• Memories 的作用用园子里的博文 Semantic Kernel 入门系列：Memory内存 中的一段话来回答：

Semantic Kernel 将 embedding 的功能封装到了 Memory 中，用来存储上下文信息，就好像电脑的内存一样，而 LLM 就像是 CPU 一样，我们所需要做的就是从内存中取出相关的信息交给 CPU 处理就好了。

摘录 Intro to Semantic Kernel – Part One 中的文字回答这个问题

关于 Plugins 与 Planner

In short, with SK, we can define Plugins that drive the semantic hardware in certain ways, and maintain collections of Plugins that can be automatically selected by SK’s Planner if a task requires multiple steps for successful completion.

关于 Memory

SK offers a Memory capability, allowing the Planner to retrieve information, from a vector database for instance, and based on that data, distill a Plan of Steps to be executed using certain plugins.

a Planner is a mechanism to autonomously orchestrate AI tasks based on a user’s request.

来自 Intro to Semantic Kernel – Part Two

讲解 Kernel Memory 的好文：Build a custom Copilot experience with your private data using and Kernel Memory

关于 kernel

One of the key concepts in Semantic Kernel is the kernel itself, which is the main object that we will use to orchestrate our LLM based workflows. Initially, the kernel has very limited functionality; all of its features are largely powered by external components that we will connect to. The kernel then acts as a processing engine that fulfils a request by invoking appropriate components to complete the given task.

来源：A Pythonista’s Intro to Semantic Kernel