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.
The Interoperability user interface now includes modernized user experiences for the DTL Editor and Production Configuration applications that are available for opt-in in all interoperability products. You can switch between the modernized and standard views. All other Interoperability screens remain in the Standard user interface. Please note that changes are limited to these two applications and we identify below the functionality that is currently available.
Quote from documentation - $SYSTEM.License.MaxConnections() returns the maximum number of connections a user can make while consuming one license unit.
Welcome to day 3 of Virtual Summit 2021! We have incredible content available to you this year and I'd just like to bring your attention to the 4(!) sessions that we have on Embedded Python.
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!
My general question is how to convert to a %Library.ListOfObjects to a Python "array-like" structure for use in Matplotlib.
Specifically, I have a Line Object which is comprised of a list of Points (see classes below). I want to pass the line to Python to create a Matplotlib Path.
In simple terms, Web scraping, web harvesting, or web data extraction is an automated process of collecting large data(unstructured) from websites. The user can extract all the data on particular sites or the specific data as per the requirement. The data collected can be stored in a structured format for further analysis.
I'm proud to announce the new release of iris-pex-embedded-python (v2.3.1) with a new command line interface.
This command line is called iop for Interoperability On Python.
First I would like to present in few words the project the main changes since the version 1.
A breif history of the project
Version 1.0 was a proof of concept to show how the interoperability framework of IRIS can be used with a python first approach while remaining compatible with any existing ObjectScript code.
What does it mean? It means that any python developer can use the IRIS interoperability framework without any knowledge of ObjectScript.
Example :
from grongier.pex import BusinessOperation
class MyBusinessOperation(BusinessOperation):
def on_message(self, request):
self.log.info("Received request")
I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.
Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.
In ObjectScript there is neat option to log errors - call Log() method of an exception intance, e.g.:
ClassMethod MyMethod() {
Try {
w1/0
}
Catch e {
do e.Log() // logging error in the app logwrite"the error happened: "_e.DisplayString()
}
}
Is there anything like that for Embedded Python? e.g:
ClassMethod MyMethod() [Language = python] {
try:
print(1/0)
except ZeroDivisionError as e:
// how can I log e into IRIS App Error Log here?
print("Caught exception: " + str(e))
}
In this article, I will show you how one can easily create and read Microsoft Word documents using InterSystems IRIS with the leverage power of embedded Python.
Setup
First things first, let’s install the Python module called python-docx. There are a lot of modules to write MS Word files in Python. However, this one is the easiest one to use.
Just execute the following command on the terminal:
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.
I copied a 5 MB messages.log file to AWS where I have iris-log-viewer app deployed. I ran the test to see how it takes in IRIS code to import the lines into a persistent table:
Since the introduction of Embedded Python there has always been doubt about its performance compared to ObjectScript and on more than one occasion I have discussed this with
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