<?php
// aiassist/search.php
if (!defined('ELASTIC_URL')) {
    define('ELASTIC_URL', 'http://localhost:9200');
}

/**
 * Выполняет поиск по указанному индексу ElasticSearch.
 *
 * @param string $index Имя индекса.
 * @param array $query Тело запроса.
 * @return array Результаты поиска.
 */
function searchIndex($index, $query) {
    $es_url = ELASTIC_URL . "/{$index}/_search";
    $ch = curl_init($es_url);
    curl_setopt_array($ch, [
        CURLOPT_RETURNTRANSFER => true,
        CURLOPT_POST => true,
        CURLOPT_POSTFIELDS => json_encode($query),
        CURLOPT_HTTPHEADER => ['Content-Type: application/json']
    ]);
    $response = curl_exec($ch);
    $httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
    if ($httpCode !== 200) {
        error_log("Ошибка поиска в ElasticSearch (индекс $index): HTTP $httpCode - " . $response . "\n", 3, __DIR__ . "/logs/search.log");
    }
    curl_close($ch);
    return json_decode($response, true);
}


function searchRefinedCasesFromBestExample($text, $embedding2048, $embedding1024 = null, $size = 5) {
    $shouldClauses = [
        [
            "multi_match" => [
                "query" => $text,
                "fields" => ["court_decision", "суть_спора"],
                "fuzziness" => "AUTO",
                "boost" => 1.5
            ]
        ],
        [
            "match_phrase" => [
                "court_decision" => [
                    "query" => $text,
                    "slop" => 2
                ]
            ]
        ]
    ];

    if ($embedding2048 !== null) {
        $shouldClauses[] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') + 1.0",
                    "params" => ["query_vector" => $embedding2048]
                ],
                "boost" => 1.5
            ]
        ];
    }

    if ($embedding1024 !== null) {
        $shouldClauses[] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_1024') + 1.0",
                    "params" => ["query_vector" => $embedding1024]
                ],
                "boost" => 1.0
            ]
        ];
    }

    $query = [
        "size" => $size,
        "query" => [
            "bool" => [
                "should" => $shouldClauses,
                "filter" => [
                    ["exists" => ["field" => "court_decision"]],
                    ["exists" => ["field" => "law_articles"]],
                    ["range" => ["case_year" => ["gte" => 2015]]]
                ]
            ]
        ]
    ];

    file_put_contents(__DIR__ . "/logs/search.log", "[" . date("Y-m-d H:i:s") . "] 🔎 Уточнённый поиск: " . json_encode($query, JSON_UNESCAPED_UNICODE) . "\n", FILE_APPEND);

    $response = searchIndex("legal_cases", $query);
    return isset($response['hits']['hits']) ? array_column($response['hits']['hits'], '_source') : [];
}


/**
 * Выполняет объединённый поиск по эмбеддингам и лексическому запросу для индекса cases.
 *
 * @param string $queryText Текст запроса.
 * @param array $queryEmbeddings Вектор запроса.
 * @param int $size Количество результатов.
 * @return array Результаты поиска.
 */
function searchCases($queryText, $queryEmbeddings, $size = 5) {
    $query = [
        "size" => $size,
        "query" => [
            "bool" => [
                "should" => [
                    [
                        "multi_match" => [
                            "query" => $queryText,
                            "fields" => ["court_decision", "суть_спора"],
                            "fuzziness" => "AUTO"
                        ]
                    ],
                    [
                        "script_score" => [
                            "query" => ["match_all" => (object)[]],
                            "script" => [
                                "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') * 1.5 + 1.0",
                                "params" => [
                                    "query_vector" => $queryEmbeddings["embedding_2048"]
                                ]
                            ]
                        ]
                    ]
                ]
            ]
        ]
    ];
    
    return searchIndex("legal_cases", $query);
}

/**
 * Выполняет поиск похожих судебных решений.
 *
 * @param array $queryParams Параметры запроса.
 * @param int $size Количество результатов.
 * @return array Результаты поиска.
 */

 function searchSimilarCases($queryParams, $size = 5) {
    $logFile = __DIR__ . "/logs/search.log";

    if (empty($queryParams['facts_short']) || empty($queryParams['facts_full'])) {
        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Ошибка: Недостаточно данных для поиска!\n", FILE_APPEND);
        return [];
    }

    $factsShort = $queryParams['facts_short']; // Для лексического поиска
    $factsFull = $queryParams['facts_full']; // Для логирования
    $normalizedEmbedding1024 = $queryParams['embedding_1024'] ?? null;
    $normalizedEmbedding2048 = $queryParams['embedding_2048'] ?? null;

