快速上手Dialogflow交互机器人

　　作者：MeshCloud脉时云公有云架构师陈博文简介：
　　Dialogflow 是Google 提供的一款人机交互平台，通过该平台可以轻松地设计出属于自己的交互机器人，比如常见的网页聊天机器人，电话智能客服等。借助Dialogflow甚至可以用于扫地机器人交互系统或者更高级的使用。
　　Dialogflow 通过客户输入的语音或者文字甚至情感分析，来识别客户的意图（Intens)，结合实体(Entities)，来进行相应的回复。
　　Dialogflow的几个优点：识别准确率高，响应速度快支持 30 多种语言和语言变体上手简单：图形界面配置；官方文档丰富、详细；网络上有案例可供参考有问题易解决：开发者社区超过150万名开发者
　　Dialogflow经典案例：
　　一、马航的订票查票机器人：
　　使用 Google Cloud 上的 Dialogflow，马来西亚航空公司和 Amadeus 创建了一个聊天机器人，使客户能够搜索、预订和支付航班 ，从而使航空公司能够满足未来的需求并增加数字渠道的收入。
　　二、达美乐披萨的订餐机器人：
　　三、KLM预定、打包机器人：
　　KLM 于 2016 年开始探索为客户提供体验的方法。他们在测试多个平台后选择了 Dialogflow。
　　常用工具
　　一、内置 Small Talk
　　Small Talk 用于为闲聊对话提供响应。 此功能可以解答代理范围之外的常见问题，极大地提升最终用户体验。
　　Small Talk 有两种版本：内置 Small Talk：为代理启用 Small Talk 后，它会自动处理闲聊对话，无需向代理添加意图。预建 Small Talk：导入预建 Small Talk 代理时，它会提供处理闲聊对话的意图。
　　二、prebuilt agent
　　由 Dialogflow 提供的一组代理，适用于常见的使用场景。 您可以这些代理为基础，构建涵盖特定场景（如外出就餐、酒店预订和导航）的对话。
　　由 Dialogflow 提供的一组代理，适用于常见的使用场景。 您可以这些代理为基础，构建涵盖特定场景（如外出就餐、酒店预订和导航）的对话。
　　如何制作一个自己的天气&新闻语音问答机器人
　　使用了文字输入Dialogflow 的方式
　　通过speech-to-text将音频麦克风流到Dialogflow 的文本意图检测API
　　案例使用了以下GCP产品：Dialogflow ES & Knowledge BasesSpeech to Text
　　其它组件：WebhookWeathers & News API
　　在这个demo中你可以使用麦克风输入，然后返回新闻或者天气
　　一、Dialogflow ES（页面配置）
　　1、意图配置
　　①配置输入
　　②配置回复
　　2、Webhook配置
　　①意图开启Fulfillment
　　②添加webhook
　　③webhook代码import requests #新闻接口 from newsapi import NewsApiClient import time import json #使用了flask框架 from flask import Flask, request import pycountry #from gevent.pywsgi import WSGIServer  app = Flask(__name__)  @app.route(＂/webhook＂, methods=[＂POST＂]) def webhook():     Dialogflow_data = json.loads(request.data)     intent =Dialogflow_data[＂queryResult＂][＂intent＂][＂displayName＂]     print(＂--------------------------------------＂)     if intent == ＂news＂:         responseText = callnewsapi()         news = responseText[＂articles＂][0][＂title＂]         print(news)         headline = ＂ headline news is %s＂%(news)         #需要按要求返回dialogflow才能回复给客户端         #＂fulfillmentText＂是客户端接收消息         res = {＂fulfillmentText＂: headline ,＂fulfillmentMessages＂: [{＂text＂: {＂text＂:[headline]}}]}         return(res)     elif intent == ＂weather＂:         CITY=Dialogflow_data[＂queryResult＂][＂parameters＂][＂geo-city＂]         key = ＂479284d0d8574437b8170935221508＂         responseText = json.loads(callweatherapi(key,CITY))         mintemp = responseText[＂data＂][＂ClimateAverages＂][0][＂month＂][7][＂avgMinTemp＂]         maxtemp = responseText[＂data＂][＂ClimateAverages＂][0][＂month＂][7][＂absMaxTemp＂]         tempres = ＂London Maxtemp is %s   Mintempe is %s  ＂%(maxtemp,mintemp)         #需要按要求返回dialogflow才能回复给客户端         #＂fulfillmentText＂是客户端接收消息         res = {＂fulfillmentText＂: tempres ,＂fulfillmentMessages＂: [{＂text＂: {＂text＂:[tempres]}}]}         return(res)   def callweatherapi(key,CITY):     time.sleep(0.01)     response = requests.post(＂http://api.worldweatheronline.com/premium/v1/weather.ashx?key=%s&q=%s&fx=no&cc=no&mca=yes&format=json＂%(key,CITY))     if response.status_code == 200:        return(response.text)  def callnewsapi():     newsapi = NewsApiClient(api_key=＂0eaad3923a654da2a2a32d84870e0405＂)     response = newsapi.get_top_headlines(language=＂es＂)     return(response)  if __name__ == ＂__main__＂:     #WSGIServer((＂0.0.0.0＂, 5000), app).serve_forever()     app.run(host=＂0.0.0.0＂, port=5000, ssl_context=(＂/root/scs1660552637313_cbw404.cn/scs1660552637313_cbw404.cn_Nginx/scs1660552637313_cbw404.cn_server.crt＂, ＂/root/scs1660552637313_cbw404.cn/scs1660552637313_cbw404.cn_Nginx/scs1660552637313_cbw404.cn_server.key＂))
　　新闻接口:http://api.worldweatheronline.com/premium/v1/weather.ashx?key=apikey&q=city&fx=no&cc=no&mca=yes&format=json
　　天气接口:#install pip install newsapi-python #usage from newsapi import NewsApiClient #init newsapi = NewsApiClient(api_key=＂API_KEY＂) # /v2/top-headlines top_headlines = newsapi.get_top_headlines(q=＂bitcoin＂,                                           sources=＂bbc-news,the-verge＂,                                           category=＂business＂,                                           language=＂en＂,                                           country=＂us＂) # /v2/everything all_articles = newsapi.get_everything(q=＂bitcoin＂,                                       sources=＂bbc-news,the-verge＂,                                       domains=＂bbc.co.uk,techcrunch.com＂,                                       from_param=＂2017-12-01＂,                                       to=＂2017-12-12＂,                                       language=＂en＂,                                       sort_by=＂relevancy＂,                                       page=2) # /v2/top-headlines/sources sources = newsapi.get_sources()
