4 ms·

Any upsides to using this vs writing 30 lines of code?

jamietanna 3y ago
There's no license on this project so the default is All-Rights-Reserved so no one aside from the original author can use it until then
- swyx 3y ago
  github's Original Sin. we could have so much more software training data if they just defaulted (defaulted! you can opt out instead of opt in) to an oss license. so much software is meant to be open source but pple just forgot/didnt bother/didnt know to slap a license on it
  - jamietanna 3y ago
    I disagree with opting people into a license, because the choice of _which_ license is a very personal and important choice. Defaulting to a very permissive license would make folks think that more restrictive (but user-benefitting) licenses like the GPL are Bad ™ when they're not, whereas defaulting to a GPL flavour would make a load of things more restrictive than they may want to be. The only person who can make that decision is the author. However, I think one thing GitHub / other source forges can do is make it _clearer_ when no license is present and that it defaults to All-Rights-Reserved
- emil14 3y ago
  Thank you, fixed, MIT license added
deleted 3y ago
[deleted]
CharlieDigital 3y ago
I'm using Semantic Kernel (https://github.com/microsoft/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/dotnet/samples/KernelSyntaxExamples/Example01_NativeFunctions.cs 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.
- emil14 3y ago
  yeah but it's not go (and go-centric)
emil14 3y ago
1) your 30 lines could become 10 lines or something 2) you can write abstract code without explicit dependency on openai and easely switch to e.g. anthropic in future without rewriting the whole thing (think of ORM for LLMs) 3) ready to use abstraction like Process that allows to chain operations (more complex stuff is much easier than writing by hand)
_lvbh 3y ago
I asked the same thing for langchain
- eigenvalue 3y ago
  Langchain is really bad. You’re so much better off just doing whatever you need to do yourself, especially when it comes to interfacing with OpenAI APIs. It has all of the baggage of a big system without any of the benefits.
  - d4rkp4ttern 3y ago
    Indeed, after being frustrated with existing frameworks, 7 months ago, we started buidling Langroid[1] Multi-Agent LLM framework. It has an elegant Inter-agent orchestration mechanism[2], among many other things. We’ve taken a measured approach to avoid bloat and excess abstractions. We have a company using it in production to assist contact-center/customer-support agents. [1] https://github.com/langroid/langroid https://github.com/langroid/langroid From the README: Langroid is an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications, from ex-CMU and UW-Madison researchers. You set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. [2] Docs on Task Delegation https://langroid.github.io/langroid/quick-start/multi-agent- https://langroid.github.io/langroid/quick-start/multi-agent-...
- emil14 3y ago
  I think the intention behind langchain is good, but the implementation suffers from bad design. We tried to fix that by having small core with as less abstractions as possible.
- emil14 3y ago
  Think of ORM for LLMs. That's not the whole purpose of "langchain-like" frameworks but on that is easy to grasp
- aldarisbm 3y ago
  Lol, yeah the hidden prompts in langchain, and the bloat turned me off it pretty quickly. Still kinda nice to get something going in less than a day

There's no license on this project so the default is All-Rights-Reserved so no one aside from the original author can use it until then

swyx 3y ago
github's Original Sin. we could have so much more software training data if they just defaulted (defaulted! you can opt out instead of opt in) to an oss license. so much software is meant to be open source but pple just forgot/didnt bother/didnt know to slap a license on it
- jamietanna 3y ago
  I disagree with opting people into a license, because the choice of _which_ license is a very personal and important choice. Defaulting to a very permissive license would make folks think that more restrictive (but user-benefitting) licenses like the GPL are Bad ™ when they're not, whereas defaulting to a GPL flavour would make a load of things more restrictive than they may want to be. The only person who can make that decision is the author. However, I think one thing GitHub / other source forges can do is make it _clearer_ when no license is present and that it defaults to All-Rights-Reserved
emil14 3y ago
Thank you, fixed, MIT license added

deleted 3y ago

[deleted]

CharlieDigital 3y ago

I'm using Semantic Kernel (https://github.com/microsoft/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/dotnet/samples/KernelSyntaxExamples/Example01_NativeFunctions.cs 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.

emil14 3y ago
yeah but it's not go (and go-centric)

emil14 3y ago

1) your 30 lines could become 10 lines or something 2) you can write abstract code without explicit dependency on openai and easely switch to e.g. anthropic in future without rewriting the whole thing (think of ORM for LLMs) 3) ready to use abstraction like Process that allows to chain operations (more complex stuff is much easier than writing by hand)

_lvbh 3y ago

I asked the same thing for langchain

eigenvalue 3y ago
Langchain is really bad. You’re so much better off just doing whatever you need to do yourself, especially when it comes to interfacing with OpenAI APIs. It has all of the baggage of a big system without any of the benefits.
- d4rkp4ttern 3y ago
  Indeed, after being frustrated with existing frameworks, 7 months ago, we started buidling Langroid[1] Multi-Agent LLM framework. It has an elegant Inter-agent orchestration mechanism[2], among many other things. We’ve taken a measured approach to avoid bloat and excess abstractions. We have a company using it in production to assist contact-center/customer-support agents. [1] https://github.com/langroid/langroid https://github.com/langroid/langroid From the README: Langroid is an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications, from ex-CMU and UW-Madison researchers. You set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. [2] Docs on Task Delegation https://langroid.github.io/langroid/quick-start/multi-agent- https://langroid.github.io/langroid/quick-start/multi-agent-...
emil14 3y ago
I think the intention behind langchain is good, but the implementation suffers from bad design. We tried to fix that by having small core with as less abstractions as possible.
emil14 3y ago
Think of ORM for LLMs. That's not the whole purpose of "langchain-like" frameworks but on that is easy to grasp
aldarisbm 3y ago
Lol, yeah the hidden prompts in langchain, and the bloat turned me off it pretty quickly. Still kinda nice to get something going in less than a day