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backend/services/ragService.js

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+const { OpenAIEmbeddings } = require('@langchain/openai');
+const { HNSWLib } = require('@langchain/community/vectorstores/hnswlib');
+const db = require('../db');
+const { ChatOllama } = require('@langchain/ollama');
+
+async function getTableData(tableId) {
+  const columns = (await db.getQuery()('SELECT * FROM user_columns WHERE table_id = $1', [tableId])).rows;
+  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 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');
+
+  return 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,
+    };
+  });
+}
+
+async function ragAnswer({ tableId, userQuestion, userTags = [], product = null }) {
+  const data = await getTableData(tableId);
+  const questions = data.map(row => row.question);
+
+  // Получаем embedding для всех вопросов
+  const embeddings = await new OpenAIEmbeddings().embedDocuments(questions);
+
+  // Создаём векторное хранилище
+  const vectorStore = await HNSWLib.fromTexts(questions, data, new OpenAIEmbeddings());
+
+  // Получаем embedding для вопроса пользователя
+  const [userEmbedding] = await new OpenAIEmbeddings().embedDocuments([userQuestion]);
+
+  // Ищем наиболее похожие вопросы (top-3)
+  const results = await vectorStore.similaritySearchVectorWithScore(userEmbedding, 3);
+
+  // Фильтруем по тегам/продукту, если нужно
+  let filtered = results.map(([row, score]) => ({ ...row, score }));
+  if (userTags.length) {
+    filtered = filtered.filter(row => row.userTags && userTags.some(tag => row.userTags.includes(tag)));
+  }
+  if (product) {
+    filtered = filtered.filter(row => row.product === product);
+  }
+
+  // Берём лучший результат
+  const best = filtered[0];
+
+  // Формируем ответ
+  return {
+    answer: best?.answer,
+    context: best?.context,
+    product: best?.product,
+    priority: best?.priority,
+    date: best?.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,
+  });
+
+  const response = await chat.invoke(`Вопрос пользователя: ${userQuestion}`);
+  return response.content;
+}
+
+module.exports = { ragAnswer, generateLLMResponse };