　　二、Speech-to-text(后面简称stt) to Dialogflow
　　1、准备工作
　　①权限配置下载service account json格式  Linux: 配置环境变量 export GOOGLE_APPLICATION_CREDENTIALS=  为1中下载的 sa 的json文件  Windows: set GOOGLE_APPLICATION_CREDENTIALS=C:UsersAdministratorDownloadssa.json
　　②python包python 包 google-cloud-speech pyaudio google-cloud-dialogflow python-dotenv uuid
　　③.env文件 用于读取配置PROJECT_ID= #这里做的西班牙语测试 LANGUAGE_CODE=es #语音的一些参数设置，保持默认 ENCODING=AUDIO_ENCODING_LINEAR_16 SAMPLE_RATE_HERZ=16000 SINGLE_UTTERANCE=false SPEECH_ENCODING=LINEAR16 SSML_GENDER=FEMALE #dialogflow的区域（有us，es，zh) LOCATION_ID=global
　　2、Speech-to-text
　　使用实时流式音频执行识别（transcribe_streaming_mic）,也就是麦克风持续输入,代码如下：#!/usr/bin/env python  ＂＂＂Google Cloud Speech API sample application using the streaming API. NOTE: This module requires the additional dependency `pyaudio`. To install using pip:     pip install pyaudio Example usage:     python transcribe_streaming_mic.py ＂＂＂  from __future__ import pision  import re import sys  from google.cloud import speech  import pyaudio from six.moves import queue   RATE = 16000 CHUNK = int(RATE / 10)  # 100ms   class MicrophoneStream(object):     ＂＂＂Opens a recording stream as a generator yielding the audio chunks.＂＂＂      def __init__(self, rate, chunk):         self._rate = rate         self._chunk = chunk           self._buff = queue.Queue()         self.closed = True      def __enter__(self):         self._audio_interface = pyaudio.PyAudio()         self._audio_stream = self._audio_interface.open(             format=pyaudio.paInt16,              channels=1,             rate=self._rate,             input=True,             frames_per_buffer=self._chunk,             stream_callback=self._fill_buffer,         )          self.closed = False          return self      def __exit__(self, type, value, traceback):         self._audio_stream.stop_stream()         self._audio_stream.close()         self.closed = True         self._buff.put(None)         self._audio_interface.terminate()      def _fill_buffer(self, in_data, frame_count, time_info, status_flags):         ＂＂＂Continuously collect data from the audio stream, into the buffer.＂＂＂         self._buff.put(in_data)         return None, pyaudio.paContinue      def generator(self):         while not self.closed:             chunk = self._buff.get()             if chunk is None:                 return             data = [chunk]             while True:                 try:                     chunk = self._buff.get(block=False)                     if chunk is None:                         return                     data.append(chunk)                 except queue.Empty:                     break              yield b＂＂.join(data)   def listen_print_loop(responses):     num_chars_printed = 0     for response in responses:         if not response.results:             continue          result = response.results[0]         if not result.alternatives:             continue          transcript = result.alternatives[0].transcript          overwrite_chars = ＂ ＂ * (num_chars_printed - len(transcript))          if not result.is_final:             sys.stdout.write(transcript + overwrite_chars + ＂r＂)             sys.stdout.flush()              num_chars_printed = len(transcript)          else:             print(transcript + overwrite_chars)              if re.search(r＂b(exit|quit)b＂, transcript, re.I):                 print(＂Exiting..