Your Mission

Let's pretend for a moment that you're an international action spy who's dedicated your life to keeping the people of the world safe from danger. You recieve the following mission:

Good day, Agent IRIS,

We're sorry for interrupting your vacation in the Bahamas, but we just received word from our London agent that a "time bomb" is set to detonate in a highly populated area in Los Angeles. Our sources say that the "time bomb" is set to trigger at 3:14 PM this afternoon.

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

Learning LLM Magic

The world of Generative AI has been pretty inescapable for a while, commercial models running on paid Cloud instances are everywhere. With your data stored securely on-prem in IRIS, it might seem daunting to start getting the benefit of experimentation with Large Language Models without having to navigate a minefield of Governance and rapidly evolving API documentation. If only there was a way to bring an LLM to IRIS, preferably in a very small code footprint....

Since the introduction of Embedded Python there has always been doubt about its performance compared to ObjectScript and on more than one occasion I have discussed this with

Hello everyone

For those interested, here is an implementation example I used in one of my projects to interoperate with MongoDB using the pymongo package.

iris-mongodb

Interoperates with MongoDB (via pymongo package) with InterSystems IRIS (including outbound adapter)

Remember to import pymongo package.

I am writing this post primarily to gather an informal consensus on how developers are using Python in conjunction with IRIS, so please respond to the poll at the end of this article! In the body of the article, I'll give some background on each choice provided, as well as the advantages for each, but feel free to skim over it and just respond to the poll.

InterSystems Announces General Availability of InterSystems IRIS, InterSystems IRIS for Health, and HealthShare Health Connect 2025.1

This article is a continuation of the IRIS JSON project and features additional methods and insights.

Let's continue with the instance methods

USER>do $System.Python.Shell()
 
ERROR #5002: ObjectScript-Error: <OBJECT DISPATCH>Shell+16^%SYS.Python.1 
*Failed to Load Python: Check documentation and messages.log, 
Check CPF parameters:[PythonRuntimeLibrary,PythonRuntimeLibraryVersion], 

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.

Imagine you’re walking down the street on a nice summer’s day, and someone walks up to you and says “Hey, you work at InterSystems, right? I’ve been hearing more and more about InterSystems IRIS lately. I know IRIS has its own programing language called ObjectBook? or InstaScript? OK, I admit it, I know it’s called ObjectScript! I know IRIS also supports Python. I’m a Python developer, so that sounds great to me. But I’m also interested in ObjectScript. For example, Python and other languages support collections. Does ObjectScript support collections?”

You’d answer “Of course!”

And then your new friend might get excited and start firing off more questions:

• How many kinds of collections does ObjectScript support?
• Can ObjectScript use Python collections?
• Can Python use ObjectScript collections?
• Which collection is best?

How would you answer? Well, you don’t have to worry about answering. All you’d have to do is send your new friend the URL of this long page.

I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:

Hi Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ Embedded Python Best Practices and News @ Global Summit 2024

https://www.youtube.com/embed/HicTI-bRa8M
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

Google Forms is the most popular solution on the market for collecting data, answering questionnaires and quizzes. So, it is the ideal solution for collecting patient data and responses in a practical way, without the need to expand or develop systems. In this article, I will detail how to create an account on Google Cloud, register the application that will consume the Google Forms API, generate the service user necessary to consume the API and finally perform actions to create new forms and collect data filled in them in an automated way in embedded Python and IRIS.