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Python Weekly

Welcome to issue 617 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

Vector Embeddings Tutorial – Create an AI Assistant with GPT-4 & Natural Language Processing

Learn about vector embeddings and how to use them in your machine learning and artificial intelligence projects. Learn how to create an AI assistant with vector embeddings.

How the Python Dataframe Interchange Protocol Makes Life Better
In this article, we answer three questions about the Python Dataframe Interchange Protocol: What it is + what problems it solves; how it works; and how extensively it's been adopted.

Introducing flake8-logging
This article introduces flake8-logging, a Flake8 plugin that helps you to improve the logging in your Python code. Flake8 is a linter that checks Python code for errors and style violations. flake8-logging extends Flake8 by adding rules for checking logging code.

How We're Building AI Search Engines using LLM Embeddings

Demo and explanation of how to use the Python sentence-transformers library to generate, store, and query LLM embeddings using the Django ORM and pgvector. This video demonstrates a prototype application that enables "AI-powered search" for job descriptions using an unstructured, English-language description of a job seeker.

Debugging Catastrophic Backtracking for Regular Expressions in Python
This post discusses the problem of catastrophic backtracking in regex, and it provides some tips on how to avoid it. It also discusses the different ways that regex engines can handle backtracking, and the trade-offs between different approaches.

Processing a 250 TB dataset with Coiled, Dask, and Xarray
The authors successfully processed 250TB of geospatial cloud data in 20 minutes using Xarray, Dask, and Coiled, highlighting the challenges and optimizations involved, all while keeping the cost at approximately $25. This achievement demonstrates the feasibility of large-scale data processing, exposes scalability issues, and explores cost-efficient strategies for such tasks.

Deploying Django with Kamal (mrsk)
If you just want to deploy containers on a remote machine, Kamal might be a nice addition to your toolbelt. It automates many common steps when deploying containers to one or more remote machines, without introducing the complexity of something like Kubernetes or having to use a managed service.

Add database search with Django and HTMX
We'll create a fast and simple database search using Django and HTMX. It's easy and fast to do with HTMX. There'll be 6 steps.

When to use classes in Python? When you repeat the same functions
In this article, we look at another heuristic for using classes in Python, with examples from real-world code, and some things to keep in mind.

Towards a new SymPy: Part 1 - Outline
This first post will outline the structure of the foundations of a computer algebra system (CAS) like SymPy, describe some problems SymPy currently has and what can be done to address them. Then subsequent posts will focus in more detail on particular components and the work that has been done and what should be done in the future.

Part 2 - Polynomials - This post will describe SymPy’s computational algebra system for polynomials and how each of these steps could be applied to speed up SymPy. I will talk a bit about FLINT and python-flint but I will also write a separate post about those because I know that some people will be more interested in using python-flint than SymPy itself and I hope to encourage them to contribute to python-flint.

How to Use Apple Vision Framework via PyObjC for Text Recognition
THis article discusses how to use the Vision framework via PyObjC, which allows you to use Objective-C frameworks from Python. The Vision framework is a machine learning framework that can be used to perform tasks such as face detection, object detection, and text recognition.

Visualizing the CPython Release Process

Altering Python attribute handling for modules

How to model a following system similar to Twitter and Instagram with Python and Django

Interesting Projects, Tools and Libraries

Litestar
Litestar is a powerful, flexible yet opinionated ASGI framework, focused on building APIs, and offers high-performance data validation and parsing, dependency injection, first-class ORM integration, authorization primitives, and much more that's needed to get applications up and running.

InstaGraph
Converts text input or URL into knowledge graph and displays.

Prompt flow
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

kr8s
A batteries-included Python client library for Kubernetes that feels familiar for folks who already know how to use kubectl.

Pyflyby
A set of productivity tools for Python.

pai
A Python REPL with a built in AI agent and code generation.

view.py
Lightning-fast, modern web framework.

django-send-sms
Send SMS from Django application using any SMS service provider just writing a single line of code.

WhatsApp-Llama
Finetune a LLM to speak like you based on your WhatsApp Conversations.

textual-web
Run TUIs and terminals in your browser.

LiteLLM
Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]

blip-caption
Generate captions for images with Salesforce BLIP.

Vanna
Personalized AI SQL Agent.

Medusa
Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads.

New Releases

Python in Visual Studio Code – September 2023 Release
This release includes the following announcements:

“Recreate” or “Use Existing” options added to the Python: Create Environment command
Experimental terminal activation using environment variables
Community-contributed yapf extension

Upcoming Events and Webinars

PyData Berlin Meetup September 2023
There will be following talks

Stochastic Gradient Langevin Dynamics - Motivation , foundation and what DL can gain
OpenAI’s Whisper: State of the Art Speech Transcription and the Voice Interface Revolution

PyData Zurich Meetup September 2023
There will be following talks

How (Not) to Use Fairness Metrics in ML
Can We Squeeze() More Out of Python’s Type System? The Challenge of Tensor Shape Annotations

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