I'm using the PEX framework to create non-polling Business Services in Python. Below is the code I've used to instantiate my Python Business Service from the application I have written:

conn = iris.connect(...)
IRIS = iris.createIRIS(conn)
#...#"Demo.PEX.FlaskPEXService"

The Service works fine, messages are received properly by the target Business Process as defined by my PEX service, however, every time I run Director.CreateBusinessService(), the created job terminates 'unexpectedly' once I close the IRIS connection.

Introducing Django

Django is a web framework designed to develop servers and APIs, and deal with databases in a fast, scalable, and secure way. To assure that, Django provides tools not only to create the skeleton of the code but also to update it without worries. It allows developers to see changes almost live, correct mistakes with the debug tool, and treat security with ease.

To understand how Django works, let’s take a look at the image:

Hello,

I need AES ECB with PKSC7 padding for an interface.
Unfortunately, the %SYSTEM.Encryption.AESEncode cannot do this.

Therefore I wanted to include the following python lib.
PyCrptydome -> https://pycryptodome.readthedocs.io/en/latest/index.html

We need to install the package offline on the system. So I downloaded it and put it in the MGR/Python/ directory.

However, when I try to install it, I get the following error message:

E:\HealthConnect\20201\bin>irispip install E:\HealthConnect\20201\Mgr\python\pycryptodome-3.18.0.tar.

InterSystems IRIS currently limits classes to 999 properties.

But what to do if you need to store more data per object?

This article would answer this question (with the additional cameo of Community Python Gateway and how you can transfer wide datasets into Python).

The answer is very simple actually - InterSystems IRIS currently limits classes to 999 properties, but not to 999 primitives. The property in InterSystems IRIS can be an object with 999 properties and so on - the limit can be easily disregarded.

FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

Introducing the FHIR - AI and OpenAPI Chain application, a solution that allows users to interact with FHIR APIs using natural language queries.

Some time ago I introduced a new driver for Django for IRIS. Now, let's see how to use Django with IRIS in practice.

1. interoperability-embedded-python

This proof of concept aims to show how the iris interoperability framework can be used with embedded python.

1.1. Table of Contents

• 1. interoperability-embedded-python

  • 1.1. Table of Contents
  • 1.2. Example
  • 1.3. Register a component

• 2. Demo

• 3. Prerequisites

• 4. Installation

  • 4.1. With Docker
  • 4.2. Without Docker
  • 4.3. With ZPM
  • 4.4. With PyPI
    • 4.4.1. Known issues

• 5. How to Run the Sample

  • 5.1. Docker containers
  • 5.2. Management Portal and VSCode
  • 5.3. Open the production

• 6. What's inside the repository

  • 6.1. Dockerfile
  • 6.2. .vscode/settings.json
  • 6.3. .vscode/launch.json

   _________ ___ ____  
  |__  /  _ \_ _|  _ \ 
    / /| |_) | || |_) |
   / /_|  __/| ||  __/ 
  /____|_|  |___|_|    

Starting in version 2021.1, InterSystems IRIS began shipping with a python runtime in the engine's kernel. However, there was no way to install packages from within the instance. The main draw of python is its enormous package ecosystem. With that in mind, I introduce my side project zpip, a pip wrapper that is callable from the iris terminal.

What is zpip?

Description

With InterSystems IRIS FHIR Server you can build a Strategy to customize the behavior of the server (see documentation for more details).

This repository contains a Python Strategy that can be used as a starting point to build your own Strategy in python.

This demo strategy provides the following features:

• Update the capability statement to remove the Account resource
• Simulate a consent management system to allow or not access to the Observation resource
  • If the User has sufficient rights, the Observation resource is returned
  • Otherwise, the Observation resource is not returned

Idea sourced from InterSystems Ideas Portal

Previous Posts:

Using AI to Simplify Clinical Documents Storage, Retrieval, and Search

Doctor-Patient Conversations: AI-Powered Transcription and Summarization

Introduction

The healthcare industry is continuously evolving, and the need for efficient document management and patient data management is more critical than ever. In this article, we will focus on the specific aspects of integrating Google Docs and Google Sheets with FHIR data in the context of healthcare data interoperability.

Introduction

A password manager is an important security tool that allows users to store and manage their passwords without the need to remember or write them down in insecure places. In this article, we will explore the development of a simple password manager using the Flask framework and the InterSystems IRIS database.

