Congratulations!

You’ve completed the GraphRAG Introduction workshop!

In this hands-on workshop, you’ve learned the fundamentals of GraphRAG - a technique that combines graph databases with generative AI to enhance LLM-generated content. You’ve explored how GraphRAG addresses limitations of traditional vector-based RAG by providing additional context through graph relationships and structured data.

What you’ve learned

You’ve gained practical experience with:

GraphRAG fundamentals - Understanding how knowledge graphs enhance retrieval-augmented generation
Knowledge graph construction - Extracting structured information from unstructured data and storing it effectively
Three types of retrievers - Vector, Vector + Cypher, and Text2Cypher retrievers, each with specific strengths and use cases
Real-world applications - Working with financial documents and exploring practical GraphRAG implementations

You now have the foundational knowledge and hands-on experience needed to start building GraphRAG applications that use graph databases to enhance AI responses.

Deepen Your Graph Database Knowledge

Continue learning with additional courses on GraphAcademy that build upon what you’ve learned:

Neo4j Fundamentals - Learn the core concepts of Neo4j, including database architecture, data modeling principles, and graph database design practices
Cypher Fundamentals - Learn Cypher, Neo4j’s query language, covering basic pattern matching through advanced query optimization
Graph Data Modeling Fundamentals - Learn how to design graph schemas, model relationships, and optimize graph structure for performance
Importing Data Fundamentals - Learn techniques for loading data from various sources into Neo4j, including CSV files, APIs, and data streams

Advanced GraphRAG Topics

Continue your GraphRAG journey with specialized courses:

Building Knowledge Graphs - Learn techniques for constructing knowledge graphs from data sources using LLMs and NLP pipelines
Using Neo4j with LangChain - Build conversational AI applications that use Neo4j’s graph capabilities through LangChain integrations
Vectors and Unstructured Data - Learn about vector embeddings, semantic search, and hybrid search strategies combining vector similarity with graph traversal

View the list of GraphRAG courses.

Start Building with Neo4j

Get hands-on experience with your own Neo4j environment.

You can use Neo4j Aura, Neo4j’s managed cloud service, which offers a free tier that includes up to 200,000 nodes and 500,000 relationships. With Aura, there is no setup required, allowing you to deploy instances quickly. It also provides automatic scaling, backups, and security updates, making it suitable for both prototyping and production applications.

Alternatively, you can use Neo4j Desktop, a local Neo4j development environment. Neo4j Desktop provides graph visualization tools, supports multiple database instances, and includes built-in monitoring and performance analysis tools. This makes it ideal for development, testing, and learning.

Neo4j & Model Context Protocol (MCP)

Enhance your development workflow with AI-powered tools. The course Developing with Neo4j MCP Tools teaches you how to use the Model Context Protocol to create intelligent AI applications with Neo4j’s MCP server and tools for natural language database interaction.

The Neo4j MCP Server enables AI assistants like GitHub Copilot and Claude to interact with your Neo4j database using natural language. With MCP Server, you can query your database using conversational language, generate Cypher queries from descriptions, explore graph schemas and relationships, and build GraphRAG applications with AI assistance.