#Vector Search

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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.

Hello Community,

Great news for developers who have just started working with InterSystems IRIS! We have hands‑on interactive tutorials available via the Instruqt platform! These are perfect for getting up to speed quickly, playing in real environments, and building confidence with IRIS‑based development.

Here is the list of available tutorials:

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One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.

In this article I will walk through a simple vector search implementation that Kwabena Ayim-Aboagye and I fleshed out using embedded python in InterSystems IRIS for Health. I'll also dive a bit into how to use embedded python and dynamic SQL generally, and how to take advantage of vector search features offered natively through IRIS.

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Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ Enhancing Customer Engagement with Vector Search - Building a Customer Facing Chatbot @ READY 2025

https://www.youtube.com/embed/T3_TOxVZKwo
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