This project is an experiment to use OpenAI API to answer to user prompts in the healthcare domain using FHIR resources and Python code.

Yet another example of applying LangChain to give some inspiration for new community Grand Prix contest.

I was initially looking to build a chain to achieve dynamic search of html of documentation site, but in the end it was simpler to borg the static PDFs instead.

Demonstration example for the current Grand Prix contest for use of a more complex Parameter template to test the AI.

Interview Questions

There is documentation. A recruitment consultant wants to quickly challenge candidates with some relevant technical questions to a role.

Can they automate making a list of questions and answers from the available documentation?

Interview Answers and Learning

One of the most effective ways to cement new facts into accessible long term memory is with phased recall.

Previous post - Using AI to Simplify Clinical Documents Storage, Retrieval and Search

Hi Community!

Just want to share with you an exercise I made to create "my own" chat with GPT in Telegram.

It became possible because of two components on Open Exchange: Telegram Adapter by @Nikolay Solovyev and IRIS Open-AI by @Kurro Lopez

So with this example you can setup your own chat with ChatGPT in Telegram.

Let's see how to make it work!

Hi colleagues!

InterSystems Grand Prix 2023 unites all the key features of InterSystems IRIS Data Platform!

Thus we invite you to use the following features and collect additional technical bonuses that will help you to win the prize!

Here we go!

• LLM AI or LangChain usage: Chat GPT, Bard and others - 6
• InterSystems FHIR SQL Builder- 5
• InterSystems FHIR - 3
• IntegratedML - 4
• Native API - 3
• Embedded Python - 4
• Interoperability - 3
• Production EXtension(PEX) - 2
• Adaptive Analytics (AtScale) Cubes usage - 3
• Tableau, PowerBI, Logi usage - 3
• InterSystems IRIS BI - 3
• Columnar Index Usage - 1
• Docker container usage - 2
• ZPM Package deployment - 2
• Online Demo - 2
• Unit Testing - 2
• Implement InterSystems Community Idea - 4
• First Article on Developer Community - 2
• Second Article On DC - 1
• Code Quality pass - 1
• First Time Contribution - 3
• Video on YouTube - 3

Problem

In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.

Hi folks

I want to tell you how you can make your own assistant based on IRIS and OpenAI (perhaps you can then move to your own AI models)

iris-recorder-helper

This is the first time I have fully tried developing an application for IRIS and I want to point out steps that may also be useful to you

Currently, many digital artists use generative AI technology as a support to accelerate the delivery of their work. Nowadays it is possible to generate a corresponding image from a text sentence. There are several market solutions for this, including some available to be used through APIs. See some at this link: https://www.analyticsvidhya.com/blog/2023/08/ai-image-generators/.

Artificial Intelligence (AI) is getting a lot of attention lately because it can change many areas of our lives. Better computer power and more data have helped AI do amazing things, like improving medical tests and making self-driving cars. AI can also help businesses make better decisions and work more efficiently, which is why it's becoming more popular and widely used. How can one integrate the OpenAI API calls into an existing IRIS Interoperability application?

Artificial intelligence is not limited only to generating images through text with instructions or creating narratives with simple directions.

You can also make variations of a picture or include a special background to an already existing one.

Additionally, you can obtain the transcription of audio regardless of its language and the speed of the speaker.

So, let's analyze how the file management works.

We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

In today's data landscape, businesses encounter a number of different challenges. One of them is to do analytics on top of unified and harmonized data layer available to all the consumers. A layer that can deliver the same answers to the same questions irrelative to the dialect or tool being used.

Hi Community

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.