对话系统Rasa - 响应 [翻译]
如果想要助手响应用户的消息，你需要管理这些响应。在训练数据，故事中，你定义了你的助手需要执行的动作。这些动作使用话语将消息发送给用户。
有三种方式管理这些话语：
将话语存储在domain中
默认的方式是将话语存储在domain文件中。这个文件可以包含自定义行为，一用的实体，slot和意图。
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# all hashtags are comments :)
intents:
- greet
- default
- goodbye
- affirm
- thank_you
- change_bank_details
- simple
- hello
- why
- next_intent
entities:
- name
slots:
name:
type: text
templates:
utter_greet:
- text: "hey there {name}!" # {name} will be filled by slot (same name) or by custom action
utter_channel:
- text: "this is a default channel"
- text: "you're talking to me on slack!" # if you define channel-specific utterances, the bot will pick
channel: "slack" # from those when talking on that specific channel
utter_goodbye:
- text: "goodbye 😢" # multiple templates - bot will randomly pick one of them
- text: "bye bye 😢"
utter_default: # utterance sent by action_default_fallback
- text: "sorry, I didn't get that, can you rephrase it?"
actions:
- utter_default
- utter_greet
- utter_goodbye
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在domian文件中，templates包括了助手可以返回给用户消息的模板。
如果你想要改变这些文本，或响应的任何部分，你需要重新训练你的助手。
更详细的介绍可以参见：Utterance templates.
创建自定义的NLG服务用语生成响应
仅仅改变文本就需要进行重新训练，针对某些工作流可能不是最优的选择。这是为什么Core允许你将响应生成在外部处理与对话学习分离开来。
助手仍将学习根据过去的对话预测操作和对用户输入做出反应，但它发送回用户的响应是在rasa核心之外生成的。
如果助手发送消息给用户，它将以POST请求调用外部的HTTP服务。为了创建该endpoint，你需要创建endpoints.yml，并将它传递给run或者server脚本。endpoints.yml内容参见：
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nlg:
url: http://localhost:5055/nlg # url of the nlg endpoint
# you can also specify additional parameters, if you need them:
# headers:
# my-custom-header: value
# token: "my_authentication_token" # will be passed as a get parameter
# basic_auth:
# username: user
# password: pass
# example of redis external tracker store config
tracker_store:
type: redis
url: localhost
port: 6379
db: 0
password: password
record_exp: 30000
# example of mongoDB external tracker store config
#tracker_store:
#type: mongod
#url: mongodb://localhost:27017
#db: rasa
#user: username
#password: password
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调用rasa run命令的时候需要加上enable-api标签。
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$ rasa run \
--enable-api \
-m examples/babi/models \
--log-file out.log \
--endpoints endpoints.yml
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POST请求的示例如下：
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{
"tracker": {
"latest_message": {
"text": "/greet",
"intent_ranking": [
{
"confidence": 1.0,
"name": "greet"
}
],
"intent": {
"confidence": 1.0,
"name": "greet"
},
"entities": []
},
"sender_id": "22ae96a6-85cd-11e8-b1c3-f40f241f6547",
"paused": false,
"latest_event_time": 1531397673.293572,
"slots": {
"name": null
},
"events": [
{
"timestamp": 1531397673.291998,
"event": "action",
"name": "action_listen"
},
{
"timestamp": 1531397673.293572,
"parse_data": {
"text": "/greet",
"intent_ranking": [
{
"confidence": 1.0,
"name": "greet"
}
],
"intent": {
"confidence": 1.0,
"name": "greet"
},
"entities": []
},
"event": "user",
"text": "/greet"
}
]
},
"arguments": {},
"template": "utter_greet",
"channel": {
"name": "collector"
}
}
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endpoint将生成具体的响应：
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{
"text": "hey there",
"buttons": [],
"image": null,
"elements": [],
"attachments": []
}
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接下来rasa将会把这个响应发回给用户。