#Embedded Python

Hi Developers,

Python has a large and powerful ecosystem that contains thousands of libraries and packages available, especially in data science.

Therefore, I wanted to have a first try in using a recent feature of IRIS called Embedded Python, to simply import a python library called datetime, generate data with a timestamp component and persist it in InterSystems IRIS for Health Data Platform. The same will work on IRIS Data Platform as well.

Continuing to observe the possibilities of Django, and usage with IRIS. The first we have looked how to define models and connect to tables already existing in IRIS, than we extended embedded Django Administration portal, with an ability to see what data we have in that models, with filters, editing and even pagination.

Time to go to real action, now we a going to create some REST API, on Django, based on the same data, we used before from the package posts-and-tags.

To do so, we will use Django REST Framework

Django REST framework is a powerful and flexible toolkit for building Web APIs.

Some reasons you might want to use REST framework:

• The Web browsable API is a huge usability win for your developers.
• Authentication policies including packages for OAuth1a and OAuth2.
• Serialization that supports both ORM and non-ORM data sources.
• Customizable all the way down - just use regular function-based views if you don't need the more powerful features.
• Extensive documentation, and great community support.
• Used and trusted by internationally recognised companies including Mozilla, Red Hat, Heroku, and Eventbrite.

You may have seen my series of articles on GlobalToJSON closing with Embedded Python.
And to me, Embedded means inside a Class and not PEX or other external variants.
The first variants showed that ePy is not an ultimate requirement. ISOS/COS may do it as well.
Here I try to position ePy inside IRIS.

Hi Community
In this article, I will demonstrate the functionality of my app iris-geo-map

I recently had the need to monitor from HealthConnect the records present in a NoSQL database in the Cloud, more specifically Cloud Firestore, deployed in Firebase. With a quick glance I could see how easy it would be to create an ad-hoc Adapter to make the connection taking advantage of the capabilities of Embedded Python, so I got to work.

TL;DR: This article describes an introductory-level project that exercises some string-manipulation functions in both ObjectScript and Python…with specs from a song.

Methods written in ObjectScript can use pass-by-reference arguments to return information to the caller. Python doesn’t support pass-by-reference arguments, so Embedded Python in IRIS doesn’t support them either. That's it, that's the end of the post, hope you liked it. 😉 But wait, what about the Classic Rock & Roll?

Hi Community

In this article, I will introduce my application IRIS-GenLab.

IRIS-GenLab is a generative AI Application that leverages the functionality of Flask web framework, SQLALchemy ORM, and InterSystems IRIS to demonstrate Machine Learning, LLM, NLP, Generative AI API, Google AI LLM, Flan-T5-XXL model, Flask Login and OpenAI ChatGPT use cases.

Overview

The online documentation contains a reference Defining and Using Class Queries -

Customizing Stored Procedures with ObjectScript directly has been useful to access NoSQL storage and external messaging via integration, to present output in tabular format.

Excuse if this is obvious to Python programmers but for those crossing over from ObjectScript this may be a useful tip.

The scenario is developing some Embedded python commands.

Testing out functionality is being confirmed via the shell:

$SYSTEM.Python.Shell()
 
Python 3.9.5 (default, Mar 14 2023, 06:58:44) [MSC v.1927 64 bit (AMD64)] on win32
Type quit() or Ctrl-D to exit this shell.
>>>

When Python evaluates an expression in the shell, it prints the result of the expression to the terminal.

Earlier this year I announced availability of a VS Code extension for coding in ObjectScript, Embedded Python or SQL using the notebook paradigm popularized by Jupyter. Today I published a maintenance release to correct a "getting started" problem.

Here's a video of the installation steps from the extension's README:

Why not try it for yourself?

In my previous articles, I described my Command Line Extension to NativeAPI.
Of course, this is also available for any other NativeAPI package.
So I created this example in Python as a demo.

As you have seen in the latest community publications, InterSystems IRIS has included since version 2024.1 the possibility of including vector data types in its database and based on this type of data vector searches have been implemented. Well, these new features reminded me of the article I published a while ago that was based on facial recognition using Embedded Python.

This example demonstrates the difference you may experience when you write to
Gllobals directly from Embedded Python compared to native ObjectScript.

To make this demo useful I start 2 background jobs that simply write sequentially
to a dedicated global. A common control method signals for a synchronous start.
Similar a common stop & view interrupts data feeding.

That's the principle process: