How to Implement RAG (Retrieval Augmented Generation) in Go

Ai Sovereignty
2026-04-17T12:05:42+00:00
Implement RAG in Go by retrieving context from a vector store and passing it to an LLM for generation.

Implement RAG in Go by chaining a vector store for retrieval with an LLM client for generation. Use the go command to manage dependencies and standard library packages like net/http for API calls.

package main

import (
	"context"
	"fmt"
	"log"
)

func main() {
	ctx := context.Background()
	query := "What is Go?"
	
	// 1. Retrieve relevant context from vector store
	context := retrieve(ctx, query)
	
	// 2. Generate answer using LLM with retrieved context
	answer := generate(ctx, query, context)
	
	fmt.Println(answer)
}

func retrieve(ctx context.Context, query string) string {
	// Replace with actual vector store logic (e.g., pgvector, chroma)
	return "Go is a statically typed, compiled programming language."
}

func generate(ctx context.Context, query, context string) string {
	// Replace with actual LLM API call (e.g., OpenAI, Anthropic)
	return fmt.Sprintf("Based on context: %s. Answer: %s", context, query)
}