# RAG in Go: A Vulnerability Research Tool

DevFeed: [RAG in Go: A Vulnerability Research Tool](<https://devfeed.tech/articles/rag-in-go-a-vulnerability-research-tool-22285.md>)

Original publisher: [Read original article](<https://www.ardanlabs.com/blog/2026/04/rag-in-go-a-vulnerability-research-tool/>)

Published: 2026-04-20T00:00:00Z

Content type: tutorial

Language: en

Sources: [William Kennedy](<https://devfeed.tech/sources/william-kennedy.md>)

Topics: [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>), [vulnerability](<https://devfeed.tech/topics/vulnerability.md>), [DuckDB](<https://devfeed.tech/topics/duckdb.md>), [JSON](<https://devfeed.tech/topics/json.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [ardan-labs](<https://devfeed.tech/tags/ardan-labs.md>), [blog](<https://devfeed.tech/tags/blog.md>), [code](<https://devfeed.tech/tags/code.md>), [database](<https://devfeed.tech/tags/database.md>), [duckdb](<https://devfeed.tech/tags/duckdb.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [go](<https://devfeed.tech/tags/go.md>), [go-programming](<https://devfeed.tech/tags/go-programming.md>), [golang](<https://devfeed.tech/tags/golang.md>), [json](<https://devfeed.tech/tags/json.md>), [llm](<https://devfeed.tech/tags/llm.md>), [programming](<https://devfeed.tech/tags/programming.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [technical](<https://devfeed.tech/tags/technical.md>), [vulnerability](<https://devfeed.tech/tags/vulnerability.md>)

## AI overview

This tutorial demonstrates how to build a retrieval-augmented generation tool in Go using the Go Vulnerability Database as internal documents. It covers ingesting zipped JSON data, generating vector embeddings, storing them in DuckDB, and retrieving documents relevant to a user query.

## Source excerpt

Introduction In the previous post, you saw how you can use tools to add information to an LLM query. In this post, we'll see another method of adding information to an LLM called RAG, or Retrieval-Augmented Generation. The idea of RAG is that you want the LLM to have access to information that wasn't available to it when it was initially trained. You do it by storing documents in your own database along with their embedding. I won't go into the technical details of embedding, but think of it as a way to convert a piece of text into a vector. The magic is that if two pieces of text have similar meaning, an embedding model can create vectors that mathematically show they are similar.