Building LLM-powered applications in Go

As Large Language Models (LLMs) and embedding models improve, more developers are integrating LLMs into their applications. Go excels in building LLM-powered applications due to its support for REST/RPC protocols, concurrency, and performance. This post demonstrates creating a Retrieval Augmented Generation (RAG) server in Go, which uses HTTP endpoints to add documents to a knowledge base and answer user questions. It explores implementing this with tools like Google Gemini API, Weaviate, LangChainGo, and Genkit for Go, highlighting Go's strengths in cloud-native application development.