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.
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.
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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?
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)
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?
In my case, what I was deploying in Docker is a small application that uses the Vector data type. With the Community version, this isn't a problem because it already includes Vector Search and vector storage. However, when I changed the IRIS image to a conventional IRIS (the latest-cd), I found that when I built the image, including the classes it had generated, it returned this error:
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.
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.
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