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.

Functioning

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.
• 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.

License

AGPL v3.0+.