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.
Hello community,
How to install the iris package installer for Python (pip) in python and import iris package in python source code.
import iris
class Solution():
iris.connection()thanks!
If anyone has experience debugging Embedded Python or has insight into why an ObjectScript method when called from a Python method would not work but would work when called directly via ObjectScript or in a Python shell, your help would be appreciated!
We have an ObjectScript ClassMethod called GetTemplateString() which takes in a templateName of String type and uses the template name to get the template object, access the Code, and read the code into a templateString. The string version of the Code is returned.
As a part of the IRIS Python 2024 contest, my colleague Damir and I went with an idea to build a platform called ShelterShare for connecting victims and volunteers for shelter requests . To do so we chose django as a framework and proceeded to build the first version with 3 different docker containers, django, iris and nginx which would then utilize IRIS as a pure Database engine via the beautifly composed django_iris (cudos to Dimitry). As we were progressing fast, we decided to explore the option of running it within the same container as IRIS by utilizing WSGI added in 2024.1.
Hi Community,
In this article, I will introduce my application iris-RAG-Gen .
Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.
Hi Contestansts!
Here are the results of bonuses gathered by applications in InterSystems Python Programming Contest 2024!
In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.
Hi Community,
We are pleased to invite you to the next InterSystems online programming contest, which is focused on Python!
🏆 InterSystems Python Contest 🏆
Duration: July 15 - August 4, 2024
Prize pool: $14,000
.jpg)
The invention and popularization of Large Language Models (such as OpenAI's GPT-4) has launched a wave of innovative solutions that can leverage large volumes of unstructured data that was impractical or even impossible to process manually until recently. Such applications may include data retrieval (see Don Woodlock's ML301 course for a great intro to Retrieval Augmented Generation), sentiment analysis, and even fully-autonomous AI agents, just to name a few!
Hi Developers!
Here are the technology bonuses for the InterSystems Python Contest 2024 that will give you extra points in the voting:
See the details below.
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.
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.
My package is based on IRIS 2024.1 release and requires attention to your processor capabilities.
I attempted to write the demo in pure ObjectScript.
Only the calculation of the description_vectoris done in embedded Python
Calculation of a vector with 384 dimensions over 2247 records takes time.
Class User.myclass Extends %Persistent
{ Property myVECTOR As %Vector(CAPTION = "Vector");
Property myProperty As %String(MAXLEN = 40) [ Required ];
}
here the GetEmbedding part from User.mymethods:
...
ClassMethod GetEmbedding(sentences As %String) As %String [ Language = python ]
{
import sentence_transformers model =
Hello everyone,
Recently, I've been working on a Business Process that processes a large JSON FHIR message containing up to 50k requests in an array within the JSON.
Currently, the code imports the JSON as a dynamic object from the original message stream, obtains an iterator from it, and processes each request one at a time in a loop.
The performance meets the requirements, even with much larger requests than the one exposed above.
Pandas is not just a popular software library. It is a cornerstone in the Python data analysis landscape. Renowned for its simplicity and power, it offers a variety of data structures and functions that are instrumental in transforming the complexity of data preparation and analysis into a more manageable form. It is particularly relevant in such specialized environments as ObjectScript for Key Performance Indicators (KPIs) and reporting, especially within the framework of the InterSystems IRIS platform, a leading data management and analysis solution.
Hi Community
In this article, I will introduce my application irisChatGPT which is built on LangChain Framework.
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!
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.
First things first, we need to load the dataset or can we just connect to it?
Current triage systems often rely on the experience of admitting physicians. This can lead to delays in care for some patients, especially when faced with inexperienced residents or non-critical symptoms. Additionally, it can result in unnecessary hospital admissions, straining resources and increasing healthcare costs.
We focused our project on pregnant women and conducted a survey with friends of ours who work at a large hospital in São Paulo, Brazil, specifically in the area of monitoring and caring for pregnant women.
With the rise of Gen AI, we believe that now users should be able to access unstructured data in a much simpler fashion. Most people have many emails that they cannot often keep track of. For example, in investment/trading strategies, professionals rely on quick decisions leveraging as much information as possible. Similarly, senior employees in a startup dealing with many teams and disciplines might find it difficult to organize all the emails that they receive. These common problems can be solved using GenAI and help make their lives easier and more organized.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.
Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.
Hi, I need to use some pythonic library from cos.
To use them I need a python dict with some python object in it
Ex in python:
obj = pythonObject("value1")
dict = {object : obj ,key : "value2"}
result = pythonFunc(dict)To do that I first tried to pass by dynamic object, to later convert them in dict from Json. But unfortunately the dynamic object doesn't accept python object inside it. And my pythonic function need to have an instance of my python object.
.png)
Hi Community,
In this article, I will introduce my application iris-image-vector-search.
The image vector retrieval demo uses IRIS Embedded Python and OpenAI CLIP model to convert images into 512 dimensional vector data. Through the new feature of Vector Search, VECTOR-COSINE is used to calculate similarity and display high similarity images.
Image retrieval has important application scenarios in the medical field, and using image retrieval can greatly improve work efficiency.
.png)
Hi Community,
In this article, I will introduce my application iris-VectorLab along with step by step guide to performing vector operations.
IRIS-VectorLab is a web application that demonstrates the functionality of Vector Search with the help of embedded python. It leverages the functionality of the Python framework SentenceTransformers for state-of-the-art sentence embeddings.
Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.
The installation steps can be viewed in the readme file. It should be noted that the BERT model used in the example has some memory requirements. If there is a long-term stuck situation during the testing process, other models such as MiniLM (which is used in the online demo) can be considered.
Accessing Amazon S3 (Simple Storage Service) buckets programmatically is a common requirement for many applications. However, setting up and managing AWS accounts is daunting and expensive, especially for small-scale projects or local development environments. In this article, we'll explore how to overcome this hurdle by using Localstack to simulate AWS services. Localstack mimics most AWS services, meaning one can develop and test applications without incurring any costs or relying on an internet connection, which can be incredibly useful for rapid development and debugging. We used ObjectScript with embedded Python to communicate with Intersystems IRIS and AWS simultaneously.Before beginning, ensure you have Python and Docker installed on your system. When Localstack is set up and running, the bucket can be created and used.
With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.
Note: For this article, I will be using a Linux system with IRIS installed.
Hi Developers!
Here're the technology bonuses for the InterSystems Vector Search, GenAI, and ML contest 2024 that will give you extra points in the voting:
See the details below.
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.cl…
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:
USER>do ##class(%SYS.Python).Shell()
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4