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RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • 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: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror 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 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)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
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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. 2 – Amazon Discounts App

Building the App

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Last updated 10 months 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 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
LogoHow to build a real-time LLM app without vector databasesStreamlit