Motivated by personal feedback by @Edilson Eberle Carvalho and
an excellent presentation of @Michael Braam related to Vector Search I'd like to share
my personal approach to Vectors.

When I started and met vectors with 256, 384, and over 1200 dimensions - I felt lost.

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.

Hey community! How are you doing?

I hope to find everyone well, and a happy 2022 to all of you!

Over the years, I've been working on a lot of different projects, and I've been able to find a lot of interesting data.

But, most of the time, the dataset that I used to work with was the customer data. When I started to join the contest in the past couple of years, I began to look for specific web datasets.

I've curated a few data by myself, but I was thinking, "This dataset is enough to help others?"

Purpose

This set of tools (RanRead, RanWrite, and the combined RanIO) is used to generate random read and write events within a database (or pair of databases) to test the IO speed of IRIS running on a specified hardware setup. While Read operations can be measured in the usual Input/Output operations per second (IOPS) since they're direct disk reads, write events are sent to the database and thus their physical writes are managed by IRIS's write daemon.

Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests, ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis. The suggested method is by running the System Performance tool that comes bundled within IRIS. Please note that this is an update to a previous release, which can be found here.

In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.

In part I of this article, we have already learned more about HL7, its messages, structure, segments, and fields. In this part II, we will show you how to transform proprietary/custom data into HL7 using IRIS for Health. For our sample, we used the data generated by the SYNTHEA bulk data generation project (https://synthea.mitre.org/downloads). So, we will show you how to convert 1000 patients from a CSV file to HL7 v2, using the Interoperability features of IRIS for Health.

We continue with this series of articles on LLM and RAG applications and in this article we will discuss the red boxed part of the following diagram:

InterSystems Data Platforms products allow you to export and import security settings in two different ways.

This article talks about those options:
- On the command line, using ^SECURITY
- Programmatically, using the Export and Import methods of classes in the Security package

Exporting settings on the command line (^SECURITY)

You can export everything or individual sections of the security settings.

InterSystems FAQ rubric

You can programmatically retrieve routine dates and sizes using the RoutineList query of the %Library.Routine (or just %Routine) class.

The RoutineList query has an argument, and the routine name to be searched can be specified by prefix match or middle match. (For wildcards, specify * or ?)

In the example below, *.MAC is specified as an argument.

A few weeks ago I posted an announcement about a JavaScript-based interface for our mg_web WebServer interfacing addon module. mg_web isn't just restricted to use by JavaScript developers though. Many readers will be ObjectScript developers who are more used to using CSP as their web gateway. Some may even have much older legacy WebLink-based applications (and be wondering how to support them given that IRIS does not support WebLink).

As a part of the IRIS Python 2024 contest, my colleague Damir and I went with an idea to build a platform called ShelterShare for connecting victims and volunteers for shelter requests .

Here I’ll walk you through the process of creating a simple Node/Express API and connect it to a InterSystems IRIS instance.

I won't go into much detail about how to work with any of the technologies I will mention in this tutorial but I will leave links, in case you want to learn more.

The objective here is to give you a practical guide on how to set up and connect a node.js back-end API to IRIS.

Before we get our hands dirty, make sure you have Node.js running on your machine. So I'll check:

Our company is in the process of converting our software for use in Intersystems IRIS and one of the major sections of the software makes use of a custom statically-linked C library using the $ZF("function-name") functionality. During this, I found out that the process for setting up the C library to be used within the database platform has changed significantly between Cache and IRIS.

This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.

Starting with one problem

Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.

First solutions in mind

We can think some usual solutions for this scenario, like: