🌿
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
Powered by GitBook
On this page
  • Contract Risk Management within Excel
  • Implementation

Was this helpful?

  1. Hands-on Development
  2. 4 (Bonus Section) – Realtime RAG with LlamaIndex and Pathway

Sample Business Use-case

PreviousImplementationNextBonus Resource: Recorded Interactions from the Archives

Last updated 1 year ago

Was this helpful?

Below is an interesting example of a business use case that is pertinent enough for companies.

Contract Risk Management within Excel

With the advent of Generative AI legal teams at corporations are looking to smartly manage risks arising through the nature and set of clauses being signed via contracts within the organization. With the help of LlamaIndex and Pathway, you can solve this problem by providing intelligent insights and interactions across all contracts stored in Google Drive or Microsoft Sharepoint (commonly used by enterprises).

Sample Demo:

With such applications, a relevant problem of legal team members and organizations is addressed. You can take inspiration from this and try to apply this to similar projects across sectors that are of interest.

Implementation

The implementation for this project is very well summarized by folks at LlamaIndex and Streamlit with the help of a blog compiled by Anup Surendran (from Pathway). Do check the post below for more insights around the same.

With this now you have covered various means by which you build RAG applications. There is also a growing field of "Advanced RAG" which is not covered in this bootcamp. But if you're curious, you can possibly start reading about it on your own too.

😄
Logo[Video] LlamaIndex on LinkedIn: Build a Live RAG Chatbot from Google Drive / Sharepoint ♻️ Excited to… | 37 commentslinkedin