [
    '192.168.1.10:4433',
    '192.168.1.11:4433',
    '192.168.1.12:4433',
  ],
});

// Patterns automatically sync across all peers
await adapter.insertPattern({
  // ... pattern data
});

// Available on all peers within ~1ms

QUIC 配置

const adapter = await createAgentDBAdapter({
  enableQUICSync: true,
  syncPort: 4433,              // QUIC server port
  syncPeers: ['host1:4433'],   // Peer addresses
  syncInterval: 1000,          // Sync interval (ms)
  syncBatchSize: 100,          // Patterns per batch
  maxRetries: 3,               // Retry failed syncs
  compression: true,           // Enable compression
});

多节点部署

# Node 1 (192.168.1.10)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.11:4433,192.168.1.12:4433 \
node server.js

# Node 2 (192.168.1.11)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.12:4433 \
node server.js

# Node 3 (192.168.1.12)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.11:4433 \
node server.js

---

距离度量

余弦相似度（默认）

最适合归一化向量和语义相似度：

# CLI
npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m cosine

# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'cosine',
  k: 10,
});

使用场景：

• 文本嵌入（BERT、GPT 等）
• 语义搜索
• 文档相似度
• 大多数通用应用

公式：cos(θ) = (A · B) / (||A|| × ||B||) 范围：[-1, 1]（1 = 相同，-1 = 相反）

欧几里得距离（L2）

最适合空间数据和几何相似度：

# CLI
npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m euclidean

# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'euclidean',
  k: 10,
});

使用场景：

• 图像嵌入
• 空间数据
• 计算机视觉
• 向量模长会产生影响的场景

公式：d = √(Σ(ai - bi)²) 范围：[0, ∞]（0 = 相同，∞ = 非常不同）

点积

最适合预先归一化的向量，可快速计算：

# CLI
npx agentdb@latest query ./vectors.db "[0.1,0.2,...]" -m dot

# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'dot',
  k: 10,
});

使用场景：

• 预先归一化的嵌入
• 快速计算相似度
• 向量已是单位长度的场景

公式：dot = Σ(ai × bi) 范围：[-∞, ∞]（值越大 = 越相似）

自定义距离度量

// Implement custom distance function
function customDistance(vec1: number[], vec2: number[]): number {
  // Weighted Euclidean distance
  const weights = [1.0, 2.0, 1.5, ...];
  let sum = 0;
  for (let i = 0; i < vec1.length; i++) {
    sum += weights[i] * Math.pow(vec1[i] - vec2[i], 2);
  }
  return Math.sqrt(sum);
}

// Use in search (requires custom implementation)

---

混合搜索（向量 + 元数据）

基本混合搜索

将向量相似度与元数据过滤结合起来：

// Store documents with metadata
await adapter.insertPattern({
  id: '',
  type: 'document',
  domain: 'research-papers',
  pattern_data: JSON.stringify({
    embedding: documentEmbedding,
    text: documentText,
    metadata: {
      author: 'Jane Smith',
      year: 2025,
      category: 'machine-learning',
      citations: 150,
    }
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
});

// Hybrid search: vector similarity + metadata filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'research-papers',
  k: 20,
  filters: {
    year: { $gte: 2023 },          // Published 2023 or later
    category: 'machine-learning',   // ML papers only
    citations: { $gte: 50 },       // Highly cited
  },
});

高级过滤

// Complex metadata queries
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'products',
  k: 50,
  filters: {
    price: { $gte: 10, $lte: 100 },      // Price range
    category: { $in: ['electronics', 'gadgets'] },  // Multiple categories
    rating: { $gte: 4.0 },                // High rated
    inStock: true,                        // Available
    tags: { $contains: 'wireless' },      // Has tag
  },
});

加权混合搜索

结合向量分数和元数据分数：

const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'content',
  k: 20,
  hybridWeights: {
    vectorSimilarity: 0.7,  // 70% weight on semantic similarity
    metadataScore: 0.3,     // 30% weight on metadata match
  },
  filters: {
    category: 'technology',
    recency: { $gte: Date.now() - 30 * 24 * 3600000 },  // Last 30 days
  },
});

---

多数据库管理

多个数据库

// Separate databases for different domains
const knowledgeDB = await createAgentDBAdapter({
  dbPath: '.agentdb/knowledge.db',
});

const conversationDB = await createAgentDBAdapter({
  dbPath: '.agentdb/conversations.db',
});

const codeDB = await createAgentDBAdapter({
  dbPath: '.agentdb/code.db',
});

// Use appropriate database for each task
await knowledgeDB.insertPattern({ /* knowledge */ });
await conversationDB.insertPattern({ /* conversation */ });
await codeDB.insertPattern({ /* code */ });

数据库分片

// Shard by domain for horizontal scaling
const shards = {
  'domain-a': await createAgentDBAdapter({ dbPath: '.agentdb/shard-a.db' }),
  'domain-b': await createAgentDBAdapter({ dbPath: '.agentdb/shard-b.db' }),
  'domain-c': await createAgentDBAdapter({ dbPath: '.agentdb/shard-c.db' }),
};

