I know that people who are completely new to VS Code, Git, Docker, FHIR, and other tools can sometimes struggle with setting up the environment. So I decided to write an article that walks through the entire setup process step by step to make it easier to get started.

I’d really appreciate it if you could leave a comment at the end - let me know if the instructions were clear, if anything was missing, or if there’s anything else you'd find helpful.

The setup includes:

✅ VS Code – Code editor
✅ Git – Version control system
✅ Docker – Runs an instance of IRIS for Health Community
✅ VS Code REST Client Extension – For running FHIR API queries
✅ Python – For writing FHIR-based scripts
✅ Jupyter Notebooks – For AI and FHIR assignments

Before you begin: Ensure you have administrator privileges on your system.

In addition to reading the guide, you can also follow the steps in the videos:

For Windows

https://www.youtube.com/embed/IyvuHbxCwCY
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In this article, we will discuss all the debugging tools included in the Microsoft Visual Studio Code IDE.

What will be covered:

• Breakpoints
• Watch window
• Call Stack

Let's start by learning about debugging requirements!

Prerequisites

There are two plugins (extensions) for debugging ObjectScript:

The first is part of the InterSystems ObjectScript Extension Pack. The second is Serenji, a standalone plugin that provides an editor, file manager, and debugging functionality. Both plugins can be installed from the plugin store. To activate key functionality, Serenji requires a license. For this article, we'll use the InterSystems ObjectScript Extension Pack to reduce the learning curve. After you've mastered the basics, you can consider purchasing a paid license for Serenji.

In this article I'll show you how to set up in your laptop, very quickly, a cluster of IRIS nodes in sharding. It's not the goal of this article neither to talk about sharding in detail nor define a deployment of a production ready architecture, but to show how to set up quickly, in your own machine, a cluster of IRIS instances configured as shard nodes, with which you'll able to play and test this functionality. If you're insterested in knowing more about sharding in IRIS, take a look at the documentation clicking here.

First and foremost, I want to remark that IRIS sharding will allow us 2 things:

• Define, load and query shard tables, which data will be distributed transparently between the cluster's nodes
• Define federated tables, which offer a global and composed view of data belonging to different tables that will be physically stored in different distributed nodes

So, as I said, we let for other article playing with shard or federated tables, and just focus now in the previous step, that is, setting up the cluster of shard nodes.

As applications grow, every database eventually hits scaling limits. Whether it's storage capacity, concurrent users, query throughput, or I/O bandwidth, single-server architectures have inherent constraints. This guide explains fundamental approaches to database scalability and shows how InterSystems IRIS implements these patterns to support enterprise-scale workloads.

We'll explore two complementary scaling strategies: horizontal scaling for user volume (distributing computational load) and sharding for data volume (partitioning datasets). Understanding the general principles behind these approaches will help you make informed decisions about when and how to scale your IRIS applications.

The examples in this guide use InterSystems IRIS in Docker containers.

Table of Contents

1. Introduction
2. Version Control
3. Exporting Classes
4. Exporting BI Analytics classes
5. Exporting globals

Introduction.

In this article, we'll cover a quick start to Git development with the InterSystems Developer Community and version control practices when working with IRIS InterSystems. We'll cover scenarios where we need to export Interoperability classes, globals, and analytics elements such as cubes and dashboards. These scenarios encompass key version control practices with IRIS.

To get started, you'll need:

1. Register and log in to GitHub
2. Be a member of the intersystems-community organization on Git

Getting started with IRIS InterSystems is very simple!

The 2024.1.4 and 2023.1.6 maintenance releases of InterSystems IRIS^® data platform, InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect are now Generally Available (GA).

Introducing Smart Clinical Sidechick — the intelligent, no-drama partner your EHR wishes it could be. She reads FHIR data in real time, interprets lab results without ghosting, and explains clinical alerts like she actually cares. Built with GPT-4 brains and YAML sass, she’s not here to replace your main EHR—just to make it look bad. Tired of irrelevant alerts and cryptic warnings? Sidechick serves up real, explainable insights, not vague “elevated risk” vibes. And when your backend crashes, she doesn’t panic—she self-heals.

using intersystemsdc/iris-ml-community:latest I failed with

Error: Invalid Community Edition license, may have exceeded core limit.
Shutting down the system : $zu(56,2)= 0Starting IRIS

What is the actual valid version with ML ??

I am brand new to using AI. I downloaded some medical visit progress notes from my Patient Portal. I extracted text from PDF files. I found a YouTube video that showed how to extract metadata using an OpenAI query / prompt such as this one:

ollama-ai-iris/data/prompts/medical_progress_notes_prompt.txt at main · oliverwilms/ollama-ai-iris

I have created a new docker stack with webgateway and IRIS for Health 2025.1. I have mapped the posts of wegateway like this:

• 8743:443
• 8780:80

I can access IRIS portal through 8743 without problems.

I also have created a FHIR repository, and I'm able to access it through 8743 port.

If you start with InterSystems ObjectScript, you will meet the XECUTE command.
And beginners may ask: Where and Why may I need to use this ?

The official documentation has a rich collection of code snippets. No practical case.
Just recently, I met a use case that I'd like to share with you.

Hi all.

I'm trying to create an indexed table with an vector field so I can search by the vector value.
I've been investigating and found that to get the vector value based on the text (token), use a Python method like the following:

I read the article by @Rodolfo Pscheidt:

https://community.intersystems.com/post/ollama-ai-iris

I forked his app and copied selected files from @Guillaume Rongier iris-rag-demo to make it containerized:

oliverwilms/ollama-ai-iris

I ran load_data.py and I got this output:

The Load Data utility it is an excellent tool to load data from CSV/TXT files into an IRIS SQL Table, but it is required send the target file to the IRIS server and write the Load Data sentence to ingest the file content. Now it is possible select a file in VSCode, set the table destination and submit the request. The vscode-load-data utility will send the file to the IRIS server and run the Load Data command to you! Very simple: