5why-analyzer/server.js

import express from 'express';
import cors from 'cors';
import axios from 'axios';

const app = express();
const PORT = 3001;

// Ollama API 設定
const OLLAMA_API_URL = "https://ollama_pjapi.theaken.com";
const MODEL_NAME = "qwen2.5:3b"; // 使用 qwen2.5:3b 模型

app.use(cors());
app.use(express.json());

// 健康檢查端點
app.get('/health', (req, res) => {
  res.json({ status: 'ok', message: 'Server is running' });
});

// 列出可用模型
app.get('/api/models', async (req, res) => {
  try {
    const response = await axios.get(`${OLLAMA_API_URL}/v1/models`);
    res.json(response.data);
  } catch (error) {
    console.error('Error fetching models:', error.message);
    res.status(500).json({ error: 'Failed to fetch models', details: error.message });
  }
});

// 5 Why 分析端點
app.post('/api/analyze', async (req, res) => {
  const { prompt } = req.body;

  if (!prompt) {
    return res.status(400).json({ error: 'Prompt is required' });
  }

  try {
    console.log('Sending request to Ollama API...');

    const chatRequest = {
      model: MODEL_NAME,
      messages: [
        {
          role: "system",
          content: "You are an expert consultant specializing in 5 Why root cause analysis. You always respond in valid JSON format without any markdown code blocks."
        },
        {
          role: "user",
          content: prompt
        }
      ],
      temperature: 0.7,
      stream: false
    };

    const response = await axios.post(
      `${OLLAMA_API_URL}/v1/chat/completions`,
      chatRequest,
      {
        headers: {
          'Content-Type': 'application/json'
        },
        timeout: 120000 // 120 seconds timeout
      }
    );

    if (response.data && response.data.choices && response.data.choices[0]) {
      const content = response.data.choices[0].message.content;
      console.log('Received response from Ollama');
      res.json({ content });
    } else {
      throw new Error('Invalid response format from Ollama API');
    }

  } catch (error) {
    console.error('Error calling Ollama API:', error.message);
    if (error.response) {
      console.error('Response data:', error.response.data);
      console.error('Response status:', error.response.status);
    }
    res.status(500).json({
      error: 'Failed to analyze with Ollama API',
      details: error.message,
      responseData: error.response?.data
    });
  }
});

// 翻譯端點
app.post('/api/translate', async (req, res) => {
  const { prompt } = req.body;

  if (!prompt) {
    return res.status(400).json({ error: 'Prompt is required' });
  }

  try {
    console.log('Translating with Ollama API...');

    const chatRequest = {
      model: MODEL_NAME,
      messages: [
        {
          role: "system",
          content: "You are a professional translator. You always respond in valid JSON format without any markdown code blocks."
        },
        {
          role: "user",
          content: prompt
        }
      ],
      temperature: 0.3,
      stream: false
    };

    const response = await axios.post(
      `${OLLAMA_API_URL}/v1/chat/completions`,
      chatRequest,
      {
        headers: {
          'Content-Type': 'application/json'
        },
        timeout: 120000
      }
    );

    if (response.data && response.data.choices && response.data.choices[0]) {
      const content = response.data.choices[0].message.content;
      console.log('Translation completed');
      res.json({ content });
    } else {
      throw new Error('Invalid response format from Ollama API');
    }

  } catch (error) {
    console.error('Error translating with Ollama API:', error.message);
    if (error.response) {
      console.error('Response data:', error.response.data);
      console.error('Response status:', error.response.status);
    }
    res.status(500).json({
      error: 'Failed to translate with Ollama API',
      details: error.message,
      responseData: error.response?.data
    });
  }
});

app.listen(PORT, () => {
  console.log(`Server is running on http://localhost:${PORT}`);
  console.log(`Ollama API URL: ${OLLAMA_API_URL}`);
  console.log(`Using model: ${MODEL_NAME}`);
});