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#Python

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Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace

Official site.

InterSystems Python Binding Documentation.

Contestant

Here are the technology bonuses for the InterSystems Full Stack Contest 2026, which will give you extra points in the voting:

  • IRIS Vector Search usage -3
  • InterSystems Native SDK for Python or Embedded Python usage -3
  • Developer Community Idea implemented - 2
  • Docker container usage -2
  • IPM Package Deployment - 2
  • Online Demo -2
  • Find and report a bug - 2
  • Article on Developer Community - 2
  • The second article on Developer Community - 1
  • Video on YouTube - 3
  • YouTube Short - 1
  • First Time Contribution - 3

See the details below.<--break->

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Contestant

Vector search is a retrieval method that converts text, images, audio, and other data into numeric vectors using an AI model, and then searches for items that are semantically close. It enables “semantic similarity search” from free text, which is difficult with keyword search alone.

However, in real use, I encountered cases where results that are “close in meaning” but logically the opposite appeared near the top of the search results.

This is a serious issue in situations where affirmation vs. negation matters. If the system returns the wrong answer, the impact can be significant, so we cannot ignore this problem.

This article does not propose a new algorithm. I wrote it to share a practical way I found useful when semantic search fails due to negation.

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Contestant

Ever since I started using IRIS, I have wondered if we could create agents on IRIS. It seemed obvious: we have an Interoperability GUI that can trace messages, we have an underlying object database that can store SQL, Vectors and even Base64 images. We currently have a Python SDK that allows us to interface with the platform using Python, but not particularly optimized for developing agentic workflows. This was my attempt to create a Python SDK that can leverage several parts of IRIS to support development of agentic systems.

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Contestant

TL;DR: This article demonstrates how to run GraphRAG-style hybrid retrieval—combining vector similarity, graph traversal, and full-text search—entirely within InterSystems IRIS using the iris-vector-graph package. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.

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Contestant

Senior engineering is defined not by the volume of code produced, but by the strategic avoidance of it. In complex integration environments, the tendency to utilize general-purpose libraries for every niche requirement introduces unnecessary overhead. True architectural maturity requires a commitment to "minimalist tooling"—prioritizing resilient, battle-tested system utilities over custom logic.

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Contestant

Introduction

Earlier this year, I set about creating kit to introduce young techy folk at a Health Tech hackathon to using InterSystems IRIS for health, particularly focusing on using FHIR and vector search.

I wanted to publish this to the developer community because the tutorials included in the kit make a great introduction to using FHIR and to building a basic RAG system in IRIS. Its an all inclusive set of tutorials to show in detail how to:

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Embeddedpy-bridge: A Toolkit for Embedded Python

Overview

Embedded Python is a game-changer for InterSystems IRIS, offering access to the vast Python ecosystem directly within the database. However, bridging the gap between ObjectScript and Python can sometimes feel like translating between two different worlds.

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Apache Airflow is the leading open-source platform to programmatically author, schedule, and monitor data pipelines and workflows using Python. Workflows are defined as code (DAGs), making them version-controlled, testable, and reusable. With a rich UI, 100+ built-in operators, dynamic task generation, and native support for cloud providers, Airflow powers ETL/ELT, ML pipelines, and batch jobs at companies like Airbnb, Netflix, and Spotify.

Airflow Application Layout

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Contestant

How to set up RAG for OpenAI agents using IRIS Vector DB in Python

In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.

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Article
· Mar 17, 2025 2m read
Ollama AI with IRIS

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:

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InterSystems Official
· Nov 19, 2025
Client SDKs available on external repositories

Hi community!

I am excited to say that since the beginning of this year we have published many of the client SDKs for InterSystems IRIS, InterSystems IRIS for Health and Health Connect to the corresponding external repositories (Maven, NuGet, npm and PyPI). This provides many benefits to you such as:

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Hi Community,

In the first part of this series, we examined the fundamentals of Interoperability on Python (IoP), specifically how it enables us to construct such interoperability elements as business services, processes, and operations using pure Python.

Now, we are ready to take things a step further. Real-world integration scenarios extend beyond simple message handoffs.They involve scheduled polling, custom message structures, decision logic, filtering, and configuration handling.In this article, we will delve into these more advanced IoP capabilities and demonstrate how to create and run a more complex interoperability flow using only Python.

To make it practical, we will build a comprehensive example: The Reddit Post Analyzer Production. The concept is straightforward: continuously retrieving the latest submissions from a chosen subreddit, filtering them based on popularity, adding extra tags to them, and sending them off for storage or further analysis.

The ultimate goal here is a reliable, self-running data ingestion pipeline. All major parts (the Business Service, Business Process, and Business Operation) are implemented in Python, showcasing how to use IoP as a Python-first integration methodology.

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Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.

I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.

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Introduction

In InterSystems IRIS 2024.3 and subsequent IRIS versions, the AutoML component is now delivered as a separate Python package that is installed after installation. Unfortunately, some recent versions of Python packages that AutoML relies on have introduced incompatibilities, and can cause failures when training models (TRAIN MODEL statement). If you see an error mentioning "TypeError" and the keyword argument "fit_params" or "sklearn_tags", read on for a quick fix.

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Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ A Day in the Life of a Developer - Data Platforms Roundup @ Ready 2025

https://www.youtube.com/embed/u7gcc629REM
[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]

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Hi all,

Let's do some more work about the testing data generation and export the result by REST API.😁

Here, I would like to reuse the datagen.restservice class which built in the pervious article Writing a REST api service for exporting the generated patient data in .csv

This time, we are planning to generate a FHIR bundle include multiple resources for testing the FHIR repository.

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Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

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Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

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