Generally speaking, InterSystems products supported dynamic objects and JSON for a long while, but version 2016.2 came with a completely new implementation of these features, and the corresponding code was moved from the ObjectScript level to the kernel/C level, which made for a substantial performance boost in these areas. This article is about innovations in the new version and the migration process (including the ways of preserving backward compatibility).

JSON is a lightweight data interchange format for representing and exchanging data between a server and a web application. Its popularity has led to its widespread use in applications based on InterSystems technology, as well as demand for a converter that transforms globals to JSON and back. Therefore, @Evgeny Shvarov suggested developing Global->JSON->Global converter. Implementing such a converter is important to ensure interoperability, simplify data exchange, support web services, and provide a standardized approach to data representation across different software ecosystems. This article, the second in the "Implemented Ideas" series, focuses on several projects created by the legend of InterSystems Developer Community @Robert Cemper that deal with this task:

• GlobalToJSON-Compact
• GlobalToJSON-Efficient
• GlobalToJSON-Academic
• GlobalToJSON-embeddedPython
• GlobalToJSON-ePython-pure
• GlobalToJSON-XLA
• JSONfile-to-Global.

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.

I have created a package that offers a utility to load a Global into JSON object and reverse
to create a Global from this type of JSON object. Efficient refers to the structure created.
Only Globals nodes containing data are presented in the generated JSON object.

Developing a Full-Stack JavaScript web app with Caché requires you to bring together the right building blocks. In this third part of this article series I will show how to link our React app to our three back-ends we created in part 2.

I am pleased to announce the next 2016.2 field test kit, 2016.2.0.595.0.

It may look like a slow week, with less than fifty changes having been checked in, but this kit includes the following fixes to problems found by you, the ones running the kits in the field:

At the end of our last lesson, we ended with our page displaying a nice (but garish) Angular Material Toolbar, and our Widget data displaying in a list of Material cards. Our page feels a bit static, and we already know that the large number of Widgets that we will be dealing with will not be especially usable on a static list. What can we do to help?

There are several options how to deliver user interface(UI) for DeepSee BI solutions. The most common approaches are:

• use native DeepSee Dashboards, get web UI in Zen and deliver it in your web apps.
• use DeepSee REST API, get and build your own UI widgets and dashboards.

The 1st approach is good because of the possibility to build BI dashboards without coding relatively fast, but you are limited with preset widgets library which is expandable but with a lot of development efforts.

The 2nd provides you the way to use any comprehensive js framework (D3, Highcharts, etc) to visualize your DeepSee data, but you need to code widgets and dashboards on your own.

Today I want to tell you about yet another approach which combines both listed above and provides Angular based web UI for DeepSee Dashboards - DeepSee Web library.

Hi,

Some time ago, InterSystems introduced the concept of %DynamicObjects.
This feature is a powerful tool that makes it very easy to convert any string of JSON text to objects and vice versa.
However, in the work that J2 Interactive is doing for our customers, there are a couple of things that "need some tweaking".

Overview

In our previous post, we discussed the motivation for developing a chatbot agent with access to FHIR resources. In this post, we will dive into the high-level design aspects of integrating a Streamlit-based chat interface with a Java SpringBoot backend, and enabling a LangChain agent with access to FHIR (Fast Healthcare Interoperability Resources) via APIs.

Foreword

InterSystems IRIS versions 2022.2 and newer feature a redesigned functionality for JSON web tokens (JWTs). Once housed under the %OAuth2 class package, the JWT class, along with other JSON web classes (JWCs), now live under %Net.JSON. This migration occured in order to modularize the JWCs. Before, they were closely intertwined with the implementation for the OAuth 2.0 framework. Now, they can be maintained and used separately from OAuth2.

or "Didn't you say you would cover Persistent Objects in Part 5, Chris?"

Yes, that was the plan. This is a pretty important topic, so it get's its own Article

Up until now, we've display widget JSON that has been created by a basic loop. Clearly this isn't of much value. Now we have our stack connected together, and we can see that the data is flowing to the Welcome page, it's time to complete the stack and start feeding our service from "real" data.

In our last lesson, we added a form to Edit our existing Widgets, and save them back to the server. However, our Form was not well structured and our Save button had no intelligence, and was not fully visible. So today, we will apply some Material components and Angular style to make the form more useful