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GraphRAG - Next-Generation RAG with Knowledge Graphs

RAG Published: 2025年11月25日 Updated: 2026年01月04日
GraphRAG Knowledge Graph RAG Neo4j Entity Extraction

What is GraphRAG?

GraphRAG combines Knowledge Graphs with Retrieval-Augmented Generation to enable deeper understanding of entity relationships beyond simple semantic similarity.

How GraphRAG Works

1. Knowledge Graph Construction

# Extract entities and relationships
entities = extract_entities(documents)
relationships = extract_relationships(entities)

# Build graph
graph = build_knowledge_graph(entities, relationships)

2. Query Processing

# Parse query for entities
query_entities = extract_entities(query)

# Traverse graph
related_entities = graph.traverse(query_entities, depth=2)

# Retrieve context
context = retrieve_documents(related_entities)

3. Generation

Combine graph context with LLM for answer generation.

GraphRAG vs Standard RAG

FeatureStandard RAGGraphRAG
SearchVector similarityGraph traversal
RelationshipsImplicitExplicit
Multi-hopLimitedNative support
Use CaseDocument QAComplex reasoning

Implementation with Neo4j

from neo4j import GraphDatabase

# Connect to Neo4j
driver = GraphDatabase.driver(uri, auth=(user, password))

# Query knowledge graph
with driver.session() as session:
    result = session.run("""
        MATCH (p:Person)-[:WORKS_AT]->(c:Company)
        WHERE c.name = $company
        RETURN p.name
    """, company="OpenAI")

Use Cases

  • Medical Research: Drug interactions, disease pathways
  • Legal Analysis: Case precedents, jurisdiction relationships
  • Financial Analysis: Company ownership, market influences
  • Recommendation Systems: Product relationships, user preferences

🛠 Key Tools

ToolPurposeLink
Neo4jGraph DatabaseDetails
LangChainGraph IntegrationDetails
spaCyEntity ExtractionDetails

FAQ

Q1: What is the difference between GraphRAG and standard RAG?

Standard RAG uses vector similarity, GraphRAG uses entity relationships in knowledge graphs.

Q2: When should I use GraphRAG?

Use when you need to understand relationships between entities or perform multi-hop reasoning.

Q3: What are the main challenges?

Building and maintaining knowledge graphs requires significant effort.

Summary

GraphRAG excels at complex reasoning tasks requiring understanding of entity relationships. While more complex to implement than standard RAG, it enables powerful applications in domains like medicine, law, and finance.

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