Interoperability on Python (IoP) is a proof-of-concept project designed to showcase the power of the InterSystems IRIS Interoperability Framework when combined with a Python-first approach.IoP leverages Embedded Python (a feature of InterSystems IRIS) to enable developers to write interoperability components in Python, which can seamlessly integrate with the robust IRIS platform. This guide has been crafted for beginners and provides a comprehensive introduction to IoP, its setup, and practical steps to create your first interoperability component. By the end of this article, you will get a

Hi Community,
In this article, I will introduce my application iris-fhir-bridge 
IRIS-FHIR-Bridge is a robust interoperability engine built on InterSystems IRIS for Health, designed to transform healthcare data across multiple formats into FHIR and vice versa. It leverages the InterSystems FHIR Object Model (HS.FHIRModel.R4.*) to enable smooth data standardization and exchange across modern and legacy healthcare systems.

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.

Implementation Steps:

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

Hi Community, 
In this article, we will explore the concepts of Dynamic SQL and Embedded SQL within the context of InterSystems IRIS, provide practical examples, and examine their differences to help you understand how to leverage them in your applications.

Hi, Community!

In the previous article, we introduced the Streamlit web framework, a powerful tool that enables data scientists and machine learning engineers to build interactive web applications with minimal effort. First, we explored how to install Streamlit and run a basic Streamlit app. Then, we incorporated some of Streamlit's basic commands, e.g., adding titles, headers, markdown, and displaying such multimedia as images, audio, and videos.

Hi Community,
In this article, I will introduce my application iris-HL7v2Gen .

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 Community,
I am getting below "The driver cannot be loaded" error on a MacBook.

obdbc8.jar file is present in the mentioned folder.
Looking forward to resolve the above issue or workaround
Thanks

Hi Community,
In this article, I will introduce my application iris-DataViz
iris-DataViz is an Exploratory Data Analysis and Visualization Streamlit Application that leverages the functionality of IRIS embedded python and SQLAlchemy to interact with IRIS, as well as the PyGWalker python library for data analysis and data Visualization. PyGWalker (Python Graphic Walker) is an interactive data visualization library built for Python, aiming to bring the ease and functionality of Tableau-style drag-and-drop visualization into Python environments.

Application Features 

Artificial intelligence (AI) has transformative potential for driving value and insights from data. As we progress toward a world where nearly every application will be AI-driven, developers building those applications will need the right tools to create experiences from these applications. Tools like vector search are essential for enabling efficient and accurate retrieval of relevant information from massive datasets when working with large language models. By converting text and images into high-dimensional vectors, these techniques allow quick comparisons and searches, even when dealing

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.

Application Features

• Ingest Documents (PDF or TXT) into IRIS
• Chat with the selected Ingested document
• Delete Ingested Documents
• OpenAI ChatGPT

By default, all files created inside a container are stored on a writable container layer. This means that:

• The data doesn't persist when that container no longer exists, and it can be difficult to get the data out of the container if another process needs it.
• A container's writable layer is tightly coupled to the host machine where the container is running. You can't easily move the data somewhere else.

In our previous article, we have explored the most common Kubernetes components:

• We started with the pods and the services we needed to communicate with each other.
• Then, we examined the  Ingress component used to Route traffic into the cluster.
• We also skimmed through an external configuration using ConfigMaps and Secrets.
• Afterward, we analyzed Data persistence with the help of Volumes.
• Finally, we took a quick look at pod blueprints with such replicating mechanisms as Deployments and StatefulSets (the latter is employed specifically for such stateful applications as databases).

Hi Community,
In this series of articles, we will explore the following InterSystems SQL usage options:

1. Embedded SQL

2. Dynamic SQL

3. Class Queries

SQL Overview

Hi Community,
In this article, I will introduce my application iris-VectorLab along with step by step guide to performing vector operations.

IRIS-VectorLab is a web application that demonstrates the functionality of Vector Search with the help of embedded python. It leverages the functionality of the Python framework SentenceTransformers for state-of-the-art sentence embeddings.

Application Features

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.

In this article, we will cover below topics:

• What is Kubernetes?
• Main Kubernetes (K8s) Components

What is Kubernetes?

Kubernetes is an open-source container orchestration framework developed by Google. In essence, it controls container speed and helps you manage applications consisting of multiple containers. Additionally, it allows you to operate them in different environments, e.g., physical machines, virtual machines, Cloud environments, or even hybrid deployment environments.

What problems does it solve?

Hi Community!
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 we need to build a custom ChatGPT AI by using LangChain Framework:
Below are the steps to build a custom ChatGPT:

Introduction

Visual Studio Code (VS Code) is a free source code editor made by Microsoft for Windows, Linux, and macOS. It provides built-in support for JavaScript, TypeScript, and Node.js. You can add extensions to provide support for numerous other languages including ObjectScript.

The InterSystems extensions enable you to use VS Code to connect to an InterSystems IRIS server and develop code in ObjectScript. The Visual Studio Code Documentation is an excellent resource on VS Code, so it is a good idea to be familiar with it.

Sometimes we need to convert FHIR message to HL7 V2, e.g. to register a patient to the PACS system.
In this article, I will explain the steps to achieve the desired by using IRIS FHIR Server production.

Below are the steps we need to follow:

1. Make sure FHIRServer production is started.
2. Register Business Service with FHIRServer endpoint.
3. Define Business Processes to convert FHIR message to SDA and then Convert SDA to HL7 v2.
4. Post JSON resource to FHIRServer endpoint and get HL7 V2 response.

Let's review the steps in detail.
 

Step 1. Make sure FHIRServer production is started

All FHIR resources have a Meta element containing metadata about the resource. Some attributes are updated by the server, others are populated by the app constructing the resource.

Here are the 3 common attributes of FHIR’s Meta element: 

1. versionId
This is an ID that identifies a saved version of the resource on the FHIR server. Many servers use a GUID here, others use an integer. But it’s a string value, so don’t write code against this assuming a sequential integer, regardless of what the server does.

In the context of HL7 FHIR (Fast Healthcare Interoperability Resources), the terms "id" and "identifier" refer to specific elements used for identifying resources within the FHIR data model. For a newbie, these terms can be confusingly similar, but they serve distinct purposes.  

Look at the below Patient resource for August T. Faulkner:

The resource has an id of “1” — generated by the FHIR server when the resource was created.
Patient August T. Faulkner also has a identifier (Medical Record Number) — possibly provided by the hospital — of 78510398960

  Hi, Community!

Since this article is an overview of Flask Login, let's begin with Flask Introduction!

What is Flask?

Hi, Community!

This article is an overview of SQLAlchemy, so let's begin!

SQLAlchemy is the Python SQL toolkit that serves as a bridge between your Python code and the relational database system of your choice. Created by Michael Bayer, it is currently available as an open-source library under the MIT License. SQLAlchemy supports a wide range of database systems, including PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server, making it versatile and adaptable to different project requirements.

Hi,
Getting the below error while posting FHIR resource to FHIR Server

Looking Forward
Thanks

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

• Step1: Install required libraries

• Step2: Create patterns and responses file

• Step3: Train the Model

• Step4: Create ChatBot Application based on the trained model

So Let us start.

Hi Community

I have created one business service created from "HS.FHIRServer.Interop.Service" in FHIR production.

I want to validate FHIR Patient resource that the age must be greater than 18 years.

How can I achieve this from objectscript as well as from DTL?
Thanks

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.

Application Features