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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
  • Link to the Project
  • Step-by-Step Process to Build the Application
  • Video Tutorial
  • Key things to note
  1. Hands-on Development
  2. Amazon Discounts App

Repository Walkthrough

PreviousHow the Project WorksNextHow to Run 'Examples'

Last updated 1 year ago

In this particular example, we'll dive a bit deeper to show you how this particular application was made so you can make one of your own.

Link to the Project

  • The repository being referred to can be found .

  • If you have a good experience with open source, visiting the above link should enable you to seamlessly build a similar project.

Step-by-Step Process to Build the Application

A good way to understand the code here would be to read the Streamlit (a Snowflake product) blog below which features the open-source application developed by Bobur.

It's a friendly and easy-to-understand blog that also shows how the application interacts with users via an HTTP REST API and works in real-time, offering support for various data types like JSON Lines and Rainforest Product API.

Video Tutorial

Once you've read the blog above, check out this video tutorial by Bobur Umurzakov (Developer Advocate at Pathway) where he gives a quick walkthrough of the code and the open-source repository.

Key things to note

  • This app is modular; you can add new data sources or interfaces.

  • You could scale it up to include more advanced features like additional data formats or APIs.

  • Streamlit and Pathway's LLM App communicate over HTTP REST API, but they can also be integrated in other ways, such as file sharing or inter-process communication.

By following this guide, you'll create a versatile application capable of real-time interactions with users, providing them with valuable insights into Amazon discounts.

here on GitHub
How to build a real-time LLM app without vector databasesStreamlit
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