ollama-webui VS private-gpt

I'm a podman beginner, trying to install ollama-webui(1) using Podman on M2 MBA.

I started up Podman Desktop, and did a terminal command "docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v ollama-webui:/app/backend/data --name ollama-webui --restart always ghcr.io/ollama-webui/ollama-webui:main" based on Github's instructions, but it gave a error message something about "host".

Do you know what is the problem and how do I overcome this?

If I run the above command using Docker Desktop, it runs and installs Ollama-WebUI just fine.

(1) https://github.com/ollama-webui/ollama-webui ("Installing with Docker")

Thank you.

I set up Ollama in a docker container (really easy) and I use the Ollama web-ui here, that's very much like ChatGPT, and I have that in a container as well.

The primary use case here seems to be that it might be possible to use this tool to spend <$20/mo for the same feature set as ChatGPT+. It does not currently make any effort to support locally-hosted open source models, which is what I would have assumed from its name.

If you're interested in a fully Libre LLM stack, I've had fun lately with ollama [0] and ollama-webui [1]. It was pretty trivial to take ollama-webui's docker-compose file and set up a locally-running chat server with Mistral 7B. Trying out different models and prompts was likewise very easy to get started with.

Mistral isn't anything like as good as GPT-4, but it's Apache licensed and fully local, which meets my definition of Libre. I'll continue to use both while the FOSS stacks catch up, but it's fun to keep up with the progress on the open source stuff as tooling develops.

[0] https://github.com/jmorganca/ollama

[1] https://github.com/ollama-webui/ollama-webui

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!