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June 10, 2024 09:07

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:


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


  • 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


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


Repository for Internet2's CLASS RAG, LLM and Cloud Workshop






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