基于Rasa框架搭建中文机器人对话系统

Rasa是一个能用于构建机器人对话系统的框架，基于Rasa框架搭建机器人对话系统，可以使用于工业各类语音智能服务场景，如：远程医疗问诊、智能客户服务、保险产品销售、金融催收服务、手机智能助手等领域。支持基于规则、填槽和机器学习来构建对话系统。主要模块包括：NLU（意图识别和实体提取）和Core（基于模型及规则进行回复）。提供了搭建对话系统的脚手架。

常用命令有：

rasa init 创建项目

rasa train 训练NLU和Core模型

rasa run actions 启动动作服务（主要是自己写的动作，系统默认的动作，不需要单独启动）

rasa shell 启动对话机器人（会自动启动默认动作服务）

涉及配置文件有：

config.yml 即对NLU和Core模型的配置

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: zhpipeline:
# # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
# # If you'd like to customize it, uncomment and adjust the pipeline.
# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
#   - name: WhitespaceTokenizer- name: JiebaTokenizer #支持中文- name: RegexFeaturizer- name: LexicalSyntacticFeaturizer- name: CountVectorsFeaturizer- name: CountVectorsFeaturizeranalyzer: char_wbmin_ngram: 1max_ngram: 4- name: DIETClassifierepochs: 100constrain_similarities: true- name: EntitySynonymMapper- name: ResponseSelectorepochs: 100constrain_similarities: true- name: FallbackClassifierthreshold: 0.3ambiguity_threshold: 0.1# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
policies:
# # No configuration for policies was provided. The following default policies were used to train your model.
# # If you'd like to customize them, uncomment and adjust the policies.
# # See https://rasa.com/docs/rasa/policies for more information.- name: MemoizationPolicy- name: TEDPolicymax_history: 5epochs: 100constrain_similarities: true- name: RulePolicy

domain.yml 领域配置

version: '2.0'
config:store_entities_as_slots: true
session_config:session_expiration_time: 60carry_over_slots_to_new_session: true
intents:
- greet:use_entities: true
- deny:use_entities: true
- request_names:use_entities: true
- goodbye:use_entities: true
- affirm:use_entities: true
- mood_great:use_entities: true
- mood_unhappy:use_entities: true
- bot_challenge:use_entities: true
entities: []
slots:first_name:type: rasa.shared.core.slots.TextSlotinitial_value: nullauto_fill: trueinfluence_conversation: truelast_name:type: rasa.shared.core.slots.TextSlotinitial_value: nullauto_fill: trueinfluence_conversation: truename_spelled_correctly:type: rasa.shared.core.slots.BooleanSlotinitial_value: nullauto_fill: trueinfluence_conversation: truerequested_slot:type: rasa.shared.core.slots.UnfeaturizedSlotinitial_value: nullauto_fill: trueinfluence_conversation: false
responses:utter_greet:- text: 嗨，你好吗?utter_cheer_up:- image: https://i.imgur.com/nGF1K8f.jpgtext: '这是某些使你嗨起来的东西:'utter_did_that_help:- text: 这对你有帮助吗?utter_happy:- text: 好极了，继续!utter_goodbye:- text: 再见utter_iamabot:- text: 我是个机器人，由Rasa框架提供支持.utter_ask_first_name:- text: 你贵姓?utter_ask_last_name:- text: 你的名字?utter_ask_name_spelled_correctly:- buttons:- payload: /affirmtitle: 是- payload: /denytitle: 否text:  姓 {first_name} 拼写对了吗?utter_submit:- text: 好的，谢谢！utter_slots_values:- text: 我记住你了， {first_name} {last_name}!
actions:
- utter_greet
- utter_slots_values
- utter_submit
- validate_name_form
forms:name_form:first_name:- type: from_textlast_name:- type: from_text
e2e_actions: []

