I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.

Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.

• Feedback : Using embedded python daily for 2 years
• Introduction
• Starting with Python
  • Python is not ObjectScript
    • Pep8
    • Modules
    • Dunders
  • Conclusion
• Embedded Python
  • What is Embedded Python ?
  • How to use Embedded Python ?
  • How I use Embedded Python
    • Use Python libraries and code as they were ObjectScript classes
      • With the language tag
      • Without the language tag
      • Conclusion
    • Use a python first approach
      • Example : iris-fhir-python-strategy
      • Remarks
      • Where to find the code
      • How to implement a Strategy
      • Implementation of InteractionsStrategy
      • Implementation of Interactions
      • Interactions in Python
      • Implementation of the abstract python class
      • Too long, do a summary

If you develop in IRIS you are confronted with two major phenomena:

• an incredibly fast and excellent designed data storage engine
• a scripting language to work on this storage engine, actually named ObjectScript

In the world of APIs, REST is very extended. But what happens when you need more flexibility in your data-fetching strategies? For instance letting the client to choose what fields is going to receive. Enter GraphQL, a query language for your APIs that provides a flexible alternative to REST.

In this post, we will:

• Compare REST and GraphQL.
• Dive into the basics of GraphQL: Queries, Mutations, and HTTP.
• Build a simple GraphQL server implementation using Graphene, SQLAlchemy, and Flask over data in InterSystems IRIS.
• Explore how to deploy your GraphQL server as a WSGI application in IRIS.

Learning LLM Magic

The world of Generative AI has been pretty inescapable for a while, commercial models running on paid Cloud instances are everywhere. With your data stored securely on-prem in IRIS, it might seem daunting to start getting the benefit of experimentation with Large Language Models without having to navigate a minefield of Governance and rapidly evolving API documentation. If only there was a way to bring an LLM to IRIS, preferably in a very small code footprint....

1. IRIS RAG Demo

This demo showcases the powerful synergy between IRIS Vector Search and RAG (Retrieval Augmented Generation), providing a cutting-edge approach to interacting with documents through a conversational interface. Utilizing InterSystems IRIS's newly introduced Vector Search capabilities, this application sets a new standard for retrieving and generating information based on a knowledge base.
The backend, crafted in Python and leveraging the prowess of IRIS and IoP, the LLM model is orca-mini and served by the ollama server.
The frontend is an chatbot written with Streamlit.

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

Hi Developers,

We are pleased to invite you all to the new InterSystems online programming contest focused on Python!

🏆 InterSystems Python Programming Contest 🏆

Duration: September 4 - 24, 2023

Prize pool: $14,000

Hi Community,

This post is a introduction of my open exchange iris-climate-change application.

iris-climate-change web application Imports The Food and Agriculture Organization (FAO) Climate Change dataset by using LOAD DATA (SQL) functionality and analyze and visualize data with the help of Python Flask Web Framework, Pandas Python data analysis Library, Plotly Open Source Graphing Library for Python and Plotly JavaScript Open Source Graphing Library.

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.

I have challenged to create a bot application using Azure Bot that can retrieve and post data to IRIS for Health.

A patient's data has already been registered in the FHIR repository of IRIS for Health.

The patient's MRN is 1001. His name is Taro Yamada. (in Japanese :山田 太郎)

This bot can post new pulse oximeter readings as an observation resource linked to the patient.

This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

Happy new year! I’m Raj Singh, InterSystems’ product manager for Developer Experience and I’d love your feedback on how you use IDEs today and your thoughts for the future.

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:

Here you'll find a simple program that uses Python in an IRIS environment and another simple program that uses ObjectScript in a Python environment. Also, I'd like to share a few of the troubles I went trough while learning to implement this.

Python in IRIS environment

Let's say, for example, you're in an IRIS environment and you want to solve a problem that you find easy, or more efficient with Python.

You can simply change the environment: create your method as any other, and in the end of it's name and specifications, you add [ Language = python ]: