The 2024.1 release of InterSystems IRIS Data Platform is now Generally Available (GA).

Release Highlights

In this release, you can expect a host of exciting updates, including:

1. Using vectors in ObjectScript: A powerful capability for optimizing data manipulation.
2. Vector Search (experimental): A cutting-edge feature for efficient data retrieval.
3. Multi-Volume Database: Enhancing scalability and storage management.
4. FastOnline Backup (experimental): Streamlining backup processes.
5. Multiple Super Server Ports: Providing flexibility in network configuration.
6. and much more!

Documentation

InterSystems announces its fourth preview, as part of the developer preview program for the 2024.1 release.  This release will include InterSystems IRIS^®,  InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect.

Highlights

InterSystems announces its second preview, as part of the developer preview program for the 2024.1 release.  This release will include InterSystems IRIS^®,  InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect.

Highlights

InterSystems announces its third preview, as part of the developer preview program for the 2024.1 release.  This release will include InterSystems IRIS^®,  InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect.

Highlights

Nowadays so much noise around LLM, AI, and so on. Vector databases are kind of a part of it, and already many different realizations for the support in the world outside of IRIS. 

Why Vector?

InterSystems announces its first preview, as part of the developer preview program for the 2024.1 release.  This release will include InterSystems IRIS^®,  InterSystems IRIS^® for Health^TM, and HealthShare^® Health Connect.

Highlights

It seems like yesterday when we did a small project in Java to test the performance of IRIS, PostgreSQL and MySQL (you can review the article we wrote back in June at the end of this article). If you remember, IRIS was superior to PostgreSQL and clearly superior to MySQL in insertions, with no big difference in queries.

Well, shortly after @Dmitry Maslennikov told me "Why don't you test it from a Python project?" Well, here is the Python version of the tests we previously performed using the JDBC connections.

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,

We're excited to share with you the recording of the next webinar in the series of InterSystems UKI Tech Talk:

👉 Analytics Capabilities using InterSystems IRIS 👈

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?

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:

With InterSystems IRIS 2022.2, we introduced Columnar Storage as a new option for persisting your IRIS SQL tables that can boost your analytical queries by an order of magnitude. The capability is marked as experimental in 2022.2 and 2022.3, but will "graduate" to a fully supported production capability in the upcoming 2023.1 release. 

As you may well remember from Global Summit 2022 or the 2022.2 launch webinar, we're releasing an exciting new capability for including in your analytics solutions on InterSystems IRIS. Columnar Storage introduces an alternative way of storing your SQL table data that offers an order-of-magnitude speedup for analytical queries. First released as an experimental feature in 2022.2, the latest 2022.3 Developer Preview includes a bunch of updates we thought were worth a quick post here.

All of us know that money is important. We constantly need to monitor all expenses to avoid looking back to the bank statement and thinking: “So, where’s my money?”

To evade financial stress, we must keep an eye on the inflow and outflow of money into our accounts.It is also important to tack when and how we spend and earn. Manually recording all transactions in order to understand where our money goes requires an effort. It demands consistency, and it is boring. Today there is a bunch of mobile or SaaS options that help you manage your finances.

Fixing the terminology

This is the third post of a series explaining how to create an end-to-end Machine Learning system.

Training a Machine Learning Model

When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline? Training could mean all the development process of a machine learning model OR the specific point in all development process that uses training data and results in a machine learning model.

Source

So Machine Learning Models are not equal Common Applications?

This is the second post of a series explaining how to create an end-to-end Machine Learning system.

Exploring Data

The InterSystems IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection (joins) which should help to create a dataset prepared to be used by a machine learning algorithm.

Posts Table ( Provided by Intersystems Team )

Tags Table ( Provided by Intersystems Team )

Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following:

• The way to launch Jupyter Notebook + Apache Spark + InterSystems IRIS
• Load a ML model into InterSystems IRIS
• K-Means clustering of the Iris Dataset
• The way to launch Apache Spark + Apache Zeppelin + InterSystems IRIS

In this series of articles, we explore Machine Learning and record linkage.

Hi all. Today we are going to use k-means algorithm on the Iris Dataset.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, python 3.6.5.

Introduction

Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM. Spark also supports Python (PySpark) and R (SparkR) and includes libraries for SQL

Hello! 

My group and I are currently doing a research project on natural language processing and iKnow plays a big role in this project.  I am aware that the algorithms iKnow use aren't public, and I respect that.

My question is, are there any public documents/research that explains, at least part of, the algorthims iKnow uses and the motivations for using them?