InterSystems Native SDK for Python is a lightweight interface to InterSystems IRIS APIs that were once available only through ObjectScript.

I'm especially interested in the ability to call ObjectScript methods, class methods, to be precise. It works, and it works great, but by default, calls only support scalar arguments: strings, booleans, integers, and floats.

But if you want to:

• Pass or return structures, such as dicts or lists
• Pass or return streams

You'll need to write some glue code or take this project (installs with pip install edpy). edpy package gives you one simple signature:

call(iris, class_name, method_name, args)

which allows you to call any ObjectScript method and get results back.

InterSystems IRIS 2022.2 has Native SDK for Python (https://docs.intersystems.com/iris20222/csp/docbook/Doc.View.cls?KEY=PA…).

We know how to traverse a global data structure using IRIS Object Script $Order function.

SET key=""
   FOR  {
     SET key=$ORDER(^myglobal(key)) 
     QUIT:key=""
     WRITE !,^myglobal(key)
   }

How to do the same from Python using IRIS Native SDK for Python? Here is a code example:

import iris

args = {'hostname':'127.0.0.1', 'port':51772,
    'namespace':'USER', 'username':'_SYSTEM', 'password':'SYS'
    }

conn = iris.

Hi developers! In this article I’m going to explore the use of Iris Python Native API in a specific problem: large strings to store.

Why Python Native API?

Python Native API for Intersystems IRIS offers an useful way to persist data that you can’t have control over the schema or if the schema changes frequently. Combining the Native API with the IRIS Globals, you can easily use Iris Database as an Document Store. You can also see more details of globals use in documentation https://learning.intersystems.com/course/view.php?id=1110&ssoPass=1, or in my article https://community.intersystems.

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

Hello Community,

In this article, I will outline and illustrate the process of implementing ObjectScript within embedded Python. This discussion will also reference other articles related to embedded Python, as well as address questions that have been beneficial to my learning journey.

As you may know, the integration of Python features within IRIS has been possible for quite some time. This article will focus on how to seamlessly incorporate ObjectScript with embedded Python.

Essentially, embedded Python serves as an extension that allows for independent writing and execution.

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

While starting the development with IRIS we have a distribution kit or in case of Docker we are pulling the docker image and then often we need to initialize it and setup the development environment. We might need to create databases, namespaces, turn on/off some services, create resources. We often need to import code and data into IRIS instance and run some custom code to init the solution.

And there plenty of templates on Open Exchange where we suggest how to init REST, Interoperability, Analytics, Fullstack and many other templates with ObjectScript. What if we want to use only Python to setup the development environment for Embedded Python project with IRIS?

So, the recent release of Embedded Python template is the pure python boilerplate that could be a starting point for developers that build python projects with no need to use and learn ObjectScript. This article expresses how this template could be used to initialize IRIS. Here we go!

Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools. 

In this series of articles we explore Data mining capabilities available with InterSystems IRIS.

Named parameters can be achieved with SQLAlchemy :  

from sqlalchemy import create_engine, text,types,engine

_engine = create_engine('iris+emb:///')

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

or with native api

from sqlalchemy import create_engine, text,types,engine

# set URL for SQLAlchemy
url = engine.url.URL.create('iris', username='SuperUser', password='SYS', host='localhost', port=33782, database='FHIRSERVER')

_engine = create_engine(url)

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

In this article, exceptions are covered.

Working with Exceptions

Instead of returning a %Status response, you can raise and throw an Exception. You are then responsible for catching the exception and validating it. IRIS provides five main classes to handle exceptions effectively. Additionally, you can create custom exception class definition based on your needs.

These exceptions are different from %Status, so you can't directly/efficiently use the exception object with $SYSTEM.OBJ.DisplayError() or $SYSTEM.Status.DisplayError().

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

Here, models as example :

• https://huggingface.co/gpt2

• https://huggingface.co/Jean-Baptiste/camembert-ner

• https://huggingface.co/bert-base-uncased

• https://huggingface.co/facebook/detr-resnet-50

• https://huggingface.co/facebook/detr-resnet-50-panoptic

• 1. iris-huggingface

• 2. Installation

  • 2.1. Starting the Production
  • 2.

Let's say you have Python including variable-length arguments methods. How can you call it from ObjectScript? 

def test1(*args):
  return sum(args)
  
def test2(**kwargs):
  a1 = kwargs.get("a1",None)
  a2 = kwargs.get("a2",None)
  return a1+a2

You can call this "a.py" from ObjectScript as below.  For **kwargs argument, create Dynamic Object in ObjectScript and put it into methods with <variablename>... (3 dots) format. 

    set a=##class(%SYS.Python).Import("a")
    write a.test1(1,2,3)   ;; 6
    set req={}
    set req.a1=10
    set req.a2=20
    write a.

With the latest improvements in support for Python in IRIS, and continued work on Python DB-API support by InterSystems. I've implemented IRIS support to the Django project where is Python DB-API is used to work with some other databases.

Let's try a simple application on Django, which stores its data in IRIS.

Modules what a topic! We don't have this notion in ObjectScript, but it's a fundamental concept in Python. Let's discover it together.

What is a Module?

I see modules as an intermediate layer between classes and packages. Let see it by example.

A bad example :

# MyClass.py
class MyClass:
    def my_method(self):
        print("Hello from MyClass!")

When you try to use this class in another script, you would do:

# class_usage.py
from MyClass import MyClass # weird, right?

my_instance = MyClass()
my_instance.my_method()

Why this is a bad example?

The Jupyter Notebook is an open source web application that can be used to create and share documents that contain live text, code, equations and visualizations. It is maintained by the people at Project Jupyter.

Jupyter name comes from the core supported programming languages that it supports: Julia, Python, and R.
It allows users to write code in "cells," which are blocks of code that can be executed individually or together. This makes it easy to test and debug code, as well as to organize and present complex analyses.

Keywords: Python, JDBC, SQL, IRIS, Jupyter Notebook, Pandas, Numpy, and Machine Learning 

1. Purpose

This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data  into an IRIS database instance via SQL syntax, for demo purpose. 

Last year I touched on a brief note on Python binding into a Cache database (section 4.7) instance.

Index

Part 1

• Introducing Flask: a quick review of the Flask Docs, where you will find all the information you need for this tutorial;
• Connecting to InterSystems IRIS: a detailed step-by-step of how to use SQLAlchemy to connect to an IRIS instance;

Part 2

• A discussion about this kind of implementation: why we should use it and situations where it is applicable.
• The OpenExchange Application: if you are not interested in the development of this example, you can go directly to this section and learn how to use it as a template for your flask-iris application.

This article presents a potential solution for semantic code search in TrakCare using IRIS Vector Search.

Here's a brief overview of results from the TrakCare Semantic code search for the query: "Validation before database object save".

• 
  

  Code Embedding model 

There are numerous embedding models designed for sentences and paragraphs, but they are not ideal for code specific embeddings.

Three code-specific embedding models were evaluated: voyage-code-2, CodeBERT, GraphCodeBERT.

ipython-iris-magic

%%iris magic for IPython, can run ObjectScript command in Notebooks

It can be run in any Notebook and does not require any special setup on IRIS or the Notebook.

Demo

Native Api

1. Integrated ML Demonstration

This repository is a demonstration of IntegratedML and Embedded Python.

• 1. Integrated ML Demonstration
• 2. Building the demo
  • 2.1. Architecture
  • 2.2. Building the nginx container
• 3. Running the demo
• 4. Python back-end
  • 4.1. Embedded Python
    • 4.1.1. Setting up the container
    • 4.1.2. Using Embedded Python
    • 4.1.3. Side By Side Comparaison
  • 4.2. Launching the server
• 5. IntegratedML
  • 5.1. Exploring both datasets
  • 5.2. Managing models
    • 5.2.1. Creating a model
    • 5.2.2. Training a model
    • 5.2.3. Validating a model
    • 5.2.4. Making predictions
• 6. Using COS
• 7. More explainability with DataRobot
• 8. Conclusion

Hi Community,
In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Motivation

This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.

My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.

So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.

How does it work?

Introduction

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.

Hey, community! 👋

We are a team of Stanford students applying technology to make sense of climate action. AI excites us because we know we can quickly analyze huge amounts of text.

As we require more reports on sustainability, such as responsibility reports and financial statements, it can be challenging to cut through the noise of aspirations and get to the real action: what are companies doing

That’s why we built a tool to match companies with climate actions scraped from company sustainability reports.

Hi all. Today we are going to use k-means algorithm on the Iris Dataset.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, python 3.6.5.

Introduction

K-Means is one of the simplest unsupervised learning algorithms that solves the clustering problem. It groups all the objects in such a way that objects in the same group (group is a cluster) are more similar (in some sense) to each other than to those in other groups. For example, assume you have an image with a red ball on the green grass. K-Means will split all pixels into two clusters.

I am demonstrating a use case of how we can create an IRIS Interoperability Production for special use in an external language. InterSystems IRIS, within Interoperability has a framework called Production Extension (PEX), using which we can create productions and program them as per their purpose using external languages like Java, Python etc, and also develop custom inbound and outbound adapters to communicate with other applications. Here in this demo, I will demonstrate a PEX framework-based production created by @Guillaume.

If you're migrating from Oracle to InterSystems IRIS—like many of my customers—you may run into Oracle-specific SQL patterns that need translation.

Take this example:

SELECT (TO_DATE('2023-05-12','YYYY-MM-DD') - LEVEL + 1) AS gap_date
FROM dual
CONNECT BY LEVEL <= (TO_DATE('2023-05-12','YYYY-MM-DD') - TO_DATE('2023-05-02','YYYY-MM-DD') + 1);

In Oracle:

• LEVEL is a pseudo-column used in hierarchical queries (CONNECT BY). It starts at 1 and increments by 1.
• CONNECT BY LEVEL <= (...) determines how many rows to

Description

This is a template for a FastApi application that can be deployed in IRIS as an native Web Application.

Installation

1. Clone the repository
2. Create a virtual environment
3. Install the requirements
4. Run the docker-compose file

git clone
cd iris-fastapi-template
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
docker-compose up

Usage

The base URL is http://localhost:53795/fastapi/.

Endpoints

• /iris - Returns a JSON object with the top 10 classes present in the IRISAPP namespace.
• /interop - A ping endpoint to test the interoperability framework of IRIS.

Globals as a Graph Data Structure in Python

How to use the IRIS Native API + Python to see globals as a Graph Network Chart.

Iris Globals

Reading the documentation these topics are related to globals:

• A global consists of a set of nodes (in some cases, only one node), identified by subscripts.
• Each node can contain a value.
• ObjectScript includes functions to iterate through the nodes of a global and quickly access values.
• A global is automatically stored in the database. When you assign a value to a node of a global variable, the data is written immediately to the database.

   _________ ___ ____  
  |__  /  _ \_ _|  _ \ 
    / /| |_) | || |_) |
   / /_|  __/| ||  __/ 
  /____|_|  |___|_|    

Starting in version 2021.1, InterSystems IRIS began shipping with a python runtime in the engine's kernel. However, there was no way to install packages from within the instance. The main draw of python is its enormous package ecosystem. With that in mind, I introduce my side project zpip, a pip wrapper that is callable from the iris terminal.

What is zpip?