I know that people who are completely new to VS Code, Git, Docker, FHIR, and other tools can sometimes struggle with setting up the environment. So I decided to write an article that walks through the entire setup process step by step to make it easier to get started.
I’d really appreciate it if you could leave a comment at the end - let me know if the instructions were clear, if anything was missing, or if there’s anything else you'd find helpful.
The setup includes:
✅ VS Code – Code editor ✅ Git – Version control system ✅ Docker – Runs an instance of IRIS for Health Community ✅ VS Code REST Client Extension – For running FHIR API queries ✅ Python – For writing FHIR-based scripts ✅ Jupyter Notebooks – For AI and FHIR assignments
Before you begin: Ensure you have administrator privileges on your system.
In addition to reading the guide, you can also follow the steps in the videos:
For Windows
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I got xDBC protocol is not compatible while executing python script. How to fix this error
C:\Users\ak\Desktop\lpyth\iris>C:/Users/ak/AppData/Local/Programs/Python/Python312/python.exe c:/Users/ak/Desktop/lpyth/iris/irisconn.py An error occurred: connection failed: IRIS xDBC protocol is not compatible
py -m pip list Package Version ------------------ --------- intersystems-iris 3.9.2
We are pretty new to Ensemble and we are considering on using a default setup for our production. Let me explain the situation. We have one sender that sends HL7 ADT messages to our system. We have 60+ other systems that need to recieve ADT messages. We where thinking on a few ways on how to do the setup.
I have the class ConfigUtils.ConfigSettingsTable, which is a persistent object. I know I need to map packages from the original namespace. In this case, I have mapped ConfigUtils.ConfigSettingsTable from the originating namespace (IRISTST database) across all other namespaces.
Our software commonly returns a full result set to the client and we use the DataTables plugin to display table data. This has worked well, but at datasets grow larger, we are trying to move some of these requests server-side so the server handles the bulk of the work rather than the client. This has had me scratching my head in so many ways.
I'm hoping I can get a mix of general best practice advice but also maybe some IRIS specific ideas.
I ask ChatGPT periodically to produce ObjectScript or plain MUMPS code for string manipulation, or for implementing known algorithms etc. Occasionally, it does make mistakes or uses non-existing class members but generally not that bad. Is there any tutorial on the subject of using AI for coding, ideally specifically for ObjectScript/MUMPS? Any AI productivity advice, or tricks you are using, or another AI flavor?
The subroutine ^routine is not executed while the queue is being processed in WorkMgr. However, it works when defined as a function. Is it mandatory to define subroutine^routine as a function for it to execute properly?
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I'm currently looking at a process where we're utilising the Class Ens.StreamContainer, and was looking to do some deletions outside of any purge routines.
Having been burned before, I wanted to make sure that deleting the container also deletes the contents within.
From looking in the class, the %OnDelete ClassMethod appears to be removing an index from a search table and nothing more.
One of our clients has a 'Notes' class with over 3 million records. We have a report that pulls data from this table that was taking about an hour to run. Our test environment (which has a copy of the production database) runs the same report query in 1 second.
We attempted to purge and rebuild indices which made an improvement (down to 15 minutes) but still not great.
We're happy to announce that registration for the event of the year — InterSystems Ready 2025 — is now open. This is the Global Summit we all know and love, but with a new name!
I'm currently testing out IRIS 2024.3 for a new project, and it's been running smooth for the most part. However, I noticed that when running some heavier analytical queries, the memory usage spikes more than I expected, even when result sets aren’t that large.
I’ve gone through the basics (buffer sizes, query plans, etc.), but I’m wondering if there are any new tweaks or recommended settings in 2024.3 specifically for managing memory better during these peak loads.
Anyone else run into something similar or have tips to fine-tune this?
Date & Time: Thursday, April 10, 12:00 pm EDT | 6:00 pm CEST
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Embeddings is a new IRIS feature empowering the latest capability in AI semantic search. This presents as a new kind of column on a table that holds vector data. The embedding column supports search for another existing column of the same table. As records are added or updated to the table, the supported column is passed through an AI model and the semantic signature is returned. This signature information is stored as the vector for future search comparison.
Does anyone know if iris merge can be called from Ansible. I have tried a couple of ways, but it doesn't seem to actually run the command on the target even though Ansible outputs it was successful.
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I am receiving the garbled text due to incorrect encoding or decoding. I tried to use the $zconvert function to convert it into the normal text but failed to do that. Can anybody suggest what I have to use to convert that into normal text?
Example: Garbled text that I am getting is "canââ¬â¢t , theyââ¬â¢re".
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