Building a Movie Recommendation Engine with Go and Redis

Discover how modern recommendation engines understand user intent beyond simple keyword matching. This text-based course guides you through building a semantic movie recommendation service using Go and Redis vector search. You will transition from understanding basic recommendation concepts to developing a fully functioning semantic search service. You will learn how to generate vector embeddings, store them in Redis, and query them efficiently using LangChainGo. What you'll learn: - Understand the foundational concepts of vector embeddings and semantic search - Configure Redis as a vector database to store and index high-dimensional data - Generate text embeddings from movie metadata using modern AI models - Write clean Go code to connect to Redis and execute vector similarity queries - Utilize LangChainGo to orchestrate the recommendation workflow - Apply best practices for structuring Go applications and handling API responses The course starts with essential terminology, introducing vector databases and embeddings before moving into step-by-step Go code implementation. You will read through practical explanations and analyze real-world code snippets to build your service. This course is designed for backend developers and Go programmers who want to learn vector search fundamentals. No prior experience with AI or vector databases is required. Start building intelligent Go applications today.