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10 Days Realtime LLM Bootcamp
  • Introduction
    • Getting Started
    • Course Syllabus
    • Course Structure
    • Prerequisites
    • Greetings from your Instructors
    • First Exercise (Ungraded)
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs
  • 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 and Further Reads (Bonus Module)
    • Let's Track Our Progress
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Tasks in the Excercise
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG Functioning and LLM Architecture: 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)
    • Track your Progress
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Using Docker to Build the App
    • Amazon Discounts App
      • How the Project Works
      • Step-by-Step Process
    • How to Run the Examples
  • Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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  • Git, Python, and Pip
  • OpenAI API Key (Recommended)
  • Note: If you're using Windows OS
  • What is Docker and how do you install it?
  1. Hands-on Development

Prerequisites

Before we dive in, let's ensure you have all the necessary prerequisites installed on your computer. Not only are these essential for what we're about to embark on, but they'll also be invaluable tools if you decide to contribute to open-source projects in the future.

Git, Python, and Pip

  • Python 3.10 or above should be installed on your machine. If not, Download Python.

  • 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 must download and install Pip 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 this documentation and install it.

OpenAI API Key (Recommended)

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

This is a good starting point if you are less confident with using open-source alternatives. If you want to use open-source models, you can find examples like the one here.

By default, OpenAI currently offers $5 in free credits for new accounts – i.e., the ones with a new phone number and email ID. Alternatively, you can sign up for free credits on platforms like Eden AI. These free credits should suffice for building your project.

As we advance, we will usetext-embedding-ada-002 in this coursework for generating the vector embeddings (OpenAI documentation) and gpt-3.5-turbo for text generation.

To create a new OpenAI API Key:

  • Log in to the OpenAI website.

  • Navigate to the API Key Management page to generate your key.

Note: If you're using Windows OS

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

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

What is Docker and how do you 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 be transported anywhere, 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.

Like Docker, there is a tool called Conda, which is showcased in one of the videos above. 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 aim to solve the problem of "it works on my machine" by isolating your project and its dependencies.

  • You can download Docker from here.

  • You can download Conda from here.

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Last updated 1 year ago

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

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