Building Vector Search Apps with PostgreSQL and LangChain in Go

Modern AI applications require efficient ways to store and search high-dimensional vector embeddings. PostgreSQL, enhanced with the pgvector extension, offers a robust and familiar relational environment to power these semantic search capabilities. By combining PostgreSQL with the LangChainGo framework, you will learn how to bridge the gap between relational databases and artificial intelligence using Go, gaining the skills to build, query, and manage a vector database for tasks like semantic movie recommendations and content discovery. What you'll learn: - Understand the fundamentals of vector embeddings, semantic search, and retrieval-augmented generation (RAG). - Configure PostgreSQL with the pgvector extension to store high-dimensional vectors alongside relational data. - Generate vector embeddings using modern embedding models integrated with Go. - Implement similarity search queries using cosine distance and other distance metrics in SQL. - Connect LangChainGo to your PostgreSQL vector store to manage document loading and indexing. - Build a practical semantic search system for recommendations using clean, idiomatic Go code. This text-only course begins with foundational definitions of vector databases and embedding models before diving into step-by-step SQL configurations and Go implementation patterns. You will progress from writing basic vector queries to structuring a complete semantic search workflow. This course is designed for Go developers and backend engineers who want to introduce AI-driven search capabilities to their applications. No prior experience with vector databases or AI frameworks is required. Start reading today to unlock the potential of vector-enabled databases in your Go projects.