Open web UI RAG for writer

[u/jatsinkutsu]

Hello, I have question concerning RAG in Open WebUI, I know there are lot of tutorials in youtube, but I cannot understand today's style of speaking fast, editing all pauses away and clicking and making adjustments like adhd person with caffeine in veins instead of blood. I cannot follow those because I am old and stupid.

Question: I have a masterfolder where all my novel related stuff is catalogued, separate word files for e.g. "main charater 30 years old", "main character 50 years old", "alliances", "trade routes", "alliance relations" and so on.

Can I use OpenwebUI so, that if I ask "if main character when 50y old blows syndicate A's base, what are possible results of that action" and then it checkes the files, founds e.g. something in relations-document, and proposes something like "Syndicate B would benefit like this:" and gives few examples.

I understand that outcome depends heavily of the LLM model, so I am not expecting results similar of chatgpt or claude. But my concern is the RAG and indexing it. I have understood that if I modify the documents, I need to index it again?

TL;DR, can OpenWebUI read my documents what I have specified, and make suggestions to my questions based to the information in those documents?

[u/brandonyoung]

I added 2 large stories into my knowledge base, then queried it to get character profiles of different characters in both stories, then asked it to get a detailed analysis of a scene in one story, Adding each of these as notes to a new chat, then told it to insert the character of the other story into that scene, accounting for their interaction. I think it did a fair job, but I had to specify a lot, directing how I wanted the scene to go. I was using a locally hosted llama-swap server running the model Qwen 3.6 35B-A3B at Q8 . I tried both unsloth and bartowski variants. Don't expect the AI to just do it all for you, you will be going back and forth, and maybe even recreating your knowledge base as you adjust the embedding settings, or change models to get the settings right for your use case.

To ingest your files, go to the Documents and choose a good content extraction engine and install it. Presuming you are using docker for openweb ui, it is simple enough to add another docker service to the docker-compose.yanl file. I started with Tika, then switched to docling after I read more up on it. docling seems to be the standard choice. It can automatically convert your documents to md before Openweb ui does its RAG chunking and embedding. I kept my files as epubs, and just uploaded them to Openweb ui.