QuestPDF VS spaCy

What is QuestPDF? QuestPDF is an open-source .NET library for PDF document generation. It uses a fluent API approach to compose together many simple elements to create complex documents.

QuestPDF looks really good (I haven't used it) but I believe they changed their license recently.

I used that same library it worked great the only issue I had was the users would often have to manually set the scaling to fit to a page. I'm sure I could've fixed this in other ways if I was more competent with CSS but ended up just switching to use https://github.com/QuestPDF/QuestPDF in a backend instead of doing everything in front end.

Give this a try: QuestPDF

I think you should ask these questions in related discussion on Github: https://github.com/QuestPDF/QuestPDF/discussions/491

May I introduce you to QuestPDF!

➡️ James Newton-King ♔ on Twitter: "Coming in .NET 8: Route tooling for ASP.NET Core" / Twitter ➡️ Announcing .NET Community Toolkit 8.1 ➡️ Uno Platform 4.7 – New Project Template, Performance Improvements and more ➡️ C# Advent 2022 Awards | Cross Cutting Concerns ➡️ GitHub - QuestPDF ➡️ DNF Summit 2023 ➡️ .NET Frontend Day

Like many have suggested, I also cast my vote on QuestPDF. No more +50MB library including a Chrome browser to render HTML so a PDF of it can be created, which took 2-3 seconds each time. But with QuestPDF, it's so much faster!

Also, the library is open-source, so you can take a look at the exact implementation of the layout engine. It is inspired by Flutter and WPF, though optimized for pageable content: https://github.com/QuestPDF/QuestPDF/tree/main/Source/QuestPDF/Elements

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.