Which SQL structure is best to find closest matching sentences across books with multiple paragraphs and sentences?

The best SQL structure to find closest matching sentences across books with multiple paragraphs and sentences would be the use of exact similarity search with a single query vector. This approach is particularly effective for vector-based searches, where semantic meanings and contexts are assessed based on vector representations.

By employing exact similarity search with a single query vector, you can utilize the inherent properties of the vectors representing sentences to compute the distance or similarity between them accurately. This method ensures you are comparing the query sentence directly against all other sentence vectors in an efficient manner to identify the closest matches. It provides a straightforward and focused approach for this type of search, which is crucial in natural language processing and AI applications.

The other options, like the FETCH PARTITIONS BY clause or GROUP BY with vector operations, generally do not directly facilitate what you need for comparing similarity among sentences. Moreover, nested queries with ORDER BY may not be optimal either, as they could complicate the retrieval process when you're trying to find the nearest neighbors in terms of semantic similarity. A single query vector allows for a more streamlined and effective search for sentence closeness, capitalizing on the advantages of vector embeddings.