#InterSystems IRIS

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.

Hello to all developers and IRIS users.
I am submitting to the competition a project for an alternative IRIS management portal that I have long wanted to create. The project was implemented on the Django framework and the intersystems_irispython-3.2.0-py3-none-any.whl library using native IRIS. The Django project is integrated with the telegram messenger and can be launched from a docker container or directly.

Hello developers,

In this article, I'll show you how to run code at compile time with ObjectScript macros.

Here's a use case that recently led me to use this feature:

As part of a medical application developed for more than 20 years, we have a large number of parameters. Although we have procedures for documenting these settings, it can be helpful to have a quick view of which settings are actually used by the application code.

Hi Community!,

I've published a new versión of IrisApiTester, I included some new features:

Why to use it

This app offers an easy interface to analyze memory:

• Filter by database (namespace), global name, used size, or allocated size;
• View a sum of the used and allocated sizes for the filters applied;
• Export the table to JSON, CSV, or XML.

How to use it

Follow the instructions on the README file from the GitHub repository, and configure the settings to connect to your instance.