原文地址:http://www.bugingcode.com/blog/ChatterBot_Dialogue_process.htmlhtml
部署機器人的各類屬性,根據前面的章節裏聊天機器人的各類屬性,對聊天機器人進行相應的配置,建立一個符合本身的機器人。python
bot = ChatBot( 'Default Response Example Bot', storage_adapter='chatterbot.storage.SQLStorageAdapter', logic_adapters=[ { 'import_path': 'chatterbot.logic.BestMatch' }, { 'import_path': 'chatterbot.logic.LowConfidenceAdapter', 'threshold': 0.65, 'default_response': 'I am sorry, but I do not understand.' } ], trainer='chatterbot.trainers.ListTrainer' )
logic_adapters:用來設置所選擇的算法,這裏選擇的是chatterbot.logic.BestMatch,也就是最匹配方式,從訓練的對話中找到最相識的語句,根據對話,提供回答。算法
trainer:選擇的是chatterbot.trainers.ListTrainersql
在trainer中,決定選擇哪一種構造方式來建立上下文的關係。數據庫
def train(self, conversation): """ Train the chat bot based on the provided list of statements that represents a single conversation. """ previous_statement_text = None for conversation_count, text in enumerate(conversation): print_progress_bar("List Trainer", conversation_count + 1, len(conversation)) statement = self.get_or_create(text) if previous_statement_text: statement.add_response( Response(previous_statement_text) ) previous_statement_text = statement.text self.storage.update(statement)
在ListTrainer中,用上下句來構建一個statement ,statement至關於存儲了一個上下對話的關係,在查找的時候,先找到最合適的上文,下文就是答案了。這就是一個訓練的過程,訓練的這一過程,主要是在構建statement,並把statement放到storage中。編程
storage_adapter有幾種可選的方案chatterbot.storage.SQLStorageAdapter,MongoDatabaseAdapter,存儲以前訓練的statement,把statement存儲在數據庫中,默認的數據庫選擇的是本地的sqlite3。session
把語料準備好,就聊天機器人進行訓練,語料的來源比較重要,像以前的小黃鴨語料的來源,主要是來源於衆包,用戶會交小黃鴨怎麼去回答問題,語料是重要的一種選擇,一個語料的質量決定了聊天機器人的可玩性。架構
訓練的過程,就是一個創建statement並存儲的過程,代碼在ListTrainer中都有詳細的體現。app
bot.train([ 'How can I help you?', 'I want to create a chat bot', 'Have you read the documentation?', 'No, I have not', 'This should help get you started: http://chatterbot.rtfd.org/en/latest/quickstart.html' ])
聊天機器人主要的過程是產生答案的過程,而答案的選擇最關鍵的就是算法的實現,以前有介紹過,可玩性比較高的聊天機器人必須擁有不一樣的算法,對不一樣的聊天內容給出不同的答案,根據輸入選擇最合適的算法,產生最好的答案。在機器人對話中,最多見的問題是一些生活的問題,好比,天氣,時間,笑話等,根據問題,選擇最匹配的算法,給出精彩的答案。ide
response = bot.get_response('How do I make an omelette?')
get_response的過程
採用的是ChatBot的方法,一開始先獲得輸入,並對數據進行過濾,在根據輸入數據選擇算法,得出答案。
def get_response(self, input_item, session_id=None): """ Return the bot's response based on the input. :param input_item: An input value. :returns: A response to the input. :rtype: Statement """ if not session_id: session_id = str(self.default_session.uuid) input_statement = self.input.process_input_statement(input_item) # Preprocess the input statement for preprocessor in self.preprocessors: input_statement = preprocessor(self, input_statement) statement, response = self.generate_response(input_statement, session_id) # Learn that the user's input was a valid response to the chat bot's previous output previous_statement = self.conversation_sessions.get( session_id ).conversation.get_last_response_statement() self.learn_response(statement, previous_statement) self.conversation_sessions.update(session_id, (statement, response, )) # Process the response output with the output adapter return self.output.process_response(response, session_id)
算法是如何進行選擇的呢? 在multi_adapter.py 算法選擇中,遍歷了全部咱們已經選擇的算法,算法經過 can_process 進行選擇,對輸入生成的statement 進行匹配,並經過confidence來進行評分,而應該還能夠進行擴展,經過不一樣的得分,來選擇算法,最佳匹配。
def process(self, statement): """ Returns the output of a selection of logic adapters for a given input statement. :param statement: The input statement to be processed. """ results = [] result = None max_confidence = -1 for adapter in self.get_adapters(): if adapter.can_process(statement): output = adapter.process(statement) if type(output) == tuple: warnings.warn( '{} returned two values when just a Statement object was expected. ' 'You should update your logic adapter to return just the Statement object. ' 'Make sure that statement.confidence is being set.'.format(adapter.class_name), DeprecationWarning ) output = output[1] results.append((output.confidence, output, )) self.logger.info( '{} selected "{}" as a response with a confidence of {}'.format( adapter.class_name, output.text, output.confidence ) ) if output.confidence > max_confidence: result = output max_confidence = output.confidence else: self.logger.info( 'Not processing the statement using {}'.format(adapter.class_name) ) # If multiple adapters agree on the same statement, # then that statement is more likely to be the correct response if len(results) >= 3: statements = [s[1] for s in results] count = Counter(statements) most_common = count.most_common() if most_common[0][1] > 1: result = most_common[0][0] max_confidence = self.get_greatest_confidence(result, results) result.confidence = max_confidence return result
ChatterBot的架構和流程基本清楚之後,就是對ChatterBot的擴展,一個好的ChatterBot聊天機器人,還有不少須要完成的地方,好比多輪對話,
我:天氣如何?
機器人:你在位置在那裏?
我:廈門
機器人:多雲轉晴,32攝氏度
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