From 23744c96f6b30a9b6a135c9fad11b6f326eb9ea7 Mon Sep 17 00:00:00 2001 From: amanda-tan Date: Fri, 7 Jun 2024 12:11:34 -0700 Subject: [PATCH] Update README.md --- README.md | 66 +++++++++++++++++++++++++++---------------------------- 1 file changed, 32 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index e647fb8..e9479c3 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,13 @@ # Developing a Web-based Q&A system using a RAG-based approach to LLMs - June 25, 2024 ## Course Information -Registration Link: https://tinyurl.com/3tnfey9n -Workshop time: 8.00 am PST/11am pm EST - 2.00pm PST / 5pm EST -Instructor: Sujee Maniyam, Node 51. See Instructor Profile [here](https://www.mongodb.com/developer/author/sujee-maniyam/). +- Registration Link: https://tinyurl.com/3tnfey9n +- Cost: $300 for NET+ Subscribers, $400 for Internet2 members, $500 for non-members +- Workshop time: 8.00 am PST/11am pm EST - 2.00pm PST / 5pm EST +- Instructor: Sujee Maniyam, Node 51. See Instructor Profile [here](https://www.mongodb.com/developer/author/sujee-maniyam/). ## 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. +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 Introductory to Intermediate @@ -22,51 +21,50 @@ Introductory to Intermediate - Deploying containerized applications ## Prerequisites -Comfortable with python programming and notebooks -Have Python development environment locally or have access to Google Colab or other Jupyter environments -A free subscription to a vector database like MongoDB Atlas -Using hosted models like chatGPT, Mistral will require a subscription -To run a local LLM, we recommend a GPU system (a laptop with GPU or Google Colab or cloud instance with GPU) +- Comfortable with python programming and notebooks +- Access to Google Colab +- A free subscription to a vector database like MongoDB Atlas +- Using hosted models like chatGPT, Mistral will require a subscription + ## Details ### 1 - Embeddings -Understanding embeddings -Various embedding models -Semantic text search using embeddings -evaluating a various embedding models +- 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 +- 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 +- 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 +- 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 +- 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) +- 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 -scale +- Containerizing applications +- Model serving +- Scalability ### 8 - Workshop / Project -Attendees will a sample application using data of interest, and LLM of their choice +- Attendees will a sample application using data of interest, and LLM of their choice