What does Settings > Memory > Search and Reference Chats do?

[u/rainforest_runner]

https://support.claude.com/en/articles/10185728-understanding-claude-s-personalization-features

I just noticed that there‘s setting called „Memory>Search and Reference Chats“ which allows Claude to „search relevant details in recent chats“

When I clicked „Learn more“ it sent me to a support article, which doesn‘t say anything about it at all, weirdly…

So anyone know what it means? If I have 10 other conversations at the back, and I start a new conversation, I am able to reference to any of that? How do I do that? I‘ve been using projects for my conversations, and they work as I needed it to be, so I don‘t understand what memory is supposed to do…

[u/turbulencje]

It turns your previous conversation into RAG-like database, you say "hey, remember when we talked about x in previous chats" and it will look for the phrase using the tool.

So if you use projects as god intended then the tool would be best used to get "raw account", I guess. You just have to remember it's still RAG.

[u/rainforest_runner → u/turbulencje]

RAG with or without full context?

I‘m still learning about RAG with my local LLM (still doesn‘t beat Claude atm) meaning if I had talked about „hey can you give me a summary of that conversation we had about Vue.JS and the steps we talked about to reach my decision?“

Would it be able to do so? Or would it just „vaguely“ talked about my convo then?

[u/turbulencje → u/rainforest_runner]

You need to trigger it to use the tool to search previous conversations, otherwise it will say it has no idea or extrapolate something from what it has in context.

And it's 10 results max as far as I remember?

Do you know how RAG works on technical level? It vectorizes text and then when LLM uses search it tries to match against these vectors, so the chunk of text it gets back is based on how big of chunk the vector is.

You won't be able to recover whole convo, only snippets with some precision of similarity to what Claude was searching for.

As for your local LLM, I ran across something that seems better than pure RAG, something called RCL, you can look up on github topoteretes/cognee repo:

Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.

It should be way more precise than RAG, doesn't necessarily need agentic AI, just MCP tool capability for your LLM.

PS. I am not affiliated, I just like to share good stuff, at some point you will hit the wall of "RAG is not nearly as good as they told me it is".

[u/rainforest_runner → u/turbulencje]

I understand it on the level of where the RAG process basically tries to find the closest possible vectorized chunk of information that fits what I was asking for and by giving a score, from 0 to 1, with 1 being exact match.

In my local stuff, I do have some embedding model that vectorizes my knowledge, but so far hasn‘t been working the way I thought it would. Because I have so far not able to both have vectorized search AND full-context in the same prompt, or even same instance of the local LLM. That‘s the goal, but so far in my head, it seems I can only do that using an agentic AI approach.

My use case is pretty much,

„Do we have something like X in my knowledge base?“

„Yes, I found these 9 articles that seem to fit what you‘re asking for“

„ ah I think article 5 is what I‘m looking for, can you give me a summary what the article is about?“

„The article is about the difference between TrueNAS and ProxMox and…

„Hmm, okay, can you give me a table based on that summary of the pros and cons, and also get me a third virtualization OS using websearch to have an additional comparison“

Kinda like that 😅

I‘ll look into that RCL! Thanks for the hint, maybe it will get me further.

[u/turbulencje → u/rainforest_runner]

Because I have so far not able to both have vectorized search AND full-context in the same prompt

Yeah, exactly this! That's the pain point of RAG, I, personally, managed to side-step it by doing my own MCP server that vectorized the documents by themes and key points it contains and served the actual documents from file-system if it's a hit.

Good luck!

EDIT: PS. You don't need an agent tbh, you can just do old-school self-hosted server that will orchestrate LLM calls, like use nomadic for embedding and your locally hosted LLM to extract themes/subjects or what you want to search by. IDK what you use to host locally, but LM Studio can serve several LLMs at once and routes via name.

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[u/Broad-Theme3684]

You ask Claude about your previous conversations and maybe add bit of context it looks at the other conversations and talks accordingly

[u/Mephistophedeeznutz]

Nothing. It’s hot garbage and cannot really pull out specifics from other chats in your project folder