InterSystems FAQ rubric

In the sample below, an image file is encoded into a Base64 string in a class property, saved, decoded again with Base64, and restored to another file.

【Usage class】

Class User.test Extends %Persistent
{
Property pics As %GlobalBinaryStream;
}

【When importing】

Are you preparing to use VS code for the first time? Just make sure you have enough privileges.

Have you defined your Iris server in your VS Code settings, and still get the following error?

VS Code accesses Iris/HealthConnect using the web application /api/atelier. If you do not have permission (i.e. you are using an LDAP user to connect and the web application is not configured to support LDAP), this is the reason why you couldn't connect.

iris-docker-multi-stage-script

A python script to keep your docker iris images in shape ;)

Witout changing your dockerfile or your code you can reduce the size of your image by 50% or more !

TL;DR

Name the builder image builder and the final image final and add this to end of your Dockerfile:

Modify your Dockerfile to use a multi-stage build:

ARG IMAGE=intersystemsdc/irishealth-community:latest
FROM $IMAGE as builder

Add this to end of your Dockerfile:

FROM $IMAGE as final

ADD --chown=${ISC_PACKAGE_MGRUSER}:${ISC_PACKAGE_IRISGROUP} https://github.com/grongierisc/iris-docker-multi-stage-script/releases/latest/download/copy-data.py /irisdev/app/copy-data.py

RUN --mount=type=bind,source=/,target=/builder/root,from=builder \
    cp -f /builder/root/usr/irissys/iris.cpf /usr/irissys/iris.cpf && \
    python3 /irisdev/app/copy-data.py -c /usr/irissys/iris.cpf -d /builder/root/ 

Boom! You're done!

Welcome back to %SYS! If you read the first article in this series, you must have already seen how to manage your users through the Security.Users class programmatically. Today, we will move on to Resources! Many of the classes in this package use a very similar set of methods.

The related package avoids adding %JSONAdaptor to each class but uses instead
SQL functions JSON_OBJECT() to create my JSON objects. With this approach, you can
add JSON to any class - even deployed ones - without any need for change or recompiling.

The trigger was the Export of M:N relationships as JSON objects or arrays.

Yet another example of applying LangChain to give some inspiration for new community Grand Prix contest.

I was initially looking to build a chain to achieve dynamic search of html of documentation site, but in the end it was simpler to borg the static PDFs instead.

Why to use it

This app offers an easy interface to analyze memory:

• 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.

Hello to all developers and IRIS users.
I am submitting to the competition a project for an alternative IRIS management portal that I have long wanted to create. The project was implemented on the Django framework and the intersystems_irispython-3.2.0-py3-none-any.whl library using native IRIS. The Django project is integrated with the telegram messenger and can be launched from a docker container or directly.

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.

Enhanced Password Management: Edit Passwords Seamlessly

In the ever-evolving landscape of digital security, robust password management tools have become indispensable. Our password management application, designed to simplify and secure your online life, now comes with an enhanced feature – the ability to edit passwords with ease.

Why is this feature a game-changer?

Have you ever thought of creating your own systems for editing users or, perhaps, even an API that you can call?

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!

Most transactional applications have a 70:30 RW profile. However, some special cases have extremely high write IO profiles.

I ran storage IO tests in the ap-southeast-2 (Sydney) AWS region to simulate IRIS database IO patterns and throughput similar to a very high write rate application.

The test aimed to determine whether the EC2 instance types and EBS volume types available in the AWS Australian regions will support the high IO rates and throughput required.