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:

Hi!

I'm playing with OAuth2 with FHIR Server, but returned tokens cause 401 or 403 errors when trying to get FHIR resources.

I tried using fhir-client.js and Postman. Access tokens returned have been failing for both, with a 401 when trying through fhir-client.js and a 403 using Postman.

Here are my settings:

TL;DR: you can try the chatbot created with IRIS Flow here; if you'd like to understand how it was created, read this article.

If you prefer, you can follow the tutorial within this article in this video:

Introduction

This article aims to give an introduction to what gRPC is and an example of how to play with the official Hello World using IRIS Embedded Python.

You can find all the code exposed here, in this project repo.

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!