这里记录从最基础的基于规则的聊天机器人，升级到基于逻辑的机器人，再升级到调用Google提供的API来让机器人能说、会听普通话。

最基本的完全基于规则式的问答：问什么就答什么，幼儿园水平。

import random

# 打招呼greetings = ['hola', 'hello', 'hi', 'Hi', 'hey!','hey']# 回复打招呼random_greeting = random.choice(greetings)

# 对于“你怎么样？”这个问题的回复question = ['How are you?','How are you doing?']# “我很好”responses = ['Okay',"I'm fine"]# 随机选一个回random_response = random.choice(responses)

# 机器人跑起来while True:    userInput = input(">>> ")    if userInput in greetings:        print(random_greeting)    elif userInput in question:        print(random_response)    # 除非你说“拜拜”    elif userInput == 'bye':        break    else:        print("I did not understand what you said")}

>>> hi
hey
>>> how are u
I did not understand what you said
>>> how are you
I did not understand what you said
>>> how are you?
I did not understand what you said
>>> How are you?
I'm fine
>>> bye

完全基于规则的问答会使召回率极低，因为难以列举的问法太多。

升级I: 现在，我们使用关键词匹配来升级我们的机器人。透过关键词来判断这句话的意图是什么（intents）。

from nltk import word_tokenizeimport random

# 打招呼greetings = ['hola', 'hello', 'hi', 'Hi', 'hey!','hey']# 回复打招呼random_greeting = random.choice(greetings)

# 对于“假期”的话题关键词question = ['break','holiday','vacation','weekend']# 回复假期话题responses = ['It was nice! I went to Paris',"Sadly, I just stayed at home"]# 随机选一个回random_response = random.choice(responses)

# 机器人跑起来while True:    userInput = input(">>> ")    # 清理一下输入，看看都有哪些词    cleaned_input = word_tokenize(userInput)    # 这里，我们比较一下关键词，确定他属于哪个问题    if  not set(cleaned_input).isdisjoint(greetings):        print(random_greeting)    elif not set(cleaned_input).isdisjoint(question):        print(random_response)    # 除非你说“拜拜”    elif userInput == 'bye':        break    else:        print("I did not understand what you said")

>>> hi
hey
>>> how was your holiday?
It was nice! I went to Paris
>>> wow, amazing!
I did not understand what you said
>>> bye

大概能发现，这依旧是文字层面的“精准对应”。

现在主流的研究方向，是做到语义层面的对应。

比如，“肚子好饿哦”， “饭点到了”都应该表示的是要吃饭了的意思。

在这个层面就需要用到word vector之类的embedding方法，这些内容将是本博客不断更新的重头戏。

升级II : 建立一个简易的知识图谱来存储“知识体系”

# 建立一个基于目标行业的database# 比如 这里我们用python自带的graphgraph = {'上海': ['苏州', '常州'],         '苏州': ['常州', '镇江'],         '常州': ['镇江'],         '镇江': ['常州'],         '盐城': ['南通'],         '南通': ['常州']}

# 明确如何找到从A到B的路径def find_path(start, end, path=[]):    path = path + [start]    if start == end:        return path    if start not in graph:        return None    for node in graph[start]:        if node not in path:            newpath = find_path(node, end, path)            if newpath: return newpath    return Noneprint(find_path('上海', "镇江"))

['上海', '苏州', '常州', '镇江']

使用python版本的prolog：PyKE，它可以构建一种复杂的逻辑网络，让你方便提取信息，而不至于需要你亲手code所有的信息:

son_of(bruce, thomas, norma)
son_of(fred_a, thomas, norma)
son_of(tim, thomas, norma)
daughter_of(vicki, thomas, norma)
daughter_of(jill, thomas, norma)

升级III:  利用Google的API（需FQ）实现文字合成语音，以及语音识别。

from gtts import gTTSimport ostts = gTTS(text='您好，我是您的私人助手，我叫小飞侠', lang='zh-tw')tts.save("hello.mp3")os.system("mpg321 hello.mp3")

同理，除了语音识别，调用Google的接口还可实现语音识别（注意：这里需要你的机器安装几个库 SpeechRecognition, PyAudio 和 PySpeech）

import speech_recognition as srfrom time import ctimeimport timeimport osfrom gtts import gTTSimport sys

# 讲出来AI的话def speak(audioString):    print(audioString)    tts = gTTS(text=audioString, lang='en')    tts.save("audio.mp3")    os.system("mpg321 audio.mp3")

# 录下来你讲的话def recordAudio():    # 用麦克风记录下你的话    r = sr.Recognizer()    with sr.Microphone() as source:        audio = r.listen(source)

    # 用Google API转化音频    data = ""    try:        data = r.recognize_google(audio)        print("You said: " + data)    except sr.UnknownValueError:        print("Google Speech Recognition could not understand audio")    except sr.RequestError as e:        print("Could not request results from Google Speech Recognition service; {0}".format(e))

    return data

# 自带的对话技能（rules）def jarvis():

    while True:

        data = recordAudio()

        if "how are you" in data:            speak("I am fine")

        if "what time is it" in data:            speak(ctime())

        if "where is" in data:            data = data.split(" ")            location = data[2]            speak("Hold on Tony, I will show you where " + location + " is.")            os.system("open -a Safari https://www.google.com/maps/place/" + location + "/&")

        if "bye" in data:            speak("bye bye")            break

# 初始化time.sleep(2)speak("Hi Tony, what can I do for you?")

# 跑起jarvis()

Hi Tony, what can I do for you?
You said: how are you
I am fine
You said: what time is it now
Mon Oct 15 18:16:54 2018
You said: where is London
Hold on Tony, I will show you where London is.
You said: ok bye bye
bye bye