Document Chunks with LLM Sherpa
2024-04-03 18:00 - 19:00 (America/Chicago) | HudsonAlpha, 601 Genome Way Northwest, Huntsville, AL, 35806
Continuing our discussion about Retrieval Augmented Generation (RAG), this week we will incorporate LLM Sherpa to provide chunks of text from PDF documents that have been retrieved from the NASA archive.
Our initial attempt used PyPDF2 to read text from the PDF documents. It was very slow and provided limited strings of text that did not match the paragraphs in the documents. We’ll take a look back at what was available at the time, and then look through the LLM Sherpa API and see what it looks like with that piece incorporated.
As we get further into this project update, it has become apparent for the need to split the monolithic application into components that can be hosted and updated separately. We will go through what has been done so far to containerize both the ChromaDB vector database and the LLM Sherpa for chunking.

Choosing an Embedding Model
2024-04-17 18:00 - 19:00
We have Josh Phillips presenting this week, so you don’t want to miss this!
Join us as we explore the crucial task of selecting optimal embedding models to enhance AI performance across a variety of applications. This meetup will delve into the Multilingual Transferable Embedding Benchmark (MTEB), a pivotal resource providing a comprehensive framework to evaluate embedding models over diverse task categories and numerous languages. The selection of the right embedding model is vital, yet challenging due to the myriad of options and their inherent trade-offs.
This presentation will not only introduce you to MTEB’s holistic approach across eight core NLP tasks but will also guide you through the practical steps of identifying, shortlisting, and benchmarking models to find the best fit for your specific needs.
Agenda:

Introduction to Embedding Models – Gain insights into why choosing the right model is critical for AI tasks.
Overview of MTEB – Understand the framework of the Multilingual Transferable Embedding Benchmark and its application across 100+ languages.
Deep Dive into MTEB Tasks – Explore the eight fundamental tasks within MTEB, including bitext mining, classification, clustering, and more.
Case Studies – Walk through real-world use cases, demonstrating how to apply MTEB in selecting models for tasks such as walking path recommendations, form filling automation, and building a documentation assistant.

Series:
Over the next several sessions, we will be diving deeper into separate components needed for RAG – hopefully resulting in a chat-based Q&A service for the NASA Technical Report Server. We were introduced to this data during our submission for the 2022 NASA SpaceApps Challenge – where we placed 2nd. Our submission was a semantic search based on the abstracts for the NSTR dataset of 10,000 papers.
I hope to have the video from our last session posted today. You can check for updates at https://hsv.ai/videos
Links:

Multilingual Transferable Embedding Benchmark (MTEB)
MTEB Github
MTEB Paper
Huntsville AI 2022 SpaceApps Submission – https://github.com/HSV-AI/spaceapps2022

Details:

Date – 04/17/2024
Time – 6-7pm
Location – HudsonAlpha
Address – 601 Genome Way Northwest, Huntsville, AL 35806
Zoom –https://us02web.zoom.us/j/81626946368?pwd=[REDACTED]

2024 SBIR Topics Round 2
2024-04-24 18:00 - 19:00
SBIR/STTR Topics
The second round of SBIR Topics for DoD were released last week. As usual, we will go through any that are labeled as Machine Learning or Artificial Intelligence – which looks like 13 SBIR and 9 STTR out of 176 total topics.
You can find the full list below in the Links, just search for Learning or Artificial.
2024 AI Symposium
The sessions from the Space and Rocket Center AI Symposium were recorded and just released on YouTube! Based on my notes from the sessions that I was able to attend, they range from high level overviews to in-depth use of AI. I believe that all of them would be considered practical rather than theoretical. Again – look for the link below.

Series (picking back up next week):
Over the next several sessions, we will be diving deeper into separate components needed for RAG – hopefully resulting in a chat-based Q&A service for the NASA Technical Report Server. We were introduced to this data during our submission for the 2022 NASA SpaceApps Challenge – where we placed 2nd. Our submission was a semantic search based on the abstracts for the NSTR dataset of 10,000 papers.
Sessions from this series so far are being posted at https://hsv.ai/videos
Links:

Defense SBIR/STTR Portal – https://www.dodsbirsttr.mil/topics-app/
Space & Rocket Center AI Symposium Videos – https://www.youtube.com/playlist?list=PLvvHQqQynqmtkQuLsvfFtmy0OohBcA5H4
Huntsville AI 2022 SpaceApps Submission – https://github.com/HSV-AI/spaceapps2022

Details:

Date – 04/24/2024
Time – 6-7pm
Location – Zoom
Zoom –https://us02web.zoom.us/j/88996968876?pwd=[REDACTED]
