ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. It is built on top of OpenAI's GPT-3.5 and GPT-4 families of large language models (LLMs) and has been fine-tuned using both supervised and reinforcement learning techniques.
InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.
But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.
I have a new project to store information from REST responses into an IRIS database. I’ll need to sync information from at least two dozen separate REST endpoints, which means creating nearly that many ObjectScript classes to store the results from those endpoints.
Could I use ChatGPT to get a headstart on creating these classes? The answer is “Yes”, which is great since this is my first attempt at using generative AI for something useful. Generating pictures of giraffes eating soup was getting kind of old….
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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).
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 !😂
1. Create a class datagen.restservice which extends %CSP.REST
Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json";
}
2.
☤ Care 🩺 Compass 🧭 - Proof-of-Concept - Demo Games Contest Entry
In today’s healthcare and social services landscape, caseworkers face overwhelming challenges. High caseloads, fragmented systems, and disconnected data often lead to missed opportunities to intervene early and effectively. This results in worker burnout and preventable emergency room visits, which are both costly and avoidable.
Care Compass was created to change that.
Hi Community,
Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.
This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.
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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.
# IRIS-Intelligent Butler
IRIS Intelligent Butler is an AI intelligent butler system built on the InterSystems IRIS data platform, aimed at providing users with comprehensive intelligent life and work assistance through data intelligence, automated decision-making, and natural interaction.
## Application scenarios
adding services, initializing configurations, etc.
Firstly, we need to understand what prompt words are and what their functions are.
Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.
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Generative artificial intelligence is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.
Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language.
Hi Community,
In this article, I will introduce my application iris-RAG-Gen .
Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.
I received some really excellent feedback from a community member on my submission to the Python 2024 contest. I hope its okay if I repost it here:
you build a container more than 5 times the size of pure IRIS
and this takes time
container start is also slow but completes
backend is accessible as described
a production is hanging around
frontend reacts
I fail to understand what is intended to show
the explanation is meant for experts other than me
The submission is here: https://openexchange.intersystems.
Hi Community
In this article, I will introduce my application irisChatGPT which is built on LangChain Framework.
First of all, let us have a brief overview of the framework.
The entire world is talking about ChatGPT and how Large Language Models(LLMs) have become so powerful and has been performing beyond expectations, giving human-like conversations. This is just the beginning of how this can be applied to every enterprise and every domain!
The introduction of InterSystems' "Vector Search" marks a paradigm shift in data processing. This cutting-edge technology employs an embedding model to transform unstructured data, such as text, into structured vectors, resulting in significantly enhanced search capabilities. Inspired by this breakthrough, we've developed a specialized search engine tailored to companies.
We harness generative artificial intelligence to generate comprehensive summaries of these companies, delivering users a powerful and informative tool.
This article aims to explore how the FHIR-PEX system operates and was developed, leveraging the capabilities of InterSystems IRIS.
Streamlining the identification and processing of medical examinations in clinical diagnostic centers, our system aims to enhance the efficiency and accuracy of healthcare workflows. By integrating FHIR standards with InterSystems IRIS database Java-PEX, the system help healthcare professionals with validation and routing capabilities, ultimately contributing to improved decision-making and patient care.
I tried OpenAI GPT's coding model a couple of weeks ago, to see whether it can do e.g. some message transformations between healthcare protocols. It surely "can", to a seemingly fair degree.
It has been nearly 3 weeks, and it's a long, long time for ChatGPT, so I am wondering how quickly it grows up by now, and whether it could do some of integration engineer jobs for us, e.g. can it create an InterSystems COS DTL tool to turn the HL7 into FHIR message?
Immediately I got some quick answers, in less than one minute or two.
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Hi Community
In this article, I will introduce my application IRIS-GenLab.
IRIS-GenLab is a generative AI Application that leverages the functionality of Flask web framework, SQLALchemy ORM, and InterSystems IRIS to demonstrate Machine Learning, LLM, NLP, Generative AI API, Google AI LLM, Flan-T5-XXL model, Flask Login and OpenAI ChatGPT use cases.
