skills.rest
latestaiagents
latestaiagentsOFFICIAL
Explore all skills by this author →
GitHub

graphrag-patterns

Combine knowledge graphs with hybrid retrieval for relationship-aware querying.

5|1|Updated Feb 4, 2026
Search Tags:#knowledge graph#rag#retrieval#reasoning#neo4j#entity extraction#graphrag
One-click install
npx skills add https://github.com/latestaiagents/agent-skills --skill graphrag-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graphrag-patterns
Source: https://github.com/latestaiagents/agent-skills/tree/main/plugins/rag-architect/skills/graphrag-patterns
Command: npx skills add https://github.com/latestaiagents/agent-skills --skill graphrag-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain-openai, langchain-community, neo4j, graspologic, networkx, json, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of performing complex reasoning over interconnected data by integrating knowledge graphs with retrieval-augmented generation (RAG). It enables more sophisticated querying and understanding of relationships within data.

Core Features & Use Cases

  • GraphRAG Architecture: Implements hybrid retrieval combining vector embeddings and knowledge graphs.
  • Entity & Relationship Extraction: Tools to build knowledge graphs from text.
  • Retrieval Strategies: Offers entity-centric, path-based, and community-based retrieval methods.
  • Use Case: When analyzing a large corpus of research papers, use this skill to identify how different concepts or researchers are related, enabling multi-hop reasoning and discovery of indirect connections.

Quick Start

Use the graphrag-patterns skill to retrieve information about entities and their relationships from a knowledge graph.

Frequently Asked Questions about graphrag-patterns

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

FAQPage Schema
How do I combine knowledge graphs with RAG for complex reasoning over interconnected data?▼

To combine knowledge graphs with RAG, you implement hybrid retrieval strategies using entity extraction and graph storage in Neo4j. This enables multi-hop reasoning by understanding structured and unstructured data connections for relationship-aware querying.

What is the best way to perform multi-hop reasoning over a large corpus of research papers?▼

The best way to perform multi-hop reasoning over research papers is building a knowledge graph from the text. By extracting entities and relationships, you can discover indirect connections between concepts and researchers using path-based retrieval methods.

Can I use Neo4j with LangChain for entity extraction and graph storage?▼

Yes, you can use Neo4j with LangChain for entity extraction and graph storage. This combination supports GraphRAG architectures by storing extracted entities in Neo4j and utilizing LangChain for hybrid retrieval combining vector embeddings with graph data.

What retrieval strategies are available for relationship-aware querying in a GraphRAG architecture?▼

Retrieval strategies available for relationship-aware querying include entity-centric, path-based, and community-based retrieval methods. These strategies enable complex reasoning by leveraging vector embeddings alongside interconnected graph data stored in Neo4j.

Does graphrag-patterns require specific Python libraries for network analysis and entity extraction?▼

Yes, graphrag-patterns requires specific Python libraries including langchain-openai, neo4j, graspologic, and networkx. These dependencies facilitate entity extraction, graph storage, network analysis, and hybrid retrieval for processing interconnected data.

When should I not use a standard vector RAG approach instead of knowledge graph retrieval?▼

You should not use standard vector RAG when your queries require multi-hop reasoning or understanding indirect connections between entities. Knowledge graph retrieval is necessary for complex reasoning over interconnected structured and unstructured data relationships.

Related Skills

Explore More Data & Analytics Skills→
Renzo-TognellaRenzo-Tognella

Knowledge Graph Construction

Construct and maintain knowledge graphs with entity extraction and community detection.

Community
Advanced
BarrettLoweBarrettLowe

graphify

Convert code, documents, papers, and images into structured knowledge graphs with community detection.

Community
Advanced
bmaddevelopment-stackbmaddevelopment-stack

hybrid_memory

Combines vector search with graph traversal for analyzing codebases.

Community
Advanced
LGuguiLGugui

AgentDB Vector Search

Implement semantic vector search with AgentDB for document retrieval.

Community
Advanced