    $shouldClauses = [];

    // 🔎 **Лексический поиск (multi_match) на основе `facts_short`**
    $shouldClauses[] = [
        "multi_match" => [
            "query"    => $factsShort,
            "fields"   => ["court_decision", "law_articles"],
            "fuzziness"=> "0",  // ❗ Отключаем fuzziness
            "boost"    => 1.5
        ]
    ];
    
    // 🔎 **Добавляем `match_phrase` для точного поиска**
    $shouldClauses[] = [
        "match_phrase" => [
            "court_decision" => [
                "query" => $factsShort,
                "slop"  => 2
            ]
        ]
    ];

    // 🔎 **Векторный поиск (script_score) на основе `embedding_2048`**
    if ($normalizedEmbedding2048 !== null) {
        $shouldClauses[] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') + 1.0",
                    "params" => [
                        "query_vector" => $normalizedEmbedding2048
                    ]
                ],
                "boost" => 1.5
            ]
        ];
    }

    // 🔎 **Векторный поиск (script_score) на основе `embedding_1024`**
    if ($normalizedEmbedding1024 !== null) {
        $shouldClauses[] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_1024') + 1.0",
                    "params" => [
                        "query_vector" => $normalizedEmbedding1024
                    ]
                ],
                "boost" => 1.0
            ]
        ];
    }

    $query = [
        "size" => $size,
        "query" => [
            "bool" => [
                "should" => $shouldClauses,
                "filter" => [
                    ["match" => ["case_category_text" => ["query" => $queryParams['category'], "fuzziness" => "AUTO"]]],
                    ["exists" => ["field" => "court_decision"]],
                    ["exists" => ["field" => "law_articles"]],
                    ["range" => ["case_year" => ["gte" => 2010]]]
                ]
            ]
        ]
    ];

    file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] 🔎 Запрос в Elasticsearch: " . json_encode($query, JSON_UNESCAPED_UNICODE) . "\n", FILE_APPEND);

    $response = searchIndex("legal_cases", $query);

    if (!isset($response['hits']['hits']) || empty($response['hits']['hits'])) {
        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Elasticsearch вернул 0 результатов!\n", FILE_APPEND);
        return [];
    }

    return array_column($response['hits']['hits'], '_source');
}


 /*
function searchSimilarCases($queryParams, $size = 5) {
    $logFile = __DIR__ . "/logs/search.log";

    if (empty($queryParams['facts']) || empty($queryParams['category'])) {
        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Ошибка: Недостаточно данных для поиска!\n", FILE_APPEND);
        return [];
    }

    // Начальные параметры
    $amount = isset($queryParams['amount']) && is_numeric($queryParams['amount']) ? floatval($queryParams['amount']) : null;
    $fuzzinessLevels = ["AUTO", "2", "5"];
    $years = [2010, 2000, null]; // Искать сначала с 2010, потом 2000, потом без ограничений
    $category = $queryParams['category'];
    $expandedSearch = false;

    $fuzzinessIndex = 0;
    $yearIndex = 0;

    do {
        $fuzziness = $fuzzinessLevels[$fuzzinessIndex] ?? "AUTO";
        $minYear = $years[$yearIndex] ?? null;

        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] 🔎 Поиск: fuzziness=$fuzziness, minYear=$minYear, category=$category\n", FILE_APPEND);

        // Создаём поисковые условия
        $shouldClauses = [
            [
                "multi_match" => [
                    "query" => $queryParams['facts'],
                    "fields" => ["court_decision", "law_articles"],
                    "fuzziness" => $fuzziness,
                    "boost" => 1.0
                ]
            ]
        ];

        // 🔍 Добавляем поиск по `embedding_2048`, если есть
        if (isset($queryParams['embedding_2048']) && is_array($queryParams['embedding_2048'])) {
            $shouldClauses[] = [
                "script_score" => [
                    "query" => ["match_all" => new stdClass()],
                    "script" => [
                        "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') + 1.0",
                        "params" => ["query_vector" => $queryParams['embedding_2048']]
                    ],
                    "boost" => 1.5
                ]
            ];
        }

        // 🔍 Добавляем поиск по `embedding_1024`, если есть
        if (isset($queryParams['embedding_1024']) && is_array($queryParams['embedding_1024'])) {
            $shouldClauses[] = [
                "script_score" => [
                    "query" => ["match_all" => new stdClass()],
                    "script" => [
                        "source" => "cosineSimilarity(params.query_vector, 'embedding_1024') + 1.0",
                        "params" => ["query_vector" => $queryParams['embedding_1024']]
                    ],
                    "boost" => 1.0
                ]
            ];
        }

