Neo4j: Cypher, GDS, GraphQL, LLM, Knowledge Graphs for RAG.

Format:

Video

Language:

English

Physical Description:

1 online resource (155 minutes)

Place of Publication:

[Place of publication not identified] : PACKT Publishing, 2025.

Language Note:

In English.

System Details:

video file

Summary:

<b>Unlock the power of Neo4j with this in-depth course covering Cypher query language, Graph Data Science (GDS) library, GraphQL integration, and building knowledge graphs using Large Language Models (LLMs). Learn hands-on techniques to apply graph databases for cutting-edge AI and Retrieval-Augmented Generation (RAG) applications.</b><h4>Key Features</h4><ul><li>Detailed introduction to Neo4j architecture and graph database principles</li><li>Step-by-step Cypher query language tutorials with practical labs</li><li>In-depth exploration of Graph Data Science algorithms for network analysis</li></ul><h4>Book Description</h4>This course offers a complete journey through Neo4j, starting with graph database fundamentals and the property graph model. You will master Cypher, Neo4j’s powerful query language, through hands-on labs covering filtering, aggregation, and advanced queries like MERGE and shortest path. Next, you’ll explore the Graph Data Science (GDS) library, applying algorithms such as centrality, community detection, and node similarity to uncover deep insights in complex networks. The course also introduces GraphQL integration to build flexible APIs that simplify querying and mutating graph data. In advanced modules, you will build knowledge graphs from unstructured data using Large Language Models (LLMs), supported by Python scripting and cloud tools like Google Colab. You’ll learn to set up Neo4j environments, install key plugins, and optimize query performance for production use. Finally, you’ll explore emerging AI trends with Retrieval-Augmented Generation (RAG) and GraphRAG techniques, enabling intelligent retrieval and content generation powered by knowledge graphs. This comprehensive approach combines theory, practical labs, and real-world use cases to prepare you for leveraging Neo4j in modern data and AI-driven applications.<h4>What you will learn</h4><ul><li>Understand and implement property graph data models in Neo4j</li><li>Write complex Cypher queries to manipulate and extract graph data efficiently</li><li>Apply graph algorithms for insights using the Graph Data Science library</li><li>Develop GraphQL schemas and queries to interface with Neo4j data</li><li>Build knowledge graphs from unstructured data leveraging LLMs</li><li>Integrate knowledge graphs with AI workflows using RAG techniques</li></ul><h4>Who this book is for</h4>This course is designed for software engineers, data scientists, database administrators, and AI practitioners eager to master graph database technologies. Prior experience with databases and basic programming knowledge (preferably Python) is recommended to maximize learning outcomes. No prior Neo4j or graph database experience is required, as the course builds knowledge progressively from fundamentals to advanced AI applications.

Notes:

Title from resource description page (viewed July 20, 2026).