Fusionpact Vectordb

⚡ 融合行动

Agent原生检索引擎

人工智能代理的混合向量+推理+记忆

 ](https://nodejs.org) ](https://www.npmjs.com/package/fusionpact)

相似性≠相关性。 FusionAction是第一个将HNSW向量搜索、基于推理的树检索和代理内存结合在一个平台上的检索引擎，专为人工智能代理和多代理系统而构建。

快速入门 · 混合检索 · 代理内存 · 多Agent · MCP服务器 · 树索引 · RAG 流程 · API 参考 · 基准测试 · 贡献

______________________________________________________________________

为什么选择FusionAct？

传统的矢量数据库检索 相似的.但相似≠相关。向向量数据库询问“2024年第三季度收入”，你可能会得到第二季度或第四季度的数据——语义相似，但 错误答案.

FusionAction通过结合 三种检索范式:

加上专门打造 代理存储器, 多代理编排，以及 MCP服务器 --全零依赖，本地优先，免费。

┌──────────────────────────────────────────────────────────┐
│             FusionPact Retrieval Engine                   │
│                                                          │
│  ┌────────────┐  ┌─────────────┐  ┌────────────────┐   │
│  │ Vector     │  │ Tree        │  │ Keyword        │   │
│  │ (HNSW)     │  │ (Reasoning) │  │ (BM25)         │   │
│  └─────┬──────┘  └──────┬──────┘  └───────┬────────┘   │
│        └────────────┬────┴─────────────────┘            │
│                     ▼                                    │
│           Reciprocal Rank Fusion                         │
│                     ▼                                    │
│  ┌──────────────────────────────────────────────────┐   │
│  │        Agent Memory (Multi-Agent)                │   │
│  │  Episodic │ Semantic │ Procedural │ Shared       │   │
│  └──────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────┐   │
│  │        MCP Server (Claude, Cursor, etc.)         │   │
│  └──────────────────────────────────────────────────┘   │
└──────────────────────────────────────────────────────────┘

______________________________________________________________________

⚡ 快速入门

# Install
npm install fusionpact

# Run the demo
npx fusionpact demo

# Start HTTP + MCP server
npx fusionpact serve --port 8080

# Start MCP server for Claude Desktop
npx fusionpact mcp

10行代码

const { create } = require('fusionpact');

const fp = create({ embedder: 'ollama' }); // or 'mock' for zero-config

// Ingest a document — auto-chunks, embeds, indexes
await fp.rag.ingest('Your document text here...', { source: 'doc.pdf' });

// Hybrid search — vector + reasoning + keyword, fused automatically
const results = await fp.retriever.retrieve('What safety protocols exist?', {
  collection: 'default',
  strategy: 'hybrid'
});

// Or build LLM-ready context directly
const context = await fp.rag.buildContext('What safety protocols exist?');
console.log(context.prompt); // Ready to paste into any LLM

______________________________________________________________________

🔀 混合检索引擎

核心区别：单个API通过多种检索策略智能路由查询，并使用倒数排名融合融合结果。

const { create } = require('fusionpact');

const fp = create({
  embedder: 'ollama',        // Local, free, private
  llmProvider: 'ollama',     // For tree reasoning
  enableHybrid: true
});

// Index a structured document with tree structure
await fp.treeIndex.indexDocument('annual-report', reportText, {
  format: 'markdown'
});

// Hybrid retrieval — automatically uses the best strategy
const results = await fp.retriever.retrieve(
  'What were the total deferred tax assets in Q3?',
  {
    collection: 'documents',       // Vector search here
    docId: 'annual-report',        // Tree reasoning here
    topK: 5,
    strategy: 'hybrid'            // Fuse all strategies
  }
);

// Each result includes:
// - score: Fused relevance score
// - content: Retrieved text
// - sources: Which strategies contributed { vector: 0.8, tree: 0.9, keyword: 0.3 }
// - citation: "Section 3 > Financial Data > Table 3.2.1"
// - reasoning: Full tree traversal reasoning trace

战略权重

const retriever = new HybridRetriever({
  engine, treeIndex, embedder,
  weights: {
    vector: 0.4,   // 40% weight to vector similarity
    tree: 0.4,     // 40% weight to reasoning-based retrieval
    keyword: 0.2   // 20% weight to keyword matching
  }
});

自适应学习

FusionAction学习哪种检索策略最适合不同的查询模式：

// Record feedback on result quality
retriever.recordFeedback('financial query', 'tree', 0.95);
retriever.recordFeedback('general search', 'vector', 0.85);

