Hi, Community!

Ready to create a retrieval-augmented generation (RAG) architecture for your next GenAI application? See how to get started:

Setting Up Your Generative AI Development Environment

https://www.youtube.com/embed/CzyQVtgdmiA?utm_source=youtube&utm_medium=social&utm_campaign=CzyQVtgdmiA
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Hi, Community!

Are you building generative AI applications? See how a retrieval-augmented generation (RAG) architecture can help:

Exploring the Benefits of RAG

https://www.youtube.com/embed/NSYVHiJPRas?utm_source=youtube&utm_medium=social&utm_campaign=NSYVHiJPRas
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Hi Community,

In this article, I will introduce my application iris-RAG-Gen .

Iris-RAG-Gen is a generative AI Retrieval-Augmented Generation (RAG) application that leverages the functionality of IRIS Vector Search to personalize ChatGPT with the help of the Streamlit web framework, LangChain, and OpenAI. The application uses IRIS as a vector store.

Application Features

• Ingest Documents (PDF or TXT) into IRIS
• Chat with the selected Ingested document
• Delete Ingested Documents
• OpenAI ChatGPT

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:

Hi, Community!

Do you think Generative AI can make your life easier? See some potential use cases for GenAI in the latest video from Learning Services:

▶ Generative AI in Use

https://www.youtube.com/embed/5D8A0PDsV2A?utm_source=youtube&utm_medium=social&utm_campaign=5D8A0PDsV2A
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Hi Community!

Need an introduction to Generative AI? Learning Services is excited to announce the first video in a new series about GenAI basics:

🧐 What Is Generative AI?

https://www.youtube.com/embed/zMfiV0RhrCA?utm_source=youtube&utm_medium=social&utm_campaign=zMfiV0RhrCA
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I try to get a vector from calling GetEmbedding, but i failed to convert it into a vector

Here is a simplyfied sample class:

Hi All,
We invite you to join our next Meetup in Boston on June 25 5:30-7:30 pm.
>> RSVP here <<

ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.

Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

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.

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.

Hey Community,

We have more exciting news! The new InterSystems online programming contest dedicated to Generative AI, Vector Search and Machine Learning is starting very soon!

🏆 InterSystems Vector Search, GenAI and ML Contest 🏆

Duration: April 22 - May 19, 2024

Prize pool: $14,000