vectordb-benchmark
semantic-kernel
| vectordb-benchmark | semantic-kernel | |
|---|---|---|
| 1 | 47 | |
| 18 | 18,846 | |
| - | 3.2% | |
| 10.0 | 9.9 | |
| 12 months ago | 2 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.
vectordb-benchmark
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Open-Source GPT-4 Platform for Markdown
If you have a small number of fixed documents e.g. <100k or so, then I agree that pickling the vectors or storing them as bytearrays would work better.
Once you reach a certain scale, it's helpful to potentially use distributed querying and/or different index types, even if you have a fairly static dataset. You can check out a billion-scale search benchmark we recently did here: https://zilliz.com/resources/milvus-performance-benchmark (you'll need to supply your email unfortunately). Here's the framework we used as well: https://github.com/zilliztech/vectordb-benchmark
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?
markprompt - AI for customer support
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
langchain - 🦜🔗 Build context-aware reasoning applications
guidance - A guidance language for controlling large language models.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
dspy - DSPy: The framework for programming—not prompting—foundation models
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
sqlite-vss - A SQLite extension for efficient vector search, based on Faiss!
gpt-llama.cpp - A llama.cpp drop-in replacement for OpenAI's GPT endpoints, allowing GPT-powered apps to run off local llama.cpp models instead of OpenAI.