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#Artificial Intelligence (AI)

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Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Learn more.

Hi Developers!

Here're the technology bonuses for the InterSystems AI Programming Contest: Vector Search, GenAI and AI Agents that will give you extra points in the voting:

  • Agent AI solution - 5
  • Vector Search usage - 4
  • Embedded Python - 3
  • LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
  • IntegratedML usage - 3
  • 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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Hi Community,

We're continuing to improve and teach our Developer Community AI, and in this iteration, we've added descriptions of Open Exchange Applications to the knowledge base!

This new feature will allow DC AI to search app descriptions to answer your questions. This will give you better answers that require programming, not just general information.

To add this knowledge to your search, just tick the checkbox Open Exchange Applications:

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Article
· Apr 1 1m read
IRIS-Intelligent Butler

# IRIS-Intelligent Butler
IRIS Intelligent Butler is an AI intelligent butler system built on the InterSystems IRIS data platform, aimed at providing users with comprehensive intelligent life and work assistance through data intelligence, automated decision-making, and natural interaction.
## Application scenarios
adding services, initializing configurations, etc. are currently being enriched
## Intelligent Butler

Open Exchange app
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Hey Community!

We're happy to share the next video in the series dedicated to Gen AI on our InterSystems Developers YouTube:

⏯ Is your data ready for AI?

https://www.youtube.com/embed/vKPUhiSYbQk
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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I just realized I never finished this serie of articles!

GIF de Shame On You Meme | Tenor

In today's article, we'll take a look at the production process that extracts the ICD-10 diagnoses most similar to our text, so we can select the most appropriate option from our frontend.

Looking for diagnostic similarities:

From the screen that shows the diagnostic requests received in HL7 in our application, we can search for the ICD-10 diagnoses closest to the text entered by the professional.

Open Exchange app
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Hi Community,

Traditional keyword-based search struggles with nuanced, domain-specific queries. Vector search, however, leverages semantic understanding, enabling AI agents to retrieve and generate responses based on context—not just keywords.

This article provides a step-by-step guide to creating an Agentic AI RAG (Retrieval-Augmented Generation) application.

Implementation Steps:

  1. Create Agent Tools
    • Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
    • Implement Vector Search Functionality
  2. Create Vector Search Agent
  3. Handoff to Triage (Main Agent)
  4. Run The Agent
Open Exchange app
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