A vector search converts your query into magic numbers, and then searches the database for other magic numbers that are "similar" (closet to it in the vector space, which is basically an N-dimensional graph of points and directions). These results are then returned as snippets to the LLM and it does stuff from that point.
The effectiveness of the vector search depends on how Open WebUI breaks up the documents into smaller sections, and how good the embeddings are.
I'm not exactly sure what you want to achieve, but you might have success in using an LLM to summarize the documents beforehand, using a specific prompt to extract the info you want, then feed that into the vector DB. This would require some scripting, of course.
The easiest thing to do is try it. See if Open WebUI's vector search is able to handle it. Make sure to use a good embedding model like nomic-embed-text (can be found on ollama.com). You can change the vector search settings in the documents settings from the workspace on OpenWebUI.
No trying to get around anything. No funny instructions like my grandma singing a lullaby about illegal activities. Just using instructions to tell a story. Even things like having a superhero in a fight is enough to trigger this. Also doesn't explain why regen makes it continue.