Hi Community,

Round 2 of the GenAI Crowdsourcing Mini-Contest is here! Everyone can join, even if you missed Round 1. You have $5 million in fantasy funds to invest in up to 5 promising submissions.

🎁 Rewards

• The top-funded submission of Round 1 will emerge victorious.
• The mastermind of the winning concept earns 5,000 points, while 3 astute "investor(s)" backing the winning idea get a chance to receive 200 bonus points each.

Dive into the game, strategically allocate your virtual investments, and aim for maximum returns! Deadline: Dec 31st, 2023

1. IRIS RAG Demo

This demo showcases the powerful synergy between IRIS Vector Search and RAG (Retrieval Augmented Generation), providing a cutting-edge approach to interacting with documents through a conversational interface. Utilizing InterSystems IRIS's newly introduced Vector Search capabilities, this application sets a new standard for retrieving and generating information based on a knowledge base. The backend, crafted in Python and leveraging the prowess of IRIS and IoP, the LLM model is orca-mini and served by the ollama server. The frontend is an chatbot written with Streamlit.

Hi Community,

InterSystems Innovation Acceleration Team invites you to take part in the GenAI Crowdsourcing Mini-Contest.

GenAI is a powerful and complex technology. Today, we invite you to become an innovator and think big about the problems it might help solve in the future.

What do you believe is important to transform with GenAI?

Your concepts could be the next big thing, setting new benchmarks in technology!

Contest Structure

     1.

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.

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/.

I created a new application to use in IRIS taking advantage of one of these APIs. I chose the Imagine API.

Hey Community,

Click play and immerse yourself in our fresh video on InterSystems Developers YouTube:

⏯ Adaptive Analytics in Action - Two Customer Use Cases @ Global Summit 2023

A "big" or "small" ask for ChatGPT?

I tried OpenAI GPT's coding model a couple of weeks ago, to see whether it can do e.g. some message transformations between healthcare protocols. It surely "can", to a seemingly fair degree. 
It has been nearly 3 weeks, and it's a long, long time for ChatGPT, so I am wondering how quickly it grows up by now, and whether it could do some of integration engineer jobs for us, e.g. can it create an InterSystems COS DTL tool to turn the HL7 into FHIR message? 

Immediately I got some quick answers, in less than one minute or two.

This article is a simple quick starter (what I did was) with SqlDatabaseChain.

Hope this ignites some interest.

Many thanks to:

sqlalchemy-iris author @Dmitry Konnov Maslennikov

Your project made this possible today.

The article script uses openai API so caution not to share table information and records externally, that you didn't intend to.

A local model could be plugged in , instead if needed.

Creating a new virtual environment

mkdir chainsql

cd chainsql

python -m venv .

Hello Community,

Let us invite you to watch the recording of the webinar: 

👉 Developing an AI Powered IRIS Application, in Python 👈

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

1. Deploy to google cloud via github workflows (One container with an IRIS database and a web server in Python (flask)) and placing secrets inside the application not through an environment variable
2. Record audio from the browser and send to the server
3. Launching cron jobs via the ZPM module
4. Integration with OpenAi

First, about the application itself

You can try it using the link and see how it works - https://iris-recorder-helper.

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.

Create new virtual environment

mkdir chainpdf

cd chainpdf

python -m venv .

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.

The latest Node.js Weekly newsletter included a link to this article:

https://thecodebarbarian.com/getting-started-with-vector-databases-in-n…

I'm wondering if anyone has been considering or actually using IRIS as a vector database for this kind of AI/ChatGPT work?

FHIR has revolutionized the healthcare industry by providing a standardized data model for building healthcare applications and promoting data exchange between different healthcare systems. As the FHIR standard is based on modern API-driven approaches, making it more accessible to mobile and web developers. However, interacting with FHIR APIs can still be challenging especially when it comes to querying data using natural language.

Introducing the FHIR - AI and OpenAPI Chain application, a solution that allows users to interact with FHIR APIs using natural language queries.

Our next Developer Meetup will take place on July 26, 17:30 pm at the CIC Venture Café in Cambridge.

Join us to learn Generative AI Use Cases + Reference Architecture in Healthcare, witness the demo of LLMs in Healthcare and share your thoughts on the topic.

RSVP here

As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:

• Users prompts
• Execution errors

In order to extract useful data to apply analytics, we used the iknowpy library - an opensource library for Natural Language Processing based in the iKnow for IRIS Data Platform. It makes possible identifies entities (phrases) and their semantic context in natural language text in several languages.

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

This post explores the potential of OpenAI's advanced language models to revolutionize healthcare through AI-powered transcription and summarization. We will delve into the process of leveraging OpenAI's cutting-edge APIs to convert audio recordings into written transcripts and employ natural language processing algorithms to extract crucial insights for generating concise summaries.

The continuing convergence of AI technologies and healthcare systems has brought forward many compelling advancements. Let's set the scene. If you have interacted with dynamic models like ChatGPT, you might have, like many of us, begun to envision its application using your unique datasets. Suppose in the healthcare sector you wish to link this technology with Electronic Health Records (EHR) or Electronic Medical Records (EMR), or perhaps you aim for heightened interoperability using FHIR's resources.

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

Project idea

Generative AIs, like the LLM models available on OpenAI, has been demonstrated remarkable power to understand and answer high level questions. They use huge amounts of data to train their models, so they can answer complex questions.

They can even use programming languages to create code based on prompts - and I have to confess that the idea of getting my work automated causes me some anxiety.

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.

Motivation

AI has made document search remarkably powerful. Question and answering over docs has never been easier with open-source tools like Chroma and Langchain to store and use vector embeddings to query across Generative AI APIs.

As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation.
But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:

As you all know, the world of artificial intelligence is already here, and everyone wants to use it to their benefit.

There are many platforms that offer artificial intelligence services for free, by subscription or private ones. However, the one that stands out because of the amount of "noise" it made in the world of computing is Open AI, mainy thanks to its most renowned services: ChatGPT and DALL-E.

What is Open AI?

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.

Hi Community, 

We invite you to join our next Developer Meetup in Boston: What happens next? Demos of the Latest Predictive Analytics technology.

Date: Monday, June 26, 2023 at 5:30 PM - 7:30 PM
Location: Venture Café Cambridge

>> RSVP here <<

Posing a question to consider during the current Grand Prix competition.

I wanted to share an observation about using PDFs with LangChain.

When loading the text out of a PDF, I noticed there was an artifact of gaps within some of the words extracted.

For example (highlighted in red)

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!

On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

Here, models as example :

• https://huggingface.co/gpt2

• https://huggingface.co/Jean-Baptiste/camembert-ner

• https://huggingface.co/bert-base-uncased

• https://huggingface.co/facebook/detr-resnet-50

• https://huggingface.co/facebook/detr-resnet-50-panoptic

• 1. iris-huggingface

• 2. Installation

  • 2.1. Starting the Production
  • 2.

Hi Community!

Please welcome a new video on InterSystems Developers YouTube Channel:

Getting Sharded with InterSystems IRIS

Here're the technology bonuses for the InterSystems IRIS Cloud SQL and IntegratedML Contest 2023 that will give you extra points in the voting:

• IntegratedML usage
• Online Demo
• Article on Developer Community
• The second article on Developer Community
• Video on YouTube
• First Time Contribution
• Community Idea Implementation
• IRIS Cloud SQL Survey

See the details below.