django-redis VS spaCy

Redis is an open-source data-structure store that can be used as a database, cache, message broker, etc. To start using Redis in your Django application, you need to first install the django-redis library. The library makes it easier to connect your Django application to Redis.

To implement caching in Django application. First, set up your Django project and application. Then install django-redis, a project by Jazzband:

django-redis

This isn't such a big deal... There are great third-party packages (i.e https://github.com/jazzband/django-redis) for whoever needs a redis cache backend in a project.

Myself, I just use good ol' memcached for all my caching needs (https://docs.djangoproject.com/en/3.2/topics/cache/#memcache...). It is rock solid and has never failed me till now!

If your example of the counter is accurate for what you want to do, it might be faster using django-redis as your cache backend and using the incr method (which adds one to a given cache key). Repeatedly inserting and deleting database records is much slower than cache operations like that would be. Or is there a way you can avoid deleting database records, and modify existing values?

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.