#Embedded Python

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.

When I install the numpy package on Python on my PC using:

pip install numpy

I can use it in python on my machine. If I do:

pip install --target C:\InterSystems\IRIS\mgr\python numpy

I get errors any time I try to import it in embedded python.

Traceback (most recent call last):
  File "c:\intersystems\iris\mgr\python\numpy\_core\__init__.py", line 23, in <module>
    from . import multiarray
  File "c:\intersystems\iris\mgr\python\numpy\_core\multiarray.py", line 10, in <module>
    from . import overrides
  File "c:\intersystems\iris\mgr\python\numpy\_core\overrides.

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?

1. A Motivating Example

Embedded Python has been around for a while. You probably followed the tutorials and learned about it. However, if you've tried to combine Python and ObjectScript in real development work, you probably ran into situations where you get an error message like this:

USER>Do ##class(MyClass).SomeMethod()

ERROR #5002: ObjectScript error: <PYTHON EXCEPTION> *<class 'ZeroDivisionError'>: division by zero

It's a long string for limited information.

Two big changes for the TestCoverage open source tool: Embedded Python support, and a new UI 

Embedded Python

Previously, TestCoverage could only track unit test coverage for code written in ObjectScript. It would ignore code written in other languages like Python in the coverage statistics.

As more and more IRIS application code is being written in Embedded Python instead of just ObjectScript, it's critical that TestCoverage can include coverage results for Embedded Python code.

Why I've decided to write this

In my last article I've talked about returning values with Python. But returning them is simple, what can make it harder is what I'm going to talk about today: where the value is treated.
 

Python object in IRIS

Following the example of the last aricle, we have the method:

Class python.returnTest [ Abstract ]
{

ClassMethod returnSomething(pValue... As %String) As %Integer [ Language = python ]
{
	return pValue
}

}

Then, we'll have as a return a Python object, that IRIS interprets as  the class %SYS.

Google Forms is the most popular solution on the market for collecting data, answering questionnaires and quizzes. So, it is the ideal solution for collecting patient data and responses in a practical way, without the need to expand or develop systems. In this article, I will detail how to create an account on Google Cloud, register the application that will consume the Google Forms API, generate the service user necessary to consume the API and finally perform actions to create new forms and collect data filled in them in an automated way in embedded Python and IRIS.

Template engines are essential for producing customizable dynamic content, without the need for compilation and opening up great possibilities of extensibility for the template user. Especially if the template engine supports Python. A popular solution for template engine is Mko. It is is a template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and API borrows from the best ideas of many others, including Django and Jinja2 templates, Cheetah, Myghty, and Genshi.

Are you using Jupyter Notebooks with IRIS? Are you using the vscode-iris-jupyter-server VS Code extension for your notebooking? If so, please let me know either via direct message or with a comment on this post. I'd like to hear more about how our customers are working with tool specifically, and with data science more generally.

Thanks!

Hello IRIS Fans and Welcome to IRIS iRacing!

Here were going to take 3 laps of your time and demonstrate how I wired up my Racing SIM to IRIS for "As Real Time as It Gets" Metrics reporting.  I missed the window for the contest, which happens quite often, but I still ended up 3rd I think in the demo race in the video below.

Technical Salad

Below are the technical ingredients for this demonstration for a salad you can post on Instragram.

• Python iRacing SDK ( https://github.

Hi, Community!

🐍Great news—you can now use Python to customize your productions and add custom code!

Using Embedded Python in the BPL and DTL Editors

I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system.

For my hundredth article on the Developer Community, I wanted to present something practical, so here's a comprehensive implementation of the GPG Interoperability Adapter for InterSystems IRIS.

Every so often, I would encounter a request for some GPG support, so I had several code samples written for a while, and I thought to combine all of them and add missing GPG functionality for a fairly complete coverage. That said, this Business Operation primarily covers data actions, skipping management actions such as key generation, export, and retrieval as they are usually one-off and performed manually anyways. However, this implementation does support key imports for obvious reasons. Well, let's get into it.

Interested in sharing your thoughts? Sign up to participate here.

Hello Community,

I got the PROTECT error while running functions. But, I could able to call the classmethods and methods in class definition with classMethodObject, classMethodValue etc.. from python. without any errors
python code

irispy.functionString('fnString','IRISPython',14)
irispy.function('fnString','IRISPython',14)

raise RuntimeError(error_message)
RuntimeError: <PROTECT> *Function not allowed

IRISPython.mac
fnString(fn1) public {
  quit "Hello "_fn1
}

​

I'm exploring this right now: given a bunch of types defined as Pydantic models, how can I come up with an equivalent %RegisteredObject/%SerialObject and convert to/from (e.g., to support persistence and match validation as much as possible)?

People who know Python better than I do (e.g., your average undergraduate from this decade): is this a stupid idea or a cool idea? Has anyone else done this before?

Hi Community,
Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.
This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

1. Create Agent Tools
   • Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
   • Implement Vector Search Functionality
2. Create Vector Search Agent
3. Handoff to Triage (Main Agent)
4. Run The Agent 

Hi Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ Embedded Python Best Practices and News @ Global Summit 2024

You can learn a lot from a first impression; we want to hear about yours.

As a continuation of our User Insights Interview program (see this post for more details), we’re expanding our scope to include Python developers, particularly those of you who are new to working with InterSystems technology. We’re looking to conduct one-on-one interviews to hear your honest thoughts about getting started: What made sense, what didn’t, and where we could improve.

Interested in sharing your thoughts? Sign up to participate here.

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.

I just realized I never finished this serie of articles!

In today's article, we'll take a look at the production process that extracts the ICD-10 diagnoses most similar to our text, so we can select the most appropriate option from our frontend.

Looking for diagnostic similarities:

From the screen that shows the diagnostic requests received in HL7 in our application, we can search for the ICD-10 diagnoses closest to the text entered by the professional.

To speed up the search process, we stored the vectorized text of the diagnosis received at the time of capturing the HL7 message in our database.

Prompt

Firstly, we need to understand what prompt words are and what their functions are.

Prompt Engineering

Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.

With the introduction of vector data types and the Vector Search functionality in IRIS, a whole world of possibilities opens up for the development of applications and an example of these applications is the one that I recently saw published in a public contest by the Ministry of Health from Valencia in which they requested a tool to assist in ICD-10 coding using AI models.

How could we implement an application similar to the one requested? Let's see what we would need:

1. List of ICD-10 codes, which we will use as context for our RAG application to search for diagnoses within the plain texts.

InterSystems Announces General Availability of InterSystems IRIS, InterSystems IRIS for Health, and HealthShare Health Connect 2025.1

The 2025.1 release of InterSystems IRIS® data platform, InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect is now Generally Available (GA). This is an Extended Maintenance (EM) release.