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.

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:

• IRIS Vector Search usage -3
• InterSystems Native SDK for Python or Embedded Python usage -3
• Developer Community Idea implemented - 2
• Docker container usage -2 
• IPM Package Deployment - 2
• Online Demo -2 
• Find and report a bug - 2
• Article on Developer Community - 2
• The second article on Developer Community - 1
• Video on YouTube - 3
• YouTube Short - 1
• First Time Contribution - 3

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-graph package. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.

Why Fraud Detection Needs Graphs

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.

Embeddedpy-bridge: A Toolkit for Embedded Python

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.

The Rut

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.

How to Build Applications with LangGraph: A Step-by-Step Guide

Tags: #LangGraph #LangChain #AI #Agents #Python #LLM #StateManagement #Workflows

Hi everyone, I want to tell you a little about LangGraph, a tool that I'm studying and developing.

Basically traditional AI applications often face challenges when dealing with complex workflows and dynamic states. LangGraph offers a robust solution, enabling the creation of stateful agents that can manage complex conversations, make context-based decisions, and execute sophisticated workflows.

Apache Airflow is the leading open-source platform to programmatically author, schedule, and monitor data pipelines and workflows using Python. Workflows are defined as code (DAGs), making them version-controlled, testable, and reusable. With a rich UI, 100+ built-in operators, dynamic task generation, and native support for cloud providers, Airflow powers ETL/ELT, ML pipelines, and batch jobs at companies like Airbnb, Netflix, and Spotify.

Airflow Application Layout

How to set up RAG for OpenAI agents using IRIS Vector DB in Python

In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.

To demonstrate this, we’ll create an OpenAI agent with knowledge of InterSystems technology. We’ll achieve this by storing embeddings of some InterSystems documentation in IRIS and then using IRIS vector search to retrieve relevant content—enabling a Retrieval-Augmented Generation (RAG) workflow.

Note: Section 1 details how process text into embeddings.

We're excited to announce the Early Access Program for IntegratedML Custom Models, a powerful new capability coming in IRIS 2026.1!

What Is It?

IntegratedML Custom Models extends the existing IntegratedML/AutoML feature by letting you deploy your own custom Python ML models directly within SQL queries.

In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama

In the generative AI usage model that we are used to, we have the following flow:

• we take texts from a data source (a file, for example) and embedding that text into vectors
• we store the vectors in an IRIS database.
• we call an LLM (Large Language Model) that accesses these vectors as context to generate responses in human language.

Hi community!

I am excited to say that since the beginning of this year we have published many of the client SDKs for InterSystems IRIS, InterSystems IRIS for Health and Health Connect to the corresponding external repositories (Maven, NuGet, npm and PyPI).

Hi,

When we open a terminal in IRIS, we are entering the ObjectScript shell. Within this shell, we can execute IRIS commands, such as:

In other words, the ObjectScript command is executed in the current shell. But it's always good to remember that IRIS has other shells

• SQL
• Python
• TSQL
• MDX

One very interesting aspect is shortcuts. We can access these shells through their calls or via shortcuts, as shown in the table below:

Hi Community,

In the first part of this series, we examined the fundamentals of Interoperability on Python (IoP), specifically how it enables us to construct such interoperability elements as business services, processes, and operations using pure Python.

Now, we are ready to take things a step further. Real-world integration scenarios extend beyond simple message handoffs.They involve scheduled polling, custom message structures, decision logic, filtering, and configuration handling.In this article, we will delve into these more advanced IoP capabilities and demonstrate how to create and run a more complex interoperability flow using only Python.

To make it practical, we will build a comprehensive example: The Reddit Post Analyzer Production. The concept is straightforward: continuously retrieving the latest submissions from a chosen subreddit, filtering them based on popularity, adding extra tags to them, and sending them off for storage or further analysis.

The ultimate goal here is a reliable, self-running data ingestion pipeline. All major parts (the Business Service, Business Process, and Business Operation) are implemented in Python, showcasing how to use IoP as a Python-first integration methodology.

Hey Community,

We're happy to invite you to a LinkedIn Live session in Dutch

🎤 Data exchange in healthcare according to the FHIR standard with Python 🎤

Event details:
📅 Date: Thursday, November 27th
🕓 Time: 1:30-2:15 PM EST

Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.

I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.

Introduction

In InterSystems IRIS 2024.3 and subsequent IRIS versions, the AutoML component is now delivered as a separate Python package that is installed after installation. Unfortunately, some recent versions of Python packages that AutoML relies on have introduced incompatibilities, and can cause failures when training models (TRAIN MODEL statement). If you see an error mentioning "TypeError" and the keyword argument "fit_params" or "sklearn_tags", read on for a quick fix.

Root Cause

• scikit-learn updated to version 1.6.0, deprecating fit_params.

Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ A Day in the Life of a Developer - Data Platforms Roundup @ Ready 2025

Hi all,

Let's do some more work about the testing data generation and export the result by REST API.😁

Here, I would like to reuse the datagen.restservice class which built in the pervious article Writing a REST api service for exporting the generated patient data in .csv

This time, we are planning to generate a FHIR bundle include multiple resources for testing the FHIR repository.

Here is some reference for you, if you want to know mare about FHIR The Concept of FHIR: A Healthcare Data Standard Designed for the Future

OK... Let's start😆

1. Create a new utility class datagen.utli.

I am documenting a demo of InterSystems IRIS featuring Embedded Python and Jupyter Notebook deployed on the same container, and an Embedded Python application developed on that Jupyter Notebook IDE.

I have used the Docker container created by @Bob Kuszewski as a development environment to demonstrate how Embedded Python app can be developed in such a setting to push and retrieve data to and from InterSystems IRIS. The benefit of using this container as the development environment is that it is a virtual environment with Jupyter IDE and IRIS connected and running side by side.

Hi Community,

Are you a Python developer? If so, you can already start building apps with InterSystems IRIS without learning a new programming language!

👨‍💻Try this exercise to get started quickly with using Python's familiar DB-API interface to connect to an InterSystems IRIS database and run SQL queries.

💬What was your experience with the exercise? Let me know in the comments!

Hi all,

It's me again 😁. In the pervious article Writing a REST api service for exporting the generated FHIR bundle in JSON, we actually generated a resource DocumentReference, with the content data encoded in Base64

Question!! Is it possible to write a REST service for decoding it? Because I am very curious what is the message data talking about🤔🤔🤔

OK, Let's start!

1. Create a new utility class datagen.utli.decodefhirjson.cls for decoding the data inside the DocumentReference
 

ClassExtends%RegisteredObject

2. Write a Python function decodebase64docref to 
a. loop through the FHIR bundle
b.

I am trying to add Plotly Bar graph in a div to a CSPpage. I am working in IRIS 2022.1. I created persistent class.

I copied relevant code into github repo:

oliverwilms/iris-python-plotly
 

iris-python-plotly/csp/otwPlotly.csp at master · oliverwilms/iris-python-plotly
 

Set tPlotlyDiv = ##class(dc.python.test).PlotlyDiv(trnYear,trnMonth)

ClassMethod PlotlyDiv(pTrnYear = 2025, pTrnMonth = 8) As %String
{
    Set importlib = ##class(%SYS.Python).Import("importlib")
    Set plotdiv = ##class(%SYS.Python).Import("plotdiv")
    Do importlib.reload(plotdiv)
    Set div = plotdiv.