＂)                 break              num_chars_printed = 0   def main():      language_code = ＂en-US＂  #  BCP-47       client = speech.SpeechClient()     config = speech.RecognitionConfig(         encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,         sample_rate_hertz=RATE,         language_code=language_code,     )      streaming_config = speech.StreamingRecognitionConfig(         config=config, interim_results=True     )      with MicrophoneStream(RATE, CHUNK) as stream:         audio_generator = stream.generator()         requests = (             speech.StreamingRecognizeRequest(audio_content=content)             for content in audio_generator         )          responses = client.streaming_recognize(streaming_config, requests)          # Now, put the transcription responses to use.         listen_print_loop(responses)   if __name__ == ＂__main__＂:     main()
　　3、Dialogflow
　　调用检测意图，代码如下:#!/usr/bin/env python  ＂＂＂DialogFlow API Detect Intent Python sample to use regional endpoint. Examples:   python detect_intent_texts_with_location.py -h   python detect_intent_texts_with_location.py --project-id PROJECT_ID    --location-id LOCATION_ID --session-id SESSION_ID    ＂hello＂ ＂book a meeting room＂ ＂Mountain View＂ ＂＂＂  import argparse import uuid    def detect_intent_texts_with_location(     project_id, location_id, session_id, texts, language_code ):      from google.cloud import dialogflow      session_client = dialogflow.SessionsClient(         client_options={＂api_endpoint＂: f＂{location_id}-dialogflow.googleapis.com＂}     )      session = (         f＂projects/{project_id}/locations/{location_id}/agent/sessions/{session_id}＂     )     print(f＂Session path: {session} ＂)      text_input = dialogflow.TextInput(text=texts, language_code=language_code)      query_input = dialogflow.QueryInput(text=text_input)      response = session_client.detect_intent(         request={＂session＂: session, ＂query_input＂: query_input}     )      print(＂=＂ * 20)     print(f＂Query text: {response.query_result.query_text}＂)     print(         f＂Detected intent: {response.query_result.intent.display_name} (confidence: {response.query_result.intent_detection_confidence,}) ＂     )     print(f＂Fulfillment text: {response.query_result.fulfillment_text} ＂)     if __name__ == ＂__main__＂:     parser = argparse.ArgumentParser(         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter     )     parser.add_argument(         ＂--project-id＂, help=＂Project/agent id.  Required.＂, required=True     )     parser.add_argument(＂--location-id＂, help=＂Location id.  Required.＂, required=True)     parser.add_argument(         ＂--session-id＂,         help=＂Identifier of the DetectIntent session. ＂ ＂Defaults to a random UUID.＂,         default=str(uuid.uuid4()),     )     parser.add_argument(         ＂--language-code＂,         help=＂Language code of the query. Defaults to ＂en-US＂.＂,         default=＂en-US＂,     )     parser.add_argument(＂texts＂, nargs=＂+＂, type=str, help=＂Text inputs.＂)      args = parser.parse_args()      detect_intent_texts_with_location(         args.project_id,         args.location_id,         args.session_id,         args.texts,         args.language_code,     )
　　4、（主要代码）将stt的结果（文字）输出到Dialogflow 意图检测，Dialogflow作出回复
　　流程：
　　代码如下： #!/usr/bin/env python  ＂＂＂Google Cloud Speech API sample application using the streaming API. NOTE: This module requires the additional dependency `pyaudio`. To install using pip:     pip install pyaudio Example usage:     python transcribe_streaming_mic.py ＂＂＂  from __future__ import pision  import re import sys  from google.cloud import speech  import pyaudio from six.moves import queue import os import uuid #调用 Dialogflow意图检测包（代码见2.dialogflow） from detect_intent_texts_with_location import detect_intent_texts_with_location from dotenv import load_dotenv  RATE = 16000 CHUNK = int(RATE / 10)  # 100ms   class MicrophoneStream(object):     ＂＂＂Opens a recording stream as a generator yielding the audio chunks.