视觉机器人vlog功能模块（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 Bases

• Speech to Text

其它组件：

• Webhook

• Weathers & 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=<json path> <json path> 为1中下载的 sa 的json文件 Windows: set GOOGLE_APPLICATION_CREDENTIALS=C:\Users\Administrator\Downloads\sa.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=<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 division 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}\n") 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,})\n" ) print(f"Fulfillment text: {response.query_result.fulfillment_text}\n") 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 division 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

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