🌿
Spring of Realtime LLMs
  • Get started with the Bootcamp!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Introductory Session | National Science Day
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • 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
    • Bonus Section: Overview of the Transformers Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformers Architecture
      • Vision Transformers
    • Graded Quiz 1
  • Prompt Engineering and Token Limits (Early Access)
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow
    • Token Limits and Hallucinations
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
  • RAG 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
    • Basic RAG Architecture with the Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Build the Dockerized App
      • Retrofitting our Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus Section) – Realtime RAG with LlamaIndex and Pathway
      • Understanding the Basics
      • Implementation
      • Sample Business Use-case
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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  • Git, Python and Pip
  • OpenAI API Key (Recommended)
  • Note: If you're using Windows OS
  • But what is Docker, and how to install it?

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  1. Hands-on Development

Prerequisites

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Last updated 1 year ago

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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).

The latter (using Docker) being a much stronger recommendation.

Don't worry, if you haven't used Docker ever. It's a popular tool with a lot of available documentation and we've added an introductory section for your convenience ahead.

But 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 (optional for this bootcamp).

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

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