🌍 Inclusion & Innovation in Education 🌍
Our project reimagines learning for all students, with a focus on accessibility and interactive experiences. Built with the goal of making education engaging and inclusive, the tool is designed to support students of all abilities in learning complex material in an intuitive way.

💡 What It Does
This educational app transforms lesson presentations into interactive study sessions:

In ObjectScript you have a wide collection of functions that return some value
typically:

set variable = $somefunction(param1,param2, ...)

There is nothing special about that.
But there is a set of functions that I classify as LEFT SIDED
The specialty of them is that you can use them also on the left of the equal operator
as a target in the SET command:

set $somefunction(param1,param2, ...) = value

In the business world, every second counts, and having high-performing applications is essential for streamlining our business processes. We understand the significance of crafting efficient algorithms, measurable through the big O notation.

We've recently made available a new version of InterSystems IRIS in the Vector Search Early Access Program, featuring a new Approximate Nearest Neighbor index based upon the Hierarchical Navigable Small World (HNSW) indexing algorithm.

Hi, Community!

Want to dive deeper into the concepts of vector searches and embeddings? Learn from an InterSystems expert:

What Are Embeddings?

https://www.youtube.com/embed/WAKwI-ijoQE?utm_source=youtube&utm_medium=social&utm_campaign=WAKwI-ijoQE
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Hi Community,

Play the new video on InterSystems Developers YouTube:

⏯ InterSystems IRIS Vector Search and the Python Ecosystem @ Global Summit 2024

https://www.youtube.com/embed/XVEvUOxJUrw
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Hi, Community!

Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:

Exploring the Benefits of RAG

https://www.youtube.com/embed/NSYVHiJPRas?utm_source=youtube&utm_medium=social&utm_campaign=NSYVHiJPRas
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Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

• Ingest Documents (PDF or TXT) into IRIS
• Chat with the selected Ingested document
• Delete Ingested Documents
• OpenAI ChatGPT

In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.

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:

Translated from the Spanish Community Article Contest.

Following the latest programming contest on OEX I had some surprising observation.
There were almost exclusive applications based on AI in combination with pre-cooked Py modules.
But digging deeper, all examples used the same technical pieces of IRIS.

Seen from point of view of IRIS it was pretty much the same whether searching for text
or searching for images or other pattern. It ended in almost exchangeable methods.

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.

ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.

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.