#Python

An experiment on how to use the LangChain framework, IRIS Vector Search, and LLMs to generate IRIS-compatible SQL from user prompts.

This article was based in this notebook. You can run it with a ready to use environment with this application in OpenExchange.

Setup

First, we need to install the necessary libraries:

Frontend development can be a daunting, even nightmarish, task for backend-focused developers. Early in my career, the lines between frontend and backend were blurred, and everyone was expected to handle both. CSS, in particular, was a constant struggle; it felt like an impossible mission.

InterSystems has been at the forefront of database technology since its inception, pioneering innovations that consistently outperform competitors like Oracle, IBM, and Microsoft.

Hello community,

How to install the iris package installer for Python (pip) in python and import iris package in python source code.

import iris
class Solution():
    iris.connection()

thanks!

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.

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.

After so many years of waiting, we finally got an official driver available on Pypi

Additionally, found JDBC driver finally available on Maven already for 3 months, and .Net driver on Nuget more than a month.

As an author of so many implementations of IRIS support for various Python libraries, I wanted to check it. Implementation of DB-API means that it should be replaceable and at least functions defined in the standard. The only difference should be in SQL.

And the beauty of using already existing libraries, that they already implemented other databases by using DB-API standard, and these libraries already expect how driver should work.

I decided to test InterSystems official driver by implementing its support in SQLAlchemy-iris library.

For a long time I have wanted to learn the Django framework, but another more pressing project has always taken priority. Like many developers, I use python when it comes to machine learning, but when I first learned web programming PHP was still enjoying primacy, and so when it was time for me to pick up a new complicated framework for creating web applications to publish my machine learning work, I still turned to PHP.

Problems with Strings

I am accessing IRIS databases with JDBC (or ODBC) using Python. I want to fetch the data into a pandas dataframe to manipulate the data and create charts from it. I ran into a problem with string handling while using JDBC. This post is to help if anyone else has the same issues. Or, if there is an easier way to solve this, let me know in the comments!

I am using OSX, so I am unsure how unique my problem is. I am using Jupyter Notebooks, although the code would generally be the same if you used any other Python program or framework.

Interoperability of systems ensures smooth workflow and management of data in today's connected digital world. InterSystems IRIS extends interoperability a notch higher with its Embedded Python feature, which lets developers seamlessly integrate Python scripts into the IRIS components, like services, operations, and custom functions.

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

https://www.youtube.com/embed/IyvuHbxCwCY
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Git link: https://github.com/ecelg/InterSystems-IRIS-as-a-Spotify-REST-client

Recently, I come up an idea in my mind that how can I put my playlist on IRIS.🧐

At the same time, I was told to pay for my Spotify subscription💸💸... ooo.. how about to get some data from the Spotify API... so I started to do study about it.

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.

Hi, Community!

In the previous article, we introduced the Streamlit web framework, a powerful tool that enables data scientists and machine learning engineers to build interactive web applications with minimal effort. First, we explored how to install Streamlit and run a basic Streamlit app. Then, we incorporated some of Streamlit's basic commands, e.g., adding titles, headers, markdown, and displaying such multimedia as images, audio, and videos.

Later, we covered Streamlit widgets, which allow users to interact with the app through buttons, sliders, checkboxes, and more. Additionally, we examined how to display progress bars and status messages and organize the app with sidebars and containers. We also highlighted data visualization, using charts and Matplotlib figures to present data interactively.

In this article, we will cover the following topics: