Post that includes the step by step explanation of the technical feature or solution.
Hi Community,
We're super excited to share that we’ve just launched a new free, interactive tutorial designed especially for those who are new to InterSystems IRIS and looking for a clear, practical introduction to its core concepts.
🧑🏫 Data Models of InterSystems IRIS 🧑💻
Hi Community!
We’re building a series of short, hands-on Instruqt tutorials to help newcomers get up to speed with InterSystems technologies faster and more effectively. To kick things off, we’ve just released a new tutorial, “Data Models of InterSystems IRIS,” covering the fundamentals of the IRIS multimodel approach. This is exactly the type of focused, concise, practical learning experience we want to expand. And this is where you come in!
We’d love to hear your ideas for other tutorial topics to help developers new to InterSystems IRIS take their first steps with confidence. Please welcome the new sweepstakes:
💡 Topics for hands-on Instruqt Tutorials 💡
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.