Building Vector Stores with Neo4j and LangChain

Modern AI applications require reliable context retrieval to generate accurate, factual responses. By combining graph databases with vector embeddings, you can build powerful search pipelines that connect semantically related information and unstructured knowledge. This course guides you step by step through structuring and querying data using Neo4j as a vector store. You will learn how to extract text from external knowledge sources, generate embeddings with OpenAI models, index them in Neo4j, and orchestrate intelligent retrieval flows with LangChain. What you'll learn: - Understand the core concepts of vector embeddings, similarity search, and hybrid graph retrieval - Configure Neo4j vector indexes to store and search high-dimensional embeddings - Connect OpenAI embedding models and language models using LangChain components - Ingest and chunk multilingual documents, including Wikipedia articles, for indexing - Construct retrieval-augmented generation (RAG) chains to answer queries with precise context - Apply best practices for evaluating query accuracy and optimizing vector retrieval performance You will begin with essential terminology and vector search fundamentals before advancing through hands-on text-based examples and practical implementation patterns. This course is designed for developers, data practitioners, and AI enthusiasts who want to integrate graph-powered vector search into their applications. Start reading today to enhance your AI retrieval capabilities.