基于rasa的情话小王子，所有训练数据均是情话，请勿上传不是情话的训练模型！ 目前采用的是Rasa bert 模型 + rasa_chinese的分词器

安装步骤：

1.在当前目录下执行sudo ./setup.sh

(*注意)

rasa仅能在Python3.8的环境下运行，目前还未支持3.9

Ubuntu: 执行setup.sh 会在/usr/local/python3下安装python3.8.9,并软连接到/usr/bin/python3.8 和 /usr/bin/pip3.8 该脚本还会自动执行pip3 install rasa

MacOS： 执行setup.sh会安装pyenv,然后通过pyenv 安装python3.8.9 ,并软连接到/usr/local/bin/python3.8 和 /usr/local/bin/pip3.8

2.如果已有Python3.8的环境，请勿执行setup.sh,根据setup.sh里面的内容执行pip install

使用步骤：

1.在代码根目录执行 python3.8 -m rasa train 即可训练当前目录下模型

2.在代码根目录执行 python3.8 -m rasa_chinese_service.nlu.tokenizers.lm_tokenizer bert-base-chinese 启动中文分词器服务

3.在代码根目录执行 python3.8 -m rasa shell打开命令行进行对话或者通过python3.8 -m rasa x 打开浏览器对话窗口

更新数据库： 常用的数据训练库是data/nlu.yml,domain.yml,data/rules.yml,data/stories.yml

1. data/nlu.yml

data/nlu.yml是用来判断用户输出的意图 如下

version: "2.0"

nlu:

• intent: greet examples: |
  • hey
  • hello
  • hi
  • hello there
  • good morning
  • good evening
  • moin
  • hey there
  • let's go
  • hey dude
  • goodmorning
  • goodevening
  • good afternoon

表示如果用户输出 hello、good morning等话语的时候，intent意图是greet打招呼

2.domain.yml

domain.yml是根据用户的意图 回复的话语 version: "2.0"

intents:

• greet
• goodbye
• affirm
• deny
• mood_great
• mood_unhappy
• bot_challenge

responses: utter_greet:

• text: "Hey! How are you?"

utter_cheer_up:

• text: "Here is something to cheer you up:" image: "https://i.imgur.com/nGF1K8f.jpg"

utter_did_that_help:

• text: "Did that help you?"

utter_happy:

• text: "Great, carry on!"

utter_goodbye:

• text: "Bye"

utter_iamabot:

• text: "I am a bot, powered by Rasa."

intent表示注册的Intent,response则表示对对应intent的回应，前面建议加上utter_

3. data/rules.yml

data/rules.yml 是将responses与intent 绑定的规则

rules:

• rule: Say goodbye anytime the user says goodbye steps:

  • intent: goodbye
  • action: utter_goodbye

• rule: Say 'I am a bot' anytime the user challenges steps:

  • intent: bot_challenge
  • action: utter_iamabot

4. data/stories.yml

data/stories.yml 是记录一种抽象的对话场景，以方便机器人辨析更长的对话链条

version: "2.0"

stories:

• story: happy path steps:

  • intent: greet
  • action: utter_greet
  • intent: mood_great
  • action: utter_happy

• story: sad path 1 steps:

  • intent: greet
  • action: utter_greet
  • intent: mood_unhappy
  • action: utter_cheer_up
  • action: utter_did_that_help
  • intent: affirm
  • action: utter_happy

• story: sad path 2 steps:

  • intent: greet
  • action: utter_greet
  • intent: mood_unhappy
  • action: utter_cheer_up
  • action: utter_did_that_help
  • intent: deny
  • action: utter_goodbye

可以看到上面这些就是一个场景抽象出来的Intent和action

5.修改完上述训练数据后，就可以使用rasa train 重新训练，用rasa shell继续测试你的模型