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 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.
In the modern healthcare landscape, finding clinically similar patients often feels like looking for a needle in a haystack. Traditional keyword searches often fail because medical language is highly nuanced; a search for "Heart Failure" might miss a record containing "Congestive Cardiac Failure."
I am excited to share iris-medmatch, an AI-powered patient matching engine built on InterSystems IRIS for Health. By leveraging Vector Search, this tool understands clinical intent rather than just matching literal strings.
Hi Community!
We’re excited to announce the launch of a new, hands-on training program:
🧑💻 Developing FHIR Applications Using Python 🧑💻
This cohort-based course takes developers from FHIR fundamentals to advanced, real-world healthcare interoperability solutions, with deeper, more practical coverage than typical industry offerings and a strong focus on production-ready skills using InterSystems technology.
Hello everyone,
I’m looking to implement Continuous Training (CT) as part of an MLOps strategy for some data science projects in IRIS. I want to automate the full cycle:
- Monitoring model performance & accuracy degradation.
- Retraining models automatically.
- Validating and updating production models.
I’ve looked into IntegratedML, but it seems more focused on the SQL interface for training (AutoML). Even with the new Custom Models (beta), which allows for more flexibility with Python, it doesn't seem to provide the "Continuous" orchestration out of the box.
I’d like to know:
Intersystems IRIS Productions provide a powerful framework for connecting disparate systems across various protocols and message formats in a reliable, observable, and scalable manner. intersystems_pyprod, short for InterSystems Python Productions, is a Python library that enables developers to build these interoperability components entirely in Python. Designed for flexibility, it supports a hybrid approach: you can seamlessly mix new Python-based components with existing ObjectScript-based ones, leveraging your established IRIS infrastructure.
1-command only required for an entire IRIS instance for Data Science projects, and leveraging this to compare query methods' speed (Dynamic SQL, Pandas Query, and Globals).
Before joining InterSystems, I worked in a team of web developers as a data scientist. Most of my day-to-day work involved training and embedding ML models in Python-based backend applications through microservices, mainly built with the Django framework and using Postgres SQL for sourcing the data.
Here are the technology bonuses for the InterSystems Full Stack Contest 2026, which will give you extra points in the voting:
See the details below.
Vector search is a retrieval method that converts text, images, audio, and other data into numeric vectors using an AI model, and then searches for items that are semantically close. It enables “semantic similarity search” from free text, which is difficult with keyword search alone.
However, in real use, I encountered cases where results that are “close in meaning” but logically the opposite appeared near the top of the search results.
This is a serious issue in situations where affirmation vs. negation matters. If the system returns the wrong answer, the impact can be significant, so we cannot ignore this problem.
This article does not propose a new algorithm. I wrote it to share a practical way I found useful when semantic search fails due to negation.
TL;DR: This article demonstrates how to run GraphRAG-style hybrid retrieval—combining vector similarity, graph traversal, and full-text search—entirely within InterSystems IRIS using the
iris-vector-graphpackage. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.
Every year, businesses and consumers lose billions to fraud. In 2024 alone, consumers reported $12.5 billion lost—a 25% increase year over year. What makes modern fraud so difficult to detect is that fraudsters rarely work alone.
Overview
Embedded Python is a game-changer for InterSystems IRIS, offering access to the vast Python ecosystem directly within the database. However, bridging the gap between ObjectScript and Python can sometimes feel like translating between two different worlds.
To make this transition seamless using embeddedpy-bridge.
This package is a developer-centric utility kit designed to provide high-level ObjectScript wrappers, familiar syntax, and robust error handling for Embedded Python.
Up until early this year, I haven't been not doing much coding at all -- I had gotten sick of it.
After many years as a hands-on software engineer and data scientist, I got burned out around 2015. I switched to business development roles focused on "external innovation," then joined InterSystems in 2019 as a product manager. I missed the creative aspects of coding, but not the tedium. The endless cycle of boilerplate, debugging, and context-switching had left me creatively depleted.