#Python

Vanna.AI - Personalized AI InterSystems OMOP Agent

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)

Or can I change namespaces within one connection object?

I have a custom Buffer class which is designed to capture written/printed statements to the device, to be able to transform the captured text to string or stream type. I have used this in ObjectScript to capture ObjectScript write statements and return a string. I would like to try to use this with a [ Language = python ] method as follows. This class will be called by a scheduled task.

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 :

In this section, we will explore how to use Python as the primary language in IRIS, allowing you to write your application logic in Python while still leveraging the power of IRIS.

• Introduction Python First Approach in IRIS
  • How to use it (irispython)
    • What is irispython?
    • Example of using irispython
    • Pros
    • Cons
    • Conclusion
  • Using WSGI
    • How to use it
    • Example of using WSGI
    • Pros
    • Cons
    • Conclusion
  • DB-API
    • How to use it
    • Example of using DB-API
    • Pros
    • Cons
    • Alternatives
    • Conclusion
  • Notebook
    • How to use it
    • Example of using Notebook
    • Pros
    • Cons
    • Conclusion
• Bonus Section
  • Using a native interpreter (no irispython)
    • How to use it
    • Pros
    • Cons
  • DB-API Community Edition
    • How to use it
    • Example of using DB-API
    • Pros
    • Cons
  • Debugging Python Code in IRIS
    • How to use it
    • Pros
    • Cons
    • Conclusion
  • IoP (Interoperability on Python)
• Conclusion

Sending emails is a common requirement in integration scenarios — whether for client reminders, automatic reports, or transaction confirmations. Static messages quickly become hard to maintain and personalize. This is where the templated_email module comes in, combining InterSystems IRIS Interoperability with the power of Jinja2 templates.

Why Jinja2 for Emails

Jinja2 is a popular templating engine from the Python ecosystem that enables fully dynamic content generation. It supports:

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:

This will be a short article about PEP 8, the Python style guide.

What is PEP 8?

In a nutshell, PEP 8 provides guidelines and best practices on how to write Python code.

This will be an introduction to Python programming in the context of IRIS.

Before anything I will cover an important topic: How python works, this will help you understand some issues and limitations you may encounter when working with Python in IRIS.

All the articles and examples can be found in this git repository: iris-python-article

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.

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.

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

Hey Community!

We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:

⏯ Chaining LLMs for Better Results using Agentic AI

https://www.youtube.com/embed/hAtuAIihBVA
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