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问题
· 四月 2, 2024

How configure GIT in LINUX

#Git #关键问题 #Red Hat Enterprise Linux (RHEL) #Caché

Hello!

Trying to configure tortoise GIT in Linux System

NAME="Red Hat Enterprise Linux Server"
VERSION="7.9 (Maipo)"

My steps

1. Enable [write-access](http://docs.intersystems.com/ens20151/csp/docbook/DocBook.UI.Page.cls?KE...) to CACHELIB database via the Management Portal. (This is required for csp-page with settings import.)
2. Import project in %SYS:

    %SYS> do $system.OBJ.ImportDir("/arch/cache-tort-git-master/","*.xml","ck",,1)

3. Now you can disable write-access to CACHELIB.
4. In Management Portal select the new %SourceControl.Git class as the desired Source Control class in all namespaces where you wish to use it. (System Administration >> Configuration >> Additional Settings >> Source Control)

 

But in cache studio there are no options to work with git.

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问题
· 四月 1, 2024

IRIS Enable Long Strings

#关键问题 #管理门户 #SQL #InterSystems IRIS

On IRIS version 2024.1.0.262.0 I have a table that store streams as %GlobalCharacterStream.

But depeding on the blob being saved I get the error: [Error: <<MAXSTRING>]

I had that with Caché, but to solve that I'd just enable the long strings on the page System Administration > Configuration > System Configuration > Memory and Startup > Enable Long Strings.

But for Iris I can't find that configuration, I looked on every config page from the portal and couldn't find it.

Btw this blobs are stored on Caché and the system using IRIS is reading from Caché and saving on IRIS.

Where, on IRIS, can I make the same configuration of Long Strings that exists on Caché?

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文章
· 四月 1, 2024 阅读大约需 2 分钟

Overview of Generative AI - Part1

#Artificial Intelligence (AI) #ChatGPT #Generative AI (GenAI) #Large Language Model (LLM) #其他​​​


Generative artificial intelligence is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

 

Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language.

 

how large language models work

 

  • Tokenizer, text to numbers: Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk. Example of tokenization
  • Predicting output tokens: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence.
  • Selection process, probability distribution: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature.


In the upcoming article, we will engage in practical demonstrations.

Thanks

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文章
· 三月 29, 2024 阅读大约需 2 分钟

.NET Client-Side Development on IRIS Data Platform

#.NET #最佳实践 #开发环境 #InterSystems IRIS

InterSystems IRIS provides a complete application development environment for building sophisticated data- and analytics-intensive applications that connect data and application silos. It is designed to work with all of the common development technologies in an open, standards-based fashion and supports both server-side and client-side programming.

InterSystems IRIS supports server-side application development with both Python and InterSystems ObjectScript. InterSystems IRIS also supports client-side development using many popular development technologies, including Java, C#/.NET, Node.js, Python, and ObjectScript.

The purpose of this article will be to focus on client-side development using a popular environment, the .NET development environment.

The ADO.NET Managed Provider, NET Native SDK, XEP API, and the Entity Framework Provider are a set of powerful APIs that combine to cover your bases regarding client-side InterSystems IRIS data platform development by leveraging the .NET framework.

ADO.NET Managed Provider

The ADO.NET Managed Provider is the InterSystems implementation of the ADO.NET data access interface, which will enable connection to IRIS from your .NET application, enabling the use of SQL queries to access data. The other three APIs use this underlying connection protocol.

.NET Native SDK

The .NET Native SDK will provide direct access to InterSystems IRIS objects, globals, and ObjectScript functionality, such as running classes and routines. Directly accessing globals, the fundamental storage structure for data in IRIS, can speed up data retrieval for your .NET application.

XEP API

The XEP API will facilitate high-speed access to InterSystems objects. This is most useful when working with high throughput objects with low to medium complexity.

The Entity Framework Provider and Object Relational Mapping (ORM)

The Entity Framework Provider is the InterSystems implementation of the Entity Framework, the object-relational mapping for ADO.NET.

  • What is Object-Relational Mapping or ORM?
    • A technique that lets you query and manipulate data from a database using an object-oriented paradigm. These techniques are often implemented as libraries, such as the SQLAlchemy library for Python.
    • If you're drawing a blank thinking of the equivalent library that implements ORM in IRIS, you should be. IRIS can be treated as a relational database (you can use SQL queries to query data stored in IRIS), so there is no need for a library implementing ORM techniques when ORM is built into the platform itself.

.NET developers can leverage any of these APIs alone or in conjunction with the stipulation of requiring the InterSystems.Data.IRISClient.dll assembly file to be referenced in their .NET project. Each API has its pros and cons, but a measured use of each one's capabilities provides a balanced approach to developing on the InterSystems IRIS data platform with the .NET Framework.

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文章
· 三月 28, 2024 阅读大约需 1 分钟

How to register and reference task schedules programmatically

#系统管理 #提示和技巧 #InterSystems IRIS #Caché #Ensemble #InterSystems IRIS for Health

InterSystems FAQ rubric

Here, we will introduce a sample code for registering and referencing task schedules.

 ①Sample of task schedule registration

*Create a task to execute do ^TEST every day at 1:00 am. 

 set task=##class(%SYS.Task).%New()
 set task.Name="MyTask1"
 set taskDescription="Execute ^xxx every day at 1:00 AM" // Optional
 set task.NameSpace="USER"
 set task.TimePeriod=0
 set task.DailyFrequency=0
 set task.DailyFrequencyTime=""
 set task.DailyIncrement=""
 set task.DailyStartTime=$ZTimeh("01:00:00")
 set task.DailyEndTime=""
 set task.TaskClass="%SYS.Task.RunLegacyTask"
 set task.Settings=$LB("ExecuteCode","do ^TEST") // Set ExecuteCode for RunLegacyTask
 write task.%Save()

② Sample of task schedule reference

*The contents registered in the task schedule are obtained programmatically.

USER>set task=##class(%SYS.Task).%OpenId(1) USER>zwrite tasktask=<object reference="">[14@%SYS.Task]
：
+----------------- attribute values ------------------
| %Concurrency = 1
| DailyEndTime = 0
| DailyFrequency = 0
| DailyFrequencyTime = ""
| DailyIncrement = ""
| DailyStartTime = 0
| DayNextScheduled = 63877
| DeleteAfterRun = 0
| Description = "Journal files are switched at midnight every day."
：
// If you want to refer to individual items, do the following:
USER>write $ZDT(task.DayNextScheduled)
11/21/2015
USER>write task.Name
Journal switching
USER>

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