// Get recommended weights for a new query
const weights = retriever.getAdaptiveWeights('new financial query');
// → { vector: 0.25, tree: 0.6, keyword: 0.15 }

______________________________________________________________________

🌲 树索引

基于推理的结构化文档检索。构建一个层次树（如智能目录），并使用LLM推理导航到最相关的部分。

const { TreeIndex, LLMProvider } = require('fusionpact');

const llm = new LLMProvider({ provider: 'ollama' }); // Free, local
const tree = new TreeIndex({ llmProvider: llm });

// Index a document
await tree.indexDocument('sec-filing', filingText, {
  format: 'markdown',
  metadata: { source: '10-K', year: 2024 }
});

// Reasoning-based search
const results = await tree.search('sec-filing', 'Total deferred tax assets', {
  maxResults: 3,
  includeReasoning: true
});

// results[0]:
// {
//   content: "Table 5.2: Deferred Tax Assets...",
//   relevanceScore: 0.95,
//   citation: "Financial Statements > Note 5 > Tax Assets > Table 5.2",
//   reasoningPath: [
//     { title: "Financial Statements", reasoning: "Deferred tax assets are in financial notes", action: "explore" },
//     { title: "Note 5: Income Taxes", reasoning: "This note covers tax-related assets", action: "explore" },
//     { title: "Table 5.2", reasoning: "Contains the deferred tax asset breakdown", action: "retrieve" }
//   ]
// }

没有法学硕士也能工作

如果没有配置LLM提供程序，TreeIndex将回退到基于关键字的树遍历——仍然有用，只是没有推理路径：

const tree = new TreeIndex(); // No LLM — keyword fallback
await tree.indexDocument('doc', text, { format: 'markdown' });
const results = await tree.search('doc', 'safety protocols');

______________________________________________________________________

🧠 代理内存

专门为AI代理构建的内存系统，有四种内存类型：

const { create } = require('fusionpact');
const fp = create({ embedder: 'ollama', enableMemory: true });

// Episodic — remember what happened
await fp.memory.remember('agent-1', {
  content: 'User prefers dark mode and concise answers',
  role: 'system',
  importance: 0.8
});

// Semantic — learn knowledge
await fp.memory.learn('agent-1',
  'OSHA 29 CFR 1910 covers general industry safety standards.',
  { source: 'regulations', category: 'compliance' }
);

// Procedural — register tools
await fp.memory.registerTool('agent-1', {
  name: 'search_incidents',
  description: 'Search EHS incident reports by category and severity',
  schema: { type: 'object', properties: { severity: { type: 'string' } } }
});

// Recall — cross-memory search
const memories = await fp.memory.recall('agent-1', 'safety compliance');
// → { episodic: [...], semantic: [...], procedural: [...], shared: [...] }

// Conversation memory
fp.memory.addMessage('agent-1', 'thread-001', { role: 'user', content: 'What are the PPE requirements?' });
fp.memory.addMessage('agent-1', 'thread-001', { role: 'assistant', content: 'PPE requirements include...' });
const history = fp.memory.getConversation('agent-1', 'thread-001');

// GDPR-friendly forget
fp.memory.forget('agent-1', { type: 'all' });

______________________________________________________________________

🤖 多代理编排

通过隔离内存、共享知识和消息路由协调多个AI代理：

const { create, AgentOrchestrator } = require('fusionpact');

const fp = create({ embedder: 'ollama', enableMemory: true });
const orchestrator = new AgentOrchestrator({
  engine: fp.engine,
  memory: fp.memory,
  retriever: fp.retriever
});

// Register agents
orchestrator.registerAgent({
  agentId: 'researcher',
  name: 'Research Agent',
  role: 'Find and analyze information',
  capabilities: ['search', 'analysis', 'summarization']
});

orchestrator.registerAgent({
  agentId: 'writer',
  name: 'Writing Agent',
  role: 'Generate reports and documentation',
  capabilities: ['writing', 'formatting', 'editing']
});

// Agent-to-agent communication
await orchestrator.send({
  from: 'researcher',
  to: 'writer',
  type: 'result',
  payload: { findings: 'Safety incidents decreased 12% YoY...' }
});

// Capability-based task delegation
await orchestrator.delegate('coordinator', 'Write a safety summary report', {
  requiredCapabilities: ['writing', 'formatting']
});
// → Automatically routes to 'writer' agent

// Collaborative retrieval across all agents
const results = await orchestrator.collaborativeRecall('safety compliance');
// → Returns memories from all agents, plus shared knowledge

// Message handling
orchestrator.onMessage('writer', async (msg) => {
  console.log(`Writer received: ${msg.type} from ${msg.from}`);
  // Process task...
});