        // 🔍 Основные фильтры
        $filterClauses = [
            ["match" => ["case_category_text" => ["query" => $category, "fuzziness" => "AUTO"]]],
            ["exists" => ["field" => "court_decision"]],
            ["exists" => ["field" => "law_articles"]]
        ];

        if ($minYear !== null) {
            $filterClauses[] = ["range" => ["case_year" => ["gte" => $minYear]]];
        }

        // 🔍 Фильтрация по сумме, если указана
        if ($amount !== null && $amount > 0) {
            $rangeMultiplier = $expandedSearch ? 2.0 : 1.3;
            $filterClauses[] = [
                "range" => ["requested_amount" => ["gte" => $amount * 0.7, "lte" => $amount * $rangeMultiplier]]
            ];
        }

        $query = [
            "size" => $size,
            "query" => [
                "bool" => [
                    "should" => $shouldClauses,
                    "filter" => $filterClauses
                ]
            ]
        ];

        // Выполняем поиск в ElasticSearch
        $response = searchIndex("legal_cases", $query);

        // Проверяем результат
        if (isset($response['hits']['hits']) && !empty($response['hits']['hits'])) {
            file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ✅ Найдено: " . count($response['hits']['hits']) . " документов.\n", FILE_APPEND);
            return array_column($response['hits']['hits'], '_source');
        }

        // Если результатов нет, пробуем ослабить параметры
        if ($fuzzinessIndex < count($fuzzinessLevels) - 1) {
            $fuzzinessIndex++;
        } elseif ($yearIndex < count($years) - 1) {
            $yearIndex++;
        } elseif (!$expandedSearch) {
            $expandedSearch = true;
            $category = "*"; // Поиск по всем категориям
        } else {
            break;
        }

    } while (true);

    file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Elasticsearch не нашел результатов даже с ослабленными параметрами!\n", FILE_APPEND);
    return [];
}

*/


 /*function searchSimilarCases($queryParams, $size = 10) {
    $logFile = __DIR__ . "/logs/search.log";

    if (empty($queryParams['facts']) || empty($queryParams['category'])) {
        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Ошибка: Недостаточно данных для поиска!\n", FILE_APPEND);
        return [];
    }

    $amount = isset($queryParams['amount']) && is_numeric($queryParams['amount']) ? floatval($queryParams['amount']) : null;
    $fuzzinessLevel = (mb_strlen($queryParams['facts']) > 100) ? "0" : "AUTO";

    // Нормализация эмбеддингов
    $normalizedEmbedding2048 = isset($queryParams['embedding_2048']) && is_array($queryParams['embedding_2048']) 
        ? normalizeEmbedding($queryParams['embedding_2048']) 
        : null;

    $normalizedEmbedding1024 = isset($queryParams['embedding_1024']) && is_array($queryParams['embedding_1024']) 
        ? normalizeEmbedding($queryParams['embedding_1024']) 
        : null;
    //$fuzzinessLevel
    // Формируем запрос в ElasticSearch
    $query = [
        "size" => $size,
        "query" => [
            "bool" => [
                "should" => [
                    [
                        "multi_match" => [
                            "query" => $queryParams['facts'],
                            "fields" => ["court_decision", "law_articles"],
                            "fuzziness" => $fuzzinessLevel
                        ]
                    ]
                ],
                "filter" => [
                    ["match" => ["case_category_text" => ["query" => $queryParams['category'], "fuzziness" => "AUTO"]]],
                    ["exists" => ["field" => "court_decision"]],
                    ["exists" => ["field" => "law_articles"]],
                    ["range" => ["case_year" => ["gte" => 2020]]]
                ]
            ]
        ]
    ];

    // Добавляем векторный поиск
    if ($normalizedEmbedding2048 !== null) {
        $query["query"]["bool"]["should"][] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') + 1.0",
                    "params" => ["query_vector" => $normalizedEmbedding2048]
                ]
            ]
        ];
    }

    if ($normalizedEmbedding1024 !== null) {
        $query["query"]["bool"]["should"][] = [
            "script_score" => [
                "query" => ["match_all" => new stdClass()],
                "script" => [
                    "source" => "cosineSimilarity(params.query_vector, 'embedding_1024') + 1.0",
                    "params" => ["query_vector" => $normalizedEmbedding1024]
                ]
            ]
        ];
    }

    if ($amount !== null && $amount > 0) {
        $query["query"]["bool"]["filter"][] = [
            "range" => ["requested_amount" => ["gte" => $amount * 0.7, "lte" => $amount * 1.3]]
        ];
    }

