Neo4j Knowledge Graphs and Semantic Search with LangChain

Modern AI applications require structured context to deliver accurate, hallucination-free insights. This text-based course guides you step by step through creating intelligent knowledge graphs, extracting entities and topics, and powering semantic search using Neo4j and LangChain. You will start with core terminology and the fundamentals of graph databases before moving into practical text-driven workflows. By exploring Cypher querying, entity extraction, and retrieval-augmented generation patterns, you will understand how to connect unstructured text to interconnected graph models. What you'll learn: - Understand graph database fundamentals and core Cypher query syntax - Model and store structured relationships and entities in Neo4j - Extract topics and entities from raw text using LangChain and modern language models - Implement semantic search workflows combining vector indexes and graph traversal - Build retrieval-augmented generation (Graph RAG) query pipelines - Apply best practices for data validation and schema design in graph-driven AI The course begins with fundamental concepts and definitions, gradually progressing through practical implementation patterns and structured coding exercises. It is designed for developers, data practitioners, and AI enthusiasts who want to master graph-based retrieval without prior graph database experience. Start reading today to add powerful knowledge graph capabilities to your AI toolkit.