const db = require('../db');
const vectorSearch = require('./vectorSearchClient');
const { getProviderSettings } = require('./aiProviderSettingsService');

console.log('[RAG] ragService.js loaded');

async function getTableData(tableId) {
  const columns = (await db.query('SELECT * FROM user_columns WHERE table_id = $1', [tableId])).rows;
  const rows = (await db.query('SELECT * FROM user_rows WHERE table_id = $1', [tableId])).rows;
  const cellValues = (await db.query('SELECT * FROM user_cell_values WHERE row_id IN (SELECT id FROM user_rows WHERE table_id = $1)', [tableId])).rows;

  const getColId = purpose => columns.find(col => col.options?.purpose === purpose)?.id;
  const questionColId = getColId('question');
  const answerColId = getColId('answer');
  const userTagsColId = getColId('userTags');
  const contextColId = getColId('context');
  const productColId = getColId('product');
  const priorityColId = getColId('priority');
  const dateColId = getColId('date');

  const data = rows.map(row => {
    const cells = cellValues.filter(cell => cell.row_id === row.id);
    return {
      id: row.id,
      question: cells.find(c => c.column_id === questionColId)?.value,
      answer: cells.find(c => c.column_id === answerColId)?.value,
      userTags: cells.find(c => c.column_id === userTagsColId)?.value,
      context: cells.find(c => c.column_id === contextColId)?.value,
      product: cells.find(c => c.column_id === productColId)?.value,
      priority: cells.find(c => c.column_id === priorityColId)?.value,
      date: cells.find(c => c.column_id === dateColId)?.value,
    };
  });
  return data;
}

async function ragAnswer({ tableId, userQuestion, userTags = [], product = null, threshold = 0.3 }) {
  console.log(`[RAG] ragAnswer called: tableId=${tableId}, userQuestion="${userQuestion}"`);
  
  const data = await getTableData(tableId);
  console.log(`[RAG] Got ${data.length} rows from database`);
  
  const questions = data.map(row => row.question && typeof row.question === 'string' ? row.question.trim() : row.question);
  const rowsForUpsert = data.map(row => ({
    row_id: row.id,
    text: row.question,
    metadata: {
      answer: row.answer || null,
      userTags: row.userTags || null,
      context: row.context || null,
      product: row.product || null,
      priority: row.priority || null,
      date: row.date || null
    }
  }));
  
  console.log(`[RAG] Prepared ${rowsForUpsert.length} rows for upsert`);
  console.log(`[RAG] First row:`, rowsForUpsert[0]);
  
  // Upsert все вопросы в индекс (можно оптимизировать по изменению)
  if (rowsForUpsert.length > 0) {
    await vectorSearch.upsert(tableId, rowsForUpsert);
    console.log(`[RAG] Upsert completed`);
  } else {
    console.log(`[RAG] No rows to upsert, skipping`);
  }
  
  // Поиск
  let results = [];
  if (rowsForUpsert.length > 0) {
    results = await vectorSearch.search(tableId, userQuestion, 3);
    console.log(`[RAG] Search completed, got ${results.length} results`);
  } else {
    console.log(`[RAG] No data in table, skipping search`);
  }
  
  // Фильтрация по тегам/продукту
  let filtered = results;
  if (userTags.length) {
    filtered = filtered.filter(row => row.metadata.userTags && userTags.some(tag => row.metadata.userTags.includes(tag)));
  }
  if (product) {
    filtered = filtered.filter(row => row.metadata.product === product);
  }
  
  // Берём лучший результат с учётом порога
  const best = filtered.find(row => row.score >= threshold);
  console.log(`[RAG] Best result:`, best);
  
  return {
    answer: best?.metadata?.answer,
    context: best?.metadata?.context,
    product: best?.metadata?.product,
    priority: best?.metadata?.priority,
    date: best?.metadata?.date,
    score: best?.score,
  };
}

async function generateLLMResponse({
  userQuestion,
  context,
  clarifyingAnswer,
  objectionAnswer,
  answer,
  systemPrompt,
  userTags,
  product,
  priority,
  date,
  rules,
  history,
  model,
  language
}) {
  console.log(`[RAG] generateLLMResponse called with:`, {
    userQuestion,
    context,
    answer,
    systemPrompt,
    userTags,
    product,
    priority,
    date,
    model,
    language
  });

  try {
    const aiAssistant = require('./ai-assistant');
    
    // Формируем промпт для LLM
    let prompt = userQuestion;
    
    if (context) {
      prompt += `\n\nКонтекст: ${context}`;
    }
    
    if (answer) {
      prompt += `\n\nНайденный ответ: ${answer}`;
    }
    
    if (userTags) {
      prompt += `\n\nТеги: ${userTags}`;
    }
    
    if (product) {
      prompt += `\n\nПродукт: ${product}`;
    }

    // Получаем ответ от AI
    const llmResponse = await aiAssistant.getResponse(
      prompt,
      language || 'auto',
      history,
      systemPrompt,
      rules
    );

    console.log(`[RAG] LLM response generated:`, llmResponse);
    return llmResponse;
  } catch (error) {
    console.error(`[RAG] Error generating LLM response:`, error);
    return 'Извините, произошла ошибка при генерации ответа.';
  }
}

module.exports = {
  ragAnswer,
  getTableData,
  generateLLMResponse
};