privateGPT VS PrivateGPT4Linux

# install developer tools xcode-select --install # create python sandbox mkdir PrivateGTP cd privateGTP/ python3 -m venv . # actiavte local context source bin/activate # privateGTP uses poetry for python module management privateGTP> pip install poetry # sync privateGTP project privateGTP> git clone https://github.com/imartinez/privateGPT # enable MPS for model loading and processing privateGTP> CMAKE_ARGS="-DLLAMA_METAL=on" pip install --force-reinstall --no-cache-dir llama-cpp-python privateGTP> cd privateGPT # Import configure python dependencies privateGTP> poetry run python3 scripts/setup # launch web interface to confirm operational on default model privateGTP> python3 -m private_gpt # navigate safari browser to http://localhost:8001/ # To bulk import documentation needed to stop the web interface as vector database not in multi-user mode privateGTP> [control] + "C" # import some PDFs privateGTP> curl "https://docs.intersystems.com/irislatest/csp/docbook/pdfs.zip" -o /tmp/pdfs.zip privateGTP> unzip /tmp/pdfs.zip -d /tmp # took a few hours to process privateGTP> make ingest /tmp/pdfs/pdfs/ # launch web interface again for query documentation privateGTP> python3 -m private_gpt

I found things like this dataset and LocalAI and I followed the article to get PrivateGPT and the GPT4ALL groovy.bin but I'm completely lost and it feels like the more I research the internet or ask BingAI for answers, the more questions I get instead. At this stage I don't know what goes where, if there's a difference between source documents and datasets, should I run this from my 2tb SSD? Should I have the data on my 8tb HDD? Will all this even work on my PC?

No, as the same as the last question, It does not have access to anything except the model data itself. However, there are some approaches that can let LLMs have access LOCAL documents, which means if you can have a program that extracts data from the database into a local folder which contains TEXT files. This could also work for 2(I didn't mention it in 2 because online datas are REALLY big. It would take the model hours to give an answer. If the database is not large then there might be a shot. Check https://github.com/imartinez/privateGPT(Must be GPT4all compatible models sadly).

I've been doing exactly this with an open source repository called PrivateGPT imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks (github.com)

Not really, you will probably need to change the data location and the LLM provider in the example code to get it running. But you don't have to implement that yourself there are a couple projects that already do that like privateGPT. I use it for searching datasheets, got it up an running in a few hours and I'm pretty happy with it so far.

PrivateGPT can do that.