This is a key usecase for text embeddings. Essentially it is a process of converting sentences or paragraphs to vectors, where the closeness of vectors then rep

This is a key usecase for text embeddings. Essentially it is a process of converting sentences or paragraphs to vectors, where the closeness of vectors then represents a semantic similarity. So you can convert all the paragraphs in your document into vectors, convert your question into a vector, and then find the e.g. 10 closest vectors, or all that fall under a certain maximum distance, etc. You can store the embeddings in a vector database, to search across multiple documents.