Description
Building a RAG application in Go (Golang) is a course using the Go programming language and modern AI technologies published by Udemy Online Academy. This course teaches developers how to build Retrieval-Augmented Generation (RAG) applications using the Go programming language and modern AI technologies. Individuals learn how to combine large language models (LLMs) with external knowledge sources to build intelligent systems that are capable of providing accurate, context-aware answers. In this hands-on course, you will build a complete and comprehensive Retrieval-Augmented Generation system from scratch using the Go programming language.
This course covers the complete RAG workflow, including document retrieval, text preprocessing, embedding generation, vector database integration, semantic search, retrieval strategies, and answer generation. Learners will also explore how to use Go’s performance, concurrency, and scalability features to build efficient AI-powered applications. By the end of the course, you will have an application that includes a streaming terminal chat REPL, a browser-based chat UI with server-sent events in the form of tokens, file and image uploads, a background file system watcher that automatically fetches documents, an evaluation harness that evaluates retrieval quality, and a Postgres + pgvector backend running in Docker.
What you will learn in Building a RAG application in Go (Golang):
- How a RAG pipeline actually works from start to finish: segmentation, embedding, vector search, query rewriting, context injection, and stream generation
- How to design Go interfaces so that LLM, embedder, and vector storage are interchangeable without touching the rest of the codebase
- How to move LLM tokens to a terminal and to a browser with server-sent events
- How to run everything in OpenAI, Ollama, LM Studio, or Groq – and how to mix and match (e.g. hosted chat with local embeds)
- How to use Postgres + pgvector for production-level vector search, including HNSW indexes and embed dimension migrations
- How to fetch documents reactively with fsnotify, unblocking half-written files, and idempotent re-fetching
- How to handle multimodal content: image upload, vision model captioning, and image rendering in chat
- And…
Course specifications
Publisher: Udemy
Instructors: Trevor Sawler
Language: English
Level: Intermediate
Number of Lessons: 45
Duration: 8 hours and 33 minutes
Course topics
Building a RAG application in Go (Golang) Prerequisites
A basic understanding of the Go programming language
Comfortable reading and writing basic Go (functions, structs, interfaces, goroutines)
Docker installed locally (for Postgres + pgvector)
An OpenAI API key or a local model runner like Ollama — the course works with either, and shows you how to switch
No prior RAG, ML, or vector database experience required
Pictures
Building a RAG application in Go (Golang) introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
4.8 GB
