#Open Exchange

Hi everyone! 👋
I’m excited to share the project I’ve submitted to the current InterSystems .Net, Java, Python, and JavaScript Contest — it’s called IRIStool and Data Manager, and you can find it on the InterSystems Open Exchange and on my GitHub page.

While working with external languages for IRIS (such as Python and Node.js), one of the first things you must accomplish is making a connection to an IRIS instance.

For instance, to make a connection in python (from https://pypi.org/project/intersystems-irispython/):

import iris

# Open a connection to the server
args = {
	'hostname':'127.0.0.1', 
	'port': 1972,
	'namespace':'USER', 
	'username':'username', 
	'password':'password'
}
conn = iris.connect(**args)

# Create an iris object
irispy = iris.createIRIS(conn)

# Create a global array in the USER namespace on the server
irispy.

If one of your packages on OEX receives a review, you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review, just let me know.

I was really surprised that such a flexible integration platform with a rich toolset specifically for app connections has no out-of-the-box Enterprise Service Bus solution. Like Apache ServiceMix, Mule ESB, SAP PI/PO, etc, what’s the reason? What do you think? Has this pattern lost its relevance completely nowadays? And everybody moved to message brokers, maybe?

Wiki time: An enterprise service bus (ESB) implements a communication system between mutually interacting software applications in a service-oriented architecture (SOA) .

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review, just let me know.

I've been trying to set up a script I can run after installing an instance to enable IPM across all namespaces. I have been able to install IPM successfully using

set r = ##class(%Net.HttpRequest).%New(),
    r.Server="pm.community.intersystems.com",
    r.SSLConfiguration="ISC.FeatureTracker.SSL.Config" 
d r.Get("/packages/zpm/latest/installer"),
    $system.OBJ.LoadStream(r.HttpResponse.Data,"c")

and then running the zpm commands

repo -r -n registry -url https://pm.community.intersystems.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review, just let me know.

Sending emails is a common requirement in integration scenarios — whether for client reminders, automatic reports, or transaction confirmations. Static messages quickly become hard to maintain and personalize. This is where the templated_email module comes in, combining InterSystems IRIS Interoperability with the power of Jinja2 templates.

Why Jinja2 for Emails

Jinja2 is a popular templating engine from the Python ecosystem that enables fully dynamic content generation.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review just let me know.

Join us on Thursday to see the new features of Deltanji source control, version 8.1.

We'll be demoing our new Git Location Driver along with other functionality and usability improvements.

The Git Location Driver enables Deltanji to integrate directly with Git repositories, makes Deltanji the perfect source control companion to those who use Git-based repos by providing tighter integration with InterSystems IRIS.

Date: Thursday, July 17th 
Time: 11 - 12pm EDT | 4 - 5pm BST | 5 - 6pm CET
Location: Online (register for details) 

Can't make it? Email info@georgejames.

Hello Community,

I encountered the following errors while installing the ZPM module on version 2025.1. The ZPM install command failed on the Community Edition of IRIS for Health.

George James Software is excited to share a new release of Deltanji source control, version 8.1. This new release introduces a Git Location Driver along with several other functionality and usability improvements. 

Highlights of this release include: 

• Git Location Driver, which enables Deltanji to integrate directly with Git repositories.
• Additional signing methods for Task Server II.
• DATA Component Driver proxy classes.
• Add or remove from Work Lists incrementally. 
• %ALL system definitions.
• New Installation defaults.

Profiling CCD Documents with LEAD North’s CCD Data Profiler
Ever opened a CCD and been greeted by a wall of tangled XML? You’re not alone. Despite being a core format for clinical data exchange, CCD's are notoriously dense, verbose, and unfriendly to the human eye. For developers and analysts trying to validate their structure or extract meaningful insights, navigating these documents can feel more like archaeology than engineering.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review just let me know.

I cloned @Yuri Marx's langchain-iris-tool repo and modified docker-compose yaml per this post:

https://community.intersystems.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review just let me know.

!

One of the challenges of creating a DICOM message is how to implement putting data in the correct place. Part of it is by inserting the data in the specific DICOM tags, while the other is to insert binary data such as a picture - In this article I will explain both.

To create a DICOM message, you can either use the  EnsLib.DICOM.File class (to create a DICOM file) or the  EnsLib.DICOM.Document class (to create a message that can be sent to PACS directly). In either case, the SetValueAt method will allow you to add your data to the DICOM tags.

I cloned iris-rag-demo from Open Exchange and issued docker-compose up -d. I went to the front end and type in the chat message:

Who was the 46th President of United States of America?

I got a run time error:

RuntimeError: ERROR <Ens>ErrBPTerminated: Terminating BP ChatProcess # due to error: ERROR #5002: ObjectScript error: <PYTHON EXCEPTION> *<class 'RuntimeError'>: <PYTHON EXCEPTION> <class 'ValueError' > ERROR #5002: ObjectScript error: <PYTHON EXCEPTION> *<class 'RuntimeError'

Traceback:

File "/irisdev/app/src/python/rag/app.py", line 28, in process_input agent_text = _service.

I cloned @Yuri Marx's langchain-iris-tool repo and typed docker-compose up -d. I see error message:

Error response from daemon: could not select device driver "nvidia" with capabilities: [[gpu]]
 

I need to add an extra blank OBX segment after a segment containing text, Total Cost:

I get the OBX segment but it is in incorrect location.

It should be after the line containing Total Cost:

It needs to contain:

OBX|1|TX|2000.02^REASON FOR REQUEST^AS4|

code:

    //Do pOutput.SetValueAt(tOBXText_"~~","ORCgrp(1).OBRuniongrp.OBXgrp("_i_").OBX:5","set") //
         If (tOBXText[ "Total Cost:") {
             Set tSegmentOBX = ##class(EnsLib.HL7.Segment).%New()
             Set tSegmentOBX.SegType = "2.3:OBX"
             Set tSC = tSegmentOBX.SetValueAt("OBX", 0, "set")
             Set tSC = pOutput.

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.