Hi Community,

We’re excited to share a brand-new Instruqt tutorial:

🧑‍🏫 RAG using InterSystems IRIS Vector Search

This hands-on lab walks you through building a Retrieval Augmented Generation (RAG) AI chatbot powered by InterSystems IRIS Vector Search. You’ll see how vector search can be leveraged to deliver up-to-date and accurate responses, combining the strengths of IRIS with generative AI.

✨ Why try it?

Table of Contents

1. Purpose of the article
2. What containers are and why they make sense with IRIS
    2.1 Containers and images in a nutshell
    2.2 Why containers are useful for developers
    2.3 Why IRIS works well with Docker
3. Prerequisites
4. Installing the InterSystems IRIS image
    4.1 Using Docker Hub
    4.2 Pulling the image
5. Running the InterSystems IRIS image
    5.1 Starting an IRIS container
    5.2 Checking container status
    5.3 Executing code in the container terminal
    5.4 Accessing the IRIS Management Portal
    5.5 Connecting the container to VS Code
    5.6 Stopping or removing the container
    5.7 Setting a specific password with a bind mount
    5.8 Using durable %SYS volumes
     5.8.1 What gets stored with durable %SYS
     5.8.2 How to enable durable %SYS
6. Using Docker Compose
    6.1 Docker Compose example
    6.2 Running Docker Compose
7. Using a Dockerfile to run custom source code
    7.1 Dockerfile example
    7.2 Docker Compose example
    7.3 Understanding layers, image tagging and build vs. run time
    7.4 Source code and init script
    7.5 Building the image with Dockerfile
    7.6 Running instructions in the containerized IRIS terminal
8. Conclusion and what’s next

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

If you thought native Go support for IRIS was exciting, wait until you see what happens when GORM enters the mix.

Just recently, we welcomed native GoLang support for InterSystems IRIS with the release of go-irisnative. That was just the beginning. Now, we’re kicking things up a notch with the launch of gorm-iris — a GORM driver designed to bring the power of Object Relational Mapping (ORM) to your IRIS + Go stack.

Why GORM?

GORM is one of the most popular ORM libraries in the Go ecosystem. It makes it easy to interact with databases using Go structs instead of writing raw SQL. With features like auto migrations, associations, and query building, GORM simplifies backend development significantly.

So naturally, the next step after enabling Go to talk natively with IRIS was to make GORM work seamlessly with it. That’s exactly what gorm-iris does.

Ready to elevate your Business Intelligence game?

We've got an engaging video, perfect for all skill levels.

Discover how to get started with IRIS BI, optimize cube performance, and drive analytics adoption. Plus, get a sneak peek at what's next!

https://www.youtube.com/embed/Ekjzny8zj98
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I am working on a tool that configures an instance to use a common default routine database across all custom Namespaces. For instances that already have distinct default routine databases for their Namespaces, this would involve a step of merging over all code from the current default routine databases to the new "super" routine database. This should be done in such a way that only merges contents in the default routine database (not mapped databases) and is able to detect/exclude contents that already exist in the target database.

I know the next ones:

1. Place all different settings in environment variables. You have a different .env file for each environment, and you must add some code to Production for reading and setting these values. It's good for deploying into containers, but challenging for management when we have a large production. I mean, we have many settings that can vary depending on the environment: active flag, pool size, timeouts, and so on. Not only endpoints.

Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

InterSystems IRIS Data Platform is a comprehensive, multi-model, multi-workload data platform that is ideal for accommodating the challenging requirements of applications for the Internet of Things. It is a complete platform for developing, executing, and maintaining IoT applications in a single, consistent, unified environment. It features a distributed architecture to support massive data-ingest rates and data volumes, while providing the flexibility and durability of an enterprise-grade transactional multi-model database to ingest, process, and persist data from a wide range of devices in different formats. It features a complete set of integration, event-processing, and integrated analytics capabilities, including full SQL support and text processing, business process orchestration, and a standards-based development environment.

Connect to, ingest, and persist a wide range of disparate device data types and formats

The data types associated with IoT applications are often heterogeneous, as they may originate from various devices with diverse functions and manufactured by different vendors. The underlying data platform must be able to ingest and process a wide range of raw data types in their original formats. Many applications also require the data platform to persist all of the disparate source data to detect deviations from normal ranges, accommodate downstream ad hoc analytics, maintain regulatory compliance, and fulfill other purposes.

It's about an example for the External Languages Contest 2025

You get almost any information about your databases in IRIS using
the System Management Portal. After passing several levels, you often
get a wide list of items, but the interesting ones are hard to find.

In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

Hi Team,

I've basic learning in Ensemble. I want to create a code as per following request. Please help in clearing following questions

We have a business process-AA. In this business process, we have onRequest method, here after performing some logic, I have to call a method ProcessAAlogic.

With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?