Introduction to Vector Search and Vector Databases with Go

Modern AI applications require more than traditional keyword search; they need to understand the meaning behind user queries. Vector search enables semantic search by representing text as numerical vectors, allowing your Go applications to find highly relevant, context-aware results. In this text-based course, you will transition from traditional databases to modern vector databases. You will understand the core mathematical concepts of embeddings, configure popular vector stores, and write Go code to query and retrieve semantically similar data for AI applications. What you'll learn: Understand the fundamentals of embeddings, vector spaces, and similarity metrics; Implement vector search workflows in Go using popular libraries and APIs; Configure and query vector databases including pgvector and Pinecone; Apply retrieval-augmented generation (RAG) patterns to enhance AI-driven responses; Optimize query performance and manage high-dimensional vector data efficiently. The course starts with foundational definitions of vector space and embeddings before guiding you through hands-on Go implementations. You will read clear explanations, analyze code snippets, and complete written exercises to solidify your understanding of modern semantic search. This course is designed for Go developers and software engineers who are new to vector search and AI engineering. No prior experience with machine learning or vector databases is required. Start reading today to unlock the power of semantic search in your Go applications.