localGPT VS private-gpt

I think local LLMs are great for tinkerers, and with quantization can run on most modern PCs. I am not comfortable sending over my personal data over to OpenAI/Anthropic, so I've been playing around with https://github.com/PromtEngineer/localGPT/, GPT4All, etc. which keep the data all local.

Sliding window chunking, RAG, etc. seem more sophisticated than the other document LLM tools, so I would love to try this out if you ever add the ability to run LLMs locally!

localGPT can parse PDF into embeddings, see <https://github.com/PromtEngineer/localGPT>.

This is going so fast that it feels like a new thing pops up every day. LocalGPT seems to have gotten a lot of traction though: https://github.com/PromtEngineer/localGPT

i am currently trying to use localGPT (https://github.com/PromtEngineer/localGPT) for a project and i encountered a problem.

I tinker a lot with electronics. I have put datasheets for components, documentation for development boards, documentation for software libraries, etc into a database with localGPT.

There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.

Check out local gpt on git hub. I tried but it had slow response for me. Other developers are fine. https://github.com/PromtEngineer/localGPT

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!