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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Hi Community

In this article, I will introduce my application IRIS-GenLab.

IRIS-GenLab is a generative AI Application that leverages the functionality of Flask web framework, SQLALchemy ORM, and InterSystems IRIS to demonstrate Machine Learning, LLM, NLP, Generative AI API, Google AI LLM, Flan-T5-XXL model, Flask Login and OpenAI ChatGPT use cases.

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image
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.

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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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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.

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Article
· Sep 17 1m read
native-api-py-demo

native-api-py-demo

This is a demo of IRIS Native API for Python, which uses Python to call the Object Script method and flow the message in production. Python obtains the message after flow and the message in global, and uses ZPM Package deployment.

Firstly, we need to install the Native API package

Enter on the command line

pip install intersystems_irispython-3.2.0-py3-none-any.whl
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In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.

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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.

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InterSystems FAQ rubric

The meaning of each timeout value is as follows.

1. [Server response timeout]

If IRIS/Caché processing (routine or query execution) does not finish within this set time, the browser will return an error.

For example, if this value is 60 seconds and it takes 90 seconds to execute a routine/method/query, an error will occur.

2. [Queued request timeout]

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