ac7467f0b4
- Added comprehensive AI Assistant system (aiassist/ directory): * Vector search and embedding capabilities * Typebot proxy integration * Elastic search functionality * Message classification and chat history * MCP proxy for external integrations - Implemented Court Status API (GetCourtStatus.php): * Real-time court document status checking * Integration with external court systems * Comprehensive error handling and logging - Enhanced S3 integration: * Improved file backup system with metadata * Batch processing capabilities * Enhanced error logging and recovery * Copy operations with URL fixing - Added Telegram contact creation API - Improved error logging across all modules - Enhanced callback system for AI responses - Extensive backup file storage with timestamps - Updated documentation and README files - File storage improvements: * Thousands of backup files with proper metadata * Fix operations for broken file references * Project-specific backup and recovery systems * Comprehensive file integrity checking Total: 26,461+ files added/modified including AWS SDK, vendor dependencies, and extensive backup system.
129 lines
4.9 KiB
PHP
129 lines
4.9 KiB
PHP
<?php
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// aiassist/search_context.php
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/**
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* Генерирует итоговый prompt для GPT, объединяя:
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* 1. Исходный текст нового спора ($combinedContent).
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* 2. Результаты поиска судебных решений в индексе legal_cases.
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* 3. Результаты поиска похожих кейсов в индексе cases и связанных судебных решений из court_decisions.
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*
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* Предполагается, что функции getTextEmbedding, searchIndex, parseSearchResults
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* и константа ELASTIC_URL уже подключены.
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*/
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$embedding = getTextEmbedding($combinedContent);
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//logMessage("Полученный эмбеддинг (первые 10 значений): " . json_encode(array_slice($embedding, 0, 10)));
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// 1. Поиск судебных решений в индексе legal_cases
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$es_url_legal = ELASTIC_URL . "/legal_cases/_search";
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$queryLegal = [
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"size" => 10,
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"query" => [
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"script_score" => [
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"query" => ["match_all" => (object)[]],
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"script" => [
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"source" => "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
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"params" => [ "query_vector" => $embedding ]
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]
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]
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]
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];
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$ch = curl_init($es_url_legal);
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curl_setopt_array($ch, [
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CURLOPT_RETURNTRANSFER => true,
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CURLOPT_POST => true,
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CURLOPT_POSTFIELDS => json_encode($queryLegal),
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CURLOPT_HTTPHEADER => ['Content-Type: application/json']
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]);
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$responseLegal = curl_exec($ch);
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curl_close($ch);
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$esResponseLegal = json_decode($responseLegal, true);
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$legalResults = parseSearchResults($esResponseLegal);
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$legalContext = "Судебные решения из практики (legal_cases):\n";
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foreach ($legalResults as $result) {
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$similarity = isset($result['similarity']) ? $result['similarity'] : 'N/A';
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$legalContext .= "- {$result['title']} (сходство: {$similarity}%)\n";
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}
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logMessage("Результаты поиска в legal_cases:\n" . $legalContext);
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// 2. Поиск похожих кейсов в индексе cases
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$es_url_cases = ELASTIC_URL . "/cases/_search";
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$queryCases = [
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"size" => 10,
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"query" => [
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"script_score" => [
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"query" => ["match_all" => (object)[]],
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"script" => [
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"source" => "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
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"params" => [ "query_vector" => $embedding ]
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]
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]
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]
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];
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$ch = curl_init($es_url_cases);
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curl_setopt_array($ch, [
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CURLOPT_RETURNTRANSFER => true,
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CURLOPT_POST => true,
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CURLOPT_POSTFIELDS => json_encode($queryCases),
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CURLOPT_HTTPHEADER => ['Content-Type: application/json']
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]);
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$responseCases = curl_exec($ch);
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curl_close($ch);
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$esResponseCases = json_decode($responseCases, true);
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$caseResults = parseSearchResults($esResponseCases);
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// Извлекаем project_id из найденных кейсов
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$projectIds = [];
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foreach ($caseResults as $case) {
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if (isset($case['project_id'])) {
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$projectIds[] = $case['project_id'];
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}
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}
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$projectIds = array_unique($projectIds);
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logMessage("Найденные project_id из кейсов: " . json_encode($projectIds));
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// 3. Поиск судебных решений для найденных кейсов в индексе court_decisions
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$courtContext = "Судебные решения, связанные с найденными кейсами (court_decisions):\n";
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if (!empty($projectIds)) {
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$es_url_court = ELASTIC_URL . "/court_decisions/_search";
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$queryCourt = [
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"size" => 10,
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"query" => [
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"terms" => [
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"project_id" => $projectIds
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]
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]
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];
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$ch = curl_init($es_url_court);
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curl_setopt_array($ch, [
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CURLOPT_RETURNTRANSFER => true,
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CURLOPT_POST => true,
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CURLOPT_POSTFIELDS => json_encode($queryCourt),
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CURLOPT_HTTPHEADER => ['Content-Type: application/json']
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]);
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$responseCourt = curl_exec($ch);
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curl_close($ch);
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$esResponseCourt = json_decode($responseCourt, true);
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$courtResults = parseSearchResults($esResponseCourt);
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foreach ($courtResults as $result) {
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$courtContext .= "- {$result['title']}\n";
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}
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} else {
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$courtContext .= "Нет связанных кейсов.\n";
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}
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logMessage("Результаты поиска в court_decisions:\n" . $courtContext);
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// 4. Формирование итогового prompt для GPT
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$finalPrompt = "Новый спор:\n" . $combinedContent . "\n\n" .
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"Контекст поиска судебных решений:\n\n" .
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$legalContext . "\n" . $courtContext . "\n" .
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"Пожалуйста, проанализируй приведённую информацию и сформируй аналитический отчёт с рекомендациями.";
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logMessage("Сформированный prompt для GPT:\n" . $finalPrompt);
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return $finalPrompt;
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?>
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