Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace
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.
A number is Esthetic if, in any base from base2 up to base10, the absolute difference between every pair of its adjacent digits is constantly equal to 1.
Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.
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Tech Talks: ➡ Practical Usage of Embedded Python - by Stefan Wittmann Product Manager, InterSystems
▶ Recording:
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BPL stands for Business Process Language. This is an XML format for describing complex information orchestration interactions between systems. InterSystems Integration engine has for two decades, provided a visual designer to build, configure, and maintain, BPL using a graphical interface. Think of it like drawing a process flow diagram that can be compiled and deployed.
With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.
I'm trying to convert a python dictionary into an objectscript array but there is an issue with the 'arrayref' function, that is not working as in the linked example.
After running many tests to kick the tires calling Python from COS (which worked fine) in any directory, I encountered problems when trying to access Python libraries like Numpy from any OTHER namespace. I saw some other posts about this but the resolution seems unclear. When I run the test program method numpytest below in %SYS it works just fine. If i run the Python method in any other namespace I get:
PITT>do ##class(test.python).numpytest() DO ##CLASS(test.python).numpytest() ^ <OBJECT DISPATCH> *python object not found
Using VECTOR_COSINE() in SQL query to perform a text similarity search on existing embeddings in a %VECTOR column.
Code is below.
Commented out sql query returns this error: SQLCODE: -29 Field 'NEW_EMBEDDING_STR' not found in the applicable tables^ SELECT TOP ? maxID , activity , outcome FROMMain .AITest ORDER BY VECTOR_COSINE ( new_embedding_str ,
Sql query as written returns ERROR #5002: ObjectScript error: <PYTHON EXCEPTION> *<class 'OSError'>: isc_stdout_write: PyArg_ParseTuple failed!
If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).
$ZF(-100) to run python script not work when directly execute in studio or in storedproc called by TrakCare, but can work when do in iris session.
The python script imports a "barcode" package. It seems that when directly execute in studio or in storedproc called by TrakCare, the package can not be imported.
Not so long ago, I came across the idea of using Python Class Definition Syntax to create IRIS classes on the InterSystems Ideas Portal. It caught my attention since integrating as many syntaxes as possible gives visibility to InterSystems’s products for programmers with experience in many languages.
Django, a high-level web framework written in Python, has become a staple for developers seeking a robust, efficient, and easy-to-learn solution for building web applications. Its popularity stems from its versatility, offering developers an efficient toolkit for building web applications. Integrating Django with InterSystems IRIS introduces a dynamic synergy, providing developers with a comprehensive web development and database management solution. That's why on the Ideas Portal, @Evgeny Shvarov suggested that having Examples to work with IRIS from Django would be beneficial. In this article, we'll explore two projects created to answer the posted idea — Django-iris by @Dmitry Maslennikovand Iris-size-django by @Heloisa Paiva.
I have a question about the python version, I read in the documentation that the compatible version is 3.9.5 for the proper functioning of python on Iris.
Do we have to stay on this version or can we use newer versions?
Since this article is an overview of Flask Login, let's begin with Flask Introduction!
What is Flask?
In the realm of web development, Python has emerged as a formidable force, offering its versatility and robustness to create dynamic and scalable applications. For that reason, tools and services compatible with this language are in demand these days. Flask is a lightweight and easy-to-use web framework for Python. It stands out as a lightweight and user-friendly option. Its simplicity and flexibility have made it a popular choice for developers, particularly for creating smaller-scale applications. It is based on the Werkzeug toolkit and provides a simple but powerful API for building web applications. Unlike its full-stack counterparts, Flask provides a core set of features, focusing on URL routing, template rendering, and request handling. This minimalist approach makes Flask lightweight and easy to learn, allowing developers to build web applications quickly and without the burden of unnecessary complexity.
It seems like yesterday when we did a small project in Java to test the performance of IRIS, PostgreSQL and MySQL (you can review the article we wrote back in June at the end of this article). If you remember, IRIS was superior to PostgreSQL and clearly superior to MySQL in insertions, with no big difference in queries.
Here you'll find a simple program that uses Python in an IRIS environment and another simple program that uses ObjectScript in a Python environment. Also, I'd like to share a few of the troubles I went trough while learning to implement this.
Python in IRIS environment
Let's say, for example, you're in an IRIS environment and you want to solve a problem that you find easy, or more efficient with Python.
You can simply change the environment: create your method as any other, and in the end of it's name and specifications, you add [ Language = python ]:
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.
I have an existing Python script that opens a child session using the pexpect library. But currently all it does is send hard-coded commands to the Cache process and expect a hard-coded response back in order to continue in the script.
I would like to run a Cache routine from the script, pass in a parameter, and wait for a response that will be different every time (a date, in this case). So the call would be something like D $$Tag^Routine(parameter) and wait for the routine to complete and return the response.
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