______________________________________________________________________

🔌 MCP服务器

FusionAction作为MCP（模型上下文协议）服务器提供。任何AI代理（Claude、Cursor、Windsurf）都可以将其用作持久内存，无需自定义集成。

Claude桌面设置

添加 ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "fusionpact": {
      "command": "npx",
      "args": ["fusionpact", "mcp"],
      "env": {
        "EMBEDDING_PROVIDER": "ollama"
      }
    }
  }
}

可用的MCP工具

______________________________________________________________________

📄 RAG 流程

一次通话中的端到端RAG：

const fp = require('fusionpact').create({ embedder: 'ollama' });

// Ingest — auto-chunks, embeds, indexes
await fp.rag.ingest(documentText, {
  source: 'safety-manual.pdf',
  title: 'Safety Manual 2024'
});

// Build context for any LLM
const ctx = await fp.rag.buildContext('What PPE is required?', {
  topK: 5,
  maxTokens: 4000,
  strategy: 'hybrid'  // Uses HybridRetriever if available
});

// ctx.prompt → Ready for any LLM
// ctx.sources → Source citations
// ctx.chunks → Number of chunks used

分块策略

const rag = new RAGPipeline(engine, {
  chunkStrategy: 'recursive',  // 'recursive' | 'sentence' | 'paragraph'
  chunkSize: 512,
  chunkOverlap: 50
});

______________________________________________________________________

🔒 多租户

零信任软隔离——租户永远看不到彼此的数据：

const tenantA = engine.tenant('shared-collection', 'acme_corp');
const tenantB = engine.tenant('shared-collection', 'globex_inc');

tenantA.insert([{ id: 'doc-1', vector: [...], metadata: { doc: 'Acme Plan' } }]);

// Tenant A queries — only sees Acme data. Always.
const results = tenantA.search(queryVec, { topK: 10 });

______________________________________________________________________

🔌 嵌入提供者

// Ollama (local, free, private)
const fp = create({ embedder: 'ollama' });

// OpenAI
const fp = create({ embedder: 'openai', openaiConfig: { apiKey: 'sk-...' } });

// Mock (for demos/testing — no dependencies)
const fp = create({ embedder: 'mock' });

______________________________________________________________________

📊 基准测试

HNSW性能（128D矢量）

运行自己的：

npx fusionpact bench --count 10000

______________________________________________________________________

🆚 比较

______________________________________________________________________

📖 API 参考

完整文档： docs/API.md文件

核心课程

______________________________________________________________________

🗺 路线图

• \[x\] 具有可配置M/ef参数的HNSW索引
• \[x\] 多租户，软隔离
• \[x\] 一键式RAG管道
• \[x\] Agent记忆（情景记忆、语义记忆、程序记忆、共享记忆）
• \[x\] 多代理编排
• \[x\] 树索引（基于推理的检索）
• \[x\] 混合检索器（矢量+树+关键字融合）
• \[x\] MCP服务器（标准输入+HTTP）
• \[x\] HTTP API服务器
• \[x\] Ollama+OpenAI嵌入提供商
• \[x\] 自适应检索学习
• \[\]SQLite/PPostgreSQL持久性
• \[\]Python SDK(pip install fusionpact)
• \[\]LangChain集成
• \[\]LlamaIdex集成
• \[\]CrewAI/AutoGen集成
• \[\]视觉RAG（PDF页面图像）
• \[\]Rust核心（NAPI绑定）
• \[\]FusionAction云（托管）
• \[\]仪表板用户界面

______________________________________________________________________

🤝 贡献

我们欢迎捐款！看 贡献.md 作为指导方针。

git clone https://github.com/FusionpactTech/fusionpact-vectordb.git
cd fusionpact-vectordb
npm install
npm test
npx fusionpact demo

______________________________________________________________________

📜 归因

FusionAction由以下人员构建和维护 FusionPact技术股份有限公司。

如果您在项目中使用FusionAct，请通过以下方式之一添加归因：

• 在应用程序的关于页面或文档中包含“Powered by FusionAct”
• 保持 NOTICE 分发中的文件
• 在项目确认书中参考FusionPact Technologies股份有限公司

看 属性.md 了解全部细节。

许可证

Apache 2.0 --在商业和开源项目中自由使用。

Apache 2.0许可证要求您：

1. 在任何重新分发中包含许可证副本
2. 包括归属于FusionPact Technologies股份有限公司的通知文件。
3. 说明您对代码所做的任何重大更改

______________________________________________________________________

内置于❤️ 通过 FusionPact技术股份有限公司。

⭐ 如果你觉得这个仓库有用，就把它标上！