git:20260911.4e0e4b5 to v3.0.0

+ ---

+ name: graph-rag-knowledge-expert

+ description: "Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop."

+ author: "Roedy Rustam"

+ version: "3.0.0"

+ ---

+

+ # GraphRAG & Knowledge Graph Expert (2026 Edition)

+

+ [English](#english) | [Bahasa Indonesia](#bahasa-indonesia)

+

+ ---

+

+ <a name="english"></a>

+ ## 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).

+

+ ---

+

+ ## 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 |

+

+ ---

+

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

+

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

+

+ ```typescript

+ // 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();

+ }

+ }

+ ```

+

+ ---

+

+ ## 3. Production Recipe: Entity & Relation Extraction (Python)

+

+ ```python

+ # graph_extractor.py - Structured Entity & Relation Extraction

+ from typing import List

+ from pydantic import BaseModel, Field

+ import instructor

+ from openai import OpenAI

+

+ client = instructor.from_openai(OpenAI())

+

+ class Entity(BaseModel):

+ name: str = Field(description="Normalized entity name, uppercase")

+ type: str = Field(description="ORGANIZATION, PERSON, TECHNOLOGY, CONCEPT, LOCATION")

+ description: str = Field(description="Summary of entity role")

+

+ class Relationship(BaseModel):

+ source_entity: str

+ target_entity: str

+ relation_type: str = Field(description="USES, DEVELOPS, OWNS, LOCATED_IN, DEPENDS_ON")

+ weight: float = Field(default=1.0, ge=0.0, le=1.0)

+ description: str

+

+ class KnowledgeGraph(BaseModel):

+ entities: List[Entity]

+ relationships: List[Relationship]

+

+ def extract_knowledge_graph(document_text: str) -> KnowledgeGraph:

+ """Extracts entities and relationships from raw text into structured schema."""

+ return client.chat.completions.create(

+ model="gpt-4o-mini",

+ response_model=KnowledgeGraph,

+ messages=[

+ {

+ "role": "system",

+ "content": (

+ "Extract all named entities and factual relationships between them. "

+ "Ensure entity names are canonicalized and relationships are directed."

+ ),

+ },

+ {"role": "user", "content": document_text},

+ ],

+ temperature=0.0,

+ )

+ ```

+

+ ---

+

+ ## 4. Microsoft GraphRAG: Hierarchical Communities

+

+ For high-level summaries ("Summarize all technical debts reported across the system"):

+ 1. **Extraction**: Chunk documents ➔ Extract Entities & Relationships.

+ 2. **Clustering**: Apply **Leiden Algorithm** to detect hierarchical communities (Level 0: Micro, Level 1: Sub-domain, Level 2: Macro domain).

+ 3. **Summarization**: LLM generates pre-computed summaries for each community cluster.

+ 4. **Global Search**: Query runs across pre-computed community summaries in parallel, eliminating the need to read millions of tokens at inference time.

+

+ ---

+

+ ## Orchestration & Integration

+

+ - **`vector-db-rag-expert`**: For hybrid dense-vector similarity search combined with graph path discovery.

+ - **`database-orm-expert`**: For maintaining transactional relational mappings alongside graph stores.

+ - **`ai-llm-integration-expert`**: Connects reasoning models to multi-hop graph context.

+ - **`search-engine-expert`**: For keyword lexical indexing of graph node attributes.

+

+ ---

+

+ <a name="bahasa-indonesia"></a>

+ ## Bahasa Indonesia

+

+ ### Deskripsi

+ Panduan ahli untuk mengimplementasikan Knowledge Graph-augmented Retrieval (GraphRAG) guna mengatasi kelemahan mendasar vector search murni: pemikiran multi-hop, penemuan relasi entitas tersembunyi, dan pemahaman korpus global. Mencakup Microsoft GraphRAG, Neo4j Text2Cypher, FalkorDB, dan pipeline pencarian hibrida Vector + Graph.

+

+ ### Kondisi Pemicu

+ - Kueri kompleks multi-hop di mana entitas saling terhubung melalui beberapa simpul perantara.

+ - Tingkat halusinasi tinggi saat menggunakan RAG vektor standar pada data relasional yang rumit.

+ - Kueri korpus global yang membutuhkan ringkasan tematik menyeluruh di ribuan dokumen.

+ - Knowledge base enterprise dengan entitas terstruktur eksplisit (organisasi, dependensi kode, regulasi).

+

+ ### Ringkasan Strategi GraphRAG

+ 1. **Mengapa GraphRAG?**: Pencarian vektor murni buta terhadap lintasan relasi multi-hop. GraphRAG menautkan entitas melalui edge terarah sehingga model dapat menavigasi dependensi secara deterministik.

+ 2. **Text2Cypher yang Aman**: Menghasilkan kueri Cypher Neo4j read-only berdasarkan schema meta yang diinjeksi secara ketat tanpa izin write (`CREATE`, `MERGE`, `DELETE`).

+ 3. **Ekstraksi Terstruktur**: Menggunakan Pydantic / Instructor untuk mengekstrak entitas dan relasi berkualifikasi tipe tinggi langsung dari teks bebas.

+ 4. **Komunitas Hirarkis (Microsoft GraphRAG)**: Algoritma Leiden untuk mengelompokkan simpul menjadi komunitas mikro hingga makro, memungkinkan kueri global tanpa membaca jutaan token saat inferensi.

+

+ ---

+

+ ## Integrasi Orkestrasi

+

+ - **`vector-db-rag-expert`**: Untuk pencarian kesamaan vektor padat hibrida yang digabungkan dengan traversal graf.

+ - **`database-orm-expert`**: Untuk pemetaan data transaksional relasional bersama penyimpanan graf.

+ - **`ai-llm-integration-expert`**: Menghubungkan model penalaran ke konteks graf multi-hop.