Vector search is a method used in information retrieval and machine learning to find similar items based on their mathematical representations as vectors. In this approach, each item is represented as a high-dimensional vector, with each dimension corresponding to a feature or characteristic of the item. Vector search algorithms then compare these vectors to find similar items, such as having similar features or being close together in the vector space. Read more here.
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:
Create Agent Tools
Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
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.
A button on a web page can capture the users voice. IRIS integration could manipulate the recordings to extract semantic meaning that IRIS vector search can then offer for new types of AI solution opportunity.
With the introduction of vector data types and the Vector Search functionality in IRIS, a whole world of possibilities opens up for the development of applications and an example of these applications is the one that I recently saw published in a public contest by the Ministry of Health from Valencia in which they requested a tool to assist in ICD-10 coding using AI models.
How could we implement an application similar to the one requested? Let's see what we would need:
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I'm trying to create an indexed table with an vector field so I can search by the vector value. I've been investigating and found that to get the vector value based on the text (token), use a Python method like the following:
In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama
In the generative AI usage model that we are used to, we have the following flow:
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...
To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.
We are a team of Stanford students applying technology to make sense of climate action. AI excites us because we know we can quickly analyze huge amounts of text.
As we require more reports on sustainability, such as responsibility reports and financial statements, it can be challenging to cut through the noise of aspirations and get to the real action: what are companies doing
That’s why we built a tool to match companies with climate actions scraped from company sustainability reports.
Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris
Part 2 – Python and Vector Search
Since we have access to the data from our external table, we can use everything that Iris has to offer with this data. Let's, for example, read the data from our external table and generate a polynomial regression with it.
Let's now consume the data from the external database to calculate a polynomial regression. To do this, we will use a python code to run a SQL that will read our MySQL table and turn it into a pandas dataframe:
In this article we will look at the use of SQL Gateway in Iris. SQL Gateway allows Iris to have access to tables from other (external) database via ODBC or JDBC. We can access Tables or Views from various databases, such as Oracle, PostgreSQL, SQL Server, MySQL and others.
You need to install the application first. If not installed, please refer to the previous article
Application demonstration
After successfully running the iris image vector search application, some data needs to be stored to support image retrieval as it is not initialized in the library.
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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:
🌍 Inclusion & Innovation in Education 🌍 Our project reimagines learning for all students, with a focus on accessibility and interactive experiences. Built with the goal of making education engaging and inclusive, the tool is designed to support students of all abilities in learning complex material in an intuitive way.
💡 What It Does This educational app transforms lesson presentations into interactive study sessions:
Welcome to the third and final publication of our articles dedicated to the development of RAG applications based on LLM models. In this final article, we will see, based on our small example project, how we can find the most appropriate context for the question we want to send to our LLM model and for this we will make use of the vector search functionality included in IRIS.
In ObjectScript you have a wide collection of functions that return some value typically:
set variable = $somefunction(param1,param2, ...)
There is nothing special about that. But there is a set of functions that I classify as LEFT SIDED The specialty of them is that you can use them also on the left of the equal operator as a target in the SET command:
In the business world, every second counts, and having high-performing applications is essential for streamlining our business processes. We understand the significance of crafting efficient algorithms, measurable through the big O notation.
Motivated by personal feedback by @Edilson Eberle Carvalho and an excellent presentation of @Michael Braam related to Vector Search I'd like to share my personal approach to Vectors.
When I started and met vectors with 256, 384, and over 1200 dimensions - I felt lost.
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