Developing a Web-based Q&A system using a RAG-based approach to LLMs - June 25, 2024

Course Information

Title: Developing a Web-based Q&A system using a RAG-based approach to LLMs
Instructor: Sujee Maniyam, Node 51
Time: Tuesday, June 25, 8.00 am PDT/11am pm EDT till 2.00pm PDT / 5pm EDT
Cost: $300 for NET+ AWS and GCP Subscribers, $400 for Internet2 members(see the list of member institutions here), $500 for non-members
Registration: https://tinyurl.com/3tnfey9n

Introduction

We have all seen how powerful and impactful Large Language Models (LLMs) like ChatGPT are. These very powerful AI models are enabling a new generation of conversational applications. This 6-hour hands-on workshop introduces the audience to LLMs, data augmentation using the Retrieval Generation Augmentation (RAG) approach, and how to how to build web using open-source tools.

Skills Level and Target Audience

Introductory to Intermediate
Research IT, Enterprise IT

What You Will Learn

Embeddings and embedding models; searching text for meanings / context using embeddings
Vector databases and performing text search with vector databases
LLMs and using them (via API and locally)
Developing applications using LLM
Developing RAG applications
Deploying containerized applications

Prerequisites

Comfortable with python programming and notebooks
Access to Google Colab
A free subscription to a vector database like MongoDB Atlas
Sign up for Mistral AI
GPU access to TBD

Details

1 - Embeddings

Understanding embeddings
Various embedding models
Semantic text search using embeddings
evaluating a various embedding models

2 - Vector Databases

Introduction to vector databases
getting started with MongoDB Atlas
Loading data into database and populate embeddings
Vector search with database and embeddings

3 - LLMs

Introduction to LLMs, the eco system
Access LLMs via API
Run LLMs locally using llama-cpp, oolama, lm-studio, jen
Experiment with different LLMs

4 - Running LLMs

We will use a framework like llama-cpp, llama-index and Langchain to run local LLMs
Run a local LLM. Experiment with various open LLMs like Mistral, Llama

5 - Develop a custom application using LLM

Use frameworks like streamlit, flask to build sample applications

6 - Building RAG Applications

Here we will query PDF documents using LLMs
Index documents with embeddings. Use various embedding models (OpenAI, Mistral, open source models)
query the documents using various LLMs (OpenAI, Mistral, LLama)

7 - Deploying RAG Applications

Containerizing applications
Model serving
Scalability

8 - Workshop / Project

Attendees will a sample application using data of interest, and LLM of their choice

CLASS/rag-llm-workshop

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