Article
· May 14, 2024 11m read
Q&A Chatbot with IRIS and langchain

TL;DR

This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.

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When developing a new Interoperability Production, it is quite natural that settings are initially added in the Production.

However, as soon as you want to move the Production from development to a test or staging environment, it becomes clear that some settings like HTTP Servers, IP addresses and/or ports need to be changed. In order to avoid these settings being overwritten during a redeployment later on, it is essential that you move these settings from the Production to the System Default settings.

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Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.

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Hi everyone,

In this article, I’m excited to introduce CodeInspector, a tool designed to simplify code validation by applying custom rules tailored to your development requirements. Whether you're managing a large codebase or working in an agile environment, CodeInspector helps ensure code quality by offering flexibility and adaptability to specific project needs.

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Here are the technology bonuses for the InterSystems Developer Tools Contest 2024 that will give you extra points in the voting:

  • IRIS Vector Search usage -3
  • Embedded Python usage -3
  • WSGI Web Apps - 2
  • InterSystems Interoperability - 3
  • InterSystems IRIS BI - 3
  • VSCode Plugin - 3
  • FHIR Tools - 3
  • Docker container usage -2
  • ZPM Package Deployment - 2
  • Online Demo -2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Embedded Python - 2
  • Code Quality pass - 1
  • Article on Developer Community - 2
  • The second article on Developer Community - 1
  • Video on YouTube - 3
  • YouTube Short - 1
  • First Time Contribution - 3

See the details below.<--break->

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Prefer not to read? Check out the demo video I created:

https://www.youtube.com/embed/-OwOAHC5b3s
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The InterSystems platforms have always offered dynamic documentation of the packages and classes in a namespace, a feature known informally as Documatic. But what if you need to publish this class reference information on a website without requiring the site to be connected to an IRIS server containing the actual classes?

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The latest "Bringing Ideas to Reality" InterSystems competition saw me trawling through the ideas portal for UI problems to have a go at.

https://www.youtube.com/embed/zw51X1JQhQ0
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A benefit of using Doxygenerate is that Doxygen does more than just HTML output. Tweak the Doxyfile that tells Doxygen what to do and you can easily create a PDF. Our example MARINA application yielded a 524-page PDF. Here's what page 94 looks like:

You can browse the whole file here.

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Monitor incremental changes in the database through scheduled tasks, display change trends through charts, set alarm thresholds, and write information to messages.log

How to use it

You can install it through Docker or ZPM

Deploying with Docker Prerequisites

Make sure you have git and Docker desktop installed.

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If you are using the client-side development paradigm (i.e. editing code in local files that get imported and compiled onto the IRIS server your `objectscript.conn` settings point to) you can now use IPM in VS Code to manage the packages in your IRIS target by launching it from the Explorer view.

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Been testing out the Production Validator toolkit, just to see what we can/not do with it. Seems really interesting and there seem to be some use cases for it that can really streamline some upgrades (or at least parts of upgrades) but I was running into so many hurdles with the documentation. I am curious if anyone else has used it.

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See the new team members in action:

https://www.youtube.com/embed/PdndX1p7pjc
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Try them online for yourself:

https://gitpod.io#snapshot/b31bdf9c-4657-402a-a2d

Get it from the Extensions view inside VS Code, or here in Marketplace.

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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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