Introduction

Hello! In this article, I will be discussing database degrade, a type of data integrity issue one can face when using IRIS. First, I will be going over a review of the structure of IRIS databases. I'll then discuss how database degrade can manifest and common causes of degrade issues. I'll then conclude with general tips we give our customers about how to prevent or prepare for database degrade issues.

There are a lot of great community articles regarding "vector search on IRIS", and samples in OpenExchange. Everytime I see these, I'm so excited to know that so many developers already try vectors on IRIS!

But if you've not tried "Vector Search on IRIS" yet, please give me one minute 😄 I create one IRIS class - and with only one IRIS class you can see how you put vector data in your IRIS database and how you compare these in your application.

When you deploy code from a repo, class (file) deletion might not be reflected by your CICD system.
Here's a simple one-liner to automatically delete all classes in a specified package that have not been imported. It can be easily adjusted for a variety of adjunct tasks:

set packages = "USER.*,MyCustomPackage.*"
set dir = "C:\InterSystems\src\"
set sc = $SYSTEM.OBJ.LoadDir(dir,"ck", .err, 1, .loaded)
set sc = $SYSTEM.OBJ.Delete(packages _ ",'" _ $LTS($LI($LFS(loaded_",",".cls,"), 1, *-1), ",'"),, .err2)

The first command compiles classes and also returns a list of loaded classes. The second command deletes all classes from specified packages, except for the classes loaded just before that.

Hello Community

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.

We have a big update this quarter.

• RHEL 10 was released yesterday, read on for what that means for you
• 2025.3 will use OpenSSL 3 across all operating systems  SUSE 15 sp6 will be the minimum OS for orgs using SUSE
• The minimum CPU standards are going up in 2025.3
• Older Windows Server operating systems will no longer be supported in 2025.3

Parallel query hinting boosts certain query performances on multi-processor systems via parallel processing. The SQL optimizer determines when this is beneficial. On single-processor systems, this hint has no effect.

Parallel processing can be managed by:

1. Setting the auto parallel option system-wide.
2. Using the %PARALLEL keyword in the FROM clause of specific queries.

%PARALLEL is ignored when it applied to:

We have released IPM 0.9.0. I previously remarked on some of the history and reasoning here; to summarize, this is a big release for two reasons: it represents a long-overdue reunification of our internal and community-driven work around IRIS-centric ObjectScript package management, and it has some backwards incompatibilities. There are several necessary backwards incompatibilities in our roadmap, and we've lumped them together; this will not be some new norm.

Prompt

Firstly, we need to understand what prompt words are and what their functions are.

Prompt Engineering

Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 3 – REST and Interoperability

Now that we have finished the configuration of the SQL Gateway and we have been able to access the data from the external database via python, and we have set up our vectorized base, we can perform some queries. For this in this part of the article we will use an application developed with CSP, HTML and Javascript that will access an integration in Iris, which then performs the search for data similarity, sends it to LLM and finally returns the generated SQL. The CSP page calls an API in Iris that receives the data to be used in the query, calling the integration. For more information about REST in the Iris see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls...

My intention is to show how simple it is to generate a lookup table taking into account the information received in our HL7 messaging. The HL7 message lookup table provided by IRIS is certainly sufficient for most of the searches we want to perform, but we always have that special field in our HIS, LIS, RIS, etc. that we'd like to search by. But that's in a segment outside of that lookup table. That field forces us to generate a specific search using the expanded search criteria.

Just like a knockout punch, without giving the opponent a chance, Kubernetes, as an open source platform, has a universe of opportunities due to its availability (i.e., the ease of finding support, services and tools). It is a platform that can manage jobs and services in containers, which greatly simplifies the configuration and automation of these processes.

But let's justify the title image and give the tool in question the “correct” name: InterSystems Kubernetes Operator.

Introduction

Managing databases and performing CRUD operations are fundamental tasks for developers building data-driven applications. While many database management systems (DBMS) exist, they can be complex and cumbersome to interact with, especially when it comes to creating databases and tables, handling constraints, and performing real-time data operations through an API.