Open WebUI’s document management loads everything into a vector database. When you use the hashtag, it will trigger a search against the vector database based on your prompt. These results are run feed into the LLM. Open WebUI should generate a hashtag that can reference all the documents. But the quality of the results will be influenced by the embeddings and the LLM that responds to you.
Install ollama. It has ROCm support (on Linux at least). Then hook it up to your favorite client. It has its own API and an openai compatible one.
KoboldCPP has ban tokens that prevent those tokens from being output. Otherwise just put it in the prompt and it should probably work.
Even the smell of Olives causes me to gag. I absolutely cannot eat them. Olive oil is fine. But actual olives, no. Doesn’t matter if they’re old, new, canned, fresh. They’re absolutely disgusting. One of the few foods I outright cannot and will not eat.
Doesn’t gnome already have this?
Lol. Git itself can act as a server over the git protocol. Might have been easier 🤪
There’s plenty of git forges that aren’t GitHub. Git itself has nothing to do with central servers and can theoretically be used in a completely decentralized manner.
I would think access to the training data, or at least no restrictions on what you can do with the model, would be a good definition.
Adam Sandler?
It’s the opening of the Canterbury Tales.
Makes sense when many of the spiders in Australia are dangerous, though.
Can you link the fork? There’s several and I wasn’t aware that one did SMS still!
Yes, but the request was explicitly for an SMS app. But yeah anything SMS is just text. It’s an outdated protocol.
But it’s not SMS anymore, unfortunately
I use a Misskey fork for micro blogging and I can’t even get Lemmy posts to load. The profiles of communities do, but that’s it.
Ah right. What I really meant to ask was if it can do protocols other than http.
Which I don’t think it can…
Are you able to tunnel ports other than 80 and 443 through Cloudflare?
A vector search converts your query into magic numbers, and then searches the database for other magic numbers that are “similar” (closet to it in the vector space, which is basically an N-dimensional graph of points and directions). These results are then returned as snippets to the LLM and it does stuff from that point.
The effectiveness of the vector search depends on how Open WebUI breaks up the documents into smaller sections, and how good the embeddings are.
I’m not exactly sure what you want to achieve, but you might have success in using an LLM to summarize the documents beforehand, using a specific prompt to extract the info you want, then feed that into the vector DB. This would require some scripting, of course.
The easiest thing to do is try it. See if Open WebUI’s vector search is able to handle it. Make sure to use a good embedding model like nomic-embed-text (can be found on ollama.com). You can change the vector search settings in the documents settings from the workspace on OpenWebUI.
Edit: https://ollama.com/library/nomic-embed-text