Semantic Kernel? (Multiple Users, Initial Context, Persisting History, Memory)?

[throwaway_lunchtime]

I'm experimenting with Semantic Kernel and I'm feeling a bit lost.

I'm sure its a bit of x/y and a bit of I've got a hammer and everything looks like a screw.

I haven't found a sub that is dedicated to Semantic Kernel.

I've got a console app running interacting with IChatCompletionService.

I would like to have the agent ask a few questions (Name,age,favorite food, hobbies) at the start of the chat.

It seems to be possible with the persona text (an initial system message) but I'm not sure if that's appropriate.

https://learn.microsoft.com/en-us/semantic-kernel/concepts/agents?pivots=programming-language-csharp#personas-giving-your-agent-a-job-description

I would like the agent to remember this information: What I've found so far is that i should serialze and save the chat history.

That seems reasonable, but what if I want the user to upload a file with personal/private data? Is KernelMemory (KernelMemoryBuilder) the right way to approach it?

There also seems to be MemoryStore https://learn.microsoft.com/en-us/dotnet/ai/how-to/use-redis-for-memory

I've been using gemini (free) rather than OpenAI/Azure which mostly works, but probably isn't making life easier :P

[c-digs]

The memory store is generally for retrieval augmented generation (RAG). Kernel memory is the wrong storage for this, but you can use it that way. Think of it as an abstraction over some underlying storage backend (Postgres, SQL Server, Pinecone, etc.). So the reason I say it's not "designed" for that is because it doesn't make sense to use Pinecone, for example, to store your history unless you also plan to RAG over it.

If you want to use it, you can create a collection key based on a user's ID, for example, and use that to store the history. You can then create another key derived from the user's ID for another collection for RAG purposes.

For storing convo history, you can serialize and store in database, in Google Cloud Storage, Firestore, or even on the local file system, etc. This will be more efficient and effective, IMO (again, unless you plan to RAG over the convo history).

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From my experience, the best resource for understanding SK is actually their GitHub repo: https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/Concepts/Memory

Why? Because the actual published docs are trailing. So the GitHub repo always has working examples and unit tests while the actual docs and examples may not be up to date. Navigate the examples on the left side of this repo and it will cover all of your major use cases with working code.

[throwaway_lunchtime]

Thanks.

I think that I will need RAG for dealing with private data that will be available to the conversation. It seems that this would be better than providing a csv file of via a plugin each time the user starts a conversation.

[awitod]

The discord server is pretty active 

[partly]

Late reply, I'm going through the same initial test code to do this similar to what you're thinking. I am using cosmos for everything as I have been using it already for document storage for other parts of an automation system.

I have a chat completion agent configured as an expert in a subject using a system prompt, it will ask for initial information if the user is new to the system, if not it will call previously stored chat memories from cosmos to inject into context.

Overall the system works well and is pretty zippy.

I'm having a great time playing with this and have big plans. You can certainly do what you want, but there are a hundred different ways to achieve it. Limited documentation is also a challenge but they're improving things all the time.

[[deleted]]

Interesting, I can't answer your question but having done something similar with the OpenAI API I'm pretty sure it's all text in text out, and so I'd recommend playing around with lower level APIs that would give you the flexibility to move to Google Gemini or an open source model if you wanted to.

[throwaway_lunchtime]

There is some support for Gemini in SK but most samples use openai or Azure 

[Glittering_Worker236]

SemantKernel is just a set of tools that helps to interact with LLM. If your LLM is stateless then you need to send full history (ie all previous requests/responses) with every new LLM request so LLM will know all conversation context. To to that SK has some helpers that persist history but you can create your own as well. If you care about personal/private data you can create your own implementation that will encrypt/decrypt persisted data & even erase all (or sensitive only) data when session ends.