Rasa Stack：创建支持上下文的人工智能助理和聊天机器人教程

nlu_md = """
## intent:greet
- hey
- hello
- hi
- good morning
- good evening
- hey there

## intent:goodbye
- bye
- goodbye
- see you around
- see you later

## intent:mood_affirm
- yes
- indeed
- of course
- that sounds good
- correct

## intent:mood_deny
- no
- never
- I don't think so
- don't like that
- no way
- not really

## intent:mood_great
- perfect
- very good
- great
- amazing
- wonderful
- I am feeling very good
- I am great
- I'm good

## intent:mood_unhappy
- sad
- very sad
- unhappy
- bad
- very bad
- awful
- terrible
- not very good
- extremely sad
- so sad
"""
%store nlu_md > nlu.md

print("The data has been successfully saved inside the nlu.md file! You can move on to the next step!")

nlu_config = """
language: en
pipeline: tensorflow_embedding
"""
%store nlu_config > nlu_config.yml

print("The configuration has been successfully stored inside the nlu_config.yml file. You can now move on to the next step!")


3.训练 NLU 模型

现在您拥有训练 NLU 模型所需的所有组件。运行下面的单元，该单元将调用 rasa.nlu 模型，传递先前定义的 nlu.md 和 nlu_config.yml 文件，并将模型保存在 models/current/nlu 目录中。

from rasa_nlu.model import Metadata, Interpreter
import json

def pprint(o):
 # small helper to make dict dumps a bit prettier
 print(json.dumps(o, indent=2))

interpreter = Interpreter.load('./models/current/nlu')
pprint(interpreter.parse(u"Hello"))

from rasa_nlu.model import Metadata, Interpreter
import json

def pprint(o):
 # small helper to make dict dumps a bit prettier
 print(json.dumps(o, indent=2))

interpreter = Interpreter.load('./models/current/nlu')
pprint(interpreter.parse(u"Hello"))

## story1
* greet
 - utter_greet

stories_md = """
## happy path
* greet
 - utter_greet
* mood_great
 - utter_happy

## sad path 1
* greet
 - utter_greet
* mood_unhappy
 - utter_cheer_up
 - utter_did_that_help
* mood_affirm
 - utter_happy

## sad path 2
* greet
 - utter_greet
* mood_unhappy
 - utter_cheer_up
 - utter_did_that_help
* mood_deny
 - utter_goodbye

## say goodbye
* goodbye
 - utter_goodbye
"""
%store stories_md > stories.md

print("The training stories have been successfully saved inside the stories.md file. You can move on to the next step!")

domain_yml = """
intents:
 - greet
 - goodbye
 - mood_affirm
 - mood_deny
 - mood_great
 - mood_unhappy

actions:
- utter_greet
- utter_cheer_up
- utter_did_that_help
- utter_happy
- utter_goodbye

templates:
 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"
"""
%store domain_yml > domain.yml

print("The domain has been successfully saved inside the domain.yml file. You can move on to the next step!")

!python -m rasa_core.train -d domain.yml -s stories.md -o models/dialogue

print("Finished training! You can move on to the next step!")

import IPython
from IPython.display import clear_output, HTML, display
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import time

interpreter = RasaNLUInterpreter('models/current/nlu')
messages = ["Hi! you can chat in this window. Type 'stop' to end the conversation."]
agent = Agent.load('models/dialogue', interpreter=interpreter)

def chatlogs_html(messages):
 messages_html = ""
 for m in messages:
 if m.endswith('.jpg'):
 messages_html += "<img src={}, alt='Tiger pub'></img>".format(m)
 else:
 messages_html += "<p>{}</p>".format(m)
 chatbot_html = """<div class="chat-window" {}</div>""".format(messages_html)
 return chatbot_html


while True:
 clear_output()
 display(HTML(chatlogs_html(messages)))
 time.sleep(0.3)
 a = input()
 messages.append(a)
 if a == 'stop':
 break
 responses = agent.handle_message(a)
 for r in responses:
 key = 'image' if 'image' in r.keys() else 'text'
 messages.append(r.get(key))