credentials.yml 证书配置，用于调用语音通道的接口

# This file contains the credentials for the voice & chat platforms
# which your bot is using.
# https://rasa.com/docs/rasa/messaging-and-voice-channelsrest:
#  # you don't need to provide anything here - this channel doesn't
#  # require any credentials#facebook:
#  verify: "<verify>"
#  secret: "<your secret>"
#  page-access-token: "<your page access token>"#slack:
#  slack_token: "<your slack token>"
#  slack_channel: "<the slack channel>"
#  slack_signing_secret: "<your slack signing secret>"#socketio:
#  user_message_evt: <event name for user message>
#  bot_message_evt: <event name for bot messages>
#  session_persistence: <true/false>#mattermost:
#  url: "https://<mattermost instance>/api/v4"
#  token: "<bot token>"
#  webhook_url: "<callback URL>"# This entry is needed if you are using Rasa X. The entry represents credentials
# for the Rasa X "channel", i.e. Talk to your bot and Share with guest testers.
rasa:url: "http://localhost:5002/api"

endpoints.yml 端点配置，如:机器人要使用的模型、动作、存储服务等。

# This file contains the different endpoints your bot can use.# Server where the models are pulled from.
# https://rasa.com/docs/rasa/model-storage#fetching-models-from-a-server#models:
#  url: http://my-server.com/models/default_core@latest
#  wait_time_between_pulls:  10   # [optional](default: 100)# Server which runs your custom actions.
# https://rasa.com/docs/rasa/custom-actions#action_endpoint:
#  url: "http://localhost:5055/webhook"
action_endpoint:url: "http://localhost:5055/webhook"# Tracker store which is used to store the conversations.
# By default the conversations are stored in memory.
# https://rasa.com/docs/rasa/tracker-stores#tracker_store:
#    type: redis
#    url: <host of the redis instance, e.g. localhost>
#    port: <port of your redis instance, usually 6379>
#    db: <number of your database within redis, e.g. 0>
#    password: <password used for authentication>
#    use_ssl: <whether or not the communication is encrypted, default false>#tracker_store:
#    type: mongod
#    url: <url to your mongo instance, e.g. mongodb://localhost:27017>
#    db: <name of the db within your mongo instance, e.g. rasa>
#    username: <username used for authentication>
#    password: <password used for authentication>
tracker_store:type: SQLdialect: sqlitedb: trackers.db# Event broker which all conversation events should be streamed to.
# https://rasa.com/docs/rasa/event-brokers#event_broker:
#  url: localhost
#  username: username
#  password: password
#  queue: queue
event_broker:type: SQLdialect: sqlitedb: events.db

names.txt 本案例使用的姓名列表。

孙悟空
猪八戒
唐三藏
沙悟净
诸葛青云

数据，主要包括：

nlu.yml 用于训练nlu模型的训练数据

version: "2.0"
nlu:
- intent: greetexamples: |- 你好- 上午好- 中午好- 嗨
- intent: goodbyeexamples: |- 再见- 回头见- 晚安
- intent: affirmexamples: |- 是的- 有- 当然- 听上去不错
- intent: denyexamples: |- 不- 不要- 没有- 没有- 我不喜欢
- intent: mood_greatexamples: |- 太好了- 感觉不错- 非常好
- intent: mood_unhappyexamples: |- 不开心- 感到沮丧- 不高兴- 不太好
- intent: bot_challengeexamples: |- 你是个机器人吗?- 你是人类吗?- 我是在和机器人讲话吗?- 我是在和人类讲话吗?
- intent: request_namesexamples: |- 我想告诉你姓名- 你知道我的姓名吗？
- lookup: namesexamples: |- 猪八戒- 孙悟空- 沙悟净- 唐三藏

rules.yml 规则，根据用户意图，进行具体的动作（包括查询、填槽、回复等）

version: "2.0"rules:- rule: Say goodbye anytime the user says goodbyesteps:- intent: goodbye- action: utter_goodbye- rule: Say 'I am a bot' anytime the user challengessteps:- intent: bot_challenge- action: utter_iamabot- rule: Activate formsteps:- intent: request_names- action: name_form- active_loop: name_form- rule: Submit formcondition:- active_loop: name_formsteps:- action: name_form- active_loop: null- slot_was_set:- requested_slot: null- action: utter_submit- action: utter_slots_values

