graph-rag-knowledge-expert · diff
git:20260907.5da0f4d to git:20260911.4e0e4b5
42 added, 1 removed. Audit A to A.
---
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"
---
# 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.
- *Panduan ahli untuk implementasi Knowledge Graph dan GraphRAG untuk mengatasi kelemahan mendasar vector search murni dalam pemikiran multi-hop, deteksi relasi entitas tersembunyi, dan ringkasan global korpus.*
+ ### 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.
+ - **`search-engine-expert`**: Pengindeksan leksikal kata kunci untuk atribut simpul graf.