"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
// 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.
exports.default = () => {
    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)',
    ];
};
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