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.
This article shares analysis in solution cycle for the Open Exchange application TOOT ( Open Exchange application )
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.
The fun semantic meaning chosen was for musical vector search, to build new skills and knowledge along the way.
Hey Community,
It's time for the first programming contest of the year, and there's a surprise so read on! Please welcome:
🏆 InterSystems AI Programming Contest: Vector Search, GenAI, and AI Agents 🏆
Duration: March 17 - April 6, 2025
Prize pool: $12,000 + a chance to be invited to the GlobalSummit2025!
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:
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
InterSystems Announces General Availability of InterSystems IRIS, InterSystems IRIS for Health, and HealthShare Health Connect 2025.1
The 2025.1 release of InterSystems IRIS® data platform, InterSystems IRIS® for HealthTM, and HealthShare® Health Connect is now Generally Available (GA). This is an Extended Maintenance (EM) release.
Release Highlights
In this exciting release, users can expect several new features and enhancements, including:
Hi all.
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
Introduction
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.
Hey, community! 👋
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.
Hi Community,
We're pleased to invite you to the upcoming webinar in Hebrew:
👉 Hebrew Webinar: GenAI + RAG - Leveraging Intersystems IRIS as your Vector DB👈
📅 Date & time: February 26th, 3:00 PM IDT
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.
For more information on using python with Iris, see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=AFL_epython
Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris
Part 1 - SQL Gateway
Hello
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
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.
Firstly, drag and drop the image or click the upload icon, select the image, and click the upload button to upload and vectorize it. This process may be a bit slow.
This process involves using embedded Python to call the CLIP model and vectorize the image into 512 dimensional vector data.
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
The 2024.3 release of InterSystems IRIS® data platform, InterSystems IRIS® for Health, and HealthShare® Health Connect is now Generally Available (GA).
In this release, you can expect a host of exciting updates, including:
🌍 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:
set $somefunction(param1,param2, ...) = value
The reason to raise that subject is that with IRIS 2024.1 there is after may years a "new kid on this block"
We continue with this series of articles on LLM and RAG applications and in this article we will discuss the red boxed part of the following diagram:
In the process of creating a RAG application, choosing an LLM model that is appropriate to your needs (trained in the corresponding subject, costs, speed, etc.) is as important as having a clear understanding of the context you want to provide. Let's start by defining the term to be clear about what we mean by context.
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.
Nevertheless, there are numerous strategies to boost the performance of systems built on the IRIS Data Platform. These strategies are equally crucial for optimizing overall efficiency.
Let's join the journey for a sneak peek into the tips for making IRIS Data Platform work better, where every little trick will help your applications shine.
1. Using Indexes
Motivated by personal feedback by @Edilson Eberle Carvalho and an excellent presentation of @Michael Braam related to Vector Search I'd like to sharemy personal approach to Vectors.When I started and met vectors with 256, 384, and over 1200 dimensions - I felt lost.However my example Vector-inside-IRIS - a simplification of iris-vector-search - worked fine. In order to understand the mechanics behind it, I decided to start in small steps.Our common 3 dimensions describe our physical world quite fine.Even the half 4th dimension (no negatives) added by Einstein is not to hard to
We've recently made available a new version of InterSystems IRIS in the Vector Search Early Access Program, featuring a new Approximate Nearest Neighbor index based upon the Hierarchical Navigable Small World (HNSW) indexing algorithm. This addition allows for highly efficient, approximate nearest-neighbor searches over large vector datasets, dramatically improving query performance and scalability.
Hi, Community!
Want to dive deeper into the concepts of vector searches and embeddings? Learn from an InterSystems expert:
Hey Developers,
Play the new video on InterSystems Developers YouTube:
⏯ Chatbots are all the RAGe: Generative AI with InterSystems IRIS @ Global Summit 2024
Hi Community,
Play the new video on InterSystems Developers YouTube:
⏯ InterSystems IRIS Vector Search and the Python Ecosystem @ Global Summit 2024
Hi, Community!
Curious how a retrieval-augmented generation (RAG) application works? Take a look at this demonstration:
Hi, Community!
Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:
Artificial intelligence (AI) has transformative potential for driving value and insights from data. As we progress toward a world where nearly every application will be AI-driven, developers building those applications will need the right tools to create experiences from these applications. Tools like vector search are essential for enabling efficient and accurate retrieval of relevant information from massive datasets when working with large language models. By converting text and images into high-dimensional vectors, these techniques allow quick comparisons and searches, even when dealing
Hi Community,
In this article, I will introduce my application iris-RAG-Gen .
Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.