Application Programming Interface (API) is a set of subroutine definitions, protocols, and tools for building application software. In general terms, it is a set of clearly defined methods of communication between various software components.
Hello all,
I have a EnsLib.HTTP.GenericMessage inbound from a webhook with a GC stream.
My router is defined as the following:
Hey Developers,
Watch this video to learn about automating the provisioning and management of FHIR services:
⏯ Automating Provisioning and Management of FHIR Services @ Ready 2025
If you have ever dug through a large IRIS namespace looking for where a particular string, method call, or pattern was used, you know the pain: there was no built-in way to do a grep-style search across your server-side ObjectScript code from VS Code — at least not without jumping through some hoops.
That is what ObjectScript Search fixes.
Try it today with a simple install from the VS Code Marketplace. If you don't like it, uninstalling is just as easy. But I think you will like it — it is a huge quality-of-life improvement for anyone doing ObjectScript development in VS Code.
InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.
But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.
I created iris-budget app for the InterSystems Full Stack Contest in 2026. By full stack, we mean a frontend web or mobile application that inserts, updates, or deletes data in InterSystems IRIS via REST API, Native API, ODBC/JDBC, or Embedded Python.
My app uses multiple REST APIs to add a new category or retrieve a list of categories of expenses and income.
I inherited /csp/coffee from module.xml in iris-fullstack-template.
For this project, I created a swagger file called "budget.json.
This is an excellent candidate for a developer community post (like Dev.to, Medium, or the InterSystems Community). It bridges the gap between high-level architecture and hands-on implementation.
Here is the summarized article format.
As applications scale, handling heavy computational tasks synchronously becomes a bottleneck. Whether it's processing large data sets, sending high-volume emails, or managing API integrations, a decoupled architecture is essential.
In the previous article, we examined how we can use the %CSP.Request and %CSP.Response classes to test a REST API without having the API fully set up and accessible across a network with an authentication mechanism. In this article, we will build on that foundation to perform some simple unit testing of one of our REST API methods.
The unit testing framework requires a couple of setup steps before we can use it. First, we have to ensure that the unit testing portion of the management portal is enabled so we can review the results of our tests.
How to set up RAG for OpenAI agents using IRIS Vector DB in Python
In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.
To demonstrate this, we’ll create an OpenAI agent with knowledge of InterSystems technology. We’ll achieve this by storing embeddings of some InterSystems documentation in IRIS and then using IRIS vector search to retrieve relevant content—enabling a Retrieval-Augmented Generation (RAG) workflow.
Note: Section 1 details how process text into embeddings.