Rasa 基于知识库的问答 音乐百科机器人

{
    "song": [
        {
            "id": 0,
            "name": "晴天",
            "singer": "周杰伦",
            "album": "叶惠美",
            "style": "流行,英伦摇滚"
        },
        {
            "id": 1,
            "name": "江南",
            "singer": "林俊杰",
            "album": "第二天堂",
            "style": "流行,中国风"
        },
    ],
    "singer": [
        {
            "id": 0,
            "name": "周杰伦",
            "gender": "male",
            "birthday": "1979/01/18"
        },
        {
            "id": 1,
            "name": "林俊杰",
            "gender": "male",
            "birthday": "1979/03/27"
        },
    ]
}

version: "3.0"
nlu:
  - intent: query_knowledge_base
    examples: |
      - 有什么好听的[歌曲](object_type)？
      - 有什么唱歌好听的[歌手](object_type)？
      - 给我列一些[男](gender)[歌手](object_type)
      - 给我列出一些[周杰伦](singer)的[歌曲](object_type)
      - [刚才那首](mention)属于什么[专辑](attribute)
      - [刚才那首](mention)是[谁](attribute)唱的
      - [刚才那首](mention)的[歌手](attribute)是谁
      - [那首歌](mention)属于什么[风格](attribute)？
      - [晴天](song)这首歌属于什么[专辑](attribute)？
      - [晴天](song)的[专辑](attribute)？
      - [江南](song)属于什么[专辑](attribute)？
      - [江南](song)在什么[专辑](attribute)里面？
      - [第一个](mention)人的[生日](attribute)
      - [周杰伦](singer)的[生日](attribute)

entities:
  - object_type
  - mention
  - attribute
  - object-type
  - song
  - singer
  - gender
slots:
  attribute:
    type: any
    mappings:
      - type: from_entity
        entity: attribute
  gender:
    type: any
    mappings:
      - type: from_entity
        entity: gender
  knowledge_base_last_object:
    type: any
    mappings:
      - type: custom
  knowledge_base_last_object_type:
    type: any
    mappings:
      - type: custom
  knowledge_base_listed_objects:
    type: any
    mappings:
      - type: custom
  knowledge_base_objects:
    type: any
    mappings:
      - type: custom
  mention:
    type: any
    mappings:
      - type: from_entity
        entity: mention
  object_type:
    type: any
    mappings:
      - type: from_entity
        entity: object_type
  singer:
    type: any
    mappings:
      - type: from_entity
        entity: singer
  song:
    type: any
    mappings:
      - type: from_entity
        entity: song

.
├── actions.py
├── config.yml
├── credentials.yml
├── data
│   ├── nlu.yml
│   ├── rules.yml
│   └── stories.yml
├── data.json
├── data_to_neo4j.py
├── dicts
│   ├── ordinal.txt
│   └── songs.txt
├── domain.yml
├── endpoints.yml
├── en_to_zh.json
├── index.html
├── index.js
├── __init__.py
├── Makefile
├── media
│   ├── demo2.png
│   └── demo.png
├── neo4j_knowledge_base.py
├── README.md
├── run_neo4j_in_docker.bash
└── tests
    └── basic.md