// Route queries to appropriate shard
function getDBForDomain(domain: string) {
  const shardKey = domain.split('-')[0];  // Extract shard key
  return shards[shardKey] || shards['domain-a'];
}

// Insert to correct shard
const db = getDBForDomain('domain-a-task');
await db.insertPattern({ /* ... */ });

---

MMR（最大边际相关性）

检索多样化结果，避免冗余：

// Without MMR: Similar results may be redundant
const standardResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: false,
});

// With MMR: Diverse, non-redundant results
const diverseResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: true,
  mmrLambda: 0.5,  // Balance relevance (0) vs diversity (1)
});

MMR 参数：

• mmrLambda = 0：相关性最高（可能存在冗余）
• mmrLambda = 0.5：平衡（默认）
• mmrLambda = 1：多样性最高（相关性可能较低）

使用场景：

• 搜索结果多样化
• 推荐系统
• 避免回音室效应
• 探索式搜索

---

上下文合成

从多条记忆生成丰富的上下文：

const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'problem-solving',
  k: 10,
  synthesizeContext: true,  // Enable context synthesis
});

// ContextSynthesizer creates coherent narrative
console.log('Synthesized Context:', result.context);
// "Based on 10 similar problem-solving attempts, the most effective
//  approach involves: 1) analyzing root cause, 2) brainstorming solutions,
//  3) evaluating trade-offs, 4) implementing incrementally. Success rate: 85%"

console.log('Patterns:', result.patterns);
// Extracted common patterns across memories

---

生产模式

连接池

// Singleton pattern for shared adapter
class AgentDBPool {
  private static instance: AgentDBAdapter;

  static async getInstance() {
    if (!this.instance) {
      this.instance = await createAgentDBAdapter({
        dbPath: '.agentdb/production.db',
        quantizationType: 'scalar',
        cacheSize: 2000,
      });
    }
    return this.instance;
  }
}

// Use in application
const db = await AgentDBPool.getInstance();
const results = await db.retrieveWithReasoning(queryEmbedding, { k: 10 });

错误处理

async function safeRetrieve(queryEmbedding: number[], options: any) {
  try {
    const result = await adapter.retrieveWithReasoning(queryEmbedding, options);
    return result;
  } catch (error) {
    if (error.code === 'DIMENSION_MISMATCH') {
      console.error('Query embedding dimension mismatch');
      // Handle dimension error
    } else if (error.code === 'DATABASE_LOCKED') {
      // Retry with exponential backoff
      await new Promise(resolve => setTimeout(resolve, 100));
      return safeRetrieve(queryEmbedding, options);
    }
    throw error;
  }
}

监控和日志记录

// Performance monitoring
const startTime = Date.now();
const result = await adapter.retrieveWithReasoning(queryEmbedding, { k: 10 });
const latency = Date.now() - startTime;

if (latency > 100) {
  console.warn('Slow query detected:', latency, 'ms');
}

// Log statistics
const stats = await adapter.getStats();
console.log('Database Stats:', {
  totalPatterns: stats.totalPatterns,
  dbSize: stats.dbSize,
  cacheHitRate: stats.cacheHitRate,
  avgSearchLatency: stats.avgSearchLatency,
});

---

CLI 高级操作

数据库导入/导出

# Export with compression
npx agentdb@latest export ./vectors.db ./backup.json.gz --compress

# Import from backup
npx agentdb@latest import ./backup.json.gz --decompress

# Merge databases
npx agentdb@latest merge ./db1.sqlite ./db2.sqlite ./merged.sqlite

数据库优化

# Vacuum database (reclaim space)
sqlite3 .agentdb/vectors.db "VACUUM;"

# Analyze for query optimization
sqlite3 .agentdb/vectors.db "ANALYZE;"

# Rebuild indices
npx agentdb@latest reindex ./vectors.db

---

环境变量

# AgentDB configuration
AGENTDB_PATH=.agentdb/reasoningbank.db
AGENTDB_ENABLED=true

# Performance tuning
AGENTDB_QUANTIZATION=binary     # binary|scalar|product|none
AGENTDB_CACHE_SIZE=2000
AGENTDB_HNSW_M=16
AGENTDB_HNSW_EF=100

# Learning plugins
AGENTDB_LEARNING=true

# Reasoning agents
AGENTDB_REASONING=true

# QUIC synchronization
AGENTDB_QUIC_SYNC=true
AGENTDB_QUIC_PORT=4433
AGENTDB_QUIC_PEERS=host1:4433,host2:4433

---

故障排除

问题：QUIC 同步无法正常工作

# Check firewall allows UDP port 4433
sudo ufw allow 4433/udp

# Verify peers are reachable
ping host1

# Check QUIC logs
DEBUG=agentdb:quic node server.js

问题：混合搜索未返回结果

// Relax filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 100,  // Increase k
  filters: {
    // Remove or relax filters
  },
});

问题：记忆整合过于激进

// Disable automatic optimization
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: false,  // Disable auto-consolidation
  k: 10,
});

---

了解更多

• QUIC 协议：docs/quic-synchronization.pdf
• 混合搜索：docs/hybrid-search-guide.md
• GitHub：https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
• 网站：https://agentdb.ruv.io

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类别：高级 / 分布式系统 难度：高级 预计用时：45-60 分钟