NLP Question Answering: Answer questions with a local LLM and a vector database on your own embedded data

A Deep Dive into Building an Advanced Q&A System on documents using a simple Retrieval Augmented Generation (RAG) Architecture.

I’ll break down RAGs for you, diving into their architectural nitty-gritty. Plus, my articles will as always have some code snippets to get you up and running.

Question Answering with RAG (DALLE 3)

What is Retrieval Augmented Generation?

In simple terms, RAG is a computer system that combines the power of finding information in any documents with the skill of creating human-like text.

It’s like having a pair of really smart and creative friends helping you out when you need to know or write about something.

In the next sections I will delve further into the topic to provide a more comprehensive explanation.

Retrieval Augmented Generation (RAG)

The RAG (Retrieval-Augmented Generation) architecture is a revolutionary approach in natural language processing, specifically designed for question-answering systems. It combines the strengths of both retrieval-based and generation-based models (LLM) to…