marker
semantic-kernel
marker | semantic-kernel | |
---|---|---|
8 | 47 | |
8,791 | 18,560 | |
- | 5.4% | |
7.8 | 9.9 | |
6 days ago | 3 days ago | |
Python | C# | |
GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
marker
-
LlamaCloud and LlamaParse
You may want to try https://github.com/VikParuchuri/surya (I'm the author). I've only benchmarked against tesseract, but it outperforms it by a lot (benchmarks in repo). Happy to discuss.
You could also try https://github.com/VikParuchuri/marker for general PDF parsing (I'm also the author) - it seems like you're more focused on tables.
-
Show HN: Texify β OCR math images to LaTeX and Markdown
Hi HN - I made texify to convert equations to markdown/LaTeX for my project marker [1] then realized it could be generally useful.
Texify converts equations and surrounding text to Markdown, with embedded LaTeX (MathJax compatible).
You can either use a GUI to select equations (inline or block) from PDFs and images to convert, or use the CLI to batch convert images. It works on CPU, GPU, or MPS (Mac).
The closest open source comparisons are pix2tex and nougat - marker is more accurate than both of them for this task. However, nougat is more for entire pages, and pix2tex is more for block equations (not inline equations and text).
I trained texify for 2 days on 4x A6000 GPUs - I was pleasantly surprised how far I could get with limited GPU resources by reframing the problem to use small parameter counts/images.
Texify is licensed for commercial use, with the weights under CC-BY-SA 4.0. Fine them here - https://huggingface.co/vikp/texify .
See the texify repo for more details, benchmarks, how to install, etc.
[1] https://github.com/VikParuchuri/marker
-
Show HN: Talk to any ArXiv paper just by changing the URL
https://github.com/VikParuchuri/marker
Both are tools to convert pdfs into Latex or Markup with latex formulas. Maybe that helps
- FLaNK Stack Weekly 11 Dec 2023
- Marker: Convert PDF to Markdown quickly with high accuracy
- FLaNK Stack for 04 December 2023
semantic-kernel
-
#SemanticKernel β πChat Service demo running Phi-2 LLM locally with #LMStudio
There is an amazing sample on how to create your own LLM Service class to be used in Semantic Kernel. You can view the Sample here: https://github.com/microsoft/semantic-kernel/blob/3451a4ebbc9db0d049f48804c12791c681a326cb/dotnet/samples/KernelSyntaxExamples/Example16_CustomLLM.cs
-
Semantic Tests for SemanticKernel Plugins using skUnit
This week, I had the chance to explore the SemanticKernel code base, particularly the core plugins. SemanticKernel comes equipped with these built-in plugins:
- FLaNK Stack for 04 December 2023
- Semantic Kernel
-
Getting Started with Semantic Kernel and C#
In this article we'll look at the high-level capabilities building AI orchestration systems in C# with Semantic Kernel, a rapidly maturing open-source AI orchestration framework.
-
Agency: Pure Go LangChain Alternative
I'm using Semantic Kernel (https://github.com/microsoft/semantic-kernel) and it's really nice. Makes building more complex workflows really simple without sacrificing control.
A bunch of examples (https://github.com/microsoft/semantic-kernel/blob/main/dotne...) for how to handle just about anything you need to do with OAI with a lot less boilerplate.
-
New: LangChain templates β fastest way to build a production-ready LLM app
I haven't tried it but there's Microsoft semantic-kernel.
https://github.com/microsoft/semantic-kernel
-
Overview: AI Assembly Architectures
Semantic Kernel github.com/microsoft/semantic-kernel
-
Automated Routing of Tasks to Optimal Models: A PR for Semantic-Kernel
The need for efficient model routing has been a point of discussion in the community. Addressing this, I've submitted a pull request to Semantic-Kernel that introduces an automated multi-model connector.
What are some alternatives?
voyager - π°οΈ An approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.
langchain - β‘ Building applications with LLMs through composability β‘ [Moved to: https://github.com/langchain-ai/langchain]
llmsherpa - Developer APIs to Accelerate LLM Projects
langchain - π¦π Build context-aware reasoning applications
PyMuPDF - PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.
guidance - A guidance language for controlling large language models.
node-gtk - GTK+ bindings for NodeJS (via GObject introspection)
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
langchain4j - Java version of LangChain
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks