InterSystems IRIS embedded vector search capabilities lets us search unstructured and semi-structured data. Data is converted to vectors (also called ‘embeddings’) and then stored and indexed in InterSystems IRIS for semantic search, retrieval-augmented generation (RAG), text analysis, recommendation engines, and other use cases.

This is a simple demo of IRIS being used as a vector database and similarity search on IRIS.

Prerequisites:

1. Python
2. InterSystems IRIS for Health - as it will be used as the vector database

Repository: https://github.

InterSystems API Manager (IAM) is a core component of the InterSystems IRIS Data Platform, offering centralized API management with a strong emphasis on security. IAM simplifies the entire API lifecycle, from creation to retirement, and provides a developer portal for easy API discovery and integration. Access control features allow administrators to define precise permissions, and IAM seamlessly integrates with the IRIS Data Platform, enhancing data management and integration capabilities.

Features of IAM include:

• API Gateway: Centralized API management and security hub.

InterSystems has also released IRIS as containerized deployments. This post is to demonstrate how InterSystems IRIS and applications those rely on IRIS a backend can be packaged into an image and be run in other machines in containers and how simple it is to do that.

A container runs image/s which have all the necessary executables, binary code, libraries, and configuration files. And the images can be moved from one machine to another, and  an images repository like Docker Hub can simplify that process.

I have used an application from Open Exchange for this demo.

Demo video: https://www.loom.

I am documenting a demo of InterSystems IRIS featuring Embedded Python and Jupyter Notebook deployed on the same container, and an Embedded Python application developed on that Jupyter Notebook IDE.

I have used the Docker container created by @Bob Kuszewski as a development environment to demonstrate how Embedded Python app can be developed in such a setting to push and retrieve data to and from InterSystems IRIS. The benefit of using this container as the development environment is that it is a virtual environment with Jupyter IDE and IRIS connected and running side by side.

The capacity of taking numerous records every second while also facilitating real-time queries simultaneously in real time is called Hybrid Transactional Analytical Processing (HTAP). It is also called Transactional analytics or Transanalytics or Translytics and is a very useful element in scenarios where there is constant flow of real time data coming from IIOT sensors or data on fluctuations in stock market, and supporting the need for querying these data sets in real-time or near real-time.

I am demonstrating a use case of how we can create an IRIS Interoperability Production for special use in an external language. InterSystems IRIS, within Interoperability has a framework called Production Extension (PEX), using which we can create productions and program them as per their purpose using external languages like Java, Python etc, and also develop custom inbound and outbound adapters to communicate with other applications. Here in this demo, I will demonstrate a PEX framework-based production created by @Guillaume.