Hi Community!

How can you create a balanced approach to using Generative AI? Get help from InterSystems experts in the latest video from Learning Services:

⚖ Weighing the Benefits and Risks of Generative AI

https://www.youtube.com/embed/VaUS5M0S0hY?utm_source=youtube&utm_medium=social&utm_campaign=VaUS5M0S0hY
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Hey Community!

We're happy to share our newest learning path dedicated to the essentials of GenAI, accessible on the Learning Portal via your SSO:

🤖 Getting Started with Generative AI 🤖

Here is what you can expect:

Looking to get started with generative AI? Try two brand-new learning paths. In Getting Started with Generative AI (2h 45m), learn the basics of interacting with GenAI. Then, try Developing Generative AI Applications (2h) to start developing your own GenAI application. Plus, earn badges for completing each path!

Hi Community,

This is a detailed, candid walkthrough of the IRIS AI Studio platform. I speak out loud on my thoughts while trying different examples, some of which fail to deliver expected results - which I believe is a need for such a platform to explore different models, configurations and limitations. This will be helpful if you're interested in how to build 'Chat with PDF' or data recommendation systems using IRIS DB and LLM models.

https://www.youtube.com/embed/bcu1gt0BDhY
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ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.

Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

TL;DR

This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.

Problem

Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.

Hi Community,

In this article, I will introduce my application iris-VectorLab along with step by step guide to performing vector operations.

IRIS-VectorLab is a web application that demonstrates the functionality of Vector Search with the help of embedded python. It leverages the functionality of the Python framework SentenceTransformers for state-of-the-art sentence embeddings.

Application Features

• Text to Embeddings Translation.
• VECTOR-typed Data Insertion.
• View Vector Data
• Perform Vector Search by using VECTOR_DOT_PRODUCT and VECTOR_COSINE functions.
• Demonstrate the difference between normal and vector search
• HuggingFace Text generation with the help of GPT2 LLM (Large Language Model) model and Hugging Face pipeline

Hi Developers!

Here're the technology bonuses for the InterSystems Vector Search, GenAI, and ML contest 2024 that will give you extra points in the voting:

• Vector Search usage - 5
• IntegratedML usage - 3
• Embedded Python - 3
• LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
• Questionnaire - 2
• Docker container usage - 2
• ZPM Package deployment - 2
• Online Demo - 2
• Implement InterSystems Community Idea - 4
• Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
• First Article on Developer Community - 2
• Second Article On DC - 1
• First Time Contribution - 3
• Video on YouTube - 3
• Suggest a new idea - 1

See the details below.

InterSystems' team is heading to the MIT Hacking Medicine GrandHack 2024, taking place from April 19-21 2024!

MIT GrandHack is a premier event that brings together innovators, entrepreneurs, and healthcare professionals from around the world to collaborate on solving some of the most pressing challenges in healthcare through technology and innovation.

What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.