An experiment on how to use the LangChain framework, IRIS Vector Search, and LLMs to generate IRIS-compatible SQL from user prompts.

This article was based in this notebook. You can run it with a ready to use environment with this application in OpenExchange.

Setup

First, we need to install the necessary libraries:

I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:

🌍 Inclusion & Innovation in Education 🌍
Our project reimagines learning for all students, with a focus on accessibility and interactive experiences. Built with the goal of making education engaging and inclusive, the tool is designed to support students of all abilities in learning complex material in an intuitive way.

💡 What It Does
This educational app transforms lesson presentations into interactive study sessions:

In ObjectScript you have a wide collection of functions that return some value
typically:

set variable = $somefunction(param1,param2, ...)

There is nothing special about that.
But there is a set of functions that I classify as LEFT SIDED
The specialty of them is that you can use them also on the left of the equal operator
as a target in the SET command:

set $somefunction(param1,param2, ...) = value

In the business world, every second counts, and having high-performing applications is essential for streamlining our business processes. We understand the significance of crafting efficient algorithms, measurable through the big O notation.

We've recently made available a new version of InterSystems IRIS in the Vector Search Early Access Program, featuring a new Approximate Nearest Neighbor index based upon the Hierarchical Navigable Small World (HNSW) indexing algorithm.

Hi, Community!

Want to dive deeper into the concepts of vector searches and embeddings? Learn from an InterSystems expert:

What Are Embeddings?

https://www.youtube.com/embed/WAKwI-ijoQE?utm_source=youtube&utm_medium=social&utm_campaign=WAKwI-ijoQE
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

Hi Community,

Play the new video on InterSystems Developers YouTube:

⏯ InterSystems IRIS Vector Search and the Python Ecosystem @ Global Summit 2024

https://www.youtube.com/embed/XVEvUOxJUrw
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

There are a lot of great community articles regarding "vector search on IRIS", and samples in OpenExchange. Everytime I see these, I'm so excited to know that so many developers already try vectors on IRIS!

But if you've not tried "Vector Search on IRIS" yet, please give me one minute 😄 I create one IRIS class - and with only one IRIS class you can see how you put vector data in your IRIS database and how you compare these in your application.

Artificial intelligence (AI) has transformative potential for driving value and insights from data. As we progress toward a world where nearly every application will be AI-driven, developers building those applications will need the right tools to create experiences from these applications. Tools like vector search are essential for enabling efficient and accurate retrieval of relevant information from massive datasets when working with large language models.

I received some really excellent feedback from a community member on my submission to the Python 2024 contest. I hope its okay if I repost it here:

With the introduction of vector data types and the Vector Search functionality in IRIS, a whole world of possibilities opens up for the development of applications and an example of these applications is the one that I recently saw published in a public contest by the Ministry of Health from Valencia in which they requested a tool to assist in ICD-10 coding using AI models.

How could we implement an application similar to the one requested? Let's see what we would need:

Hey Community!

We're happy to share our newest learning path dedicated to the essentials of GenAI, accessible on the Learning Portal via your SSO:

🤖 Getting Started with Generative AI 🤖

Here is what you can expect:

Translated from the Spanish Community Article Contest.

Following the latest programming contest on OEX I had some surprising observation.
There were almost exclusive applications based on AI in combination with pre-cooked Py modules.
But digging deeper, all examples used the same technical pieces of IRIS.

Seen from point of view of IRIS it was pretty much the same whether searching for text
or searching for images or other pattern. It ended in almost exchangeable methods.