Question

我用rasa编写了一个简单的机器人。为了处理消息，我创建了flask应用程序并将代理加载到该应用程序中。我从请求中收到用户消息和ID，并将其放入代理handle_text方法中，然后得到响应。问题是当我讲了一个故事中定义的一个故事后。我的经纪人停止了回答。

这是我的烧瓶应用程序

app = Flask(__name__)


# Define rasa interpreter
interpreter = None

# Define rasa agent
agent = None


@app.route('/')
def index():

    # Receive message from request
    message = request.args.get('msg')

    # Receive user id from request
    user_id = request.args.get('uid')

    #  Validation
    if not message:
        return 'No message specified in field \'msg\''
    if not user_id:
        return 'No user id specified in field \'uid\''

    # Put received message into rasa agent
    answers = agent.handle_text(message, sender_id=user_id)

    # Define text for the response
    text = None

    if len(answers) > 0:
        text = "User: {} | {}".format(user_id, answers[0].get('text'))
    else:
        text = "User: {} | Nothing to answer".format(user_id)

    return text


if __name__ == '__main__':

    # Load rasa interpreter
    interpreter = RasaNLUInterpreter(NLU_PATH)

    # Load rasa agent
    agent = Agent.load(CORE_PATH, interpreter=interpreter)

    app.run()

我的故事。md是

## Simple flow
* greet
  - utter_greet
* bye 
  - utter_bye 
## Order pizza
* greet
  - utter_greet
* order_pizza_type
  - utter_finish_order_pizza
* bye
  - utter_bye
## Story
* order_pizza_type
  - utter_finish_order_pizza
## Generated Story -1054914010798310995
* greet
    - utter_greet
* order_pizza_type{"Country": "mexican"}
    - utter_finish_order_pizza
* bye
    - utter_bye
## New Story
* greet
  - utter_greet
* order_pizza_wish
  - utter_finish_order_pizza
* bye
    - utter_bye

和我的config.yml

language: "en"

pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "ner_crf"
- name: "tokenizer_whitespace"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
  intent_tokenization_flag: true
  intent_split_symbol: "+"

policies:
  - name: "KerasPolicy"
    featurizer:
    - name: MaxHistoryTrackerFeaturizer
      max_history: 5
      state_featurizer:
        - name: BinarySingleStateFeaturizer
  - name: "MemoizationPolicy"
    max_history: 5
  - name: "FallbackPolicy"
    nlu_threshold: 0.4
    core_threshold: 0.3

我的预期结果

$ curl -X GET "https://localhost?msg=hello&uid=1"
$ curl -X GET "https://localhost?msg=I want to order pizza&uid=1"
$ curl -X GET "https://localhost?msg=Bye&uid=1"
$ curl -X GET "https://localhost?msg=hello&uid=1"

响应

> User: 1 | Hey! How are you?
> User: 1 | Ok I will deliver pizza for you
> User: 1 | Bye
> User: 1 | Hey! How are you?

但是我的实际结果是

$ curl -X GET "https://localhost?msg=hello&uid=1"
$ curl -X GET "https://localhost?msg=I want to order pizza&uid=1"
$ curl -X GET "https://localhost?msg=Bye&uid=1"
$ curl -X GET "https://localhost?msg=hello&uid=1"

响应

> User: 1 | Hey! How are you?
> User: 1 | Ok I will deliver pizza for you
> User: 1 | Bye
> User: 1 | Nothing to answer

说完一个故事情节后，您看不到第二条消息“ hello”的任何响应。

Answer 1

与评论中的建议相同，我建议使用interactive learning来调试您的机器人并创建新的培训案例。当前，您的训练数据非常稀疏。

您是否使用augmentation进行培训？如果您没有以其他方式指定参数，则默认扩展设置为20。

如果您使用增强功能，我建议您还添加另一个简短的故事来处理独立的greet：

## Simple flow
* greet
  - utter_greet

还有一件事：建议使用一般意图，并通过公认的实体来区分它们。因此，最好有一个意图order_pizza_type或什至order_pizza_wish，然后再放置order_pizza，order的位置，而不是food_type和product_to_order （例如pizza）等等。如果您有非常相似的意图，例如order_pizza_type和order_pizza_wish，NLU将很难区分它们。

一个故事讲完后，Rasa代理停止响应

1 个答案: