Embedded Python refers to the integration of the Python programming language into the InterSystems IRIS kernel, allowing developers to operate with data and develop business logic for server-side applications using Python.
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!
I'm currently facing an issue with a Python script in my IRISHealth environment and would appreciate your insights.
I've written a class method, `getTokenCount`, in Python, which uses the `tiktoken` module. However, when I run the script in the terminal using `do ##class(python.openaiUtils).getTokenCount("")`, I encounter the following error:
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.
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:
MIRROR is the best solution for almost immediate replications to a Failover Server. The related mechanics are based on Global Journaling.
Globals hold Data and Classes and Routines and more ... If Mirroring is in place all is in sync. With minimum delays This is of course rather useful for code changes in Classes, Routines, ....
To what extent is Embedded Python covered by Mirroring? Or: What is required to Synchronize EmbeddedPython like Mirroring.
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?
I'm attempting to compile a basic Python code on a remote server, but it appears that the compiler doesn't recognize the language.
The remote server is running a virtual machine with Oracle Linux Server 7.9 (64-bit), and it has IRIS for UNIX (Red Hat Enterprise Linux for x86-64) 2021.1 (Build 215U) [HealthConnect:3.3.0] installed.
When I try to compile a script that includes a Python ClassMethod, such as this "testpy.cls":
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.
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.
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:
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.
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.
For several years now Visual Studio Code has supported the notebook coding paradigm with a maturing UX and an API that is enabling a notebook extensions ecosystem to grow. One of the best-known notebook platforms is Jupyter Notebooks. A Microsoft team publishes an extension that allows VS Code to handle .ipynb notebook files. These can either work against a local Python environment or connect to a Jupyter Server, which typically hosts remote Python environments with beefier resources.
What if your InterSystems IRIS environments, whether local on your workstation or remote in your organization / cloud, could operate as Jupyter Servers? And not only for Embedded Python but also for ObjectScript and SQL
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Hi all, I am trying to execute a query like the below code. set statement = ##class(%ResultSet).%New("some_class:query_method"). // here query method is empty and with rowspec some columname
statement.Execute(param1)
I want to fetch data type of column value returned from above. eg - Name - VARCHAR, amount - INTEGER etc. How can I get it. Or if not possible directly. Is there any other way to validate or get datatype of values returned. Line we have type() in python3
In a previous question, I have illustrated a few problems using Embedded Python interactively as you would do from Docker console or IRIS terminal. Investigation of the causes brought a rather clear picture. It's a classic impedance mismatch
In my previous articles, I described my Command Line Extension to NativeAPI. Of course, this is also available for any other NativeAPI package. So I created this example in Python as a demo.
Customizing Stored Procedures with ObjectScript directly has been useful to access NoSQL storage and external messaging via integration, to present output in tabular format.
First of all, let us have a brief overview of the framework.
The entire world is talking about ChatGPT and how Large Language Models(LLMs) have become so powerful and has been performing beyond expectations, giving human-like conversations. This is just the beginning of how this can be applied to every enterprise and every domain!
Welcome dear members of the Community to the presentation and first article of a small project that will demonstrate the capabilities of InterSystems IRIS to provide full backup functionality for a web application developed in Angular. This article will be limited to presenting the concept as well as the InterSystems IRIS functionalities used in a general way, going into more detail in subsequent articles.
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