stories.yml 故事情节，描述对话的流程

version: "2.0"stories:- story: happy pathsteps:- intent: greet- action: utter_greet- intent: mood_great- action: utter_happy- story: sad path 1steps:- 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 2steps:- intent: greet- action: utter_greet- intent: mood_unhappy- action: utter_cheer_up- action: utter_did_that_help- intent: deny- action: utter_goodbye- story: interactive_story_1steps:- intent: greet- action: utter_greet- intent: request_names- action: name_form- active_loop: name_form- slot_was_set:- requested_slot: first_name- slot_was_set:- name_spelled_correctly: None- slot_was_set:- first_name: None- slot_was_set:- requested_slot: last_name- slot_was_set:- name_spelled_correctly: None- slot_was_set:- last_name: None- slot_was_set:- requested_slot: null- active_loop: null- action: utter_submit- action: utter_slots_values

下面举例创建mybot的过程：

1、使用rasa init 创建mybot项目，目录结果如下：

涉及的配置文件在上文已经逐一列出，下面看看自定义的action是如何写的，主要完成了输入验证的过程，默认的action是以utter_打头，系统默认支持。

actions.py

import yaml
import pathlib
from typing import Text, List, Any, Dict, Optionalfrom rasa_sdk import Tracker, FormValidationAction
from rasa_sdk.executor import CollectingDispatcher
from rasa_sdk.types import DomainDictnames = pathlib.Path("names.txt").read_text().split("\n")class ValidateNameForm(FormValidationAction):def name(self) -> Text:return "validate_name_form"async def required_slots(self,slots_mapped_in_domain: List[Text],dispatcher: "CollectingDispatcher",tracker: "Tracker",domain: "DomainDict",) -> Optional[List[Text]]:first_name = tracker.slots.get("first_name")if first_name is not None:if first_name not in names:return ["name_spelled_correctly"] + slots_mapped_in_domainreturn slots_mapped_in_domainasync def extract_name_spelled_correctly(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict) -> Dict[Text, Any]:intent = tracker.get_intent_of_latest_message()return {"name_spelled_correctly": intent == "affirm"}def validate_name_spelled_correctly(self,slot_value: Any,dispatcher: CollectingDispatcher,tracker: Tracker,domain: DomainDict,) -> Dict[Text, Any]:"""Validate `first_name` value."""if tracker.get_slot("name_spelled_correctly"):return {"first_name": tracker.get_slot("first_name"), "name_spelled_correctly": True}return {"first_name": None, "name_spelled_correctly": None}def validate_first_name(self,slot_value: Any,dispatcher: CollectingDispatcher,tracker: Tracker,domain: DomainDict,) -> Dict[Text, Any]:"""Validate `first_name` value."""# If the name is super short, it might be wrong.print(f"姓 = {slot_value} 长度 = {len(slot_value)}")if len(slot_value) <=1:dispatcher.utter_message(text=f"姓太短了，你确定拼写对了？")return {"first_name": None}else:return {"first_name": slot_value}def validate_last_name(self,slot_value: Any,dispatcher: CollectingDispatcher,tracker: Tracker,domain: DomainDict,) -> Dict[Text, Any]:"""Validate `last_name` value."""# If the name is super short, it might be wrong.print(f"名字 = {slot_value} 长度 = {len(slot_value)}")if len(slot_value) <= 1:dispatcher.utter_message(text=f"名字太短了，你确定拼写对了？")return {"last_name": None}else:return {"last_name": slot_value}

2、启动自动定义的actions服务：

3、训练NLU和Core模型：

4、启动机器人进行对话

分期业务效果：

实际尝试：可以自动完成正常的分期业务流程，中间可以穿插其它的对话内容，如：分期时间长、手续费太贵、担心被套路等。

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