Generative AI refers to algorithms and models in artificial intelligence that are capable of generating new data or content that is similar to existing data. These models are trained on large datasets and learn to generate new examples that mimic the patterns and characteristics of the original data.
Keywords: IRIS, Agents, Agentic AI, Smart Apps
Transformer based LLMs appear to be a pretty good "universal logical–symbolic abstractor". They started to bridge up the previous abyss among human languages and machine languages, which in essence are all logic symbols that could be mapped into the same vector space.
Wondering for 3 years we might be able to just use English (etc human natural languages) to do IRIS implementations as well, one day.
There are always those who say that their child looks more like their mother or their father. How about using math and Artificial Intelligence to be sure? The facial-matching application can answer you.
See these results:
Hi developers!
I'm testing vibecoding with ObjectScript and my silicon friend created a code-block that got me thinking "what's wrong"?
Here is the piece of code:
for i=0:1:(json.%Size()-1) {
set p = json.%Get(i)
if (p="value1")!(p="value2") {
quit 1
}
AI wanted to quit from a method with a return value. Good intention, but bad use of the command.
And ObjectScript compiler compiles this code with no error(?) (syntax linter in VSCode says it's a syntax, kudos @Brett Saviano ).
But in action, it produces <COMMAND>, of course.
I wanted to try vibecoding a real backend + frontend setup on InterSystems IRIS, ideally using something realistic rather than a toy example. The goal was simple: take an existing, well-known persistent package in IRIS and quickly build a usable UI and API around it — letting AI handle as much of the boilerplate as possible. Here is the result of the experiments.
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.