narrator
semantic-kernel
| narrator | semantic-kernel | |
|---|---|---|
| 5 | 47 | |
| 4,284 | 18,846 | |
| - | 3.2% | |
| 6.4 | 9.9 | |
| about 1 month ago | 3 days ago | |
| Python | C# | |
| - | 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.
narrator
- FLaNK Stack for 04 December 2023
- David Attenborough narrates your life
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David Attenborough is now narrating my life
This is hilarious. Can’t wait for the Werner Herzog version.
And you can use it yourself: https://github.com/cbh123/narrator
semantic-kernel
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#SemanticKernel – 📎Chat 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
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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
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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.
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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.
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New: LangChain templates – 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
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Overview: AI Assembly Architectures
Semantic Kernel github.com/microsoft/semantic-kernel
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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?
Jlama - Jlama is a modern Java inference engine for LLMs
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
langchain4j - Java version of LangChain
langchain - 🦜🔗 Build context-aware reasoning applications
nougat - Implementation of Nougat Neural Optical Understanding for Academic Documents
guidance - A guidance language for controlling large language models.
onnx-models - A copy of ONNX models, datasets, and code all in one GitHub repository. Follow the README to learn more.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
incubator-gluten - Gluten is a middle layer responsible for offloading JVM-based SQL engines' execution to native engines.
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks