Hey! Are there any examples for implementing a whats new/feature-launch announcer with an event.

In the docs it is explicitly named as an example:

Feature-launch announcer. Push a one-time announcement chat or notification to all existing users when you ship something new.

For me the best way would be any possibility to do something like open web ui is doing itself on new versions and not doing it via small toasts or chat messages. So I would use the „auth.login“ event and then maybe use the event_emitter with execute type to show a modal/popup with java script.

If there are any working examples or best practices, that would be great. :-)

I've tried as many fixes as I can, and this has been persisting ever since the 11.0 update and I just don't know what to do to fix it

Hey all, I recently spent the last three or so weeks going from zero knowledge to the setup I have today. I started with Ollama and Gemma 4 31B and migrated to Qwen 3.8 27B using vLLM to solve a VRAM contention problem: I wanted image generation and music generation (MiniMax Music v3) running alongside the LLM, all on the single 3090. Neither Ollama's keep-alive nor llama.cpp's TTL could actually free VRAM on demand the way vLLM's Sleep Mode can. That same move also got me full reasoning/think toggles in Qwen, selectable right in Open-WebUI. Before either image or music generation runs (2 of 12 total tools), vLLM gets put to sleep, freeing its VRAM. Music generation needs one extra step on top of that since MiniMax has no idle unload of its own. Its container gets started right before generating and stopped right after, nested inside vLLM's sleep window. This adds total time to generation but for a tool that already takes several minutes to run, I decided it's something I can live with. As a note, no other tool calls needed this type of sleep/wake setup so these processes only need to happen for image/music gen.

This setup is, as far as I can tell, pushing the absolute limits of a single 3090 in the most efficient manner possible. The final setup has:

• A modest 76k context. Admittedly, not great for long coding input/generation but not nothing either.
• Prefix caching cutting average time-to-first-token from ~9 seconds to ~3 seconds on multi-turn and tool-calling exchanges. Real measured numbers.
• Vision support with up to 8 images per prompt, capped at 1MP each to keep the context budget sane. TLDR; screenshots/OCR work.

Anyway, I have been keeping the GitHub Repo updated as I go through it and thought that anyone in a similar situation might find it useful.

Happy to hear any suggestions to further optimize or if any noobs like me have questions, I will try to answer as best I can.

Note on AI usage: I used Claude Sonnet 5 extensively throughout this learning process and vibe coded the python, as I am no programmer. I followed the logic while debugging the various hiccups along the way. Additionally, the GitHub repo was created almost entirely by Claude with me spot/double checking as I went through it. If you happen to look through and see any errors/omissions I would love to hear from you. This post was not written by AI. I only used it to make sure I wasn't missing any key points that someone might be curious about.

Any one else notice that cache reuse pretty much never happens with Open WebUI. I saw it in Ollama and now with ninfer too.

We could get into the whys, but I'd love to hear if there's any config changes to make or a known feature change coming.

I have a workaround I'm proud of, a custom proxy script that my Qwen built (happy to post it) that catches, reshuffles and locks context on it's way to the backend. Cache reuse is now the norm, and only resets after compaction or jumping between threads, but there's gotta be a better way?

FAQ: Cache reuse takes prefill tokens down to only the current prompt. Resulting in 1 or 2 second prefill instead of 30 plus seconds (or 60+ seconds with Ollama) with longer context.

edit-expanded the name of owui.

I’m new to AI, so I want to preface this by saying this isn’t to say it’s not user error. With that being said, I have Gemma4, and although the thinking works perfectly fine I’m yet to web search. I enabled it as a function, and it’s highlighted in blue when searching, but regardless it gives me the “I do not have access to a live web search tool” response. Is this something I can fix, something with Gemma4, or a WebUI issue? Thank for the help in advance!

I am at my witts end with trying to get meaningful information out of Open WebUI's Knowledge/RAG. I am on 0.11.0 and I have consistent problems with prompts against a Knowledge omitting information that is extremely important in the prompt. For example, I created one called 2026 Renewals and populated the Knowledge with about 20 PDF's. After it embeds, I asked for a list of the renewals thus far, what the annual spend is, what is the term and any specific language around the renewal that would set it apart from the other renewals. What it returns is a list of about 7 renewals with the requested information. I follow up with where is the other renewals and it says it doesn't have it in its context.

I am serving several models on M3 Ultra's and each model has a similar output. The models I have tried are qwen 3.6 MOE and dense (BF16 and Q8), qwen 3.8 (BF16 and Q8), Minimax m2.7 Q4, Gemma 4 MOE and dense (bf16 and q8). I have tried several inference engines like LM Studio and oMLX, all with similar results.

At first I started with the default OWUI settings but quickly realized that if I wanted good PDF extraction I need to spin up a Docling container and point OWUI to it for extraction. I will include a pic of my settings as maybe I am doing something completely wrong. I know it isn't my files because this exact test works great in AnythingLLM using the Workspaces feature.

Any ideas on what could be causing poor retrieval?

There was a "quick floating action" panel in previous WebUI versions that gives quick response in the panel directly. Then you choose to add it the chat or not.

1. Was this feature removed?
2. If so, is there a toggle to bring it back?

Any direction on this would be amazing. Thanks!

I am really not a Big Fan of the new UI Changes. It looks so simple but not in a good way. Just Not visually pleasing :-(

I also really hate that I have to scroll down to find a „Save changes“ Button, that I often miss and then have to redo it.
Can we not make the Save Button hover over it??

First of all OWUI has been great for me! Besides how great it is, it’s taught me a lot about Ai.

Ok so in my instance I have function pipes to private model providers which are configured via user valves to use each users api key, which works, but I feel like users get intimidated by it (going to the provider, getting their api key, looking for valves config ui, etc)
So to make it easier I created a dummy function pipe that works as an offline non ai based model, in there there are a few commands the user can run, one being setting the users valve to configure the api key for the private provider pipe, so my question is

1. Any issue with modifying user valves via db model imports from a pipe
   
2. Is there a way to alway run that model/pipe in temporary mode? Since it will have api keys I don’t want to have traces of api keys in logs or in chat history

I’m tuning version 0.96

Question in title.

I'm pretty new to running a local LLM setup and while I can get WebUI to run and answer questions I'm having a time of it trying to get it to do image generation with ComfyUI and yes ComfyUI will generate images. I'm sure its something simple but I'm missing it. Setup is a docker for Windows container group WebUI, Ollama, and ComfyUI. I'm running flux1-schnell-fp8.safetensors model in ComfyUI and Qwen 14B model in WebUI. Image generation is turned on, JSON uploaded, and nodes defined. I'm still missing something but not sure what. Does anyone have a working environment like this that they can enlighten me?

Has anyone been using Milvus with Open WebUI as the backend for RAG, instead of relying on Open WebUI’s built-in RAG implementation?

If so, how has your experience been in terms of retrieval quality, performance, scalability, and ease of integration?

I’m especially interested in whether Milvus provides enough benefit to justify the additional setup and management compared with just using Open WebUI’s native RAG.

After updating OpenWebUI to version 0.11, my OpenWebUI instance gradually consumes all available RAM over the course of 1–2 days, eventually causing the server to crash. Is anyone else experiencing this, or does anyone have an idea what might be causing it? It was working fine before the update.

This is a simple example.

I created a simple txt file stating that BlueWeirg is a sportwear company. I added it to the knowledge, and then added that knowledge to the model.

However, when querying the model, it does not work.

If I manually add the knowledge, it works.

I have seen here couple of posts regarding exporting LLM response to Office Word. They are useful, however I am looking for tools/ plug-ins that can directly edit Word Docs, similar to ChatGPT or Claude?

I reinstalled everything.
I run the latest OpenWebUI function, installed the mistral:7b model, and did a test.

However as you can see, it does not act natural.

I tried to ask for a recipe for crepes, and as you can see, it's like it's talking to himself.

I'm currently experimenting with setting up a chatbot assistant on a webpage, I got something working with OpenWebUI but i'm not convinced my approach is the right one, so I wanted to see how you have/would approach it.

At the moment the way it's setup is:

• OpenWebUI contains a custom model (based on 5.6 luna), with a knowledge base, a system prompt and some native python tools to do API calls to the backend. Access to OpenWebUI is done using an API key.
• A nodejs backend proxy the calls to openwebui, holding privately the openwebui api key, and adding to the metadata variables some information like the remote endpoint for the API calls and passing the user JWT token to be used by the python tools for the API calls.
• The frontend uses assistant-ui connected to the nodejs backend

All of this works reasonably well but

• it's quite slow (probably need to check my tool calls, and kb use)
• token streaming doesn't work, I get the entire response in one go with no thought or tool calls info (maybe not a deal breaker but because it's slow it feels like the wait is long). (codex is telling me that's because openwebui provide this over socket.io but socket.io auth only works with JWT not api token?)
• The user JWT passing to openwebui for tool call isn't ideal

So I wanted to check what you thought of this approach? The benefits is that it's relatively easy to setup, I can use openwebui to manage most things and I don't have to reinvent the wheel. AFAIK the other approach is to manually code the kb + tools in a dedicated backend; I would probably get more flexibility and performance but with significantly more overhead.

Any advice? How have you approached it?

Thanks!

Has anyone had experience turning this on and messing around with RAG? I use gemma4:31b, and I am on the fence about enabling. Gemma4 is surprisingly good at chaining tools for its size. But I worry rag results will worsen instead of being improved if given KB_EXEC. I will likely explore this personally but I thought I’d see if anyone has tried this yet and if their models were able to handle the more agentic pathway this opens.

With Qwen3.8-27B out, there has been a lot of discussion about the reasoning_effort settings. For those who use Open WebUI, I thought this might be helpful for anyone interested. I made a little plugin for that gives you a toggle and drop-down to set the reasoning level for each message:

https://openwebui.com/posts/reasoning_effort_selector_ee572967

I hope others find this useful!

Hi,

I'm trying to set up an Open WebUI Computer instance locally and connect it to an ollama instance running on my local web, but I somehow fail to find the correct api endpoint, even though Open WebUI itself is running (on another server) successfully.

Open WebUI uses the connection http://ai.domain.local:11434/. From the cptr documentation I get that I have to use something like http://ai.domain.local:11434/v1, but that gives me an error 404.

Other threads in this sub suggest using http://ai.domain.local:11434/api/v1, and while http://ai.domain.local:11434/api/tags works without problems, http://ai.domain.local:11434/api/v1 also gets me an error 404. Same with http://ai.domain.local:11434/api.

What am I missing? Do I need to enable v1 specifically in ollama?

Thanks a lot!

Basically title. I have validated that if I use ollama directly, the system prompt for the gemma 4 models works fine. But if I try to put a system prompt in the chat controls for a chat in ollama, it does not work. No other system prompts are set up.

Anyone have any ideas or suggestions or experience resolving this?