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#SQL

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SQL is a standard language for storing, manipulating and retrieving data in relational databases.

Nowadays so much noise around LLM, AI, and so on. Vector databases are kind of a part of it, and already many different realizations for the support in the world outside of IRIS.

Why Vector?

  • Similarity Search: Vectors allow for efficient similarity search, such as finding the most similar items or documents in a dataset. Traditional relational databases are designed for exact match searches, which are not suitable for tasks like image or text similarity search.
  • Flexibility: Vector representations are versatile and can be derived from various data types, such as text (via embeddings like Word2Vec, BERT), images (via deep learning models), and more.
  • Cross-Modal Searches: Vectors enable searching across different data modalities. For instance, given a vector representation of an image, one can search for similar images or related texts in a multimodal database.

And many other reasons.

So, for this pyhon contest, I decided to try to implement this support. And unfortunately I did not manage to finish it in time, below I'll explain why.

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Case description

Let’s imagine that you are a Python developer or have a well-trained team specialized in Python, but the deadline you got to analyze some data in IRIS is tight. Of course, InterSystems offers many tools for all kinds of analyses and treatments. However, in the given scenario, it is better to get the job done using the good old Pandas and leave the IRIS for another time.

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InterSystems FAQ rubric

It can be retrieved using the schema INFORMATION_SCHEMA.

INFORMATION_SCHEMA is a system schema and is not displayed by default in the SQL menu of the Management Portal.

The method to display it is as follows.

  1. Open Management Portal → System Explorer → SQL menu.
  2. Check "System" on the left of the schema drop-down.
  3. Select INFORMATION_SCHEMA from the schema dropdown.

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Earlier this year I announced availability of a VS Code extension for coding in ObjectScript, Embedded Python or SQL using the notebook paradigm popularized by Jupyter. Today I published a maintenance release to correct a "getting started" problem.

Here's a video of the installation steps from the extension's README:

Why not try it for yourself?

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The related package avoids adding %JSONAdaptor to each class but uses instead
SQL functions JSON_OBJECT() to create my JSON objects. With this approach, you can
add JSON to any class - even deployed ones - without any need for change or recompiling.

The trigger was the Export of M:N relationships as JSON objects or arrays.

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Enhanced Password Management: Edit Passwords Seamlessly

In the ever-evolving landscape of digital security, robust password management tools have become indispensable. Our password management application, designed to simplify and secure your online life, now comes with an enhanced feature – the ability to edit passwords with ease.

Why is this feature a game-changer?

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