＂＂＂      def __init__(self, rate, chunk):         self._rate = rate         self._chunk = chunk                   self._buff = queue.Queue()         self.closed = True      def __enter__(self):         self._audio_interface = pyaudio.PyAudio()         self._audio_stream = self._audio_interface.open(             format=pyaudio.paInt16,             channels=1,             rate=self._rate,             input=True,             frames_per_buffer=self._chunk,             stream_callback=self._fill_buffer,         )          self.closed = False          return self      def __exit__(self, type, value, traceback):         self._audio_stream.stop_stream()         self._audio_stream.close()         self.closed = True         self._buff.put(None)         self._audio_interface.terminate()      def _fill_buffer(self, in_data, frame_count, time_info, status_flags):         ＂＂＂Continuously collect data from the audio stream, into the buffer.＂＂＂         self._buff.put(in_data)         return None, pyaudio.paContinue      def generator(self):         while not self.closed:             chunk = self._buff.get()             if chunk is None:                 return             data = [chunk]             while True:                 try:                     chunk = self._buff.get(block=False)                     if chunk is None:                         return                     data.append(chunk)                 except queue.Empty:                     break              yield b＂＂.join(data)   def listen_print_loop(responses):      load_dotenv(verbose=True)     num_chars_printed = 0     for response in responses:         if not response.results:             continue           result = response.results[0]         if not result.alternatives:             continue          transcript = result.alternatives[0].transcript          overwrite_chars = ＂ ＂ * (num_chars_printed - len(transcript))          if not result.is_final:             sys.stdout.write(transcript + overwrite_chars + ＂r＂)             sys.stdout.flush()              num_chars_printed = len(transcript)          else:         #从.env中导出Project_id等配置，可以通过修改.env修改             TEXT=transcript + overwrite_chars             print(transcript + overwrite_chars)             PROJECT_ID = os.getenv(＂PROJECT_ID＂)             SESSION_ID = uuid.uuid1()             LANGUAGE_CODE = os.getenv(＂LANGUAGE_CODE＂)         #Location_ID             LOCATION_ID = os.getenv(＂LOCATION_ID＂)         #意图检测 TEXT为mic接收到的语音转成的文字（代码见2.dialogflow）             detect_intent_texts_with_location(PROJECT_ID, LOCATION_ID, SESSION_ID, TEXT, LANGUAGE_CODE)             # Exit recognition if any of the transcribed phrases could be             # one of our keywords.             #对麦克风说exit即可退出             if re.search(r＂b(exit|quit)b＂, transcript, re.I):                 print(＂Exiting..＂)                 break              num_chars_printed = 0   def main():     language_code = ＂en-US＂  # BCP-47      client = speech.SpeechClient()     config = speech.RecognitionConfig(         encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,         sample_rate_hertz=RATE,         language_code=language_code,     )      streaming_config = speech.StreamingRecognitionConfig(         config=config, interim_results=True     )      with MicrophoneStream(RATE, CHUNK) as stream:         audio_generator = stream.generator()         requests = (             speech.StreamingRecognizeRequest(audio_content=content)             for content in audio_generator         )          responses = client.streaming_recognize(streaming_config, requests)           listen_print_loop(responses)  if __name__ == ＂__main__＂:     main()
　　Location_id:（上面意图检测API的location_id参数）
　　国家/地区分组
　　地理位置
　　地区 ID
　　美洲
　　爱荷华
　　us-central1
　　美洲
　　蒙特利尔
　　northamerica-northeast1
　　美洲
　　南卡罗来纳
　　us-east1
　　美洲
　　俄勒冈
　　us-west1
　　欧洲
　　比利时
　　europe-west1
　　欧洲
　　伦敦
　　europe-west2
　　欧洲
　　法兰克福
　　europe-west3
　　亚太地区
　　悉尼
　　australia-southeast1
　　亚太地区
　　东京
　　asia-northeast1
　　亚太地区
　　孟买
　　asia-south1
　　亚太地区
　　新加坡
　　asia-southeast1
　　全球
　　全球服务，静态数据在美国
　　global（首选）、us 或无区域（默认）
　　5、测试
　　①Dialogflow web测试 ：Fulfillment 通过 webhook response 格式化数据后返回给客户端 ，测试成功
　　②程测试：mic -- stt -- dialogflow --client（default welcome intent --default response）
　　测试成功：
　　可以看到语音输入的内容转成了文字，并发送给了dialogflow的意图检测，并匹配意图给出相应的回复
　　③全流程测试：mic -- stt -- dialogflow -- fulfillment -- webhook -- api -- client
　　对麦克风说：noticias(西语新闻）
　　返回：头条新闻的标题，测试成功
　　三、总结
　　至此，一个天气&新闻语音问答机器人就做好了
　　官方还提供了另外的集成或者使用方式，可以供大家参考学习。希望这篇文章对大家有抛砖引玉的作用，以便大家能做出更高级更智能、更符合自己项目的交互机器人