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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In the previous article, we saw different modules in IRIS AI Studio and how it could help explore GenAI capabilities out of IRIS DB seamlessly, even for a non-technical stakeholder. In this article, we will deep dive into "Connectors" module, the one that enables users to seamlessly load data from local or cloud sources (AWS S3, Airtable, Azure Blob) into IRIS DB as vector embeddings, by also configuring embedding settings like model and dimensions.

Hi Community,

In this article, I will introduce my application iris-VectorLab along with step by step guide to performing vector operations.

IRIS-VectorLab is a web application that demonstrates the functionality of Vector Search with the help of embedded python. It leverages the functionality of the Python framework SentenceTransformers for state-of-the-art sentence embeddings.

Application Features

• Text to Embeddings Translation.
• VECTOR-typed Data Insertion.
• View Vector Data
• Perform Vector Search by using VECTOR_DOT_PRODUCT and VECTOR_COSINE functions.
• Demonstrate the difference between normal and vector search
• HuggingFace Text generation with the help of GPT2 LLM (Large Language Model) model and Hugging Face pipeline

Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.

Most examples I've seen so far in OEX or DC left the impression that VECTORs
are just something available with SQL with the 3 Functions especially around VECTOR_Search.
* TO_VECTOR()
* VECTOR_DOT_PRODUCT ()
* VECTOR_COSINE ()

There is a very useful summary hidden in iris-vector-search demo package.
From there you find everything you need over several links and corners.