/**
 * Vector similarity search over stored snippet embeddings.
 *
 * Uses sqlite-vec vector_top_k() for ANN search instead of in-memory cosine
 * similarity computation over all embeddings.
 */

import type Database from 'better-sqlite3';
import { SqliteVecStore } from './sqlite-vec.store.js';

// ---------------------------------------------------------------------------
// Types
// ---------------------------------------------------------------------------

export interface VectorSearchResult {
	snippetId: string;
	score: number;
}

export interface VectorSearchOptions {
	repositoryId: string;
	versionId?: string;
	profileId?: string;
	limit?: number;
}

// ---------------------------------------------------------------------------
// Math helpers
// ---------------------------------------------------------------------------

/**
 * Compute cosine similarity between two Float32Array vectors.
 *
 * Returns a value in [-1, 1] where 1 is identical direction. Returns 0 when
 * either vector has zero magnitude to avoid division by zero.
 */
export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
	if (a.length !== b.length) {
		throw new Error(`Embedding dimension mismatch: ${a.length} vs ${b.length}`);
	}

	let dot = 0;
	let normA = 0;
	let normB = 0;

	for (let i = 0; i < a.length; i++) {
		dot += a[i] * b[i];
		normA += a[i] * a[i];
		normB += b[i] * b[i];
	}

	const denom = Math.sqrt(normA) * Math.sqrt(normB);
	if (denom === 0) return 0;
	return dot / denom;
}

// ---------------------------------------------------------------------------
// VectorSearch class
// ---------------------------------------------------------------------------

export class VectorSearch {
	private readonly sqliteVecStore: SqliteVecStore;

	constructor(private readonly db: Database.Database) {
		this.sqliteVecStore = new SqliteVecStore(db);
	}

	/**
	 * Search stored embeddings by cosine similarity to the query embedding.
	 *
	 * Uses in-memory cosine similarity computation. The vec_embedding column
	 * stores raw Float32 bytes for forward compatibility with vector-capable
	 * libSQL builds; scoring is performed in JS using the same bytes.
	 *
	 * @param queryEmbedding - The embedded representation of the search query.
	 * @param options - Search options including repositoryId, optional versionId, profileId, and limit.
	 * @returns Results sorted by descending cosine similarity score.
	 */
	vectorSearch(queryEmbedding: Float32Array, options: VectorSearchOptions): VectorSearchResult[] {
		return this.sqliteVecStore
			.queryNearestNeighbors(queryEmbedding, options)
			.map((result) => ({ snippetId: result.snippetId, score: result.score }));
	}
}