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.

All source code to the article is available at: https://github.com/antonum/ha-iris-k8s

Index

Part 1

• Introducing Flask: a quick review of the Flask Docs, where you will find all the information you need for this tutorial;
• Connecting to InterSystems IRIS: a detailed step-by-step of how to use SQLAlchemy to connect to an IRIS instance;

Part 2

• A discussion about this kind of implementation: why we should use it and situations where it is applicable.

Hi Developers!

Often I find questions on how to install IRIS, connect to IRIS from IDE, setup the environment, compile, debug, maintain the repository.

Here below possibly the shortest way to set up all the environment and start development with ObjectScript on InterSystems IRIS.

Prerequisites

Make sure you have Git, Docker, and VSCode installed

Install Docker and ObjectScript extensions into VSCode

Sign in or Create an account on Github

Here we go!

Hi folks,

I have published a new package on OEX.

In short, it is a string datatype that support various configuration options and flexibility.

Why should you connect Flask to InterSystems IRIS?

The first thing that comes to mind when we think about combining Flask with IRIS is a portal to interact with your clients and partners. A good example would be a website for patients to access their clinical exams. Of course, this case would require a whole new layer of security, which we did not cover in our last article. However, we can effortlessly add it with Werkzeug, for instance.

Most of us are more or less familiar with Docker. Those who use it like it for the way it lets us easily deploy almost any application, play with it, break something and then restore the application with a simple restart of the Docker container.

We continue with the series of articles related to the QuinielaML application. In this article we are going to cover how to prepare the raw data that we have captured using the Embedded Python functionality.

Welcome everybody!

Hi Community,

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

IRIS-HL7v2Gen is a CSP application that facilitates the dynamic generation of HL7 test messages. This process is essential for testing, debugging, and integrating healthcare data systems. The application allows users to generate a wide variety of HL7 message types, validate their structure against HL7 specifications, explore the message hierarchy, and transmit messages over TCP/IP to production systems. These features are particularly useful in settings where compliance with HL7 standards is mandatory for interoperability between different healthcare organizations or systems.

Application Features

• Dynamic HL7 Message Generation: Instantly create HL7 messages for a range of message types, facilitating comprehensive testing.
• Message Structure Exploration: Visualize the structure of generated messages based on HL7 specifications.
• Value Set Visualization View predefined sets of allowable coded values for specific fields.
• Message Validation: Validate messages against HL7 standards to ensure compliance.
• TCP/IP Communication: Easily transmit messages to production using TCP/IP settings.
• Broad Message Type Support: Supports 184 different HL7 message types, ensuring versatility for various healthcare integration needs.
• ClassMethod: Generate a Test Message by Invoking a Class Method
• Version Support: Currently Supports HL7 Version 2.5

InterSystems has also released IRIS as containerized deployments. This post is to demonstrate how InterSystems IRIS and applications those rely on IRIS a backend can be packaged into an image and be run in other machines in containers and how simple it is to do that.

A container runs image/s which have all the necessary executables, binary code, libraries, and configuration files. And the images can be moved from one machine to another, and an images repository like Docker Hub can simplify that process.

I have used an application from Open Exchange for this demo.

Hi Developers!

"objectscript.conn" :{
      "ns": "IRISAPP",
      "active": true,
      "docker-compose": {
        "service": "iris",
        "internalPort": 52773
      }

I want to share with you a nice new feature I came across in a new 0.8 release of VSCode ObjectScript plugin by @Dmitry Maslennikov and CaretDev.

The release comes with a new configuration setting "docker-compose" which solves the issue with ports you need to set up to make your VSCode Editor connect to IRIS. It was not very convenient if you had more than one docker container with IRIS running on the same machine. Now, this is solved!

Read below how it works now.

Data Collection

This is a step-by-step instruction guide for creating a task to collect data about the InterSystems database and globals therein (as seen in the associated Open Exchange App - find all the associated code there)

Why to use it

This app offers an easy interface to analyze storage:

• Filter by database (namespace), global name, used size, or allocated size;
• View a sum of the used and allocated sizes for the filters applied;
• Export the table to JSON, CSV, or XML.

How to use it

Follow the instructions on the README file from the GitHub repository, and configure the settings to connect to your instance.

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