1-command only required for an entire IRIS instance for Data Science projects, and leveraging this to compare query methods' speed (Dynamic SQL, Pandas Query, and Globals).

One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.

Hi all,

Let's do some more work about the testing data generation and export the result by REST API.😁

Here, I would like to reuse the datagen.restservice class which built in the pervious article Writing a REST api service for exporting the generated patient data in .csv

This time, we are planning to generate a FHIR bundle include multiple resources for testing the FHIR repository.

IRIS Audio Query is a full-stack application that transforms audio into a searchable knowledge base.

As part of a recent documentation technical project to optimize the search, I needed to use Embedded Python in my ObjectScript code. The main blocker was passing a Python list from a Python class method to a ObjectScript method. Sending the list by reference to the python method, populating it with the Insert() method, and returning the reference to the ObjectScript method resulted in an list with type %SYS.Python, a process that was straightforward but not efficient.

Imagine you’re walking down the street on a nice summer’s day, and someone walks up to you and says “Hey, you work at InterSystems, right? I’ve been hearing more and more about InterSystems IRIS lately. I know IRIS has its own programing language called ObjectBook? or InstaScript? OK, I admit it, I know it’s called ObjectScript! I know IRIS also supports Python. I’m a Python developer, so that sounds great to me. But I’m also interested in ObjectScript. For example, Python and other languages support collections. Does ObjectScript support collections?”

You’d answer “Of course!”

And then your new friend might get excited and start firing off more questions:

• How many kinds of collections does ObjectScript support?
• Can ObjectScript use Python collections?
• Can Python use ObjectScript collections?
• Which collection is best?

How would you answer? Well, you don’t have to worry about answering. All you’d have to do is send your new friend the URL of this long page.

This article is a continuation of the IRIS JSON project and features additional methods and insights.

Let's continue with the instance methods

Template engines are essential for producing customizable dynamic content, without the need for compilation and opening up great possibilities of extensibility for the template user. Especially if the template engine supports Python. A popular solution for template engine is Mko. It is is a template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and API borrows from the best ideas of many others, including Django and Jinja2 templates, Cheetah, Myghty, and Genshi.

1. A Motivating Example

Embedded Python has been around for a while. You probably followed the tutorials and learned about it. However, if you've tried to combine Python and ObjectScript in real development work, you probably ran into situations where you get an error message like this:

I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.

For those of you who are not familiar with EMPI functionality here's a little introduction:

Enterprise Master Patient Index

In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:

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

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.

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.

Hello interface engineers and app developers,

Did you know that you can use Python in productions or integrations?