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backend/services/ragService.js

@@ -1,17 +1,13 @@
-const { HNSWLib } = require('@langchain/community/vectorstores/hnswlib');
 const db = require('../db');
-const { ChatOllama } = require('@langchain/ollama');
-const { OllamaEmbeddings } = require('@langchain/ollama');
+const vectorSearch = require('./vectorSearchClient');
 const { getProviderSettings } = require('./aiProviderSettingsService');
-const { OpenAIEmbeddings } = require('@langchain/openai');
 
 console.log('[RAG] ragService.js loaded');
 
 async function getTableData(tableId) {
-  const columns = (await db.getQuery()('SELECT * FROM user_columns WHERE table_id = $1', [tableId])).rows;
-  console.log('RAG getTableData: columns:', columns);
-  const rows = (await db.getQuery()('SELECT * FROM user_rows WHERE table_id = $1', [tableId])).rows;
-  const cellValues = (await db.getQuery()('SELECT * FROM user_cell_values WHERE row_id IN (SELECT id FROM user_rows WHERE table_id = $1)', [tableId])).rows;
+  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');
@@ -35,147 +31,142 @@ async function getTableData(tableId) {
       date: cells.find(c => c.column_id === dateColId)?.value,
     };
   });
-  const questions = data.map(row => row.question);
-  console.log('RAG getTableData: questions:', questions);
-  if (!questions.length) {
-    console.warn('RAG getTableData: questions array is empty! Проверьте структуру колонок и наличие данных.');
-  }
   return data;
 }
 
-async function getEmbeddingsProvider(providerName = 'ollama') {
-  const settings = await getProviderSettings(providerName);
-  if (!settings) throw new Error('Embeddings provider settings not found');
-  switch (providerName) {
-    case 'openai':
-      return new OpenAIEmbeddings({
-        apiKey: settings.api_key,
-        baseURL: settings.base_url,
-        model: settings.selected_model || undefined,
-      });
-    case 'ollama': {
-      // Fallback: если не задан base_url, пробуем env, host.docker.internal, localhost
-      let baseUrl = settings.base_url;
-      if (!baseUrl) {
-        baseUrl = process.env.OLLAMA_BASE_URL;
-      }
-      if (!baseUrl) {
-        // Если в Docker — используем host.docker.internal
-        baseUrl = 'http://host.docker.internal:11434';
-      }
-      // Если всё равно нет — последний fallback
-      if (!baseUrl) {
-        baseUrl = 'http://localhost:11434';
-      }
-      return new OllamaEmbeddings({
-        model: settings.embedding_model || process.env.OLLAMA_EMBED_MODEL || 'mxbai-embed-large',
-        baseUrl,
-      });
-    }
-    // case 'gemini':
-    //   return new GeminiEmbeddings({ apiKey: settings.api_key });
-    // Добавьте другие провайдеры по аналогии
-    default:
-      throw new Error('Unknown embeddings provider: ' + providerName);
-  }
-}
-
-async function ragAnswer({ tableId, userQuestion, userTags = [], product = null, embeddingsProvider = 'ollama', threshold = 0.3 }) {
-  console.log('[RAG] Используется провайдер эмбеддингов:', embeddingsProvider);
+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);
-  // Триммируем вопросы для чистоты сравнения
+  console.log(`[RAG] Got ${data.length} rows from database`);
+  
   const questions = data.map(row => row.question && typeof row.question === 'string' ? row.question.trim() : row.question);
-
-  // Получаем embeddings-инстанс динамически
-  const embeddingsInstance = await getEmbeddingsProvider(embeddingsProvider);
-
-  // Получаем embedding для всех вопросов
-  const embeddings = await embeddingsInstance.embedDocuments(questions);
-  console.log('Questions embedding length:', embeddings[0]?.length, 'Total questions:', questions.length);
-
-  // Получаем embedding для вопроса пользователя (trim)
-  const userQuestionTrimmed = userQuestion && typeof userQuestion === 'string' ? userQuestion.trim() : userQuestion;
-  const [userEmbedding] = await embeddingsInstance.embedDocuments([userQuestionTrimmed]);
-  console.log('User embedding length:', userEmbedding?.length, 'User question:', userQuestionTrimmed);
-
-  // Явно сравниваем embeddings (отладка)
-  console.log('[RAG] Embedding сравнение:');
-  embeddings.forEach((emb, idx) => {
-    const dot = emb.reduce((sum, v, i) => sum + v * userEmbedding[i], 0);
-    console.log(`  [${idx}] dot-product: ${dot} | question: "${questions[idx]}"`);
-  });
-
-  // Создаём массив метаданных для каждого вопроса
-  const metadatas = data.map(row => ({
-    id: row.id,
-    answer: row.answer,
-    userTags: row.userTags,
-    context: row.context,
-    product: row.product,
-    priority: row.priority,
-    date: row.date,
+  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
+    }
   }));
-
-  // Создаём векторное хранилище
-  const vectorStore = await HNSWLib.fromTexts(questions, metadatas, embeddingsInstance);
-
-  // Ищем наиболее похожие вопросы (top-3)
-  const results = await vectorStore.similaritySearchVectorWithScore(userEmbedding, 3);
-  console.log('[RAG] Результаты поиска по векторам (score):', results.map(([doc, score]) => ({ ...doc.metadata, score })));
-
-  // Фильтруем по тегам/продукту, если нужно
-  let filtered = results.map(([doc, score]) => ({ ...doc.metadata, score }));
+  
+  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.userTags && userTags.some(tag => row.userTags.includes(tag)));
+    filtered = filtered.filter(row => row.metadata.userTags && userTags.some(tag => row.metadata.userTags.includes(tag)));
   }
   if (product) {
-    filtered = filtered.filter(row => row.product === product);
+    filtered = filtered.filter(row => row.metadata.product === product);
   }
-  console.log('[RAG] Отфильтрованные результаты:', filtered);
-
+  
   // Берём лучший результат с учётом порога
   const best = filtered.find(row => row.score >= threshold);
-  console.log(`[RAG] Выбранный ответ (порог ${threshold}):`, best);
-
-  // Формируем ответ
+  console.log(`[RAG] Best result:`, best);
+  
   return {
-    answer: best?.answer,
-    context: best?.context,
-    product: best?.product,
-    priority: best?.priority,
-    date: best?.date,
+    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 }) {
-  // Подставляем значения в шаблон промта
-  let prompt = (systemPrompt || '')
-    .replace('{context}', context || '')
-    .replace('{clarifyingAnswer}', clarifyingAnswer || '')
-    .replace('{objectionAnswer}', objectionAnswer || '')
-    .replace('{answer}', answer || '')
-    .replace('{question}', userQuestion || '')
-    .replace('{userTags}', userTags || '')
-    .replace('{product}', product || '')
-    .replace('{priority}', priority || '')
-    .replace('{date}', date || '')
-    .replace('{rules}', rules || '')
-    .replace('{history}', history || '')
-    .replace('{model}', model || '')
-    .replace('{language}', language || '');
-
-  const chat = new ChatOllama({
-    baseUrl: process.env.OLLAMA_BASE_URL || 'http://localhost:11434',
-    model: process.env.OLLAMA_MODEL || 'qwen2.5',
-    system: prompt,
-    temperature: 0.7,
-    maxTokens: 1000,
-    timeout: 30000,
+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
   });
 
-  const response = await chat.invoke(`Вопрос пользователя: ${userQuestion}`);
-  return response.content;
+  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, generateLLMResponse }; 
+module.exports = {
+  ragAnswer,
+  getTableData,
+  generateLLMResponse
+};