Go RAG Implementation: Vector Search and Embeddings in 2025 introduces modern techniques for integrating retrieval-augmented generation systems using the Go programming language. As large language models increasingly rely on external knowledge, efficient vector search and embedding generation are essential for accurate and scalable RAG pipelines. This article examines the technical stack, Go-based vector search implementations, and best practices for deploying robust RAG systems. Familiarity with Go and basic concepts in machine learning and vector databases is assumed. Current RAG Tech Stack Overvie The Retrieval-Augmented Generation (RAG) pipeline has evolved significantly in 2025, with advancements in each stage from chunking to orchestration. Modern implementations leverage a combination of best-in-class tools, frameworks, and models to optimize performance, scalability, and accuracy. Chunking and Document Loadin Text chunking remains a foundational step in RAG...