Easy, easy, I'm not promoting a war against the machines in the best sci-fi way to avoid world domination of Ultron or Skynet. Not yet, not yet 🤔

I invite you to challenge the machines through the creation of a very simple game using ObjectScript with embedded Python.

I have to say that I got super excited with the feature of Embedded Python on InterSystems IRIS, it's incredible the bunch of possibilities that opens to create fantastic apps.

Let's build a tic tac toe, the rules are quite simple and I believe that everyone knows how to play.

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

I was looking at the thread of messages on the topic of "Performance when constructing a comma-separated string", and I started writing a response but got distracted, the page refreshed, and I lost my text. I couldn't spend the time rewriting my response, so I started writing this document instead.

I started writing MUMPS at the beginning of my career. I wrote very tight and dense code blocks where exercises such as the string example were authentic challenges.

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 

The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently. Such applications may include data retrieval (see Don Woodlock's ML301 course for a great intro to Retrieval Augmented Generation), sentiment analysis, and even fully-autonomous AI agents, just to name a few!

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.

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.

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.

InterSystems has been at the forefront of database technology since its inception, pioneering innovations that consistently outperform competitors like Oracle, IBM, and Microsoft. By focusing on an efficient kernel design and embracing a no-compromise approach to data performance, InterSystems has carved out a niche in mission-critical applications, ensuring reliability, speed, and scalability.

Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python. This article details how to create new SQL Stored Procedures in InterSystems IRIS Database using Embedded Python.

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.

Introduction

In some of the last few articles I've talked about types between IRIS and Python, and it is clear that it's not that easy to access objects from one side at another. 

Fortunately, work has already been done to create SQLAlchemy-iris (follow the link to see it on Open Exchange), which makes everything much easier for Python to access IRIS' objects, and I'm going to show the starters for that.

Thank you @Dmitry Maslennikov !

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

Iris-python-template

Template project with various Python code to be used with InterSystems IRIS Community Edition with container.

Featuring :

• Notebooks
  • Embedded Python Kernel
  • ObjectScript Kernel
  • Vanilla Python Kernel
• Embedded Python
  • Code example
  • Flask demo
• IRIS Python Native APIs
  • Code example

2. Table of Contents

• 1. iris-python-template
• 2. Table of Contents
• 3. Installation
  • 3.1. Docker
• 4. How to start coding
  • 4.1. Prerequisites
    • 4.1.1. Start coding in ObjectScript
    • 4.1.2. Start coding with Embedded Python
    • 4.1.3. Start coding with Notebooks
• 5. What's inside the repository
  • 5.1.

Interoperability on Python (IoP) is a proof-of-concept project designed to showcase the power of the InterSystems IRIS Interoperability Framework when combined with a Python-first approach.IoP leverages Embedded Python (a feature of InterSystems IRIS) to enable developers to write interoperability components in Python, which can seamlessly integrate with the robust IRIS platform. This guide has been crafted for beginners and provides a comprehensive introduction to IoP, its setup, and practical steps to create your first interoperability component. By the end of this article, you will get a clear understanding of how to use IoP to build scalable, Python-based interoperability solutions.

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.

With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.

Setting up Embedded Python to Use the Sentence Transformers Library

Note: For this article, I will be using a Linux system with IRIS installed.

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.

Learn how to design scalable, autonomous AI agents that combine reasoning, vector search, and tool integration using LangGraph.

Too Long; Didn't Read

• AI Agents are proactive systems that combine memory, context, and initiative to automate tasks beyond simple chatbots.
• LangGraph is a framework that enables us to build complex AI workflows, utilizing nodes (tasks) and edges (connections) with built-in state management.

If a picture is worth a thousand words, what's a video worth? Certainly more than typing a post.

Please check out my "Coding talks" on InterSystems Developers YouTube:

1. Analysing InterSystems IRIS System Performance with Yape. Part 1: Installing Yape

Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

Extracting and plotting pButtons data including timeframes and iostat.

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.

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:

!

Now that we have a good understanding of Python and its features, let's explore how we can leverage Python within IRIS.

• Introduction to Python in IRIS
• Language Tag
  • How to use it?
  • Pros
  • Cons
  • Conclusion
• Importing Python Modules (pypi modules)
  • How to use it
  • Pros
  • Cons
  • Conclusion
• Importing Python Modules (custom modules)
  • How to use it
  • Pros
  • Cons
  • Conclusion

A customer recently asked if IRIS supported OpenTelemetry as they where seeking to measure the time that IRIS implemented SOAP Services take to complete.

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:

I'm a huge sci-fi fan, but while I'm fully onboard the Star Wars train (apologies to my fellow Trekkies!), but I've always appreciated the classic episodes of Star Trek from my childhood. The diverse crew of the USS Enterprise, each masterminding their unique roles, is a perfect metaphor for understanding AI agents and their power in projects like Facilis. So, let's embark on an intergalactic mission, leveraging AI as our ship's crew and  boldly go where no man has gone before!

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.

This formation can mostly be done using copy paste and will guide you through everystep before challenging you with a global exercise.
We are available to answer any question or doubt in the comment of that post, on teams or even by mail at lucas.enard@intersystems.com .

The InterSystems IRIS is a great data platform and it is met the current features required by the market. In this article, you see the top 10:

Note: this list was updated because many features are added to IRIS in last 3 years (thanks @Kristina.

If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).