Key Features

Our password manager application will provide the following key features:

1. User registration with account creation.
2. User authentication during login.
3. Adding new passwords with a title, login, and password.

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.

Our objective

In the last article, we talked about a few starters for Django. We learned how to begin the project, ensure we have all the requisites, and make a CRUD. However, today we are going a little further. 
Sometimes we need to access more complex methods, so today, we will connect IRIS to a Python environment, build a few functions and display them on a webpage. It will be similar to the last discussion, but further enough for you to make something new, even though not enough to feel lost.

As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation.
But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:

My major interest is  Working with Globals in Embedded Python.
So I checked the available official documentation.

#1 Introduction to Globals
an attempt of a generic description of what a global is. Pointing to

#2 A Closer Look at ObjectScript
But where is Embedded Python ?
Way down you see

#3 Embedded Python

3.1 Embedded Python Overview
3.1.1 Work with Globals
Great if you have never seen a Global before
Otherwise a shocking primitive example
3.2 Using Embedded Python
Last hope: >>> but there is just NOTHING visible.
This is more than just disappointing! Even IRIS Native API for Python is more detailed.

Hi folks!

How can I refer to a classmethod of the same class while coding another classmethod with Embedded python?

I know that I can call it with iris.cls(classname).MethodName(), but it's more cumbersome even comparing with ObjectScript, where I can call ..MethodName().

Compare ObjectScript:

do ..SetupGame()

and the same call in EmbeddedPython:

    iris.cls('eshvarov.sample.SeaBattle.GamePython').SetupGame()

Thoughts?

Hi folks!

I have a need to use symilar to $property function from Python. Here is the code:

obj=iris.cls('some.persistent.class'for

How could I do this? Any trick?

For now I plan to implement a helper function in iris that I will call, but I doubt also how can I transfer oref to IRIS.

Thoughts?

Hi folks!

Consider I need to call a python function which name contains "_" symbol (which is quite often in Python). How it could be called from ObjectScript?

E.g. here is the code:

Set##class%SYS.Python

Thanks in advance!

I want to connect IRIS system as it has all the database tables. on top of that I am creating a REST API in python. How can I connect to IRIS DB. here is my example code for connection

def connect():

connection_string = "localhost:1972/USER"

username = "_SYSTEM"

password = "SYS"

conn = iris.connect(connection_string, username, password)

after this connection is created but how can I get tables data. Please let me know more about how we can integrate IRIS database into a python REST API.

Demonstration example for the current Grand Prix contest for use of a more complex Parameter template to test the AI.

Interview Questions

There is documentation. A recruitment consultant wants to quickly challenge candidates with some relevant technical questions to a role.

Can they automate making a list of questions and answers from the available documentation?

Interview Answers and Learning

One of the most effective ways to cement new facts into accessible long term memory is with phased recall.

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

Here, models as example :

• https://huggingface.co/gpt2

• https://huggingface.co/Jean-Baptiste/camembert-ner

• https://huggingface.co/bert-base-uncased

• https://huggingface.co/facebook/detr-resnet-50

• https://huggingface.co/facebook/detr-resnet-50-panoptic

• 1. iris-huggingface

• 2. Installation

  • 2.1. Starting the Production
  • 2.2. Access the Production

Hi folks!

Just want to introduce you a new util to import CSV into IRIS - csvgenpy!

Install

USER>zpm "install csvgenpy"

Use:

do ##class(shvarov.csvgenpy.csv).Generate("file or url","table","schema")

Example:

This will create table and class data.titanic in IRIS and will load the data.  you can proof it with:

So that's it!

Basically it does the same thing as csvgen does, but uses python library sqlalchemy which works because of embedded python and sqlalchemy-iris by @Dmitry Maslennikov

P.S. csvgenpy can import some "complicated" for csvgen csv as this one

Let me introduce my new project, which is irissqlcli, REPL (Read-Eval-Print Loop)  for InterSystems IRIS SQL 

• Syntax Highlighting
• Suggestions (tables, functions)
• 20+ output formats
• stdin support
• Output to files 

Install it with pip

pip install irissqlcli

Or run with docker

docker run -it caretdev/irissqlcli irissqlcli iris://_SYSTEM:SYS@host.docker.internal:1972/USER

Connect to IRIS

$ irissqlcli iris://_SYSTEM@localhost:1972/USER -W
Password for _SYSTEM:
Server:  InterSystems IRIS Version 2022.3.0.606 xDBC Protocol Version 65
Version: 0.1.0
[SQL]_SYSTEM@localhost:USER> select $ZVERSION
+---------------------------------------------------------------------------------------------------------+
| Expression_1                                                                                            |
+---------------------------------------------------------------------------------------------------------+
| IRIS for UNIX (Ubuntu Server LTS for ARM64 Containers) 2022.3 (Build 606U) Mon Jan 30 2023 09:05:12 EST |
+---------------------------------------------------------------------------------------------------------+
1 row in set
Time: 0.063s
[SQL]_SYSTEM@localhost:USER> help
+----------+-------------------+------------------------------------------------------------+
| Command  | Shortcut          | Description                                                |
+----------+-------------------+------------------------------------------------------------+
| .exit    | \q                | Exit.                                                      |
| .mode    | \T                | Change the table format used to output results.            |
| .once    | \o [-o] filename  | Append next result to an output file (overwrite using -o). |
| .schemas | \ds               | List schemas.                                              |
| .tables  | \dt [schema]      | List tables.                                               |
| \e       | \e                | Edit command with editor (uses $EDITOR).                   |
| help     | \?                | Show this help.                                            |
| nopager  | \n                | Disable pager, print to stdout.                            |
| notee    | notee             | Stop writing results to an output file.                    |
| pager    | \P [command]      | Set PAGER. Print the query results via PAGER.              |
| prompt   | \R                | Change prompt format.                                      |
| quit     | \q                | Quit.                                                      |
| tee      | tee [-o] filename | Append all results to an output file (overwrite using -o). |
+----------+-------------------+------------------------------------------------------------+
Time: 0.012s
[SQL]_SYSTEM@localhost:USER>

The Jupyter Notebook is an open source web application that can be used to create and share documents that contain live text, code, equations and visualizations. It is maintained by the people at Project Jupyter.

Jupyter name comes from the core supported programming languages that it supports: Julia, Python, and R.
It allows users to write code in "cells," which are blocks of code that can be executed individually or together. This makes it easy to test and debug code, as well as to organize and present complex analyses.

Hey Community,

Watch this video to see how to connect to InterSystems Cloud Services from your Python application using the InterSystems DB-API driver interface:

⏯ Connecting to InterSystems Cloud Services with Python

Let's say you have Python including variable-length arguments methods. How can you call it from ObjectScript? 

deftest1(*args)returntest2

You can call this "a.py" from ObjectScript as below.  For **kwargs argument, create Dynamic Object in ObjectScript and put it into methods with <variablename>... (3 dots) format. 

Do you like playing ChatGPT? With this way, you can call ChatGPT APIs not only from Language=python method but from ObjectScript world.  As described in OpenAI page , ChatCompletion.create method of OpenAI library has **kwargs argument.

Many factors affect a person's quality of life, and one of the most important is sleep. The quality of our sleep determines our ability to function during the day and affects our mental and physical health. Good quality sleep is critical to our overall health and well-being. Therefore, by analyzing indicators preceding sleep, we can determine the quality of our sleep. This is precisely the functionality of the Sheep's Galaxy application.

We have a rule to disable a user account if they have not logged in for a certain number of days. IRIS Audit database logs many events such as login failures for example. It can be configured to log successful logins as well. We have IRIS clusters with many IRIS instances. I like to run queries against audit data from ALL IRIS instances and identify user accounts which have not logged into ANY IRIS instance.

Within IRIS I defined a task to export audit data every day.

I provide some sample files in my GitHub repo.

I wrote ObjectScript code to import all files into otw.audit.consolidator class.

I want to use Python SQLAlchemy, pandas and sqlalchemy-iris (created by Dmitry Maslenikov) to copy consolidated audit data from my consolidator class in my IRIS container to my InterSystems Cloud SQL deployment.

I updated my Dockerfile to pip3 install sqlalchemy-iris and pandas (dataframes).

docker-compose build –no-cache in my personal AWS took 700 seconds.

Hi Community,
In this article, I will introduce my application iris-mlm-explainer

This web application connects to InterSystems Cloud SQL to create, train, validate, and predict ML models, make Predictions and display a dashboard of all the trained models with an explanation of the workings of a fitted machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, partial dependence plots, SHAP (interaction) values, visualization of individual decision trees, etc.