semantic-kernel
langchaingo
| semantic-kernel | langchaingo | |
|---|---|---|
| 47 | 8 | |
| 18,332 | 3,116 | |
| 4.2% | - | |
| 9.9 | 9.8 | |
| 4 days ago | 7 days ago | |
| C# | Go | |
| MIT License | 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.
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.
langchaingo
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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Build a Serverless GenAI solution with Lambda, DynamoDB, LangChain and Amazon Bedrock
This use-case here is a similar one - a chat application. I will switch back to implementing things in Go using langchaingo (I used Python for the previous one) and continue to use Amazon Bedrock. But there are few unique things you can explore in this blog post:
- LangChain for Go, the easiest way to write LLM-based programs in Go
- Langchaingo – LangChain in Idiomatic Go
- Agency: Pure Go LangChain Alternative
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Building LangChain applications with Amazon Bedrock and Go - An introduction
langchaingo is the LangChain implementation for the Go programming language. This blog post covers how to extend langchaingo to use foundation model from Amazon Bedrock.
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Zep: A long-term memory store for LLM apps, written in Go
Langchain Go is being actively developed https://github.com/tmc/langchaingo
What are some alternatives?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
yao - :rocket: A performance app engine to create web services and applications in minutes.Suitable for AI, IoT, Industrial Internet, Connected Vehicles, DevOps, Energy, Finance and many other use-cases.
langchain - 🦜🔗 Build context-aware reasoning applications
guidance - A guidance language for controlling large language models.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
zep - Zep: Long-Term Memory for AI Assistants.
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
TaskEaseGPT - (WIP) A user-friendly, AI-powered task manager emphasizing efficient work over planning. Streamlines workflow with intelligent task generation & execution. Boost your productivity today!
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
langchaingo-amazon-bedrock-llm - Amazon Bedrock extension for langchaingo