version: "3.0"
nlu:
  - intent: goodbye
    examples: |
      - 拜拜
      - 再见
      - 拜
      - 退出
      - 结束
  - intent: greet
    examples: |
      - 你好
      - 您好
      - Hello
      - hello
      - Hi
      - hi
      - 喂
      - 在么
  - intent: query_knowledge_base
    examples: |
      - 有什么好听的[歌曲](object_type)？
      - 有什么唱歌好听的[歌手](object_type)？
      - 给我列一些[歌曲](object_type)
      - 给我列一些[歌手](object_type)
      - 给我列一些[男](gender)[歌手](object_type)
      - 给我列一些[男](gender)的[歌手](object_type)
      - 给我列一些[女](gender)[歌手](object_type)
      - 给我列一些[女](gender)的[歌手](object_type)
      - 给我列一些[男性](gender)[歌手](object_type)
      - 给我列一些[女性](gender)[歌手](object_type)
      - 给我[男性](gender)[歌手](object_type)
      - 给我[女性](gender)[歌手](object_type)
      - 给我列出一些[周杰伦](singer)的[歌曲](object_type)
      - 给我列出[周杰伦](singer)的[歌曲](object_type)
      - 给我列出[周杰伦](singer)唱的[歌曲](object_type)
      - 列出[周杰伦](singer)的[歌曲](object_type)
      - 给我列[周杰伦](singer)的[歌曲](object_type)
      - [林俊杰](singer)都有什么[歌曲](object_type)
      - [林俊杰](singer)有什么[歌曲](object_type)
      - [刚才那首](mention)属于什么[专辑](attribute)
      - [刚才那首](mention)是[谁](attribute)唱的
      - [刚才那首](mention)的[歌手](attribute)是谁
      - [那首歌](mention)属于什么[风格](attribute)？
      - [最后一个](mention)属于什么[风格](attribute)？
      - [第一个](mention)属于什么[专辑](attribute)？
      - [第一个](mention)的[专辑](attribute)
      - [第一个](mention)是[谁](attribute)唱的？
      - [最后一个](mention)是[哪个](attribute)唱的？
      - [舞娘](song)是[哪个歌手](attribute)唱的？
      - [晴天](song)这首歌属于什么[专辑](attribute)？
      - [晴天](song)的[专辑](attribute)？
      - [江南](song)属于什么[专辑](attribute)？
      - [江南](song)在什么[专辑](attribute)里面？
      - [第一个](mention)人的[生日](attribute)
      - [周杰伦](singer)的[生日](attribute)
  - intent: play_song
    examples: |
      - 播放这首歌
      - 播这首歌
  - intent: play_album
    examples: |
      - 播放这个专辑
      - 播这个专辑
  - synonym: "1"   # 同义词，第一个 -> 1
    examples: |
      - 第一个
      - 首个
      - 第一首
  - synonym: "2"
    examples: |
      - 第二个
      - 第二首
  - synonym: "3"
    examples: |
      - 第三个
      - 第三首
  - synonym: LAST
    examples: |
      - 最后一个
      - 最后那个
      - 最后的
  - synonym: birthday
    examples: |
      - 生日
  - synonym: song
    examples: |
      - 歌曲
  - synonym: singer
    examples: |
      - 歌手
      - 谁
      - 哪个
      - 哪个歌手
  - synonym: album
    examples: |
      - 专辑
  - synonym: "4"
    examples: |
      - 第四个
      - 第四首
  - synonym: style
    examples: |
      - 风格
      - 类型
      - 流派
  - synonym: male
    examples: |
      - 男
      - 男性
  - synonym: famale
    examples: |
      - 女
      - 女性

version: "3.0"
stories:
  - story: greet
    steps:
      - intent: greet
      - action: utter_greet
  - story: knowledge query
    steps:
      - intent: query_knowledge_base
      - action: action_response_query
      - intent: query_knowledge_base
      - action: action_response_query
  - story: say goodbye
    steps:
      - intent: goodbye
      - action: utter_goodbye

version: "3.0"
rules:
  - rule: 处理NLU低置信度时的规则
    steps:
      - intent: nlu_fallback
      - action: action_default_fallback
  - rule: 处理知识图谱查询
    steps:
      - intent: query_knowledge_base
      - action: action_response_query

version: "3.0"
session_config:
  session_expiration_time: 60
  carry_over_slots_to_new_session: true
intents:
  - goodbye
  - greet
  - query_knowledge_base:
      use_entities: []
  - play_song
  - play_album
entities:
  - object_type
  - mention
  - attribute
  - object-type
  - song
  - singer
  - gender
slots:
  attribute:
    type: any
    mappings:
      - type: from_entity
        entity: attribute
  gender:
    type: any
    mappings:
      - type: from_entity
        entity: gender
  knowledge_base_last_object:
    type: any
    mappings:
      - type: custom
  knowledge_base_last_object_type:
    type: any
    mappings:
      - type: custom
  knowledge_base_listed_objects:
    type: any
    mappings:
      - type: custom
  knowledge_base_objects:
    type: any
    mappings:
      - type: custom
  mention:
    type: any
    mappings:
      - type: from_entity
        entity: mention
  object_type:
    type: any
    mappings:
      - type: from_entity
        entity: object_type
  singer:
    type: any
    mappings:
      - type: from_entity
        entity: singer
  song:
    type: any
    mappings:
      - type: from_entity
        entity: song
responses:
  utter_greet:
    - text: 你好，我是 Silly, 一个可以利用知识图谱帮你查询歌手、音乐和专辑的机器人。
  utter_goodbye:
    - text: 再见！
  utter_default:
    - text: 系统不明白您说的话
  utter_ask_rephrase:
    - text: 抱歉系统没能明白您的话，请您重新表述一次
actions:
  - action_response_query
  - utter_goodbye
  - utter_greet
  - utter_default
  - utter_ask_rephrase

recipe: default.v1
language: zh

pipeline:
  - name: JiebaTokenizer
  - name: LanguageModelFeaturizer
    model_name: bert
    model_weights: bert-base-chinese
  - name: RegexFeaturizer
  - name: DIETClassifier
    epochs: 1000
    learning_rate: 0.001
  - name: FallbackClassifier
    threshold: 0.4
    ambiguity_threshold: 0.1
  - name: EntitySynonymMapper     

policies:
  - name: MemoizationPolicy
  - name: TEDPolicy 
  - name: RulePolicy
    core_fallback_threshold: 0.3
    core_fallback_action_name: "action_default_fallback"

action_endpoint:
  url: "http://localhost:5055/webhook"

