Semantic Search with Pinecone and LangChain in Go

Modern applications need to understand the meaning behind user data, not just match exact keywords. By leveraging vector databases, you can build powerful semantic search and recommendation systems directly in Go. This text-based course guides you through the fundamentals of vector embeddings, setting up Pinecone, and writing clean Go code using LangChainGo to create an intelligent movie recommendation service. What you'll learn: - Understand the foundational concepts of vector embeddings and semantic search. - Configure Pinecone as a high-performance vector store for your application data. - Implement LangChainGo to orchestrate data ingestion and embedding generation. - Build a semantic recommendation engine using structured data patterns. - Apply modern Go practices, including context propagation and Go modules, to your AI workflows. - Query and filter vector indices efficiently to deliver fast, relevant results. You will start with core terminology, exploring how machines represent meaning through high-dimensional vectors. From there, you will progress to practical integration, reading through clear code explanations to connect your Go services with Pinecone and LangChainGo. This course is designed for Go developers who are new to AI and vector databases. No prior machine learning background is required to succeed. Start reading today to bring semantic intelligence to your Go applications.