The similarity between JSON objects + arrays and Globals in IRIS or Caché is evident.
With small and medium size JSON objects navigation across %Dynamic Objects is comfortable.
But with large and/or deep cascaded objects it becomes a challenge.

The presented tool offers 3 variants

• loading an already existing %Dyamic object or Array into a global of your choice
• loading a %Stream containing a JSON object into a global of your choice
• loading an external File containing a JSON object into a global of your choice

There are a lot of great community articles regarding "vector search on IRIS", and samples in OpenExchange. Everytime I see these, I'm so excited to know that so many developers already try vectors on IRIS!

But if you've not tried "Vector Search on IRIS" yet, please give me one minute 😄 I create one IRIS class - and with only one IRIS class you can see how you put vector data in your IRIS database and how you compare these in your application.

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

Data Collection

This is a step-by-step instruction guide for creating a task to collect data about the InterSystems database and globals therein (as seen in the associated Open Exchange App - find all the associated code there)

Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here

Striking a Balance Between Health Analytics and Engaging Features

In the previous part of this series, we saw how to reference a web page that will enhance our dashboard experience. Now we will look into referencing data that is already in our cubes.

In this example, we will be referencing the controller object and we will be extracting data from it. This data will then be displayed as text in our Dashboard. In Part 5, we will show how to incorporate this data into other charting libraries.

If one of your packages on OEX receives a review you get notified by OEX only own YOUR package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

Enhanced Password Management: Edit Passwords Seamlessly

In the ever-evolving landscape of digital security, robust password management tools have become indispensable. Our password management application, designed to simplify and secure your online life, now comes with an enhanced feature – the ability to edit passwords with ease.

Why is this feature a game-changer?

My newest app includes a java routine to read data from Excel 95, 97, 2000, XP, and 2003 workbooks and write the data into IRIS globals using Java Native API library.

If you have Git and Docker installed, clone/git pull the repo into any local directory

$ git clone https://github.com/oliverwilms/excel-java-iris.git

Open the terminal in this directory and run:

$ docker-compose up -d

Execute java class IRISNative

The latest release of Serenji by George James Software introduces zero configuration debugging, so it's ready to go in just one click - no matter where you are or what you're doing, your on-the-spot debugger is ready to go. 

We have introduced a host of new features to enhance the debugging experience so you can focus on identifying and fixing errors, without losing focus by spending time setting up a launch configuration. 

Check out the new features in our release notes. 

If you've already started using Serenji 3.2.0 let us know how you're getting on! 

Hi Community,

Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.

This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

1. Create Agent Tools
   • Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
   • Implement Vector Search Functionality
2. Create Vector Search Agent
3. Handoff to Triage (Main Agent)
4. Run The Agent

Killer presentations are essential for opening doors, doing business and delighting customers. These types of presentations move away from the traditional and tiring topics and portions of texts, or even pictures of happy people to get closer to the interests of their audience.The best presentations materialize what customers are looking for into something they can see working. Instead of promises, examples in execution and the ability to be very close to what you want to do.