31–40 of 114 posts
Are embeddings useful for something of the scale compared to just keyword search (aka grep)?
That's a whole lot of text to say "it's markdown".
Earlier quoted context omitted.
that sounds horribly token inefficient, just create a tool call if you are all in on the agentic approach and hide memory retrieval behind an optimized api
They already have. The tool is called bash and optimized api is grep, the storage is the file system. We’ve been here many months. What people are exploring are other options as far as I can tell. What you are offering is “don’t do that, this already works”. Which I guess is fine, but apparently not everyone is fully satisfied with the current generation of tooling.
Also fwiw grep is pretty poorly suited to semantic search and will only return the most basic of matches.
If you are really trying to build a useful memory search tool there are much better options than plain text search.
It’s good that a lot of people are trying a lot of things when it comes to agentic memory. Sadly none of it represents a complete solution at this time. But we need the experimentation.
I still dont understand what "agentic memory" is . agents can already call sql / rag and grep through files or whatever. why is "agentic memory" a special thing.
Earlier quoted context omitted.
I still dont understand what "agentic memory" is . agents can already call sql / rag and grep through files or whatever. why is "agentic memory" a special thing.
Memory isn’t the same thing as rag it’s usually just a text based index of past events and the llm reads it and decides what’s important rather than querying a db
- memory systems are a specific type of knowledge base where you generate all the documents. You might as well generate them to be less than your embedding token limit to obviate the need for chunking.
- embedding models are getting better and are no longer just semantic averaging.
- small models are getting dirt cheap, making parallel reads cost manageable
What they describe is sort of the simplest architecture that takes advantage of these observations. I believe them when they say it works well.
I do suspect though that things like keyword lookup will completely fail if every memory is just a vector. Hence why something like Typesense hybrid search can still be useful.
That's a whole lot of text to say "it's markdown".
The markdown explanation I was also unimpressed with, but RAG over hyperlinks is convincing to me.
Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…
Does anyone else not use memory? I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database stru…