Yet another example of applying LangChain to give some inspiration for new community Grand Prix contest.

I was initially looking to build a chain to achieve dynamic search of html of documentation site, but in the end it was simpler to borg the static PDFs instead.

To install the Python binding on Windows one must follow the instructions in the following:

https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to-...

The reason is that ActiveState Python version 2.7.X is built with Visual Studio 2008 and Microsoft provides Visual Studio 2008, which one must install, so that Python C extensions can be built.

Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.

Hi,

this is a public announcement for the first release of Intersystems Cache Object-Relational Mapper in Python 3. Project's main repository is located at Github (healiseu/IntersystemsCacheORM).

About the project

CacheORM module is an enhanced OOP porting of Intersystems Cache-Python binding. There are three classes implemented:

• CacheClient This is the super class of CachePython module. It wraps two functions from intersys.pythonbind module pythonbind3.connection() and pythonbind3.database().

• CacheQuery A subclass of CacheClient that wraps methods and adds extra functionality in intersys.pythonbind.databaseand intersys.pythonbind.query classes

• CacheClass A subclass of CacheClient, that wraps methods and adds extra functionality in intersys.pythonbind.databaseand intersys.pythonbind.object classes

The intersys.pythonbind package is a Python C extension that provides Python application with transparent connectivity to the objects stored in the Caché database.

Creating a Chatbot with IRIS and Python

In this article I'm going to show how to integrate the InterSystems IRIS Database with Python to serve a Machine
Learning Model of Natural Language Processing (NLP).

In this short article we talk about how to get Yape running in a docker container to avoid having to setup python on your machine.

It's been a while since the last article in this series, so let's recap quickly.

In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph.
Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.

1. IRIS RAG Demo

This demo showcases the powerful synergy between IRIS Vector Search and RAG (Retrieval Augmented Generation), providing a cutting-edge approach to interacting with documents through a conversational interface. Utilizing InterSystems IRIS's newly introduced Vector Search capabilities, this application sets a new standard for retrieving and generating information based on a knowledge base.
The backend, crafted in Python and leveraging the prowess of IRIS and IoP, the LLM model is orca-mini and served by the ollama server.
The frontend is an chatbot written with Streamlit.

Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

If you are looking for a slick way to integrate your IRIS solution in the Amazon Web Services ecosystem, server less application, or boto3 powered python script, using the IRIS Python Native API could be the way to go. You don't have to build out to far with a production implementation until you'll need to reach out and get something or set something in IRIS to make your application do its awesome sauce, so hopefully you will find value in this article and build something that matters or doesn't matter at all to anybody else but you as that is equally important.

Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.

To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.

You can try it out here.

Here's a sneak peek of the results:

Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

Hi Community,


In this article, I will introduce Python Flask Web Framework. Together we will create a minimal web application to connect to IRIS and get data from it.

Below you can find the steps we will need to follow:

• Step 1 : Introduction to Python Flask Web Framework
• Step 2 : Installation of Flask module
• Step 3 : Creation of web application using Flask
• Step 4 : Use of HTML Templates
• Step 5 : Installation of IRIS Python Native module
• Step 6 : Establishment of a connection with IRIS
• Step 7 : Transferring data from IRIS to Flask and displaying it

So Let's start with step 1

Step1-Introduction to Python Flask Web Framework

Flask is a small and lightweight Python web framework that provides useful tools and features that make creating web applications in Python easier. It gives developers flexibility and is a more accessible framework for new developers since it allows to build a web application quickly using only a single Python file. Flask is also extensible and doesn’t requires a particular directory structure or complicated boilerplate code before getting started.

For more details please view Flask Documentations

Introducing Django

Django is a web framework designed to develop servers and APIs, and deal with databases in a fast, scalable, and secure way. To assure that, Django provides tools not only to create the skeleton of the code but also to update it without worries. It allows developers to see changes almost live, correct mistakes with the debug tool, and treat security with ease.

To understand how Django works, let’s take a look at the image: