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3 Week Bootcamp: Building Realtime LLM Application
  • Introduction
    • Timelines and Structure
    • Course Syllabus
    • Meet your Instructors
    • Action Items
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors Simplified
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention, Transformers Architecture, and Further Reads (Bonus Module)
    • Graded Quiz 1
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Ungraded Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Your Task
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG: Pre-Trained and Fine-Tuned LLMs
    • In-Context Learning
    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • LLM Architecture Diagram and Various Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in Retrieval-Augmented Generation (RAG)
    • Key Benefits of RAG for Enterprise-Grade LLM Applications
    • Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search in Vector Embeddings (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Building the Dockerized App
    • Amazon Discounts App
      • How the Project Works
      • Repository Walkthrough
    • How to Run 'Examples'
  • Bonus Resource: Recorded Interactions from the Archives
  • Bootcamp Keynote Session on Vision Transformers
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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On this page
  • Git, Python and Pip
  • OpenAI API Key (Recommended)
  • Note: If you're using Windows OS
  • What is Docker and how to install it?
  1. Hands-on Development

Prerequisites

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Last updated 1 year ago

Alright, let's get the ball rolling! Let's kick things off by ensuring you have everything you need installed on your computer. And remember, persistence is crucial in this journey.

Nailing a new framework flawlessly from the get-go is as rare as acing a complex algorithm on the first try. And finding joy in debugging? That's like getting used to a 3 AM alarm—tough, but part of the process.

The magic happens when, after persistence. That's when you'll see the true power of your skills and the impact you can create. Plus, the frameworks we're diving into are designed for production-grade applications, meaning the potential for real-world impact is enormous and genuinely empowering.

Are you geared up? Let's embrace this challenge with enthusiasm. 😊 These steps aren't just for today; they're your stepping stones to the exciting world of open-source contributions. So, let's get to it!

Git, Python and Pip

  • Python 3.10 or 3.11 should be installed on your machine. If not, you can here.

  • Pip: Comes pre-installed with Python 3.4+. It is the standard package manager for Python. You can check if it's downloaded by typing the below command in your terminal/command prompt.

    pip --version

  • If Pip is not installed, you'll get an error. In that case, you need to download and install to manage project packages.

  • Git should be installed on your machine. If you've installed XCode (or its Command Line Tools), Git may already be installed. To find out, open a Terminal or Command Prompt, and enter git --version. If it's not installed, refer to and install it.

OpenAI API Key (Recommended)

This key is required if you plan to use OpenAI models for embedding and generation.

If you are less confident with using open-source alternatives, this is a good starting point. By default, OpenAI currently offers $5 in free credits for new accounts – i.e. the ones with a new phone number and email ID. These free credits should suffice for building your project.

Going forward, we will use text-embedding-ada-002 for generating the vector embeddings () and gpt-3.5-turbo for text generation.

To create a new OpenAI API Key:

Note: If you're using Windows OS

The example ahead only supports Unix-like systems (such as Linux, macOS, and BSD).

What is Docker and how to install it?

Think of Docker as a shipping container for your app. Just as a shipping container can hold all sorts of goods (clothes, electronics, etc.) and can be transported anywhere in the world, Docker bundles your app and everything it needs to run into a 'container.' This makes it easy to share and run your app on any computer.

Similar to Docker, there is a tool called Conda which is showcased in one of the videos ahead. Conda lets you create separate environments to manage different sets of Python packages, ensuring your code runs the same way on any computer.

Conda and Docker both aim to solve the problem of "it works on my machine" by isolating your project and its dependencies.

to the OpenAI website.

Navigate to the page to generate your key.

But the good news is that you have an easy fix. If you are a Windows user, you can use or Dockerize the app to run as a container.

You can download Docker

You can download Conda .

Now that we have the prerequisites, let's proceed.

😄
🤷‍♀️
Download Python
Pip
this documentation
OpenAI documentation
Log in
API Key Management
Windows Subsystem for Linux (WSL)
from here
from here