[liampulles]

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.

[madhatter999]

Thank you for the answer! Wouldn't it make sense to compare the potential answers to the questions to the parts of the documents?

[liampulles]

If the potential answers are given, sure