BrainChulo VS private-gpt

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.

PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.

https://github.com/zylon-ai/private-gpt

Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion

Run https://github.com/imartinez/privateGPT

Then

make ingest /path/to/folder/with/files

Then chat to the LLM.

Done.

Docs: https://docs.privategpt.dev/overview/welcome/quickstart

PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.

I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].

The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.

[0] https://gpt4all.io/index.html

[1] https://www.danswer.ai/

[2] https://github.com/imartinez/privateGPT

https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...

https://github.com/imartinez/privateGPT

I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.

This resulted in Python package I call OnPrem.LLM.

In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/

Enjoy!