As a part of the IRIS Python 2024 contest, my colleague Damir and I went with an idea to build a platform called ShelterShare for connecting victims and volunteers for shelter requests . To do so we chose django as a framework and proceeded to build the first version with 3 different docker containers, django, iris and nginx which would then utilize IRIS as a pure Database engine via the beautifly composed django_iris (cudos to Dimitry). As we were progressing fast, we decided to explore the option of running it within the same container as IRIS by utilizing WSGI added in 2024.1.

You can learn a lot from a first impression; we want to hear about yours.

As a continuation of our User Insights Interview program (see this post for more details), we’re expanding our scope to include Python developers, particularly those of you who are new to working with InterSystems technology. We’re looking to conduct one-on-one interviews to hear your honest thoughts about getting started: What made sense, what didn’t, and where we could improve.

Interested in sharing your thoughts? Sign up to participate here.

This will be a short article about Python dunder methods, also known as magic methods.

What are Dunder Methods?

Dunder methods are special methods in Python that start and end with double underscores (__). They allow you to define the behavior of your objects for built-in operations, such as addition, subtraction, string representation, and more.

Some common dunder methods include:

• __init__(self, ...): Called when an object is created.
  • Like our %OnNew method in ObjectScript.
• __str__(self): Called by the str() built-in function and print to represent the object as a

This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.

Starting with one problem

Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization of each tag and the experience of any community member browsing the subjects tends to decrease.

First solutions in mind

We can think some usual solutions for this scenario, like:

• Take a volunteer to read all posts and fix the mistakes.
• Pay a company to fix all mistakes.
• Send an email to each post writer to review the texts from past.

In my previous articles, I described my Command Line Extension to NativeAPI.
Of course, this is also available for any other NativeAPI package.
So I created this example in Python as a demo.

In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.

Matplotlib: Crafting Visualizations with Precision

Matplotlib is a versatile and powerful library for creating static, animated, and interactive visualizations in Python.

New to coding in InterSystems IRIS® data platform? Try this brand-new learning program to get the basics, and see how to develop an application with InterSystems ObjectScript alongside your language of choice: Java, .NET, Python, or Node.js. 

Hi all. Today we are going to install Jupyter Notebook and connect it to Apache Spark and InterSystems IRIS.

Note: I have done the following on Ubuntu 18.04,  Python 3.6.5.

Introduction

If you are looking for well-known, widely-spread and mainly popular among Python users notebook instead of Apache Zeppelin, you should choose Jupyter notebook. Jupyter notebook is a very powerful and great data science tool. it has a big community and a lot of additional software and integrations.

Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

INTRO

Barricade is a tool developed by ICCA Ops to streamline and scale support for FHIR-to-OMOP transformations for InterSystems OMOP. Our clients will be using InterSystems OMOP to transform FHIR data to this OMOP structure. As a managed service, our job is to troubleshoot any issues that come with the transformation process. Barricade is the ideal tool to aid us in this process for a variety of reasons. First, effective support demands knowledge across FHIR standards, the OHDSI OMOP model, and InterSystems-specific operational workflows—all highly specialized areas.

This is a benchmark built in python and objectscript in InterSystems IRIS.

The objective is to compare the speed for sending back and forth a thousand request/message from a BP to a BO in python and in objectscript.

See https://github.com/LucasEnard/benchmark-python-objectscript for more information.

IMPORTANT : Here are the results of time in seconds, for sending 1000 messages back and forth from a bp to a bo using python, graph objectscript and objectscript.

String messages are composed of ten string variables.

FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

Introducing the FHIR - AI and OpenAPI Chain application, a solution that allows users to interact with FHIR APIs using natural language queries.

For the upcoming Python contest, I would like to make a small demo, on how to create a simple REST application using Python, which will use IRIS as a database. Using this tools

• FastAPI framework, high performance, easy to learn, fast to code, ready for production
• SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL
• Alembic is a lightweight database migration tool for usage with the SQLAlchemy Database Toolkit for Python.
• Uvicorn is an ASGI web server implementation for Python.

You know that feeling when you get your blood test results and it all looks like Greek? That's the problem FHIRInsight is here to solve. It started with the idea that medical data shouldn't be scary or confusing – it should be something we can all use. Blood tests are incredibly common for checking our health, but let's be honest, understanding them is tough for most folks, and sometimes even for medical staff who don't specialize in lab work. FHIRInsight wants to make that whole process easier and the information more actionable.

I'm proud to announce the new release of iris-pex-embedded-python (v2.3.1) with a new command line interface.

This command line is called iop for Interoperability On Python.

First I would like to present in few words the project the main changes since the version 1.

A breif history of the project

Version 1.0 was a proof of concept to show how the interoperability framework of IRIS can be used with a python first approach while remaining compatible with any existing ObjectScript code.

What does it mean? It means that any python developer can use the IRIS interoperability framework without any knowledge of ObjectScript.

Example :

from grongier.pex import BusinessOperation

class MyBusinessOperation(BusinessOperation):

    def on_message(self, request):
        self.log.info("Received request")

Great, isn't it?

