Background

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.

Hi ,

I am sending my DTL response (which is an xml stream) to an FTP out location.

My XML stream has CRLF , but after sending FTP'ing out , it displays as LF

before writing it to FTP

At FTP out location

What Chatset will do it ? Currently Charset is set as Binary.

Appreciate quick response.

Are you using Jupyter Notebooks with IRIS? Are you using the vscode-iris-jupyter-server VS Code extension for your notebooking? If so, please let me know either via direct message or with a comment on this post. I'd like to hear more about how our customers are working with tool specifically, and with data science more generally.

Thanks!

Hi,

i'm with a trouble to take the api/monitor/alerts using prometheus.

i'm using prometheus 3.2.1 with IRIS 2022.1, the api metrics is working fine, but with the alerts, i'm receiving the following error:

and this is the answer in the request:

I'm trying to put this global into a readable list. any suggestions
The nested $bl's are giving me a problem.

As I begin our adventure into using an External FHIR Repository, I wanted to see what opinions people have about a design Idea I was working on.

I want to create a way that my Team has the ability to send a Message Type, and the system knows from that Message Type which FHIR queries to execute and return the Response back to the user, sort of like a FHIR Query Hub. Does this sound reasonable?

Kong provides an open source configuration management tool (written in Go), called decK (which stands for declarative Kong)

Hi everyone,

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?

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 have a class with a projection that creates some persistent classes (chunks) and a non-persistent "Daemon" class that I want to have running in the background, doing some processing. The daemon needs to look at the "chunk" classes, and I want it to be simple to start the daemon -- I am currently calling the daemon's start method at the end of the CreateProjection method.

Nearline FHIR® Ingestion to InterSystems OMOP from AWS HealthLake

InterSystems FAQ rubric

You can use the WriteToConsoleLog method of the %SYS.System class to write any message to the console log.

The following is an example:

I have an old .NET 4.5 project, working with database InterSystems.Cache. The project is deployed on a Windows server. Data is retrieved using the InterSystems.Data.CacheClient.dll library.

But now I'm planning migration of the project to .NET 8 to container with linux centos.

How can I get data from InterSystems.Cache in this case?

As I understand it, InterSystems.Data.CacheClient.dll is only for the .NET framework.

On Encounter container we want to map some data from Z segments is that possible? Presently the mapping from HL7to SDA3 is taking place from HL7 defines segments . is there a way to push the data from Z segment.