{
    "song": [
        {
            "id": 0,
            "name": "晴天",
            "singer": "周杰伦",
            "album": "叶惠美",
            "style": "流行,英伦摇滚"
        },
        {
            "id": 1,
            "name": "江南",
            "singer": "林俊杰",
            "album": "第二天堂",
            "style": "流行,中国风"
        },
        {
            "id": 2,
            "name": "舞娘",
            "singer": "蔡依林",
            "album": "舞娘",
            "style": "流行"
        },
        {
            "id": 3,
            "name": "后来",
            "singer": "刘若英",
            "album": "我等你",
            "style": "流行,抒情,经典"
        }
    ],
    "singer": [
        {
            "id": 0,
            "name": "周杰伦",
            "gender": "male",
            "birthday": "1979/01/18"
        },
        {
            "id": 1,
            "name": "林俊杰",
            "gender": "male",
            "birthday": "1979/03/27"
        },
        {
            "id": 2,
            "name": "蔡依林",
            "gender": "female",
            "birthday": "1980/09/15"
        },
        {
            "id": 3,
            "name": "刘若英",
            "gender": "female",
            "birthday": "1969/06/01"
        }
    ]
}

import os
import json

from typing import Any, Dict, List, Text

from rasa_sdk import utils
from rasa_sdk.executor import CollectingDispatcher
from rasa_sdk.knowledge_base.actions import ActionQueryKnowledgeBase
from rasa_sdk.knowledge_base.storage import InMemoryKnowledgeBase

USE_NEO4J = bool(os.getenv("USE_NEO4J", False))

if USE_NEO4J:
    from neo4j_knowledge_base import Neo4jKnowledgeBase


class EnToZh:
    def __init__(self, data_file):
        with open(data_file) as fd:
            self.data = json.load(fd)

    def __call__(self, key):
        return self.data.get(key, key)


class MyKnowledgeBaseAction(ActionQueryKnowledgeBase):
    def name(self) -> Text:
        return "action_response_query"

    def __init__(self):
        if USE_NEO4J:
            print("using Neo4jKnowledgeBase")
            knowledge_base = Neo4jKnowledgeBase(
                "bolt://localhost:7687", "neo4j", "43215678"
            )
        else:
            print("using InMemoryKnowledgeBase")
            knowledge_base = InMemoryKnowledgeBase("data.json")

        super().__init__(knowledge_base)

        self.en_to_zh = EnToZh("en_to_zh.json")

    async def utter_objects(
        self,
        dispatcher: CollectingDispatcher,
        object_type: Text,
        objects: List[Dict[Text, Any]],
    ) -> None:
        """
        Utters a response to the user that lists all found objects.
        Args:
            dispatcher: the dispatcher
            object_type: the object type
            objects: the list of objects
        """
        if objects:
            dispatcher.utter_message(text="找到下列{}:".format(self.en_to_zh(object_type)))

            repr_function = await self.knowledge_base.get_representation_function_of_object(
                    object_type
            )


            for i, obj in enumerate(objects, 1):
                dispatcher.utter_message(text=f"{i}: {repr_function(obj)}")
        else:
            dispatcher.utter_message(
                text="我没找到任何{}.".format(self.en_to_zh(object_type))
            )

    def utter_attribute_value(
        self,
        dispatcher: CollectingDispatcher,
        object_name: Text,
        attribute_name: Text,
        attribute_value: Text,
    ) -> None:
        """
        Utters a response that informs the user about the attribute value of the
        attribute of interest.
        Args:
            dispatcher: the dispatcher
            object_name: the name of the object
            attribute_name: the name of the attribute
            attribute_value: the value of the attribute
        """
        if attribute_value:
            dispatcher.utter_message(
                text="{}的{}是{}。".format(
                    self.en_to_zh(object_name),
                    self.en_to_zh(attribute_name),
                    self.en_to_zh(attribute_value),
                )
            )
        else:
            dispatcher.utter_message(
                text="没有找到{}的{}。".format(
                    self.en_to_zh(object_name), self.en_to_zh(attribute_name)
                )
            )

rasa train
rasa run --cors "*"

rasa run actions

python -m http.server

pip install neo4j

python data_to_neo4j.py

set USE_NEO4J=1

rasa run actions