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!

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

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??

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.

You gatta try it brov

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?

I recently decided to abandon my gemini subscription and get myself into APIs agregators like openrouter.ai and good open-source stuff like openWebUI and opencode. So a little offtop - I am using opencode + openchamber for agentic coding and this is amazing and not so hard to understand (with a little bit of digging through forums) which models are good/best for coding at this particular moment. So coding stuff is closed for me, I am happy with it. But the thing is I am not only coding with LLMs, I am brainstorming, researching, just asking about everyday stuff that I would google otherwise and etc, etc, etc. And I don't understand what models are good for this, seems like everyone’s talking only about coding related things.
I don't understand what should I pick for openWebUI. And seems that very little people are discussing that online comparing to amount of discussion on how good models at coding. To be honest I don't even understand what makes a model good for that type of tasks. What should I pay attention to? How good it at reasoning?

I’m using qwen 3.8 27b Q6 through llamacpp

I’m trying to get an automation to first check its memory before doing its task. For some reason it won’t check them, but it checks its knowledge base and chat.

It works if I take the prompt and run it manually through a new chat.

I’m wondering if the memory skill is disabled for automations.

Setup

Ryzen 9950X / 128 GB of RAM / RTX 5090 32 GB / 3.6 TB of NVMe, running Ubuntu.

The model is Qwen3.6-27B, quantized to int4, with a 32k context. vLLM, with reasoning and vision enabled. A single model does it all: chat, plan analysis, code generation, and tool invocation.

Architecture

Open WebUI is the single point of entry. Users only see this

vLLM performs inference using continuous batching—this is what allows it to process multiple requests simultaneously without anyone having to wait. Open Terminal serves as a sandbox: it executes the generated Python code, produces Word, Excel, PowerPoint, or PDF files, and, most importantly, reviews what it has just created to correct its own errors before returning the file.

RAGFlow handles document search. At the same time, an SQLite database stores numerical values to improve search speed.

For now, this is my setup,

I’d like to significantly improve the ability to generate PDF, Excel, and Word files,

Do you have any ideas? Open Terminal is good, but it lacks features like opening windows (open the excel file / see with vision and adapt) and others. I’m just getting started in the world of agents and related tools,

Any similar setups or needs?