#Tips & Tricks

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Pieces of experience in InterSystems Technology which solve some particular problem in elegant or unusual way.

Hi everyone.

I'm going to give you a quick tip on how to implement an AI agent to search the Intersystems documentation integrated into Teams.

Yes, I know the documentation page has its own AI search engine and it's quite effective, but this way we'd have faster access, especially if Teams is your company's corporate tool.

You can also create another AI agent to search articles published in the developer community (which also has its own integrated AI search engine).

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Contestant

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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