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.

What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

Why Are Insights from Unstructured Data Important?
According to an IDC (International Data Corporation) report, 80% of worldwide data is projected to be unstructured by 2025, posing a significant concern for 95% of businesses.

In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity. 

New Updates  ⛴️ 

• Added installation through Docker. Run `./build.sh` after cloning to get the application & IRIS instance running in your local
• Connect via InterSystems Extension in vsCode - Thanks to @Evgeny.

Keywords:  Vibe coding, Windsurf, IRIS, TIE

Why not?   "Vibe coding" is never about the vibe!

Has anyone not been trying "vibe coding" so far?

Even merely 3 years ago, if anyone asked

• "Could I do IRIS implementation for NHS TIE in English or Spanish or just Chinese ?", or
• "Can I just instruct TIE in English to build itself an e2e route, to pick up a PDF report then turn into ORU/MDM message and submit into the PAS ?", or
• "Could we query IRIS database in English only, and build up dashboard or ad hoc report of my own by English instructions?

Keywords:  IRIS, Agents, Agentic AI, Smart Apps

Motive?

Transformer based LLMs appear to be a pretty good "universal logical–symbolic abstractor".  They started to bridge up the previous abyss among human languages and machine languages, which in essence are all logic symbols that could be mapped into the same vector space. 

Objective?

Wondering for 3 years we might be able to just use English (etc human natural languages) to do IRIS implementations as well, one day.

Hi developers!

I'm testing vibecoding with ObjectScript and my silicon friend created a code-block that got me thinking "what's wrong"?

Here is the piece of code:

for i=0:1:(json.%Size()-1) {

set p = json.%Get(i)

if (p="value1")!(p="value2") {

quit 1
}

AI wanted to quit from a method with a return value. Good intention, but bad use of the command.

And ObjectScript compiler compiles this code with no error(?) (syntax linter in VSCode says it's a syntax, kudos @Brett Saviano ).

But in action, it produces <COMMAND>, of course.

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.

Hey Community!

We're happy to share a new video from our InterSystems Developers YouTube:

     ⏯ Develop the Next Generation of Health IT with InterSystems@ Ready 2025

10:47 AM — Jose Garcia's creatinine test results arrive at the hospital FHIR server. 2.1 mg/dL — a 35% increase from last month.

What happens next?

• Most systems: ❌ The result sits in a queue until a clinician reviews it manually — hours or days later.
• This system: 👍 An AI agent evaluates the trend, consults clinical guidelines, and generates evidence-based recommendations — in seconds, automatically.

No chatbot. No manual prompts. No black-box reasoning.

This is event-driven clinical decision support with full explainability:

✅ Triggered automatically by FHIR events ✅ Multi-agent reasoning (context, guidelines, recommendations) ✅ Complete audit trail in SQL (every decision, every evidence source) ✅ FHIR-native outputs (DiagnosticReport published to server)

Built with:

• InterSystems IRIS for Health — Orchestration, FHIR, persistence, vector search
• CrewAI — Multi-agent framework for structured reasoning

You'll learn: 🖋️ How to orchestrate agentic AI workflows within production-grade interoperability systems — and why explainability matters more than accuracy alone.

How I Vibecoded a Backend (and Frontend) on InterSystems IRIS

I wanted to try vibecoding a real backend + frontend setup on InterSystems IRIS, ideally using something realistic rather than a toy example. The goal was simple: take an existing, well-known persistent package in IRIS and quickly build a usable UI and API around it — letting AI handle as much of the boilerplate as possible. Here is the result of the experiments.

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.

Some concepts make perfect sense on paper, whereas others require you to get your hands dirty. Take driving, for example. You can memorize every component of the engine mechanics, but that does not mean you can actually drive.

You cannot truly grasp it until you are in the driver's seat, physically feeling the friction point of the clutch and the vibration of the road beneath. While some computing concepts are intuitive, Intelligent Agents are different. To understand them, you have to get in the driver's seat.

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ HealthShare Vision & Roadmap - Fueling Faster AI Adoption Through Trusted Health Data @ READY 2025

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ Enhancing Customer Engagement with Vector Search - Building a Customer Facing Chatbot @ READY 2025

Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.
This project explores how ChatGPT and Anthropic Claude, two of the most advanced AI models, can interact with IRIS using Custom GPTs and the Model Context Protocol (MCP).

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.

To demonstrate this, we’ll create an OpenAI agent with knowledge of InterSystems technology. We’ll achieve this by storing embeddings of some InterSystems documentation in IRIS and then using IRIS vector search to retrieve relevant content—enabling a Retrieval-Augmented Generation (RAG) workflow.

Note: Section 1 details how process text into embeddings.

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ Understanding AI Ready Data @ READY 2025

Hi,

We're working on new capabilities to help you build Agents and AI applications faster with InterSystems IRIS. In order to better understand which entry points and development methodologies would help you most, we've created this brief survey: Building AI solutions with InterSystems IRIS. 

Filling it in should not take much more than 5 minutes, and your feedback on this exciting topic will help us fine tune our designs and prioritize the right features.

Thanks in advance!
benjamin
 

Vibe the Module, Not the Data

While working with the FHIR to OMOP Service, I've seen good FHIR synthetic data being created using commercial LLM's etc, custom tailored for ConditionOnset with the typical amazement on return, but witnessed some questionable trust first hand on a call.  This approach also falls short generating gigantic payloads so I can go back to my interests on the backend and ensure smooth data transition.

Hey Community!

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

⏯  The Next Leap Beyond Text Generation

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ Data Platforms Vision and Roadmap - The AI Era Runs on Data @ READY 2025

Hey Community!

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

⏯  From Words to Molecules: How AI Is Transforming Medicine and Beyond

Hi, 

We very much appreciate the interest in the Developer Community for IRIS Vector Search and hope our technology has helped many of you build innovative applications or advanced your R&D efforts. With a dedicated index, integrated embeddings generation, and deep integration with our SQL engine now available in InterSystems IRIS, we're looking at the next frontier, and would love to hear your feedback on the technology to prioritize our investments.

This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

Hey Community!

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

⏯  Before the Lightbulb: Understanding the First Phase of the AI Revolution in Medicine