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#Framework

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A framework is a toolset to simplify or/and to make the development process more effective.

Introduction

InterSystems IRIS allows you to build REST APIs using ObjectScript classes and the %CSP.REST framework. This enables the development of modern services to expose data for web apps, mobile apps, or system integrations.

In this article, you'll learn how to create a basic REST API in InterSystems IRIS, including:

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For a long time I have wanted to learn the Django framework, but another more pressing project has always taken priority. Like many developers, I use python when it comes to machine learning, but when I first learned web programming PHP was still enjoying primacy, and so when it was time for me to pick up a new complicated framework for creating web applications to publish my machine learning work, I still turned to PHP.

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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.

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Hi, Community!

In the previous article, we introduced the Streamlit web framework, a powerful tool that enables data scientists and machine learning engineers to build interactive web applications with minimal effort. First, we explored how to install Streamlit and run a basic Streamlit app. Then, we incorporated some of Streamlit's basic commands, e.g., adding titles, headers, markdown, and displaying such multimedia as images, audio, and videos.

Later, we covered Streamlit widgets, which allow users to interact with the app through buttons, sliders, checkboxes, and more. Additionally, we examined how to display progress bars and status messages and organize the app with sidebars and containers. We also highlighted data visualization, using charts and Matplotlib figures to present data interactively.

In this article, we will cover the following topics:

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Hi, Community!

In this article, I will introduce Python Streamlit Web Framework.

Below, you can find the topics we will cover:

  • 1-Introduction to Streamlit Web Framework
  • 2-Installation of Streamlit module
  • 3-Running Streamlit Application
  • 4-Streamlit Basic commands
  • 5-Display multimedia
  • 6-Input widgets
  • 7-Display progress and status
  • 8-Sidebar and container
  • 9-Data Visualization
  • 10-Display a DataFrame

So, let's start with the first topic.

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Article
· Aug 19, 2024 4m read
Accessing Azure Blob Storage

Accessing an Azure cloud storage to upload/download blobs is quite easy using the designated %Net.Cloud.Storage.Client class API methods, or using the EnsLib.CloudStorage.* inbound/outbound adaptors.

Note that you'll need to have the %JavaServer External Language Server up and running to use the cloud storage API or adaptors, since they both use the PEX framework using the Java Server.

Here is a quick summary:

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