#Python

Hi Developers,

New video, recorded by @Benjamin De Boe, is already on InterSystems Developers YouTube:

⏯ Python and InterSystems IRIS

https://www.youtube.com/embed/1alOEeLEPfs
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama

In the generative AI usage model that we are used to, we have the following flow:

Hi everyone! If it doesn't bother you, could you help me teach my bots to talk?

Open my chatbot here: Help my chatbots to talk!

What? Aren't your chatbots smarts?

Hi Developers!

Here're the technology bonuses for the InterSystems Vector Search, GenAI, and ML contest 2024 that will give you extra points in the voting:

• Vector Search usage - 5
• IntegratedML usage - 3
• Embedded Python - 3
• LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
• Questionnaire - 2
• Docker container usage - 2
• ZPM Package deployment - 2
• Online Demo - 2
• Implement InterSystems Community Idea - 4
• Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
• First Article on Developer Community - 2
• Second Article On DC - 1
• First Time Contribution - 3
• Video on YouTube - 3
• Suggest a new idea - 1

See the details below.

In this article, exceptions are covered.

Working with Exceptions

Instead of returning a %Status response, you can raise and throw an Exception. You are then responsible for catching the exception and validating it. IRIS provides five main classes to handle exceptions effectively. Additionally, you can create custom exception class definition based on your needs.

In this GitHub we gather information from a csv, use a DataTransformation to make it into a FHIR object and then, save that information to a FHIR server all that using only Python.

The objective is to show how easy it is to manipulate data into the output we want, here a FHIR Bundle, in the IRIS full Python framework.

Hello interface engineers and app developers,

Did you know that you can use Python in productions or integrations?

Hi Community,

New video is already on InterSystems Developers YouTube:

⏯ Embedded Python with Interoperability

https://www.youtube.com/embed/b6Fbg0JDmPA
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

Hi, Community!

In this article, I will introduce Python Streamlit Web Framework.

Below, you can find the topics we will cover:

• 1-Introduction to Streamlit Web Framework
• 2-Installation of Streamlit module
• 3-Running Streamlit Application
• 4-Streamlit Basic commands
• 5-Display multimedia
• 6-Input widgets
• 7-Display progress and status
• 8-Sidebar and container
• 9-Data Visualization
• 10-Display a DataFrame

So, let's start with the first topic.

Hi folks

I want to tell you how you can make your own assistant based on IRIS and OpenAI (perhaps you can then move to your own AI models)

iris-recorder-helper

This is the first time I have fully tried developing an application for IRIS and I want to point out steps that may also be useful to you

Hi Community,

In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

In the previous articles, we learned the basics of using IMAP protocol to handle messages from mailboxes in an e-mail server. That was cool and interesting, but you could take advantage of implementations created by other ones, available in libraries ready to use.