Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace
Hi everyone! 👋
I’m excited to share the project I’ve submitted to the current InterSystems .Net, Java, Python, and JavaScript Contest — it’s called IRIStool and Data Manager, and you can find it on the InterSystems Open Exchange and on my GitHub page.
In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.
This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.
You can find them at the Open Exchange:
In this article we'll cover:
InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.
The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.
Neuschwanstein Castle
Tabular data storages based on what is formally known as the relational data model will be celebrating their 50th anniversary in June 2020. Here is an official document – that very famous article. Many thanks for it to Doctor Edgar Frank Codd. By the way, the relational data model is on the list of the most important global innovations of the past 100 years published by Forbes.
On the other hand, oddly enough, Codd viewed relational databases and SQL as a distorted implementation of his theory. For general guidance, he created 12 rules that any relational database management system must comply with (there are actually 13 rules). Honestly speaking, there is zero DBMS's on the market that observes at least Rule 0. Therefore, no one can call their DBMS 100% relational :) If you know any exceptions, please let me know.
While working with external languages for IRIS (such as Python and Node.js), one of the first things you must accomplish is making a connection to an IRIS instance.
For instance, to make a connection in python (from https://pypi.org/project/intersystems-irispython/):
import iris
# Open a connection to the server
args = {
'hostname':'127.0.0.1',
'port': 1972,
'namespace':'USER',
'username':'username',
'password':'password'
}
conn = iris.connect(**args)
# Create an iris object
irispy = iris.createIRIS(conn)
# Create a global array in the USER namespace on the server
irispy.set("myGlobal", "hello world!")
I’m excited to share the project I’ve submitted to the current InterSystems .Net, Java, Python, and JavaScript Contest — it’s called FHIR Data Explorer with Hybrid Search and AI Summaries, and you can find it on the InterSystems Open Exchange and on my GitHub page.
When developing Python applications with InterSystems IRIS, you can quickly end up with several execution contexts:
python3 process that loads the Embedded Python libraries from a local IRIS installation;These three cases are useful, but they do not behave exactly the same way for imports, system configuration, object APIs, and SQL access.
This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.
This article is based on this Kaggle Notebook, which you can run and directly edit yourself.
Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.
This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.
Hi Community,
Enjoy the new video on InterSystems Developers YouTube:
⏯ Productive Data Science with InterSystems IRIS and Python @ Ready 2025
Hi Community,
Enjoy the new video on InterSystems Developers YouTube:
⏯ Succeeding with Python Development on InterSystems IRIS @ Ready 2025
Solar irradiance forecasting is critical for grid stability in photovoltaic (PV) power plants. This article replicates and extends the methodology of Lara-Benítez et al. (2023) "Short-term solar irradiance forecasting in streaming with deep learning" replacing the original offline simulation with a fully operational streaming pipeline built on InterSystems IRIS. We leverage IRIS Interoperability Productions as the streaming backbone, Embedded Python to run MLP, LSTM, and CNN deep learning models, and IntegratedML as an AutoML baseline.
This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:
https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models
https://github.com/JorgeIvanJH/Explainability-in-ML-models
We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline
Ever since I started using IRIS, I have wondered if we could create agents on IRIS. It seemed obvious: we have an Interoperability GUI that can trace messages, we have an underlying object database that can store SQL, Vectors and even Base64 images. We currently have a Python SDK that allows us to interface with the platform using Python, but not particularly optimized for developing agentic workflows. This was my attempt to create a Python SDK that can leverage several parts of IRIS to support development of agentic systems.
Senior engineering is defined not by the volume of code produced, but by the strategic avoidance of it. In complex integration environments, the tendency to utilize general-purpose libraries for every niche requirement introduces unnecessary overhead. True architectural maturity requires a commitment to "minimalist tooling"—prioritizing resilient, battle-tested system utilities over custom logic. This assessment examines our PGP encryption/decryption pipeline to demonstrate how shifting from application-level libraries to OS-native delegation enhances system durability.
Today I have published a new Open Exchange package for generation of Synthetic Data directly into IRIS.
It can be a frustrating process to find decent datasets when you are looking to make a demo app. Maybe the dataset doesn't matter that much, but you still want it to appear somewhat genuine and with several linked tables that are usable directly within IRIS with the neat implicit joins with ->. Maybe you just want linked tables that are easily installable with IPM to benchmark queries, this dataset generation would be perfect.
A microservice is an architectural style that structures an application as a collection of small, autonomous services. Each component is developed around a specific business capability, can be deployed independently, and is typically managed by a miniature, specialized, self-governing team. (Source: https://microservices.io/)
I'm a huge sci-fi fan, but while I'm fully onboard the Star Wars train (apologies to my fellow Trekkies!), but I've always appreciated the classic episodes of Star Trek from my childhood. The diverse crew of the USS Enterprise, each masterminding their unique roles, is a perfect metaphor for understanding AI agents and their power in projects like Facilis. So, let's embark on an intergalactic mission, leveraging AI as our ship's crew and boldly go where no man has gone before
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:
Since I started using Claude Code, my motivation to create things has skyrocketed.
Previously, even if I wanted to build something, actually doing the coding felt like a hassle, so unless there was a very strong need, I rarely went as far as programming. But now, if I just jot down the specifications, Claude Code handles the rest automatically, resulting in a dramatic improvement in productivity.
I come from a generation native to ObjectScript, so I used to feel some hesitation when it came to switching to Python.
Working with files often starts off simple. open the file, read, and process. That approach works perfectly well, until the file happens to be an Excel file.
A Common Assumption
At first, an Excel file (.xlsx) looks like just another data file, rows, columns and values. nothing unusual. So it's natural to assume it can be read the same way as a .txt ot .csv file. But that's where things start to break.
Why Excel files behave differently
The key difference is how the data is stored:
-> .txt / .csv - plain text, line-by-line.
-> .
This article will introduce you to the concept of virtual environments in Python, which are essential for managing dependencies and isolating project from the OS.
A virtual environment is a folder that contains :
Virtual environments will help you to isolate your project from the OS Python installation and from other projects.
Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.
And now it is possible to use with InterSystems IRIS as well.
An online demo is available and it uses IRIS Cloud SQL as a data source.
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Apache Superset provides a bunch of examples, which were successfully loaded to IRIS without any issues, and displayed on example dashboards.
Hi all.
I have a rather strange problem.
I've created a method in Python to create a vector for a vector search. So far, so good.
If I call this method from the terminal, it works correctly:
But if I make this same call from a code block in a Business Process, it gets stuck, doesn't respond, and throws the following error:
Does anyone know what's happening and how to fix it?
Thank you in advance
Earlier this year, I set about creating kit to introduce young techy folk at a Health Tech hackathon to using InterSystems IRIS for health, particularly focusing on using FHIR and vector search.
I wanted to publish this to the developer community because the tutorials included in the kit make a great introduction to using FHIR and to building a basic RAG system in IRIS.
InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.
But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.
1 Introduction
In environments that use InterSystems IRIS, globals are the physical foundation of data storage. Although system queries and administrative tools exist for metric inspection, global growth analysis is usually reactive: the problem is generally only noticed when there is disk pressure or performance impact.
The facial recognition has become the most popular method for validating people's identities, thus enabling access to systems, confirmation of personal and documentary data, and approval of actions and documents.
The challenges are related to performance when the database is very large, accuracy, and especially the privacy of biometric facial data. For these challenges, nothing is better than using InterSystems Vector Search, as it allows:
Hello Community,
We are excited to announce that registration is now open for the second cohort of the course:
🧑💻 Developing FHIR Applications Using Python 🧑💻
This hands-on program is designed for developers who want to build real-world FHIR applications using Python and InterSystems IRIS for Health.
👉 Watch 5-minute course overview
📅 Second cohort starts March 29, 2026
I created iris-budget app for the InterSystems Full Stack Contest in 2026. By full stack, we mean a frontend web or mobile application that inserts, updates, or deletes data in InterSystems IRIS via REST API, Native API, ODBC/JDBC, or Embedded Python.
My app uses multiple REST APIs to add a new category or retrieve a list of categories of expenses and income.
I inherited /csp/coffee from module.xml in iris-fullstack-template.
For this project, I created a swagger file called "budget.json.