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The FHIR® SQL Builder, or Builder, is a component of InterSystems IRIS for Health. It is a sophisticated projection tool used to create custom SQL schemas using data in an InterSystems IRIS for Health FHIR repository without moving the data to a separate SQL repository. The Builder is designed specifically to work with FHIR repositories and multi-model databases in InterSystems IRIS for Health.
The objective of the Builder is to enable data analysts and business intelligence developers to work with FHIR using familiar analytic tools, without having to learn a new query syntax.
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FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.
Introducing the FHIR - AI and OpenAPI Chain application, a solution that allows users to interact with FHIR APIs using natural language queries.
Previous post - Using AI to Simplify Clinical Documents Storage, Retrieval and Search
This post explores the potential of OpenAI's advanced language models to revolutionize healthcare through AI-powered transcription and summarization. We will delve into the process of leveraging OpenAI's cutting-edge APIs to convert audio recordings into written transcripts and employ natural language processing algorithms to extract crucial insights for generating concise summaries.
The continuing convergence of AI technologies and healthcare systems has brought forward many compelling advancements. Let's set the scene. If you have interacted with dynamic models like ChatGPT, you might have, like many of us, begun to envision its application using your unique datasets. Suppose in the healthcare sector you wish to link this technology with Electronic Health Records (EHR) or Electronic Medical Records (EMR), or perhaps you aim for heightened interoperability using FHIR's resources.
In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.
AI has made document search remarkably powerful. Question and answering over docs has never been easier with open-source tools like Chroma and Langchain to store and use vector embeddings to query across Generative AI APIs.
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As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation.
But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:
Demonstration example for the current Grand Prix contest for use of a more complex Parameter template to test the AI.
There is documentation. A recruitment consultant wants to quickly challenge candidates with some relevant technical questions to a role.
Can they automate making a list of questions and answers from the available documentation?
One of the most effective ways to cement new facts into accessible long term memory is with phased recall.
Hi Community!
Just want to share with you an exercise I made to create "my own" chat with GPT in Telegram.
It became possible because of two components on Open Exchange: Telegram Adapter by @Nikolay Solovyev and IRIS Open-AI by @Kurro Lopez 
So with this example you can setup your own chat with ChatGPT in Telegram.
Let's see how to make it work!
Let's say you have Python including variable-length arguments methods. How can you call it from ObjectScript?
def test1(*args):
return sum(args)
def test2(**kwargs):
a1 = kwargs.get("a1",None)
a2 = kwargs.get("a2",None)
return a1+a2You can call this "a.py" from ObjectScript as below. For **kwargs argument, create Dynamic Object in ObjectScript and put it into methods with <variablename>... (3 dots) format.
set a=##class(%SYS.Python).Import("a")
write a.test1(1,2,3) ;; 6
set req={}
set req.a1=10
set req.a2=20
write a.In recent years, artificial intelligence technologies for text generation have developed significantly. For example, text generation models based on neural networks can produce texts that are almost indistinguishable from texts written by humans.
ChatGPT is one such service. It is a huge neural network trained on a large number of texts, which can generate texts on various topics and be matched to a given context.
A new task for people is to develop ways to recognize texts written not only by people but also by artificial intelligence (AI).
Keywords: ChatGPT, COS, Lookup Table, IRIS, AI
Here is another quick note before we move on to GPT-4 assisted automation journey. Below are some "little" helps ChatGPT had already been offering, here and there, during daily works.
And what could be the perceived gaps, risks and traps to LLMs assisted automation, if you happen to explore this path too. I'd also love to hear anyone's use cases and experiences on this front too.
One of the simplest tasks could be Lookup tables.
Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.
A thought comes to mind while playing. By going slightly theoretical or philosophical, eventually interoperability standards such as HL7 and FHIR etc are kind of "languages", right? HL7 has its own grammar, rules, vocabulary and even dialects - every system speaks its own tone.