lib/services/vector_service.dart

import 'dart:convert';
import 'package:flutter/services.dart';
import 'package:sqflite/sqflite.dart';

/// Result of a vector-similarity search against the scam_embeddings table.
class VectorMatch {
  final String category;
  final double distance;
  final bool isHighRisk;
  final bool isMediumRisk;

  bool get isScam => isMediumRisk || isHighRisk;
  String get threatLevel => isHighRisk ? 'HIGH' : isMediumRisk ? 'MEDIUM' : 'LOW';

  VectorMatch({
    required this.category,
    required this.distance,
    required this.isHighRisk,
    required this.isMediumRisk,
  });
}

/// Encapsulates sqlite-vec Layer 2 of the Kavacha pipeline.
///
/// Design principle: NEVER throw out. All failures are caught and logged.
/// The pipeline must continue with layers 1, 3, 4 if this layer is unavailable.
class VectorService {
  static final VectorService _instance = VectorService._internal();
  factory VectorService() => _instance;
  VectorService._internal();

  static bool _isReady = false;
  static bool get isReady => _isReady;

  /// The sqlite-vec version string, available after successful [initialize].
  static String vecVersion = 'not loaded';

  /// Initialize the sqlite-vec extension and create the virtual table.
  ///
  /// Must be called once, after the database is opened, before the pipeline runs.
  /// Safe to call multiple times — subsequent calls are no-ops.
  static Future<void> initialize(Database db) async {
    if (_isReady) return;
    try {
      // CRITICAL: enable_load_extension MUST come before load_extension.
      // SQLite on Android disables extension loading by default.
      await db.execute("PRAGMA enable_load_extension = 1");
      await db.execute("SELECT load_extension('libsqlitevec')");
      await db.execute("PRAGMA enable_load_extension = 0");

      // Verify extension loaded correctly.
      final version = await db.rawQuery("SELECT vec_version()");
      vecVersion = version.first.values.first?.toString() ?? 'unknown';
      print('[VectorService] sqlite-vec version: $vecVersion');

      // Create virtual vector table for scam embeddings (128-dimension).
      await db.execute('''
        CREATE VIRTUAL TABLE IF NOT EXISTS scam_embeddings
        USING vec0(
          embedding FLOAT[384],
          +category TEXT,
          +source TEXT,
          +created_at INTEGER
        )
      ''');

      _isReady = true;
      print('[VectorService] Layer 2 ready');
    } catch (e) {
      _isReady = false;
      vecVersion = 'not loaded';
      print('[VectorService] Failed to load: $e');
      print('[VectorService] Layer 2 disabled — pipeline continues without it');
      // Do NOT rethrow — Kavacha pipeline must continue with layers 1, 3, 4.
    }
  }

  /// Store a confirmed scam as an embedding for future matching.
  Future<void> addScamEmbedding({
    required Database db,
    required List<double> embedding,
    required String category,
    required String source,
  }) async {
    if (!_isReady) return;
    try {
      await db.execute(
        'INSERT INTO scam_embeddings(embedding, category, source, created_at) VALUES (?, ?, ?, ?)',
        [
          '[${embedding.join(',')}]',
          category,
          source,
          DateTime.now().millisecondsSinceEpoch,
        ],
      );
    } catch (e) {
      print('[VectorService] addScamEmbedding error: $e');
    }
  }

  /// Find the closest scam pattern in the embedding store.
  ///
  /// Returns [VectorMatch] if a candidate is found, [null] if not or layer is disabled.
  Future<VectorMatch?> findClosest({
    required Database db,
    required List<double> queryEmbedding,
    double highRiskThreshold = 0.15,
    double mediumRiskThreshold = 0.35,
  }) async {
    if (!_isReady) return null;
    try {
      final results = await db.rawQuery('''
        SELECT category, source, distance
        FROM scam_embeddings
        WHERE embedding MATCH ?
        ORDER BY distance
        LIMIT 1
      ''', ['[${queryEmbedding.join(',')}]']);

      if (results.isEmpty) return null;

      final distance = (results.first['distance'] as num).toDouble();
      final category = results.first['category'] as String;

      return VectorMatch(
        category: category,
        distance: distance,
        isHighRisk: distance < highRiskThreshold,
        isMediumRisk: distance < mediumRiskThreshold,
      );
    } catch (e) {
      print('[VectorService] findClosest error: $e');
      return null;
    }
  }

  /// Convenience accessor that uses StorageService's shared DB.
  Future<VectorMatch?> findClosestScam(List<double> queryEmbedding) async {
    if (!_isReady) return null;
    // This path is used by layer2_vector.dart which has its own db reference.
    // The caller passes the embedding; DB must be provided via findClosest().
    // Return null here to signal the caller to use findClosest() directly.
    return null;
  }

  /// Seed the vector store with pre-computed scam embeddings from a JSON asset.
  ///
  /// Call once on first install. Skips silently if embeddings already exist.
  Future<void> seedEmbeddings(Database db, String assetPath) async {
    if (!_isReady) return;
    try {
      final countResult = await db.rawQuery(
        'SELECT COUNT(*) as c FROM scam_embeddings',
      );
      final existing = (countResult.first['c'] as int);
      if (existing > 0) {
        print('[VectorService] Already seeded ($existing embeddings), skipping');
        return;
      }

      print('[VectorService] Seeding embeddings from $assetPath');
      final String jsonString = await rootBundle.loadString(assetPath);
      final List<dynamic> data = jsonDecode(jsonString);

      final batch = db.batch();
      for (final item in data) {
        final List<dynamic> rawEmbedding = item['embedding'] as List<dynamic>;
        final embeddingStr =
            '[${rawEmbedding.map((v) => (v as num).toDouble()).join(',')}]';
        batch.execute(
          'INSERT INTO scam_embeddings(embedding, category, source, created_at) VALUES (?, ?, ?, ?)',
          [
            embeddingStr,
            item['category'] as String,
            item['source'] as String,
            DateTime.now().millisecondsSinceEpoch,
          ],
        );
      }
      await batch.commit(noResult: true);
      print('[VectorService] Seeded ${data.length} embeddings');
    } catch (e) {
      print('[VectorService] Seed error: $e');
    }
  }

  /// Returns the count of embeddings currently in the store.
  Future<int> embeddingCount(Database db) async {
    if (!_isReady) return 0;
    try {
      final result = await db.rawQuery(
        'SELECT COUNT(*) as c FROM scam_embeddings',
      );
      return (result.first['c'] as int);
    } catch (_) {
      return 0;
    }
  }
}