OpenWebUI Filters

Mirrored at Github: https://github.com/ProjectMoon/open-webui-filters

My collection of OpenWebUI Filters.

So far:

• Memory Filter: A basic narrative memory filter intended for long-form storytelling/roleplaying scenarios. Intended as a proof of concept/springboard for more advanced narrative memory.
• GPU Scaling Filter: Reduce number of GPU layers in use if Ollama crashes due to running out of VRAM.
• Output Sanitization Filter: Remove words, phrases, or characters from the start of model replies.

Memory Filter

Super hacky, very basic automatic narrative memory filter for OpenWebUI, that may or may not actually enhance narrative generation!

This is intended to be a springboard for a better, more comprehensive filter that can coherently keep track(ish?) of plot and character developments in long form story writing/roleplaying scenarios, where context window length is limited (or ollama crashes on long context length models despite having 40 GB of unused memory!).

Configuration

The filter exposes two settings:

• Summarization model: This is the model used for extracting and creating all of the narrative memory, and searching info. It must be good at following instructions. I use Gemma 2.
  • It must be a base model. If it's not, things will not work.
  • If you don't set this, the filter will attempt to use the model in the conversation. It must still be a base model.
• Number of messages to retain: Number of messages to retain for the context. All messages before that are dropped in order to manage context length.

Ideally, the summarization model is the same model you are using for the storytelling. Otherwise you may have lots of model swap-outs.

The filter hooks in to OpenWebUI's RAG settings to generate embeddings and query the vector database. The filter will use the same embedding model and ChromaDB instance that's configured in the admin settings.

Usage

Enable the filter on a model that you want to use to generate stories. It is recommended, although not required, that this be the same model as the summarizer model (above). If you have lots of VRAM or are very patient, you can use different models.

User input is pre-processed to 'enrich' the narrative. Replies from the language model are analyzed post-delivery to update the story's knowlege repository.

You will see status indicators on LLM messages indicating what the filter is doing.

Do not reply while the model is updating its knowledge base or funny things might happen.

Function

What does it do?

• When receiving user input, generate search queries for vector DB based on user input + last model response.
• Search vector DB for theoretically relevant character and plot information.
• Ask model to summarize results into coherent and more relevant stuff.
• Inject results as contextual info for the model.
• After receiving model narrative reply, generate character and plot info and stick them into the vector DB.

Limitations and Known Issues

What does it not do?

• Handle conversational branching/regeneration. In fact, this will pollute the knowledgebase with extra information!
  • Bouncing around some ideas to fix this. Basically requires building a "canonical" branching story path in the database?
• Proper context "chapter" summarization (planned to change).
• Work properly when switching conversations due to OpenWebUI limitations. The chat ID is not available on incoming requests for some reason, so a janky workaround is used when processing LLM responses. Fixed! (but still in a very hacky way)
• Clear out information of old conversations or expire irrelevant data.

Other things to do or improve:

• Set a minimum search score, to prevent useless stuff from coming up.
• Figure out how to expire or update information about characters and events, instead of dumping it all into the vector DB.
• Improve multi-user handling. Should technically sort of work due to messages having UUIDs, but is a bit messy. Only one collection is used, so multiple users = concurrency issues?
• Block user input while updating the knowledgebase.

GPU Scaling Filter

This is a simple filter that reduces the number of GPU layers in use by Ollama when it detects that Ollama has crashed (via empty response coming in to OpenWebUI). Right now, the logic is very basic, just using static numbers to reduce GPU layer counts. It doesn't take into account the number of layers in models or dynamically monitor VRAM use.

There are three settings:

• Initial Reduction: Number of layers to immediately set when an Ollama crash is detected. Defaults to 20.
• Scaling Step: Number of layers to reduce by on subsequent crashes (down to a minimum of 0, i.e. 100% CPU inference). Defaults to 5.
• Show Status: Whether or not to inform the user that the conversation is running slower due to GPU layer downscaling.

Output Sanitization Filter

This filter is intended for models that often output unwanted characters or terms at the beginning of replies. I have noticed this especially with Beyonder V3 and related models. They sometimes output a ":" or "Name:" in front of replies. For example, if system prompt is "You are Quinn, a helpful assistant." the model will often reply with "Quinn:" as its first word.

There is one setting:

• Terms: List of terms or characters to remove. This is a list, and in the UI, each item should be separated by a comma.

For the above example, the setting textbox should have :,Quinn: in it, to remove a single colon from the start of replies, and Quinn: from the start of replies.

Other Notes

Terms are removed in the order defined by the setting. The filter loops through each term and attempts to remove it from the start of the LLM's reply.

License

All filters are licensed under AGPL v3.0+. The code is free software, that you can run, redistribute, modify, study, and learn from as you see fit, as long as you extend that same freedom to others, in accordance with the terms of the AGPL. Make sure you are aware how this might affect your OpenWebUI deployment, if you are deploying OpenWebUI in a public environment!