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.
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.
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.
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.
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.
Application Features
Text to Embeddings Translation.
VECTOR-typed Data Insertion.
View Vector Data
Perform Vector Search by using VECTOR_DOT_PRODUCT and VECTOR_COSINE functions.
Demonstrate the difference between normal and vector search
HuggingFace Text generation with the help of GPT2 LLM (Large Language Model) model and Hugging Face pipeline
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.
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
▶ Recording:
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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.
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.
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.
As you have seen in the latest community publications, InterSystems IRIS has included since version 2024.1 the possibility of including vector data types in its database and based on this type of data vector searches have been implemented. Well, these new features reminded me of the article I published a while ago that was based on facial recognition using Embedded Python.
On February 8, 2024, we asked for input from the IRIS community regarding exam topics for our InterSystems IRIS Developer Professional 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!
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.
The Certification Team of InterSystems Learning Services is developing an InterSystems IRIS Developer Professional certification exam, and we are reaching out to our community for feedback that will help us evaluate and establish the contents of this exam.
The architect of the JSON schema (MS) asked if IRIS could perform schema validation. I asked on Discord objectscript channel how we could validate a Dynamic Object against a JSON schema. Dmitry Maslennikov replied that probably the easiest way would be to use python, but it would require converting ObjectScript JSON to Python dict.
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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:
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:
By update
Open Exchange app
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