BrainChulo VS long_term_memory

On BrainChulo, we’re going 100% guidance mode, see for instance an implementation of Chain of Thoughts on top of a thin guidance wrapper: https://github.com/ChuloAI/BrainChulo/blob/main/app/guidance_tooling/guidance_agent/agent.py

Awesome resource! If I may suggest that you'd add one, some friends and I are working on data retrieval with llm project as well, with our differentiating marker being that we are trying to implement guidance in order to improve the agent efficiency. If you guys wanna take a look :) https://github.com/ChuloAI/BrainChulo

Plug: https://github.com/iGavroche/BrainChulo - BrainChulo currently works on top of Ooba but uses its own UI interface. Its first goal is to provide a production-level way to do Retrieveal Augmentation on Open Source LLMs via vector stores and good prompt engineering.

Regarding this, I've joined a project that is doing some nice progress on this front. Still WIP but we're getting there, checkout BrainChulo :)

This is exactly what BrainChulo aims to do. You should check it out: https://github.com/CryptoRUSHGav/BrainChulo/ and feel free to drop on the discord to give us your feedback, your use-case, or if you need help getting started.

The download script is broken. I posted a working version on my repo: https://github.com/CryptoRUSHGav/BrainChulo

I'm hoping that many of you brilliant people can join me in our common quest to add long-term memory to our favorite camelid, Vicuna. The repository is called BrainChulo, and it's just waiting for your contributions.

what you're probably thinking of is this: https://github.com/wawawario2/long_term_memory

I do believe there are some proposed solutions in order to retain memories such as LTM or Long Term Memory. I think there are some extensions for ooba's webui that implements that. see: https://github.com/wawawario2/long_term_memory

If I understand correctly, one of the extensions you have enabled as an option for the gui to be able to select is the long_term_memory extension. That extension enables people to store in their conversations with the model into a long-term memory upon the next chat. I didn't know if you had a method that interfaced with that so that you could enable the long-term memory read or not. Here is where it explains how long_term_memory works: https://github.com/wawawario2/long_term_memory

I saw [this](https://github.com/wawawario2/long_term_memory) as a possible option, but now that I know about SuperBooga I am now not sure which would be better for my purposes.

I thought about Langchain, but it already does the functionality that we have built in which is.. just feed the context back to the model. What I am aiming to do.. is inject relevant memories with the combined text and mix it with the prompt. So the responses will be very fine tuned to the prompt itself based on the context/and memory! so when you say prompt something and it has a memory.. it will take the memory + prompt and the context of the conversation and give a very fine tuned response. https://github.com/wawawario2/long_term_memory

Is this going to function at all similarly to https://github.com/wawawario2/long_term_memory ?

There is the long term memory extension that might assist with this. I found out about it though and haven't gotten it to load successfully yet.. https://github.com/wawawario2/long_term_memory