Neo4j: Cypher, GDS, GraphQL, LLM, Knowledge Graphs for RAG: Master Neo4j, Cypher, Graph Data Science, GraphQL, and AI-powered knowledge graphs for RAG

Nitin Gujral

$64.99

Video Jun 2025 1hr 40mins 1st Edition

Nitin Gujral

$64.99

Video Jun 2025 1hr 40mins 1st Edition

What do you get with a video?

Download this video in MP4 format

DRM FREE - Read whenever, wherever and however you want

Download Code

Key benefits

Detailed introduction to Neo4j architecture and graph database principles
Step-by-step Cypher query language tutorials with practical labs
In-depth exploration of Graph Data Science algorithms for network analysis

Description

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.

Who is this book for?

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.