We continue with this series of articles on LLM and RAG applications and in this article we will discuss the red boxed part of the following diagram:

Motivated by personal feedback by @Edilson Eberle Carvalho and
an excellent presentation of @Michael Braam related to Vector Search I'd like to share
my personal approach to Vectors.

When I started and met vectors with 256, 384, and over 1200 dimensions - I felt lost.

InterSystems IRIS 2024 recently introduced the vector types.
This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications.
In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

Hey Developers,

Play the new video on InterSystems Developers YouTube:

⏯ Chatbots are all the RAGe: Generative AI with InterSystems IRIS @ Global Summit 2024

https://www.youtube.com/embed/mj6NshZ4T0U
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Hi, Community!

Curious how a retrieval-augmented generation (RAG) application works? Take a look at this demonstration:

Exploring a Basic RAG Application

https://www.youtube.com/embed/bYzAziVjwRA?utm_source=youtube&utm_medium=social&utm_campaign=bYzAziVjwRA
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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:

This is an attempt to run a vector search demo completely in IRIS
There are no external tools and all you need is a Terminal / Console and the management portal.
Special thanks to Alvin Ryanputra as his package iris-vector-search that was the base
of inspiration and the source for test data.

I try to get a vector from calling GetEmbedding, but i failed to convert it into a vector

Here is a simplyfied sample class:

Current triage systems often rely on the experience of admitting physicians. This can lead to delays in care for some patients, especially when faced with inexperienced residents or non-critical symptoms. Additionally, it can result in unnecessary hospital admissions, straining resources and increasing healthcare costs.

In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity.

DNA Similarity and Classification was developed as a REST API utilizing InterSystems Vector Search technology to investigate genetic similarities and efficiently classify DNA sequences. This is an application that utilizes artificial intelligence techniques, such as machine learning, enhanced by vector search capabilities, to classify genetic families and identify known similar DNAs from an unknown input DNA.

Hello developers,

Our project was designed to optimize patient clinical outcomes by reducing hospitalization time and supporting the development of resident and novice physicians. Additionally, it contributes to lowering financial waste in the healthcare system by improving the monitoring of pregnant patients, thereby decreasing risks and enhancing their safety.

Using the most accessible tool, the smartphone, was the obvious choice to make patients' lives easier.

https://www.youtube.com/embed/OL3kdE-JL4c?si=HV6kopsNGJLCVSdl
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Hi Community,

Here is a brief walkthrough on the capabilities of IRIS AI Studio platform. It covers one complete flow from loading data into IRIS DB as vector embeddings and retrieving information through 4 different channels (search, chat, recommender and similarity). In the latest release, added docker support for local installation and live version to explore.

https://www.youtube.com/embed/X1gzz3Qs2dw
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