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graph-rag

Build and query Neo4j knowledge graphs with SPARQL for RAG systems.

4|1|Updated Jan 7, 2026
Search Tags:#knowledge graph#rag#neo4j#sparql#graph embedding#hybrid retrieval
One-click install
npx skills add https://github.com/an8079/take-skills --skill graph-rag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graph-rag
Source: https://github.com/an8079/take-skills/tree/main/skills/graph-rag
Command: npx skills add https://github.com/an8079/take-skills --skill graph-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires neo4j, torch, faiss-cpu, transformers, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of extracting and utilizing complex relationships within data by building and querying knowledge graphs, enhancing information retrieval through advanced RAG techniques.

Core Features & Use Cases

  • Knowledge Graph Construction: Build and manage knowledge graphs using tools like Neo4j.
  • RAG Enhancement: Integrate knowledge graphs into Retrieval Augmented Generation systems for more context-aware responses.
  • Hybrid Search: Implement search strategies that combine vector embeddings with graph traversal.
  • Use Case: Enhance a customer support chatbot by connecting user queries to a knowledge graph of product features and troubleshooting steps, providing more accurate and detailed answers.

Quick Start

Use the graph-rag skill to build a knowledge graph from the provided product data.

Frequently Asked Questions about graph-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a knowledge graph for RAG to improve context-aware retrieval?▼

You can build a knowledge graph for RAG by structuring domain-specific data into Neo4j, enabling semantic search and complex relationship analysis to provide more context-aware generation. This approach extracts complex relationships to enhance information retrieval.

Do I need Neo4j and SPARQL to implement a knowledge graph for semantic search?▼

Yes, implementing this knowledge graph RAG system requires Neo4j for graph storage and SPARQL for querying. You also need Python libraries like torch, faiss-cpu, and transformers to execute graph embedding and hybrid retrieval operations.

How do I query a knowledge graph using SPARQL for domain-specific knowledge representation?▼

You can query a knowledge graph using SPARQL to extract complex data relationships for domain-specific knowledge representation. This allows the RAG system to perform semantic search and analyze complex data relationships during retrieval.

Can I use Python transformers and FAISS for graph embedding in a RAG system?▼

Yes, you can use Python libraries including transformers and faiss-cpu for graph embedding and hybrid retrieval within your RAG system. These dependencies work alongside Neo4j to enable vector embeddings combined with graph traversal for enhanced information retrieval.

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