Hello Community,

I encountered the following errors while installing the ZPM module on version 2025.1. The ZPM install command failed on the Community Edition of IRIS for Health.

George James Software is excited to share a new release of Deltanji source control, version 8.1. This new release introduces a Git Location Driver along with several other functionality and usability improvements.

Highlights of this release include:

Profiling CCD Documents with LEAD North’s CCD Data Profiler
Ever opened a CCD and been greeted by a wall of tangled XML? You’re not alone. Despite being a core format for clinical data exchange, CCD's are notoriously dense, verbose, and unfriendly to the human eye. For developers and analysts trying to validate their structure or extract meaningful insights, navigating these documents can feel more like archaeology than engineering.

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.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own 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.

One of the challenges of creating a DICOM message is how to implement putting data in the correct place. Part of it is by inserting the data in the specific DICOM tags, while the other is to insert binary data such as a picture - In this article I will explain both.

To create a DICOM message, you can either use the EnsLib.DICOM.File class (to create a DICOM file) or the EnsLib.DICOM.Document class (to create a message that can be sent to PACS directly). In either case, the SetValueAt method will allow you to add your data to the DICOM tags.

Prefer not to read? Check out the demo video I created:

https://www.youtube.com/embed/-OwOAHC5b3s
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I cloned @Yuri.Marx's langchain-iris-tool repo and typed docker-compose up -d. I see error message:

Error response from daemon: could not select device driver "nvidia" with capabilities: [[gpu]]

Hi Community,

I would like to present my last package OpenAPI-Suite, this is a set of tools to generate ObjectScript code from an OpenAPI specification version 3.0. In short, these packages allow to:

• Generate server-side class. It’s pretty similar to the generated code by ^%REST but the added value is the version 3.0 support.
• Generate HTTP client classes.
• Generate client production (business services, business operation, business process, Ens.Request, Ens.Response) classes.
• A web interface to generate and download the code or generate and compile directly on the server.
• Convert specification from version 1.x, 2.x to version 3.0.

Hi Community,

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

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.

Hello there 🙋‍♂️,

I'm looking towards a functionality alike Python's difflib, but made with Intersystems products. I already know could embed Python code within IRIS but thought maybe there was a development already done for handling deltas, with a different approach or extra utils.

Even if I find such a library after comparing both might still go with difflib or any other more suitable. Intend to use it for a cross-environment checklist/check tool.

Please find below a sample of HTML rendering computed and made with difflib, with a few lines of code: