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#Generative AI (GenAI)

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Generative AI refers to algorithms and models in artificial intelligence that are capable of generating new data or content that is similar to existing data. These models are trained on large datasets and learn to generate new examples that mimic the patterns and characteristics of the original data.

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

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As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:

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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.

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Hey Community,

Watch the latest video on InterSystems Developers YouTube:

⏯ Using Vector Search for Generative AI

https://www.youtube.com/embed/IapbO3j1Qmw
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With rapid evolution of Generative AI, to embrace it and help us improve productivity is a must. Let's discuss and embrace the ideas of how we can leverage Generative AI to improve our routine work.

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Hi Developers,
We will be hosting the first in-person meetup in 2024 in Boston on January 24. Join us to explore the latest trends, tools, and features for innovating with health data.

RSVP here

Presentations:

"Integrate third-party Clinical Decision Support services into the EHR with CDS Hooks" - by Frank Pandolfe, Clinical Product Manager, InterSystems.
"Connecting GenAi into Clinical Workflows" - by Nicholai Mitchko, Manager, Solution Partner Sales Engineering, InterSystems

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Looking to get started with generative AI? Try two brand-new learning paths. In Getting Started with Generative AI (2h 45m), learn the basics of interacting with GenAI. Then, try Developing Generative AI Applications (2h) to start developing your own GenAI application. Plus, earn badges for completing each path!

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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.

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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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Hi Community,

This is a detailed, candid walkthrough of the IRIS AI Studio platform. I speak out loud on my thoughts while trying different examples, some of which fail to deliver expected results - which I believe is a need for such a platform to explore different models, configurations and limitations. This will be helpful if you're interested in how to build 'Chat with PDF' or data recommendation systems using IRIS DB and LLM models.

https://www.youtube.com/embed/bcu1gt0BDhY
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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

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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,

Thank you for participating in our recent mini-contest! We received many great ideas, and we hope you enjoyed the process.

The mastermind of the winning concept will receive 5,000 points, while the astute "investors" in said concept will receive 200 points each.

The Winning Concept: Senior people loneliness

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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.

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Hi Community!

How can you create a balanced approach to using Generative AI? Get help from InterSystems experts in the latest video from Learning Services:

⚖ Weighing the Benefits and Risks of Generative AI

https://www.youtube.com/embed/VaUS5M0S0hY?utm_source=youtube&utm_medium=social&utm_campaign=VaUS5M0S0hY
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The introduction of InterSystems' "Vector Search" marks a paradigm shift in data processing. This cutting-edge technology employs an embedding model to transform unstructured data, such as text, into structured vectors, resulting in significantly enhanced search capabilities. Inspired by this breakthrough, we've developed a specialized search engine tailored to companies.

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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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