go-openai VS semantic-kernel

Unfortunately, you're using sashabaranov/go-openai, which has different api behavior than the official langchain python package [0].

I was hoping this would be a drop in replacement for langchain (so DS ppl can code in python and gophers can port it to production).

[0] - https://github.com/sashabaranov/go-openai#why-dont-we-get-th...

what do you offer that https://github.com/sashabaranov/go-openai does not ?

We will use this great library to connect to the openAPI API https://github.com/sashabaranov/go-openai

This was built using an AWS Lambda function built in GO with the https://github.com/sashabaranov/go-openai module

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

This week, I had the chance to explore the SemanticKernel code base, particularly the core plugins. SemanticKernel comes equipped with these built-in plugins:

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.

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.

I haven't tried it but there's Microsoft semantic-kernel.

https://github.com/microsoft/semantic-kernel

Semantic Kernel github.com/microsoft/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.