chore: align infrastructure with server

app/server/api/canvas/index.get.ts

@@ -31,11 +31,16 @@ export default defineEventHandler(async (event) => {
     orderBy: { updatedAt: 'desc' }
   })
 
+  const totalCount = await prisma.canvas.count()
+
+  const canvases = canvasList.map(c => ({
+    ...c,
+    nodeCount: Array.isArray(c.nodes) ? (c.nodes as any[]).length : 0,
+    nodes: undefined
+  }))
+
   return {
-    canvases: canvasList.map(c => ({
-      ...c,
-      nodeCount: Array.isArray(c.nodes) ? (c.nodes as any[]).length : 0,
-      nodes: undefined
-    }))
+    canvases,
+    totalCount
   }
 })

app/server/api/nodes/[id]/execute.post.ts

@@ -8,6 +8,8 @@ export default defineEventHandler(async (event) => {
   const body = await readBody(event)
   const { canvasId, context } = body
 
+  console.log(`[Execute] nodeId=${nodeId} canvasId=${canvasId} userId=${user.id}`)
+
   if (!nodeId || !canvasId) {
     throw createError({ statusCode: 400, message: 'Node ID and Canvas ID are required' })
   }
@@ -17,6 +19,8 @@ export default defineEventHandler(async (event) => {
     where: { id: canvasId }
   })
 
+  console.log(`[Execute] Canvas found: ${!!canvas}, nodes count: ${(canvas?.nodes as any[])?.length}`)
+
   if (!canvas) {
     throw createError({ statusCode: 404, message: 'Canvas not found' })
   }
@@ -30,11 +34,14 @@ export default defineEventHandler(async (event) => {
   const nodes = (canvas.nodes as any[]) || []
   const nodeIdx = nodes.findIndex(n => n.id === nodeId)
   
+  console.log(`[Execute] Node found at index: ${nodeIdx}`)
+
   if (nodeIdx === -1) {
-    throw createError({ statusCode: 404, message: 'Node not found in canvas' })
+    throw createError({ statusCode: 404, message: `Node ${nodeId} not found in canvas ${canvasId}` })
   }
 
   const node = nodes[nodeIdx]
+  console.log(`[Execute] Node type: ${node.type}`)
 
   try {
     let result = ''
@@ -42,15 +49,17 @@ export default defineEventHandler(async (event) => {
     if (node.type === 'ai') {
       // Формируем промпт с учетом контекста от соседей
       let fullPrompt = node.prompt || node.text || 'Analyze the provided context.'
-      
+
       if (context && context.length > 0) {
         const contextText = context.map((c: any) => `[Node ${c.id}]: ${c.text}`).join('\n\n')
         fullPrompt = `CONTEXT INFORMATION:\n${contextText}\n\nUSER REQUEST/INSTRUCTION:\n${fullPrompt}`
       }
 
-      console.log(`[AI Execute] Generating response with context for node ${nodeId}...`)
-      result = await generateAIResponse(fullPrompt)
-      
+      // Модель можно задать прямо в узле: node.model = 'qwen_coder' | 'llama' | 'gemma' | 'laguna' | 'nemotron'
+      const model = node.model || undefined
+      console.log(`[AI Execute] model=${model || 'default'} nodeId=${nodeId}`)
+      result = await generateAIResponse(fullPrompt, 'You are a helpful assistant.', model)
+
       // Сохраняем результат в текст узла
       nodes[nodeIdx].text = result
 

app/server/utils/ai.ts

@@ -1,31 +1,89 @@
-export const generateAIResponse = async (prompt: string, systemPrompt = 'You are a helpful assistant.') => {
-  const config = useRuntimeConfig()
-  const apiKey = config.deepseekApiKey || process.env.DEEPSEEK_API_KEY
+/**
+ * XFlow AI Utility — OpenRouter Integration (Stable Models)
+ *
+ * Поддерживаемые модели (наиболее стабильные и без жестких rate-limit):
+ *
+ *  • poolside/laguna-m.1:free           — быстрые инженерные задачи, Claude-based (default)
+ *  • nvidia/nemotron-3-super-120b-a12b:free — MoE 120B, тяжёлая аналитика, сложные рассуждения
+ */
+
+const OPENROUTER_BASE_URL = 'https://openrouter.ai/api/v1/chat/completions'
+
+/** Доступные модели с описанием для выбора в UI */
+export const AI_MODELS = {
+  /** Сложные инженерные и архитектурные задачи, высокая стабильность */
+  laguna: 'poolside/laguna-m.1:free',
+  /** MoE 120B тяжёлая аналитика, архитектурный анализ, сложные рассуждения */
+  nemotron: 'nvidia/nemotron-3-super-120b-a12b:free',
+} as const
+
+export type AiModelKey = keyof typeof AI_MODELS
+
+/** Дефолтная модель, если не указана */
+const DEFAULT_MODEL: AiModelKey = 'laguna'
+
+export const generateAIResponse = async (
+  prompt: string,
+  systemPrompt = 'You are a helpful assistant.',
+  modelKey?: AiModelKey | string,
+): Promise<string> => {
+  const apiKey =
+    process.env.NUXT_OPEN_ROUTER_API_KEY ||
+    process.env.OPEN_ROUTER_API_KEY
 
   if (!apiKey) {
-    throw new Error('DEEPSEEK_API_KEY is not configured')
+    throw new Error('OPEN_ROUTER_API_KEY is not configured')
   }
 
+  // Резолвим модель: принимаем как ключ ('laguna') так и полное имя
+  const resolvedModel: string =
+    modelKey && modelKey in AI_MODELS
+      ? AI_MODELS[modelKey as AiModelKey]
+      : modelKey && modelKey.includes('/')
+        ? modelKey
+        : AI_MODELS[DEFAULT_MODEL]
+
+  console.log(`[AI] Using model: ${resolvedModel}`)
+
   try {
-    const response = await $fetch('https://api.deepseek.com/v1/chat/completions', {
+    const response = await $fetch<any>(OPENROUTER_BASE_URL, {
       method: 'POST',
       headers: {
         'Authorization': `Bearer ${apiKey}`,
-        'Content-Type': 'application/json'
+        'Content-Type': 'application/json',
+        'HTTP-Referer': 'https://a-crm.controlsystem.kz',
+        'X-Title': 'XFlow AI Node',
       },
       body: {
-        model: 'deepseek-chat',
+        model: resolvedModel,
         messages: [
           { role: 'system', content: systemPrompt },
-          { role: 'user', content: prompt }
+          { role: 'user', content: prompt },
         ],
-        stream: false
-      }
-    }) as any
+        stream: false,
+      },
+    })
 
-    return response.choices[0].message.content
+    const content = response?.choices?.[0]?.message?.content
+    if (!content) {
+      throw new Error('Empty response from AI model')
+    }
+    return content
   } catch (error: any) {
-    console.error('DeepSeek AI error:', error.data || error.message)
-    throw new Error(`AI generation failed: ${error.message}`)
+    console.error(`[AI] Error with model ${resolvedModel}:`, error?.data || error?.message)
+    throw new Error(`AI generation failed (${resolvedModel}): ${error?.message}`)
   }
 }
+
+/** Хелпер: список моделей для фронтенда */
+export const getAvailableModels = () =>
+  Object.entries(AI_MODELS).map(([key, modelId]) => ({
+    key,
+    modelId,
+    label: modelLabels[key as AiModelKey],
+  }))
+
+const modelLabels: Record<AiModelKey, string> = {
+  laguna: '🏊 Laguna M.1 — стабильная инженерная (Claude)',
+  nemotron: '🔬 Nemotron Super 120B — тяжёлая аналитика (NVIDIA)',
+}