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

• 🧠 Python + smolagents to orchestrate the agent’s “brain”
• 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

• IRIStool and Data Manager
• FHIR Data Explorer

1. Fhir-client-python

This is a simple fhir client in python to practice with fhir resources and CRUD requests to a fhir server.
Note that for the most part auto-completion is activated, that's the main reason to use fhir.resources.

GitHub

• 1. Fhir-client-python
• 2. Prerequisites
• 3. Installation
  • 3.1. Installation for development
  • 3.2. Management Portal and VSCode
  • 3.3. Having the folder open inside the container
• 4. FHIR server
• 5. Walkthrough
  • 5.1. Part 1
  • 5.2. Part 2
  • 5.3. Part 3
  • 5.4. Part 4
  • 5.5. Conclusion of the walkthrough
• 6. How it works
  • 6.1. The imports
  • 6.2. Creation of the client
  • 6.3.

We are happy to share interesting information with you, as well as tell you why Python is good, where it is used.

Among the most used libraries are NumPy and Pandas. NumPy (Numerical Python) is used to sort large datasets. It simplifies mathematical operations and their vectorization on arrays. Pandas offers two data structures: Series (a list of elements) and Data Frames (a table with multiple columns). This library converts data into a Data Frame, allowing you to remove and add new columns, as well as perform various operations.

A Quick Start to InterSystems Cloud SQL Data in Databricks

Are you curious about how to run Python scripts directly in your InterSystems IRIS or Caché terminal? 🤔 Good news it's easy! 😆 IRIS supports Embedded Python, allowing you to use Python interactively within its terminal environment. 

How to access the Python Shell?

To launch the Python shell from the IRIS terminal, simply run the following command:

do ##class(%SYS.Python).Shell()

This opens an interactive Python shell inside the IRIS terminal. From here, you can write and run Python code just as you would in a normal Python environment.

Introduction

Since version 2019.2, InterSystems IRIS has provided their Native API for Python as a high-performance data access method. The Native API allows you to directly interact with the native IRIS data structure.

In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.
The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS :
We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one.
That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.

Context

The Web Server Gateway Interface (WSGI) is a simple calling convention for web servers to forward requests to web applications or frameworks written in the Python programming language. WSGI is a Python standard described in detail in PEP 3333.

🤔 Ok, great definition and what the point with iris ?

IRIS 2024.2+ has a new feature that allows you to run WSGI applications directly on IRIS. This feature is a great way to integrate IRIS with other Python frameworks and libraries.

Case description

Let’s imagine that you are a Python developer or have a well-trained team specialized in Python, but the deadline you got to analyze some data in IRIS is tight. Of course, InterSystems offers many tools for all kinds of analyses and treatments. However, in the given scenario, it is better to get the job done using the good old Pandas and leave the IRIS for another time.
    For the abovementioned situation and many other cases, you might want to fetch tables from IRIS to manage data outside InterSystems’ products.

I implemented a Python Flask application for the 2024 Python Contest with a page that provides common form fields for an outgoing email such as the To and CC fields. And it lets you input a message as well as uploading text based attachments.
Then using LlamaIndex in Python, the app analyzes the content you put in and returns to you in a result box if there is anything that should stop you from sending that email.
Take a look at the Github repo here.

Git link: https://github.com/ecelg/InterSystems-IRIS-as-a-Spotify-REST-client

Recently, I come up an idea in my mind that how can I put my playlist on IRIS.🧐

At the same time, I was told to pay for my Spotify subscription💸💸... ooo.. how about to get some data from the Spotify API... so I started to do study about it.

Like most of the development, let's start from Documentation of  the API https://developer.spotify.com/documentation/web-api

In order to get the data, i am required to request an access token from for the token endpoint URL.🧐

curl -X POST "https://accounts.

Hello everyone, I’m a French student in academical exchange for my fifth year of engineering school and here is my participation in the FHIR for Women's Health contest.

This project is supposed to be seen as the backend of a bigger application. It can be plugged into a Front End app and help you gather information from your patients. It will read your data in local and use a Data Transformation to make it into a FHIR object before sending it to the included local FHIR server.

I wanted to participate because Women's Health is a really important topic that must be discussed more.

Creating a Chatbot with IRIS and Python

In this article I'm going to show how to integrate the InterSystems IRIS Database with Python to serve a Machine Learning Model of Natural Language Processing (NLP).

Why Python?

With a large adoption and use in the world, Python have a great community and a lot of accelerators | libraries to deploy any kind of application. If you are curious (https://www.python.org/about/apps/).

Iris Globals

As I start to learn about ^globals they became familiar to use as a fast way to ingest data in a out-of-box data model.

Hey Developers,

You're very welcome to join the upcoming InterSystems webinar called "InterSystems IRIS Tech Talk: Python"! 

Date: Wednesday, June 08, 2022
Time: 11:00 AM EDT 

In this tech talk, we’ll go into detail about the breadth of support Python developers have using the InterSystems IRIS data platform, including: