#码力全开·技术π对# 如何在我的 Web 应用中集成 Google AI 的自然语言处理（NLP）服务？

const dialogflow = require('@google-cloud/dialogflow');
const { v4 } = require('uuid');

// 初始化Dialogflow会话客户端
const sessionClient = new dialogflow.SessionsClient({
  keyFilename: 'path/to/your-service-account-key.json'
});

async function detectIntent(query, sessionId) {
  const sessionPath = sessionClient.projectAgentSessionPath(
    'your-project-id',
    sessionId || v4()
  );
  
  const request = {
    session: sessionPath,
    queryInput: {
      text: {
        text: query,
        languageCode: 'zh-CN' // 根据业务需求设置语言
      }
    }
  };
  
  const [response] = await sessionClient.detectIntent(request);
  return response;
}

用户 → Web前端 → 后端API → Dialogflow → 业务系统 → 返回响应

{
  "entities": [
    {
      "name": "medical_procedure",
      "entries": [
        { "value": "MRI", "synonyms": ["核磁共振", "磁共振成像"] },
        { "value": "CT", "synonyms": ["计算机断层扫描"] }
      ]
    }
  ]
}

// 上下文设置示例
const request = {
  session: sessionPath,
  queryInput: {...},
  queryParams: {
    contexts: [
      {
        name: 'projects/your-project-id/agent/sessions/123/contexts/order-status',
        lifespanCount: 3
      }
    ]
  }
};

"responses": [
  { "text": ["我明白了，您需要帮助解决X问题", "关于X问题，我可以这样帮助您"] }
]

const language = require('@google-cloud/language');
const client = new language.LanguageServiceClient();

async function analyzeSentiment(text) {
  const document = {
    content: text,
    type: 'PLAIN_TEXT'
  };
  
  const [result] = await client.analyzeSentiment({document});
  return result.documentSentiment;
}

// 根据情感分数调整响应策略
const sentiment = await analyzeSentiment(userQuery);
if(sentiment.score < -0.5) {
  // 特别处理负面情绪的用户
}

// 使用Redis存储活跃会话
const redisClient = require('redis').createClient();
const SESSION_TTL = 1800; // 30分钟

async function getSession(userId) {
  let sessionId = await redisClient.get(`dialogflow:${userId}`);
  if(!sessionId) {
    sessionId = v4();
    await redisClient.setex(`dialogflow:${userId}`, SESSION_TTL, sessionId);
  }
  return sessionId;
}

const { PubSub } = require('@google-cloud/pubsub');
const pubSubClient = new PubSub();

async function publishQuery(query, sessionId) {
  const topicName = 'dialogflow-queries';
  const dataBuffer = Buffer.from(JSON.stringify({ query, sessionId }));
  
  await pubSubClient.topic(topicName).publish(dataBuffer);
}

// 消费者服务处理长时间运行的任务

const NodeCache = require('node-cache');
const queryCache = new NodeCache({ stdTTL: 600 });

async function handleQuery(query) {
  const cacheKey = `query:${md5(query)}`;
  let response = queryCache.get(cacheKey);
  
  if(!response) {
    response = await detectIntent(query);
    queryCache.set(cacheKey, response);
  }
  
  return response;
}

const { Logging } = require('@google-cloud/logging');
const logging = new Logging();
const log = logging.log('dialogflow-requests');

async function logRequest(query, response, latency) {
  const metadata = {
    severity: 'INFO',
    labels: { intent: response.queryResult.intent.displayName }
  };
  
  const entry = log.entry(metadata, {
    query,
    detectedIntent: response.queryResult.intent.displayName,
    confidence: response.queryResult.intentDetectionConfidence,
    responseTime: latency
  });
  
  await log.write(entry);
}

const { DialogflowCX } = require('@google-cloud/dialogflow-cx');
const client = new DialogflowCX.VersionsClient();

async function createNewVersion(agentId, trainingPhrases) {
  const request = {
    parent: agentId,
    version: {
      displayName: `v-${new Date().toISOString()}`,
      trainingPhrases: trainingPhrases
    }
  };
  
  const [operation] = await client.createVersion(request);
  return operation.promise();
}

intentName: "Product Inquiry"
trainingPhrases: ["关于XX产品的功能", "我想了解YY型号的特点"]
response: "XX产品具备A、B、C功能..."

{
  "fulfillmentText": "请问您想了解哪款产品？",
  "fulfillmentMessages": [
    {
      "card": {
        "title": "产品选择",
        "buttons": [
          {"text": "产品A", "postback": "product_a"},
          {"text": "产品B", "postback": "product_b"}
        ]
      }
    }
  ]
}