TL;DR

This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.

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:

In this article, I’d like to present details of which technologies we had been using to develop our application for the IRIS for Health Contest:

IRIS Megazord

In the first article about IRIS Megazord, Henrique explains what drove us to create such an application. It basic is a composition of these previous project which we did:

Motivation

This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.

My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.

So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.

In the previous articles, we learned the basics of using IMAP protocol to handle messages from mailboxes in an e-mail server. That was cool and interesting, but you could take advantage of implementations created by other ones, available in libraries ready to use.

Have you tried the InterSystems learning platform lab for IRIS IntegratedML? In that lab you can train and test a model on a readmission dataset and be able to predict when a patient will be readmitted or not, or calculate its probability of being readmitted.

You can try it without any installation on your system, all you have to do is start a virtual lab environment (Zeppelin) and play it around!

In the first part we got a quick introduction on the IMAP protocol commands, now it's time to use IRIS and implement them and create our own IMAP client!

Introduction

In the first article, a simple tutorial helped you to set up your FHIRaaS deployment.

Now, let's move forward and introduce a JS library to access the FHIR resource.

In the end, two examples of usage of this library will be presented, exploring the Appointment FHIR resource type.

SMART on FHIR JavaScript Library

FHIR is a REST API, so you can use any HTTP client in order to use it. But, it’s always a good idea to have help.