graph-rag-knowledge-expert

GraphRAG & Knowledge Graph Expert (2026 Edition)

English | Bahasa Indonesia

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English

Description

Expert guide for implementing Knowledge Graph-augmented Retrieval (GraphRAG), solving the fatal weaknesses of vector search: multi-hop reasoning, relationship discovery, and global corpus understanding. Covers Microsoft GraphRAG, Neo4j Text2Cypher, FalkorDB, and hybrid Vector + Graph retrieval pipelines.

Trigger Conditions

• Complex multi-hop queries where entities are linked through multiple intermediate nodes.
• High hallucination rate using standard vector RAG on interconnected data.
• Global corpus queries requiring domain-wide thematic summarization across thousands of documents.
• Enterprise knowledge bases containing explicitly structured relational entities (e.g., organizations, code dependencies, regulatory rules).

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1. Why GraphRAG Over Pure Vector Search?

Capability	Pure Vector Search (RAG)	GraphRAG (Graph + Vector)
Direct Similarity ("What is X?")	🟢 Fast, accurate	🟢 High accuracy
Multi-Hop Traversal ("How does X affect Z via Y?")	🔴 Blind (returns fragmented chunks)	🟢 Explores interconnected graph edges
Global Corpus Query ("What are the main themes across all documents?")	🔴 Fails (limited to Top-K chunks)	🟢 Hierarchical Community Summaries
Hallucination Rate on Complex Queries	🔴 Moderate to High (context stitching)	🟢 Grounded in explicit knowledge edges

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2. Production Recipe: Text2Cypher Knowledge Graph Querying (TypeScript)

Using Neo4j with deterministic schema introspection, preventing arbitrary syntax hallucinations.

// text2cypher.ts - Safe Neo4j Query Generation & Execution
import neo4j, { Driver } from 'neo4j-driver';
import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';

export class GraphRAGService {
  private driver: Driver;

  constructor(uri: string, user: string, pass: string) {
    this.driver = neo4j.driver(uri, neo4j.auth.basic(user, pass));
  }

  // 1. Fetch live Graph Schema to ground the LLM
  private async getGraphSchema(): Promise<string> {
    const session = this.driver.session();
    try {
      const result = await session.run(`
        CALL apoc.meta.schema() YIELD value
        RETURN value
      `);
      return JSON.stringify(result.records[0]?.get('value') || {});
    } finally {
      await session.close();
    }
  }

  // 2. Synthesize strict read-only Cypher query
  public async queryGraph(userQuestion: string): Promise<any[]> {
    const schema = await this.getGraphSchema();

    const { text: cypherQuery } = await generateText({
      model: openai('gpt-4o-mini'),
      system: `
        You are an expert Neo4j Cypher generator.
        Generate ONLY valid, read-only CYPHER queries based on this schema:
        ${schema}

        Rules:
        - Never generate CREATE, MERGE, DELETE, or SET statements.
        - Always use parameterization where appropriate.
        - Output ONLY the raw Cypher query, without markdown or backticks.
      `,
      prompt: `Translate this question into Cypher: ${userQuestion}`,
    });

    const sanitizedCypher = cypherQuery.trim().replace(/^```cypher|```$/g, '');

    // 3. Execute with read-only transaction
    const session = this.driver.session({ defaultAccessMode: neo4j.session.READ });
    try {
      const res = await session.run(sanitizedCypher);
      return res.records.map((r) => r.toObject());
    } finally {
      await session.close();
    }
  }

  public async close(): Promise<void> {
    await this.driver.close();
  }
}