    // Логируем запрос
    file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] 🔎 Запрос в Elasticsearch: " . json_encode($query, JSON_UNESCAPED_UNICODE) . "\n", FILE_APPEND);

    try {
        $response = searchIndex("legal_cases", $query);
        if (!isset($response['hits']['hits']) || empty($response['hits']['hits'])) {
            file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Elasticsearch вернул 0 результатов!\n", FILE_APPEND);
            return [];
        }

        // Формируем выходные данные
        $results = [];
        foreach ($response['hits']['hits'] as $doc) {
            $source = $doc['_source'];
            $results[] = [
                'case_id' => $source['case_id'] ?? 'Неизвестный ID',
                'court' => $source['court'] ?? 'Неизвестный суд',
                'court_decision' => $source['court_decision'] ?? 'Текст решения отсутствует',
                'case_url' => $source['case_url'] ?? 'Нет ссылки'
            ];
        }

        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ✅ Найдено " . count($results) . " документов.\n", FILE_APPEND);
        return $results;
    } catch (Exception $e) {
        file_put_contents($logFile, "[" . date("Y-m-d H:i:s") . "] ❌ Ошибка Elasticsearch: " . $e->getMessage() . "\n", FILE_APPEND);
        return [];
    }
}

*/

/**
 * Преобразует ответ ElasticSearch в удобный массив результатов.
 *
 * @param array $esResponse Ответ от ElasticSearch.
 * @return array Массив результатов.
 */
function parseSearchResults($esResponse) {
    $results = [];
    if (isset($esResponse['hits']['hits'])) {
        foreach ($esResponse['hits']['hits'] as $hit) {
            $results[] = $hit['_source'];
        }
    }
    return $results;
}

/**
 * Выполняет поиск по индексу legal_chunks.
 *
 * @param string $queryText Текст запроса.
 * @param array $queryEmbeddings Вектор запроса (например, embedding_1024, embedding_2048).
 * @param int $size Количество результатов.
 * @return array Результаты поиска.
 */
function searchLegalChunks($queryText, $queryEmbeddings, $size = 5) {
    // Формируем запрос
    $query = [
        "size" => $size,
        "query" => [
            "bool" => [
                "should" => [
                    // Поиск по полям с текстом (например, court_decision, text)
                    [
                        "multi_match" => [
                            "query" => $queryText,
                            "fields" => ["court_decision", "text", "case_category_text", "defendant", "plaintiff"],
                            "fuzziness" => "AUTO",
                            "boost" => 1.5
                        ]
                    ],
                    // Поиск по вектору (embedding_2048)
                    [
                        "script_score" => [
                            "query" => ["match_all" => new stdClass()],
                            "script" => [
                                "source" => "cosineSimilarity(params.query_vector, 'embedding_2048') + 1.0",
                                "params" => ["query_vector" => $queryEmbeddings["embedding_2048"]]
                            ],
                            "boost" => 1.5
                        ]
                    ],
                    // Поиск по вектору (embedding_1024)
                    [
                        "script_score" => [
                            "query" => ["match_all" => new stdClass()],
                            "script" => [
                                "source" => "cosineSimilarity(params.query_vector, 'embedding_1024') + 1.0",
                                "params" => ["query_vector" => $queryEmbeddings["embedding_1024"]]
                            ],
                            "boost" => 1.0
                        ]
                    ]
                ],
                "filter" => [
                    ["exists" => ["field" => "court_decision"]],
                    ["exists" => ["field" => "law_articles"]],
                    // Можно добавить дополнительные фильтры, например, по году или категориям
                    ["range" => ["case_year" => ["gte" => 2015]]]
                ]
            ]
        ]
    ];

    // Логируем запрос
    file_put_contents(__DIR__ . "/logs/search.log", "[" . date("Y-m-d H:i:s") . "] 🔎 Запрос в Elasticsearch для legal_chunks: " . json_encode($query, JSON_UNESCAPED_UNICODE) . "\n", FILE_APPEND);

    // Выполняем поиск
    $response = searchIndex("legal_chunks", $query);

    // Если результат есть, возвращаем его
    if (isset($response['hits']['hits']) && !empty($response['hits']['hits'])) {
        return array_column($response['hits']['hits'], '_source');
    } else {
        file_put_contents(__DIR__ . "/logs/search.log", "[" . date("Y-m-d H:i:s") . "] ❌ Elasticsearch не нашел результатов для legal_chunks!\n", FILE_APPEND);
        return [];
    }
}

?>