Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.

The installation steps can be viewed in the readme file. It should be noted that the BERT model used in the example has some memory requirements. If there is a long-term stuck situation during the testing process, other models such as MiniLM (which is used in the online demo) can be considered.

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

For my intern project, I am building a Flask REST API backend. My goal is to host it on InterSystems IRIS using the WSGI interface. This is a relatively new approach and is currently only being used in a handful of projects such as AskMe. To help others get started, I decided to write this article to simplify the process.

Creating a Basic Flask App

First, let’s create a minimal Flask application. Here is the code:

from flask import Flask
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

@app.route('/test')
def test():
    return "Test"
if __name__ == "__main__":
    app.

This will be a short article about Python dunder methods, also known as magic methods.

What are Dunder Methods?

Dunder methods are special methods in Python that start and end with double underscores (__). They allow you to define the behavior of your objects for built-in operations, such as addition, subtraction, string representation, and more.

Some common dunder methods include:

• __init__(self, ...): Called when an object is created.
  • Like our %OnNew method in ObjectScript.
• __str__(self): Called by the str() built-in function and print to represent the object as a

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

• 🧠 Python + smolagents to orchestrate the agent’s “brain”
• 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

This article will introduce you to the concept of virtual environments in Python, which are essential for managing dependencies and isolating project from the OS.

What is a Virtual Environment?

A virtual environment is a folder that contains :

• A specific version of Python
• At start an empty site-packages directory

Virtual environments will help you to isolate your project from the OS Python installation and from other projects.

How to use it?

Introducing Smart Clinical Sidechick — the intelligent, no-drama partner your EHR wishes it could be. She reads FHIR data in real time, interprets lab results without ghosting, and explains clinical alerts like she actually cares. Built with GPT-4 brains and YAML sass, she’s not here to replace your main EHR—just to make it look bad. Tired of irrelevant alerts and cryptic warnings? Sidechick serves up real, explainable insights, not vague “elevated risk” vibes. And when your backend crashes, she doesn’t panic—she self-heals.

In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

You know that feeling when you get your blood test results and it all looks like Greek? That's the problem FHIRInsight is here to solve. It started with the idea that medical data shouldn't be scary or confusing – it should be something we can all use. Blood tests are incredibly common for checking our health, but let's be honest, understanding them is tough for most folks, and sometimes even for medical staff who don't specialize in lab work. FHIRInsight wants to make that whole process easier and the information more actionable.

This project is implemented as a tool for viewing temperature data taken from a temperature sensor installed in Rasperry PI and located in a remote place where there is Internet, for example, in dacha.

The module for regular reading and sending information is implemented in python and is built in the same project. If the temperature in the country house becomes lower or higher than the threshold values, then a notification will be sent to the telegram channel.

🛠️ Managing InterSystems InterSystems API Manager (IAM = Kong Gateway) configurations in CI/CD

🔍 Context: InterSystems IAM configurations 

As part of integrating InterSystems IRIS into a secure and controlled environment, InterSystems IAM relies on Kong Gateway to manage exposed APIs.
Kong acts as a modern API Gateway, capable of handling authentication, security, traffic management, plugins, and more.

However, maintaining consistent Kong configurations (routes, services, plugins, etc.) across different environments (development, testing, production) is a major challenge.

Last Chapter: Creating a REST client to get Tracks from Spotify REST API - Part2 Save and Refresh Token

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

Ok, now I am pretty sure i have a valid token for making query.😀

Shall we try to query something from the API.

Again, its time to go through the API document https://developer.spotify.com/documentation/web-api/tutorials/getting-started

Search for Request artist data

the suggested code is like the following

curl "https://api.spotify.

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?

Using Flask, REST API, and IAM with InterSystems IRIS

Part 3 – IAM

InterSystems API Manager (IAM) is a component that allows you to monitor, control, and manage traffic from HTTP-based APIs. It also acts as an API gateway between InterSystems IRIS applications and servers.

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 1 - SQL Gateway

Hello

In this article we will look at the use of SQL Gateway in Iris. SQL Gateway allows Iris to have access to tables from other (external) database via ODBC or JDBC. We can access Tables or Views from various databases, such as Oracle, PostgreSQL, SQL Server, MySQL and others.

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.

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.

In this post, we'll discuss our project that leverages Pulumi and Docker Compose to automate the deployment of InterSystems WSGI applications on AWS. The focus is on simplicity and efficiency, using pre-built infrastructure templates for provisioning and scaling AWS resources.

Overview

This repository automates the deployment of a WSGI-based application using AWS infrastructure templates and Pulumi’s Python SDK. The infrastructure is provisioned with Pulumi's declarative approach, while Docker Compose handles application orchestration.

INTRO

Barricade is a tool developed by ICCA Ops to streamline and scale support for FHIR-to-OMOP transformations for InterSystems OMOP. Our clients will be using InterSystems OMOP to transform FHIR data to this OMOP structure. As a managed service, our job is to troubleshoot any issues that come with the transformation process. Barricade is the ideal tool to aid us in this process for a variety of reasons. First, effective support demands knowledge across FHIR standards, the OHDSI OMOP model, and InterSystems-specific operational workflows—all highly specialized areas.

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.

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

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 
a

Intersystems IRIS Productions provide a powerful framework for connecting disparate systems across various protocols and message formats in a reliable, observable, and scalable manner. intersystems_pyprod, short for InterSystems Python Productions, is a Python library that enables developers to build these interoperability components entirely in Python. Designed for flexibility, it supports a hybrid approach: you can seamlessly mix new Python-based components with existing ObjectScript-based ones, leveraging your established IRIS infrastructure.

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 2 – Python and Vector Search

Since we have access to the data from our external table, we can use everything that Iris has to offer with this data. Let's, for example, read the data from our external table and generate a polynomial regression with it.

For more information on using python with Iris, see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=AFL_epython

Let's now consume the data from the external database to calculate a polynomial regression. To do this, we will use a python code to run a SQL that will read our MySQL table and turn it into a pandas dataframe:

I implemented a Python Flask application for the 2024 Python Contest with a page that provides common form fields for an outgoing email such as the To and CC fields. And it lets you input a message as well as uploading text based attachments.
Then using LlamaIndex in Python, the app analyzes the content you put in and returns to you in a result box if there is anything that should stop you from sending that email.
Take a look at the Github repo here.

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 3 – REST and Interoperability

Now that we have finished the configuration of the SQL Gateway and we have been able to access the data from the external database via python, and we have set up our vectorized base, we can perform some queries. For this in this part of the article we will use an application developed with CSP, HTML and Javascript that will access an integration in Iris, which then performs the search for data similarity, sends it to LLM and finally returns the generated SQL. The CSP page calls an API in Iris that receives the data to be used in the query, calling the integration. For more information about REST in the Iris see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cl…

Hi everyone! 👋
I’m excited to share the project I’ve submitted to the current InterSystems .Net, Java, Python, and JavaScript Contest — it’s called IRIStool and Data Manager, and you can find it on the InterSystems Open Exchange and on my GitHub page.

Using Flask, REST API, and IAM with InterSystems IRIS

Part 2 – Flask App

Flask is a web development microframework written in Python. It is known for being simple, flexible, and enabling rapid application development.

Installing Flask is very simple. Once you have python installed correctly on your operating system, we need to install the flask library with the pip command. For REST API consumption, it is advisable to use the requests library. The following link provides a guide to installing flask: https://flask.palletsprojects.com/en/stable/installation/