I'm exploring this right now: given a bunch of types defined as Pydantic models, how can I come up with an equivalent %RegisteredObject/%SerialObject and convert to/from (e.g., to support persistence and match validation as much as possible)?
People who know Python better than I do (e.g., your average undergraduate from this decade): is this a stupid idea or a cool idea? Has anyone else done this before?
Is there a way to remote connect to IRIS terminal from my local machine?
I can remote connect to Studio & SMP from my locally windows installed IRIS cube to another IRIS installation in a Linux server, but not the case when trying to connect to the Terminal, is there a way to do so? I'm currently using ssh client but that one times out quickly
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:
Create Agent Tools
Add Ingest functionality: Automatically ingests and index documents (e.g., InterSystems IRIS 2025.1 Release Notes).
See the Langchain IRIS Tool in action on YouTube. You can see IRIS metrics, discover classes, generate fake data, and so on. Project using Ollama, IRIS VectorDB, Streamlit and Langchain.
I deployed an IRIS REST application using Installer class. I think I created the namespace FEEDER database with %DB_Default resource and I used the same resource in Web Application roles. I allowed Unauthenticated Authentication method. I used ^%ISCLOG and reviewed ^ISCLOG. I do not understand why I get 403 Forbidden response.
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Does %OSCertificateStore only check the trusted root folder in windows?
Can it be used for Personal store on servers or is there another condition can be used?
Used it for a first time and writing a function to check specific ones being used for expiry but had one this week that was to be installed in personal rather than the trusted root and didn't know if stating OSCertificateStore or a url otherwise to look in the personal installed certs on the server could be used instead so stuck with the original way (which can get confusing)
You can learn a lot from a first impression; we want to hear about yours.
As a continuation of our User Insights Interview program (see this post for more details), we’re expanding our scope to include Python developers, particularly those of you who are new to working with InterSystems technology. We’re looking to conduct one-on-one interviews to hear your honest thoughts about getting started: What made sense, what didn’t, and where we could improve.
The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below. Note: I classified each answer by the assertiveness that I consider as good, average and bad.
Note 2: The article has 4 parts, each one for an exam area.
The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below. Note: I classified each answer by the assertiveness that I consider as good, average and bad.
Note 2: The article has 4 parts, each one for an exam area.
The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below. Note: I classified each answer by the assertiveness that I consider as good, average and bad.
Note 2: The article has 4 parts, each one for an exam area.
The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below. Note: I classified each answer by the assertiveness that I consider as good, average and bad.
Note 2: The article has 4 parts, each one for an exam area.
Trying to start investigating an error we are seeing with multiple of the same messages getting sent to the same vendor. We receive an HL7 message with an RTF embedded from our EMR, send it through a DTL to just update the Patient Class, and then send it onto the Operation which is TCP.
I want to create a scheduler to montor the status of list of backend jobs ( say limit is 10). there going to be job queue. Need to pick a job form job queue when one of the current processing job is finished. What is the best way to implement this
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Hello and welcome to the Developer Ecosystem News!
The first quarter of the year was full of exciting activities in the InterSystems Developer Ecosystem. In case you missed something, we've prepared a selection of the hottest news and topics for you to catch up on!
This guideline provides an overview of how to design and implement a REST API interface for querying patient demographic data from an Electronic Patient Record (EPR) system using HealthConnect. The process involves sending a query request with the patient's identification number, retrieving the response from the EPR system, extracting the required patient demographic data from the HL7 message, and sending it as a JSON response to the supplier. The high-level process diagram is shown below (Screenshot 1).
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.