Yes, yes! Welcome! You haven't made a mistake, you are in your beloved InterSystems Developer Community in Spanish.

You may be wondering what the title of this article is about, well it's very simple, today we are gathered here to honor the Inquisitor and praise the great work he performed. 

So, who or what is the Inquisitor?

Perfect, now that I have your attention, it's time to explain what the Inquisitor is. The Inquisitor is a solution developed with InterSystems technology to subject public contracts published daily on the platform  https://contrataciondelestado.es/ to scrutiny.

Hi, 

We very much appreciate the interest in the Developer Community for IRIS Vector Search and hope our technology has helped many of you build innovative applications or advanced your R&D efforts. With a dedicated index, integrated embeddings generation, and deep integration with our SQL engine now available in InterSystems IRIS, we're looking at the next frontier, and would love to hear your feedback on the technology to prioritize our investments.

Introduction

In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.

This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.

You can find them at the Open Exchange:

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?

Enter IRIS Audio Query - a full-stack application that transforms audio into a searchable knowledge base. With it, you can:

IRIS Audio Query is a full-stack application that transforms audio into a searchable knowledge base.

Project Structure

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?

Vanna.AI - Personalized AI InterSystems OMOP Agent

Hey Community,

We're excited to invite you to the next InterSystems UKI Tech Talk webinar: 

👉AI Vector Search Technology in InterSystems IRIS

⏱ Date & Time: Thursday, September 25, 2025 10:30-11:30 UK

Speakers:
👨‍🏫 @Saurav Gupta, Data Platform Team Leader, InterSystems
👨‍🏫 @Ruby Howard, Sales Engineer, InterSystems

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.

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

• 🧠 Python + smolagents to orchestrate the agent’s “brain”
• 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

Hey Community, 

Last week, the InterSystems team held our monthly Developer Meetup in a new venue for the first time ever! In the AWS Boston office location in the Seaport, over 71 attendees showed up to chat, network, and listen to talks from two amazing speakers. The event was a huge success; we had a packed house, tons of engagement and questions, and attendees lining up to chat with our speakers afterwards! 

Jayesh presents on Testing Frameworks for Agentic Systems to a full house

#InterSystems Demo Games entry

⏯️ Care Compass – InterSystems IRIS powered RAG AI assistant for Care Managers

Care Compass is a prototype AI assistant that helps caseworkers prioritize clients by analyzing clinical and social data. Using Retrieval Augmented Generation (RAG) and large language models, it generates narrative risk summaries, calculates dynamic risk scores, and recommends next steps. The goal is to reduce preventable ER visits and support early, informed interventions.

☤ Care 🩺 Compass 🧭 - Proof-of-Concept - Demo Games Contest Entry

Introducing Care Compass: AI-Powered Case Prioritization for Human Services

In today’s healthcare and social services landscape, caseworkers face overwhelming challenges. High caseloads, fragmented systems, and disconnected data often lead to missed opportunities to intervene early and effectively. This results in worker burnout and preventable emergency room visits, which are both costly and avoidable.

Care Compass was created to change that.

InterSystems IRIS 2024 recently introduced the vector types. This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications. In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

At its essence, a vector type is a structured collection of numerical values arranged in a predefined order. These values serve to represent different attributes, features, or characteristics of an object.

Hi Community,

Enjoy the new video on InterSystems Developers YouTube from our Tech Video Challenge:

⏯  Genes in DNA Sequences as vector representation using IRIS vector database

Learn how to design scalable, autonomous AI agents that combine reasoning, vector search, and tool integration using LangGraph.

Too Long; Didn't Read

Hi Community,

Enjoy the new video in a series of videos created by our Project Managers to highlight some of the interesting features of the new 2025.1 release:

⏯ RAG with built-in Vector Search in InterSystems IRIS 2025.1

You know that feeling when you get your blood test results and it all looks like Greek? That's the problem FHIRInsight is here to solve. It started with the idea that medical data shouldn't be scary or confusing – it should be something we can all use. Blood tests are incredibly common for checking our health, but let's be honest, understanding them is tough for most folks, and sometimes even for medical staff who don't specialize in lab work. FHIRInsight wants to make that whole process easier and the information more actionable.

🤖 Why We Built FHIRInsight

Hi all!

I want to create an %Embedding.Config to use with an %Embedding property. I followed the documentation for %Embedding.OpenAI, and it works fine after setting sslConfig, modelName, and apiKey.

However, I need to use AzureOpenAI. While the embedding process is similar to OpenAI's, Azure requires additional connection parameters, like an endpoint. My question is: is it possible to configure these extra parameters with %Embedding.Config, and if so, how?

documentation reference

I am testing vectorsearch, while doing so I am trying to paginate my resultset for a "next page" function to give me the first, second, third 15 entries within a table. 

For this I have two embedding classes. One with a HNSW Index (vectornomicembedtextlatest) , and one without (vectornomicembedtexttest).

Calling SELECT ID,PRIMKEY FROM SQLUser.vectornomicembedtexttest LIMIT 5 OFFSET 1 works fine with the first entry having the rowID of 486448. (I deleted old entries in the beginning and reused the table)

This article presents a potential solution for semantic code search in TrakCare using IRIS Vector Search.

Here's a brief overview of results from the TrakCare Semantic code search for the query: "Validation before database object save".

• Code Embedding model 

There are numerous embedding models designed for sentences and paragraphs, but they are not ideal for code specific embeddings.

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.

I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:

Who hasn't been developing a beautiful example using a Docker IRIS image and had the image generation process fail in the Dockerfile because the license under which the image was created doesn't contain certain privileges?

An experiment on how to use the LangChain framework, IRIS Vector Search, and LLMs to generate IRIS-compatible SQL from user prompts.

This article was based in this notebook. You can run it with a ready to use environment with this application in OpenExchange.

Setup

First, we need to install the necessary libraries:

!pip install --upgrade --quiet langchain langchain-openai langchain-iris pandas

Next, we import the required modules and set up the environment:

Background

Embeddings is a new IRIS feature empowering the latest capability in AI semantic search.
This presents as a new kind of column on a table that holds vector data.
The embedding column supports search for another existing column of the same table.
As records are added or updated to the table, the supported column is passed through an AI model and the semantic signature is returned.
This signature information is stored as the vector for future search comparison.
Subsequently when search runs, a comparison of the stored signatures occurs without any further AI model processing overhead.

Hi Community,
Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.
This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

See the Langchain IRIS Tool in action on YouTube. You can see IRIS metrics, discover classes, generate fake data, and so on. Project using Ollama, IRIS VectorDB, Streamlit and Langchain.

I just realized I never finished this serie of articles!

In today's article, we'll take a look at the production process that extracts the ICD-10 diagnoses most similar to our text, so we can select the most appropriate option from our frontend.

Looking for diagnostic similarities:

From the screen that shows the diagnostic requests received in HL7 in our application, we can search for the ICD-10 diagnoses closest to the text entered by the professional.