Hello everybody,

I've been experimenting with Embedded Python and have been following the steps outlined in this documentation: https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls...

I'm trying to convert a python dictionary into an objectscript array but there is an issue with the 'arrayref' function, that is not working as in the linked example.

This is a snapshoot of my IRIS terminal:

Hi Developers,

Join us at the upcoming Developer Roundtable on April 25th at 9 am ET | 3 pm CET. 📍
We will have 2 topics covered by the invited experts and open discussion as always.

Tech Talks:
➡ Practical Usage of Embedded Python - by Stefan Wittmann Product Manager, InterSystems
➡ Monitoring and Alerting Capabilities of InterSystems IRIS - by Mark Bolinsky, Principal Technology Architect, InterSystems

▶ Register via >> this Global Masters challenge <<
Please note: access to the roundtable requires registration as an attendee.

I recently had the need to monitor from HealthConnect the records present in a NoSQL database in the Cloud, more specifically Cloud Firestore, deployed in Firebase. With a quick glance I could see how easy it would be to create an ad-hoc Adapter to make the connection taking advantage of the capabilities of Embedded Python, so I got to work.

This is an attempt to run a vector search demo completely in IRIS
There are no external tools and all you need is a Terminal / Console and the management portal.
Special thanks to Alvin Ryanputra as his package iris-vector-search that was the base
of inspiration and the source for test data.

Hi All,

On February 8, 2024, we asked for input from the IRIS community regarding exam topics for our InterSystems IRIS Developer Specialist exam. We will close the window for providing feedback on the exam topics on Friday, March 8, 2024. If you would like to have your say in what topics are covered on the exam, this is your last chance!

How can I access the survey? You can access it here

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 Everyone,

The Certification Team of InterSystems Learning Services is developing an InterSystems IRIS Developer Specialist certification exam, and we are reaching out to our community for feedback that will help us evaluate and establish the contents of this exam.

We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.

Embedded Python is about to get a whole lot more powerful, and we’re looking for a few volunteers to give it a try.

While starting the development with IRIS we have a distribution kit or in case of Docker we are pulling the docker image and then often we need to initialize it and setup the development environment. We might need to create databases, namespaces, turn on/off some services, create resources. We often need to import code and data into IRIS instance and run some custom code to init the solution.

And there plenty of templates on Open Exchange where we suggest how to init REST, Interoperability, Analytics, Fullstack and many other templates with ObjectScript. What if we want to use only Python to setup the development environment for Embedded Python project with IRIS?

So, the recent release of Embedded Python template is the pure python boilerplate that could be a starting point for developers that build python projects with no need to use and learn ObjectScript. This article expresses how this template could be used to initialize IRIS. Here we go!

It's true! QuinielaML has incorporated the most important leagues in Europe (and Brazil) into its prediction service, so, dear members of the Developer Community, wherever you are from, you will be able to have the predictions of your favorite leagues at your disposal.

From the predictions screen you will have access to each of the new leagues included, being able to record the matches for each journey:

With ECP we have the option to have a collection of Frontend instances
All Frontend servers typically have a common Master in the background
Concentrating data on the Master server is the primary goal.

As a side effect, this applies also to Classes, Routines, .. anything stored in Globals.
This is probably not the most efficient setup. But rather common anyhow.

Is embedded Python code also stored in Globals?

What is the recommended solution for a similar installation?

Introduction

Data analytics is a crucial aspect of business decision-making in today's fast-paced world. Organizations rely heavily on data analysis to make informed decisions and stay ahead of the competition. In this article, we will explore how data analytics can be performed using Pandas and Intersystems Embedded Python. We will discuss the basics of Pandas, the benefits of using Intersystems Embedded Python, and how they can be used together to perform efficient data analytics.