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#ChatGPT

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ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. It is built on top of OpenAI's GPT-3.5 and GPT-4 families of large language models (LLMs) and has been fine-tuned using both supervised and reinforcement learning techniques.

Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.

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· 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

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Prompt

Firstly, we need to understand what prompt words are and what their functions are.

Prompt Engineering

Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.

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Hi Community,

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

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

  • Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
  • Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
  • Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
  • Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
  • Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
  • Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
  • MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
  • Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
  • Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

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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
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