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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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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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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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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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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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.

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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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Problem

Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.

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Hi Developers!

Here're the technology bonuses for the InterSystems Vector Search, GenAI, and ML contest 2024 that will give you extra points in the voting:

  • Vector Search usage - 5
  • IntegratedML usage - 3
  • Embedded Python - 3
  • LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
  • Questionnaire - 2
  • Docker container usage - 2
  • ZPM Package deployment - 2
  • Online Demo - 2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
  • First Article on Developer Community - 2
  • Second Article On DC - 1
  • First Time Contribution - 3
  • Video on YouTube - 3
  • Suggest a new idea - 1

See the details below.<--break->

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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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Artificial Intelligence (AI) is getting a lot of attention lately because it can change many areas of our lives. Better computer power and more data have helped AI do amazing things, like improving medical tests and making self-driving cars. AI can also help businesses make better decisions and work more efficiently, which is why it's becoming more popular and widely used. How can one integrate the OpenAI API calls into an existing IRIS Interoperability application?

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Hi Community!

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 we need to build a custom ChatGPT AI by using LangChain Framework:

Below are the steps to build a custom ChatGPT:

  • Step 1: Load the document

  • Step 2: Splitting the document into chunks

  • Step 3: Use Embedding against Chunks Data and convert to vectors

  • Step 4: Save data to the Vector database

  • Step 5: Take data (question) from the user and get the embedding

  • Step 6: Connect to VectorDB and do a semantic search

  • Step 7: Retrieve relevant responses based on user queries and send them to LLM(ChatGPT)

  • Step 8: Get an answer from LLM and send it back to the user

For more details, please Read this article

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We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

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What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

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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.

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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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