Knowledge Graphs and LLMs: A Powerful Combination for Enterprise AI

The Problem with Vanilla RAG

Vector search retrieves text chunks, not structured relationships. Multi-hop questions like 'Which customers bought from suppliers with quality issues in 2025?' require graph traversal. RAG retrieves vaguely related documents; knowledge graphs answer precisely.

from neo4j import GraphDatabase
import anthropic

client = anthropic.Anthropic()
driver = GraphDatabase.driver('bolt://localhost:7687', auth=('neo4j', 'password'))

def query_and_explain(question: str, schema: str) -> str:
    cypher_resp = client.messages.create(
        model='claude-sonnet-4-6', max_tokens=512,
        system=f'Convert to Cypher for Neo4j. Schema: {schema}',
        messages=[{'role': 'user', 'content': question}]
    )
    cypher = cypher_resp.content[0].text
    with driver.session() as s:
        results = s.run(cypher).data()
    explain_resp = client.messages.create(
        model='claude-sonnet-4-6', max_tokens=1024,
        messages=[{'role': 'user', 'content': f'Q: {question}\nResults: {results}\nExplain in plain English.'}]
    )
    return explain_resp.content[0].text

Enterprise Use Cases

• Compliance reasoning: which regulations apply to this transaction type in this jurisdiction
• Supply chain analysis: multi-hop queries across supplier and distributor networks
• HR and org chart queries: reporting relationships and performance metrics
• Product catalog: hierarchical taxonomies with attribute inheritance