def run(self, dispatcher, tracker, domain):
graph_database = GraphDatabase()
# first need to know the entity type we are looking for
entity_type = get_entity_type(tracker)
if entity_type is None:
dispatcher.utter_template("utter_rephrase", tracker)
return []
# check what attributes the NER found for entity type
attributes = get_attributes_of_entity(entity_type, tracker)
# query knowledge base
entities = graph_database.get_entities(entity_type, attributes)
# filter out transactions that do not belong the set account (if any)
if entity_type == "transaction":
account_number = tracker.get_slot("account")
entities = self._filter_transaction_entities(
entities, account_number)
if not entities:
dispatcher.utter_template(
"I could not find any entities for '{}'.".format(entity_type),
tracker)
return []
# utter a response that contains all found entities
# use the 'representation' attributes to print an entity
entity_representation = schema[entity_type]["representation"]
dispatcher.utter_message(
"Found the following '{}' entities:".format(entity_type))
sorted_entities = sorted(
[to_str(e, entity_representation) for e in entities])
for i, e in enumerate(sorted_entities):
dispatcher.utter_message(f"{i + 1}: {e}")
# set slots
# set the entities slot in order to resolve references to one of the found
# entites later on
entity_key = schema[entity_type]["key"]
slots = [
SlotSet("entity_type", entity_type),
SlotSet("listed_items",
list(map(lambda x: to_str(x, entity_key), entities))),
]
# if only one entity was found, that the slot of that entity type to the
# found entity
if len(entities) == 1:
slots.append(SlotSet(entity_type, to_str(entities[0], entity_key)))
reset_attribute_slots(slots, entity_type, tracker)
return slots
def run(self, dispatcher, tracker, domain):
graph_database = GraphDatabase()
car_series_list = graph_database.get_entities(entity_type='车系')
dispatcher.utter_message(template="utter_answer", answer="小通找到了下列车系：")
car_series_list_slot = ['车系']
for i, e in enumerate(car_series_list):
answer = str(i + 1) + ": " + e['name']
dispatcher.utter_message(template="utter_answer", answer=answer)
car_series_list_slot.append(e['name'])
slots = [SlotSet("listed_items", car_series_list_slot)]
return slots
from graph_database import GraphDatabase
from py2neo import Graph, Node, data, Path, Relationship
graphDatabase = GraphDatabase()
attributes = [{'name': '2021款 精英型'}]
result = graphDatabase.get_entities(entity_type='车型', attributes=attributes)
print(result) #[{'id': 1, 'label': '车型', 'name': '2021款 精英型'}]
attributes = [{'id': 1}]
result = graphDatabase.get_entities(entity_type='车型', attributes=attributes)
print(result) #[{'id': 1, 'label': '车型', 'name': '2021款 精英型'}]
result = graphDatabase.get_entities(entity_type='车系')
print(result) #[{'id': 1, 'label': '车型', 'name': '2021款 精英型'}]
for i, e in enumerate(result):
print(f"{i + 1}: {e['name']}")
#
# result = graphDatabase.get_entities(entity_type='车型')
# print(result) #[{'id': 955, 'label': '车型', 'name': '2021款 精英型'}, {'id': 963, 'label': '车型', 'name': '2019款 2.4L汽油手动四驱高底盘先锋版长厢'}......]
#
# result = graphDatabase.get_entities(entity_type='车身',limit=2)
# print(result) #[{'id': 957, 'label': '车身', '车身结构': '皮卡', '车门数(个)': 4.0, '高度(mm)': 1809.0, '座位数(个)': '5', '后排车门开启方式': '平开门', 'name': '车身节点', '轴距(mm)': 3155.0, '货箱尺寸(mm)': '1485x1510x530'}, {'id': 965, 'label': '车身', '车身结构': '皮卡', '车门数(个)': 4.0, '高度(mm)': 1809.0, '座位数(个)': '5', '后排车门开启方式': '平开门', 'name': '车身节点', '轴距(mm)': 3470.0, '货箱尺寸(mm)': '1800x1510x530'}]
#
# result = graphDatabase.get_entities(entity_type='发动机',limit=2)
# print(result) #[{'id': 961, 'label': '发动机', '最大扭矩(N·m)': '310', '最大功率(kW)': 130.0, 'name': '发动机节点'}, {'id': 968, 'label': '发动机', '每缸气门数(个)': '4', '供油方式': '多点电喷', '排量(L)': '2.4', '进气形式': '自然吸气', '最大扭矩(N·m)': '200', '环保标准': '国V', '最大功率(kW)': 105.0, '最大功率转速(rpm)': '5250', '最大马力(Ps)': 143.0, '最大扭矩转速(rpm)': '2500-3000', '缸体材料': '未知', '气缸数(个)': '4', '气缸排列形式': 'L', '缸盖材料': '铝合金', 'name': '发动机节点', '燃料形式': '汽油', '燃油标号': '92号', '配气机构': '未知'}]
#
# attributes = [{'供油方式':'直喷','排量(L)':'2.0'}]
# result = graphDatabase.get_entities(entity_type='发动机',attributes=attributes,limit=2)
# print(result) #[{'id': 1029, 'label': '发动机', '进气形式': '涡轮增压', '每缸气门数(个)': '4', '供油方式': '直喷', '排量(L)': '2.0', '最大扭矩(N·m)': '375', '环保标准': '国VI', '最大功率(kW)': 120.0, '最大马力(Ps)': 163.0, '缸体材料': '未知', '气缸排列形式': 'L', '气缸数(个)': '4', '缸盖材料': '未知', 'name': '发动机节点', '燃料形式': '柴油', '燃油标号': '0号', '配气机构': '未知'}, {'id': 1039, 'label': '发动机', '进气形式': '涡轮增压', '每缸气门数(个)': '4', '供油方式': '直喷', '排量(L)': '2.0', '最大扭矩(N·m)': '350', '环保标准': '国VI', '最大功率(kW)': 160.0, '最大马力(Ps)': 218.0, '缸体材料': '未知', '气缸排列形式': 'L', '气缸数(个)': '4', '缸盖材料': '铝合金', 'name': '发动机节点', '燃料形式': '汽油', '燃油标号': '92号', '配气机构': '未知'}]
#
# entity_id = 1029
# aimed_attribute = '供油方式'
