Made in RAG (Local LLM Q&A System) With Docker + ollama + R2R

Build a local LLM with RAG-based Q&A system in just 2 hours of practice using Docker + ollama + R2R framework. (Applicable for internal/in-house Q&A systems, personal portfolios, AI-enhanced work productivity, and even commercial services)

(5.0) 3 reviews

38 learners

Level Basic

Course period Unlimited

Docker
Docker
AI
AI
wsl
wsl
LLM
LLM
Docker
Docker
AI
AI
wsl
wsl
LLM
LLM

Reviews from Early Learners

5.0

5.0

최성동

43% enrolled

The explanation is easy and well understood

What you will gain after the course

  • You will learn how to run LLMs locally.

  • Implementing the AI Q&A system I've only heard about on a local machine.

  • Build an AI Q&A system based on specific domain knowledge.

  • You will learn how to build a Docker-based AI system.

  • You will learn how to utilize AI open-source projects.

  • We will be implementing a local (or generative AI API-based) LLM-powered RAG system for internal/in-house use.

  • You will take a step closer to implementing an AI system that once felt far out of reach.

  • You will learn about 'ollama', the ultimate LLM open framework.

I want to try building an LLM-based Q&A system... but how do I make it?

AI technology development, which feels infinitely difficult!

1) We invite you to the world of AI with a local LLM Q&A system using Docker, ollama, and the R2R framework!

2) From quickly learning AI infrastructure technology with a light step to utilizing open-source projects applicable to real-world tasks!

3) You can build an On-premise system without worrying about security, even for internal/in-house use!

Features of this course

📌 After taking the course, you can immediately build in-house Local and commercial LLM-based RAG systems that can be used in practice!

📌 You can develop a sense for artificial intelligence (AI) related open-source projects.

📌 You will gain opportunities to develop and utilize AI technology, which may have previously felt out of reach.

📌 You will experience infrastructure and operating system concepts such as Docker and WSL, which are essential for Artificial Intelligence (AI) technology infrastructure.

Recommended for these types of people

I want to try utilizing AI and artificial intelligence technology within my company.

Programmers who hear about AI utilization everywhere and want to try it out, but don't know where to start!

I want to add AI experience to my programmer portfolio~
Programmers and job seekers who are secretly afraid because they have never used related technologies in the AI era!

I'm curious about AI-related open-source projects!
Developers who are curious about how to use relevant open-source projects in the AI era!

After taking this course,

  • Before you know it, you will find yourself having taken a step forward in utilizing AI technology.

  • You will make a name for yourself as the developer who built the AI system within the company.

  • You will find yourself surprised, thinking, "Was AI development really this close to me!?"

  • You will gain a much deeper understanding of the concepts of Artificial Intelligence and AI.

  • You will learn about infrastructure concepts and usage methods such as WSL and Docker.

  • You will learn about AI Q&A systems through LLM-based RAG systems.

What you will learn.

Convenient embedding tasks through the R2R dashboard

Conveniently perform domain knowledge embedding required for Q&A systems.

Build an LLM-based Q&A system that used to feel so far out of reach!

Experience an LLM Q&A system built on ollama, docker, and wsl!

The person who created this course

Instructor Introduction

Notes before taking the course

Practice Environment

  • The lecture is conducted based on Windows 10.

  • You will be using a Linux kernel-based virtual machine provided by Windows. (WSL)

  • You will be using it after installing the Docker system on WSL.


Based on the lecture content, it is recommended to proceed with the version included for R2R (3.6.0).

However, assuming that the R2R framework will continue to improve in the future, there is no major problem with proceeding with the latest version. (You must take into account that some commands or settings may change.)

For version 3.6.0, please refer to the Release Tags in the official R2R GitHub repository or the link separately archived by the author.

github

docker hub : docker pull kojeomstudio/r2r:3.6.0

  • You can also pull version 3.6.0 directly. (Image created by the author)


p.s. Since there are few changes to the R2R-dashboard, you may use the latest version.

p.s2 If you wish to proceed with the lecture in a Mac OS environment, please refer to the Mac OS setup process organized in "Community -> Frequently Asked Questions"! :)

Learning Materials

Recommended for
these people

Who is this course right for?

  • A programmer who wants to implement an AI system

  • An aspiring programmer who wants to build a portfolio of AI systems

  • Programmers who want to build a domain-specific AI Q&A system in-house.

  • People who want to run LLMs locally

  • A programmer who wants to go with the flow of AI.

  • Programmers and aspiring developers who want to build Docker-based AI systems

  • Someone who is new to AI open-source projects

  • Programmers who want to apply AI open-source projects to practical work

Need to know before starting?

  • Docker

  • Basic concepts of LLM

  • The desire and will to improve AI utilization skills

  • Interest in building AI system infrastructure

  • Interest in open-source projects

Hello
This is kojeomstudio

1,172

Learners

39

Reviews

2

Answers

4.8

Rating

6

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Instructor Introduction

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Curriculum

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7 lectures ∙ (1hr 47min)

Published: 
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Reviews

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3 reviews

5.0

3 reviews

  • sund님의 프로필 이미지
    sund

    Reviews 5

    Average Rating 4.0

    5

    43% enrolled

    The explanation is easy and well understood

    • kaciaryu9603님의 프로필 이미지
      kaciaryu9603

      Reviews 4

      Average Rating 5.0

      5

      100% enrolled

      • ssyi4412님의 프로필 이미지
        ssyi4412

        Reviews 3

        Average Rating 5.0

        5

        43% enrolled

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