import { SqlQuery } from '../types';

// Storage for the AI embeddings index. Two tables:
//
// - `note_embeddings_meta` holds the per-chunk metadata. It is a regular table
//   so the metadata (chunk text, source note ID, model identifier) can be
//   inspected and managed even on platforms where the sqlite-vec extension
//   isn't available.
//
// - `note_embeddings_vec` is the sqlite-vec virtual table holding the actual
//   vectors. It is created lazily by the embeddings code when sqlite-vec is
//   available, because virtual-table syntax differs by extension version and
//   trying to create it unconditionally would break startup on platforms
//   without the prebuilt.
//
// Neither table is synced — embeddings are large, model-specific, and easily
// re-derivable on demand.

export default (): (SqlQuery|string)[] => {
	return [
		`CREATE TABLE IF NOT EXISTS note_embeddings_meta (
			id INTEGER PRIMARY KEY AUTOINCREMENT,
			note_id TEXT NOT NULL,
			chunk_index INTEGER NOT NULL,
			model_id TEXT NOT NULL,
			chunk_text TEXT NOT NULL,
			created_time INT NOT NULL DEFAULT 0
		)`,
		'CREATE INDEX IF NOT EXISTS note_embeddings_meta_note_id ON note_embeddings_meta(note_id)',
		'CREATE UNIQUE INDEX IF NOT EXISTS note_embeddings_meta_note_chunk ON note_embeddings_meta(note_id, chunk_index)',
	];
};