🕸️ Graph RAG：用知识图谱给AI装上GPS

// 从文档中提取实体和关系
const { EntityExtractor } = require('./entity-extractor');
const { GraphStore } = require('./graph-store');

async function buildKnowledgeGraph(documents) {
    const graph = new GraphStore();
    
    for (const doc of documents) {
        // 提取实体
        const entities = await EntityExtractor.extract(doc.content);
        
        // 提取关系
        const relations = await EntityExtractor.extractRelations(doc.content, entities);
        
        // 存入图谱
        await graph.addEntities(entities);
        await graph.addRelations(relations);
    }
    
    return graph;
}

// Graph RAG 查询实现
async function graphRAGQuery(query, vectorStore, graphStore) {
    // 并行执行向量检索和图谱检索
    const [vectorResults, graphResults] = await Promise.all([
        vectorStore.search(query, { topK: 5 }),
        graphStore.search(query, { maxHops: 2 })
    ]);
    
    // 融合结果
    const fusedResults = fuseResults(vectorResults, graphResults);
    
    // 组装上下文
    const context = buildContext(fusedResults);
    
    // 生成回答
    return await generateAnswer(query, context);
}

# SKILL.md - Graph RAG Skill
name: graph-rag
description: 使用知识图谱增强RAG检索

tools:
  - name: graph_query
    description: 查询知识图谱
    parameters:
      query: string
      max_hops: number
      
  - name: vector_search
    description: 向量相似度搜索
    parameters:
      query: string
      top_k: number

workflow:
  - step: 解析用户查询
    action: extract_entities
  - step: 并行检索
    action: parallel_search
    targets: [graph, vector]
  - step: 融合结果
    action: fuse_and_rank
  - step: 生成回答
    action: generate_with_context