dspy
semantic-kernel
dspy | semantic-kernel | |
---|---|---|
22 | 47 | |
10,820 | 18,332 | |
17.5% | 4.2% | |
9.9 | 9.9 | |
7 days ago | 3 days ago | |
Python | C# | |
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.
dspy
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
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Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
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Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
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Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
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AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
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Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
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?
open-interpreter - A natural language interface for computers
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
playground - Play with neural networks!
langchain - 🦜🔗 Build context-aware reasoning applications
MLflow - Open source platform for the machine learning lifecycle
guidance - A guidance language for controlling large language models.
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
AgentOoba - An autonomous AI agent extension for Oobabooga's web ui
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks