Imagine you’re walking down the street on a nice summer’s day, and someone walks up to you and says “Hey, you work at InterSystems, right? I’ve been hearing more and more about InterSystems IRIS lately. I know IRIS has its own programing language called ObjectBook? or InstaScript? OK, I admit it, I know it’s called ObjectScript! I know IRIS also supports Python. I’m a Python developer, so that sounds great to me. But I’m also interested in ObjectScript. For example, Python and other languages support collections. Does ObjectScript support collections?”

You’d answer “Of course!”

And then your new friend might get excited and start firing off more questions:

• How many kinds of collections does ObjectScript support?
• Can ObjectScript use Python collections?
• Can Python use ObjectScript collections?
• Which collection is best?

How would you answer? Well, you don’t have to worry about answering. All you’d have to do is send your new friend the URL of this long page.

Picture a dark room. The only light comes from a wall of displays and a blinking prompt that politely informs you:

“LINK TO M-U5K-3T: HIGH LATENCY – DIRECT CONTROL DISABLED.”

Perfect.

You are supposed to be a Rover Commander in the Musketeer Corps. Somewhere absurdly far away, you have an autonomous unit parked on a hostile exoplanet. It wants you dead, but it’s also covered in minerals that could fund a small civilization.

The ore on M-U5K-3T is everywhere. Unfortunately, everything else on the surface is actively trying to kill you.

So, we don't pilot. We script.

One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.

In this article, I aim to demonstrate a couple of methods for easily adding validation to REST APIs on InterSystems IRIS Data Platform. I believe a specification-first approach is an excellent idea for API development. IRIS already has features for generating an implementation stub from a specification and publishing that specification for external developers (use it with iris-web-swagger-ui for the best results). The remaining important thing not yet implemented in the platform is the request validator. Let's fix it!

PEP 578 added Python Audit hooks. A rich variety of events (module load, os interactions and so on) triggers audit events which you can subscribe to.

Here's how to do that. First create an embedded python hook:

Class User.Python
{

/// do ##class(User.Python).Audit()
ClassMethod Audit() [ Language = python ]
{
import sys
import time
def logger(event,args):
     if event=='import':
        module = args[0]
        print(f"Loading {module}")
        if module == "numpy":
            print(f"Module {module} forbidden. Terminating process in 3.")
            time.

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.

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.

Hi developers!
Happy holidays!

In ObjectScript there is neat option to log errors - call Log() method of an exception intance, e.g.:

ClassMethod MyMethod() {

Try {

w 1/0

}

Catch e {

do e.Log() // logging error in the app log

write "the error happened: "_e.DisplayString()

}

}

Is there anything like that for Embedded Python? e.g:

ClassMethod MyMethod() [Language = python] {

 try:

   print(1/0)

 except ZeroDivisionError as e:

   // how can I log e into IRIS App Error Log here?

   print("Caught exception: " + str(e))

}

Any ideas?

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

Hi everyone,
I'm getting prepared to take the following certification exam: "InterSystems IRIS Development Professional".

Can you give some advice on how to prepare (aside from the official course page: https://www.intersystems.com/certifications/intersystems-iris-development-professional/)?
Do you have examples of quiz questions that simulate the real exam or any material that helped you getting prepared?

Thanks you in advance,

Arber

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.

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
 

Class datagen.utli.decodefhirjson Extends %RegisteredObject
{
}

2. Write a Python function decodebase64docref to 

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.

Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

1. Create a class datagen.restservice which extends  %CSP.REST

Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json";
}

2.

With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?

Enter IRIS Audio Query - a full-stack application that transforms audio into a searchable knowledge base.

IRIS Audio Query is a full-stack application that transforms audio into a searchable knowledge base.

Project Structure

community/
├── app/                   # FastAPI backend application
├── baml_client/           # Generated BAML client code
├── baml_src/              # BAML configuration files
├── interop/               # IRIS interoperability components
├── iris/                  # IRIS class definitions
├── models/                # Data models and schemas
├── twelvelabs_client/     # TwelveLabs API client
├── ui/                    # React frontend application
├── main.

As part of a recent documentation technical project to optimize the search, I needed to use Embedded Python in my ObjectScript code. The main blocker was passing a Python list from a Python class method to a ObjectScript method. Sending the list by reference to the python method, populating it with the Insert() method, and returning the reference to the ObjectScript method resulted in an list with type %SYS.Python, a process that was straightforward but not efficient.
I explored an alternative method: converting a Python list to an ObjectScript list using JSON as the intermediary format.

Interoperability on Python (IoP) is a proof-of-concept project designed to showcase the power of the InterSystems IRIS Interoperability Framework when combined with a Python-first approach.IoP leverages Embedded Python (a feature of InterSystems IRIS) to enable developers to write interoperability components in Python, which can seamlessly integrate with the robust IRIS platform. This guide has been crafted for beginners and provides a comprehensive introduction to IoP, its setup, and practical steps to create your first interoperability component. By the end of this article, you will get a clear understanding of how to use IoP to build scalable, Python-based interoperability solutions.

This article is a continuation of the IRIS JSON project and features additional methods and insights.

Let's continue with the instance methods

%GetTypeOf(Key)

This instance method is used to determine the JSON data type of the %DynamicObject or %DynamicArray.

It returns one of the following strings:

USER>Set array = [1,"test",true,12.

I have a custom Buffer class which is designed to capture written/printed statements to the device, to be able to transform the captured text to string or stream type. I have used this in ObjectScript to capture ObjectScript write statements and return a string. I would like to try to use this with a [ Language = python ] method as follows. This class will be called by a scheduled task.

/// ObjectScript code which initializes buffer to capture statements written in nested method call
ClassMethod CollectStringFromBuffer() 
{
    set buffer = ##class(CustomClass.Buffer).%New()
    do buffer.

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.

I am looking to create a Python virtual environment (venv) so that my imported/installed python packages can be separate on different namespaces in IRIS. I am able to go and create an environment, activate it, and install packages, but I am not sure how to ensure that Embedded Python methods actually point to this virtual environment. 

Is the best solution to just load the virtual environment at runtime, in each method? That seems like a bad solution. Has anyone run into this and found a good solution?

I am writing this post primarily to gather an informal consensus on how developers are using Python in conjunction with IRIS, so please respond to the poll at the end of this article! In the body of the article, I'll give some background on each choice provided, as well as the advantages for each, but feel free to skim over it and just respond to the poll.

As most of you are probably aware, Python is currently the most popular programming language among developers around the world - and for good reason.

I was wondering what best practice was for using macros in Embedded Python, i.e. iris.execute('$$$MACRO()') or something else. Does anyone have insight into this?