一、pyahocorasick

• pip install pyahocorasick ：安裝名為 pyahocorasick 的第三方庫
  👉 這個庫是一個 Aho-Corasick 多模匹配算法 的 Python 實現，常用于高效的多關鍵詞搜索。

• -i https://pypi.tuna.tsinghua.edu.cn/simple/ ：指定 pip 使用 清華鏡像源，下載會更快、更穩定，尤其是在國內網絡環境下。

2.介紹

? pyahocorasick 是 Python 的一個第三方庫，
實現了 Aho–Corasick 多模式串匹配算法（自動機算法）。

👉 簡單理解：
如果你有 很多關鍵詞，想要在 一段文本 里 一次性高效匹配出所有關鍵詞的位置，用普通的 for 循環挨個匹配會很慢，而 Aho–Corasick 算法可以用 O(n + m + k) 的時間（接近線性時間）搞定，非常高效。

二、創建醫療知識圖譜

#選材自開源項目(劉煥勇，中國科學院軟件研究所)，數據集來自互聯網爬蟲數據
import os
import json
from py2neo import Graph,Nodeclass MedicalGraph:def __init__(self):cur_dir = '/'.join(os.path.abspath(__file__).split('/')[:-1])self.data_path = os.path.join(cur_dir, 'data/medical2.json')self.g = Graph("bolt://localhost:7687", auth=("neo4j", "weixuanlv0304"))'''讀取文件'''def read_nodes(self):# 共７類節點drugs = [] # 藥品foods = [] #　食物checks = [] # 檢查departments = [] #科室producers = [] #藥品大類diseases = [] #疾病symptoms = []#癥狀disease_infos = []#疾病信息# 構建節點實體關系rels_department = [] #　科室－科室關系rels_noteat = [] # 疾病－忌吃食物關系rels_doeat = [] # 疾病－宜吃食物關系rels_recommandeat = [] # 疾病－推薦吃食物關系rels_commonddrug = [] # 疾病－通用藥品關系rels_recommanddrug = [] # 疾病－熱門藥品關系rels_check = [] # 疾病－檢查關系rels_drug_producer = [] # 廠商－藥物關系rels_symptom = [] #疾病癥狀關系rels_acompany = [] # 疾病并發關系rels_category = [] #　疾病與科室之間的關系count = 0for data in open(self.data_path):disease_dict = {}count += 1print(count)data_json = json.loads(data)disease = data_json['name']disease_dict['name'] = diseasediseases.append(disease)disease_dict['desc'] = ''disease_dict['prevent'] = ''disease_dict['cause'] = ''disease_dict['easy_get'] = ''disease_dict['cure_department'] = ''disease_dict['cure_way'] = ''disease_dict['cure_lasttime'] = ''disease_dict['symptom'] = ''disease_dict['cured_prob'] = ''if 'symptom' in data_json:symptoms += data_json['symptom']for symptom in data_json['symptom']:rels_symptom.append([disease, symptom])if 'acompany' in data_json:for acompany in data_json['acompany']:rels_acompany.append([disease, acompany])if 'desc' in data_json:disease_dict['desc'] = data_json['desc']if 'prevent' in data_json:disease_dict['prevent'] = data_json['prevent']if 'cause' in data_json:disease_dict['cause'] = data_json['cause']if 'get_prob' in data_json:disease_dict['get_prob'] = data_json['get_prob']if 'easy_get' in data_json:disease_dict['easy_get'] = data_json['easy_get']if 'cure_department' in data_json:cure_department = data_json['cure_department']if len(cure_department) == 1:rels_category.append([disease, cure_department[0]])if len(cure_department) == 2:big = cure_department[0]small = cure_department[1]rels_department.append([small, big])rels_category.append([disease, small])disease_dict['cure_department'] = cure_departmentdepartments += cure_departmentif 'cure_way' in data_json:disease_dict['cure_way'] = data_json['cure_way']if  'cure_lasttime' in data_json:disease_dict['cure_lasttime'] = data_json['cure_lasttime']if 'cured_prob' in data_json:disease_dict['cured_prob'] = data_json['cured_prob']if 'common_drug' in data_json:common_drug = data_json['common_drug']for drug in common_drug:rels_commonddrug.append([disease, drug])drugs += common_drugif 'recommand_drug' in data_json:recommand_drug = data_json['recommand_drug']drugs += recommand_drugfor drug in recommand_drug:rels_recommanddrug.append([disease, drug])if 'not_eat' in data_json:not_eat = data_json['not_eat']for _not in not_eat:rels_noteat.append([disease, _not])foods += not_eatdo_eat = data_json['do_eat']for _do in do_eat:rels_doeat.append([disease, _do])foods += do_eatrecommand_eat = data_json['recommand_eat']for _recommand in recommand_eat:rels_recommandeat.append([disease, _recommand])foods += recommand_eatif 'check' in data_json:check = data_json['check']for _check in check:rels_check.append([disease, _check])checks += checkif 'drug_detail' in data_json:drug_detail = data_json['drug_detail']producer = [i.split('(')[0] for i in drug_detail]rels_drug_producer += [[i.split('(')[0], i.split('(')[-1].replace(')', '')] for i in drug_detail]producers += producerdisease_infos.append(disease_dict)return set(drugs), set(foods), set(checks), set(departments), set(producers), set(symptoms), set(diseases), disease_infos,\rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,\rels_symptom, rels_acompany, rels_category'''建立節點'''def create_node(self, label, nodes):count = 0for node_name in nodes:node = Node(label, name=node_name)self.g.create(node)count += 1print(count, len(nodes))return'''創建知識圖譜中心疾病的節點'''def create_diseases_nodes(self, disease_infos):count = 0for disease_dict in disease_infos:node = Node("Disease", name=disease_dict['name'], desc=disease_dict['desc'],prevent=disease_dict['prevent'] ,cause=disease_dict['cause'],easy_get=disease_dict['easy_get'],cure_lasttime=disease_dict['cure_lasttime'],cure_department=disease_dict['cure_department'],cure_way=disease_dict['cure_way'] , cured_prob=disease_dict['cured_prob'])self.g.create(node)count += 1print(count)return'''創建知識圖譜實體節點類型schema'''def create_graphnodes(self):Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos,rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,rels_symptom, rels_acompany, rels_category = self.read_nodes()self.create_diseases_nodes(disease_infos)self.create_node('Drug', Drugs)print(len(Drugs))self.create_node('Food', Foods)print(len(Foods))self.create_node('Check', Checks)print(len(Checks))self.create_node('Department', Departments)print(len(Departments))self.create_node('Producer', Producers)print(len(Producers))self.create_node('Symptom', Symptoms)return'''創建實體關系邊'''def create_graphrels(self):Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos, rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,rels_symptom, rels_acompany, rels_category = self.read_nodes()self.create_relationship('Disease', 'Food', rels_recommandeat, 'recommand_eat', '推薦食譜')self.create_relationship('Disease', 'Food', rels_noteat, 'no_eat', '忌吃')self.create_relationship('Disease', 'Food', rels_doeat, 'do_eat', '宜吃')self.create_relationship('Department', 'Department', rels_department, 'belongs_to', '屬于')self.create_relationship('Disease', 'Drug', rels_commonddrug, 'common_drug', '常用藥品')self.create_relationship('Producer', 'Drug', rels_drug_producer, 'drugs_of', '生產藥品')self.create_relationship('Disease', 'Drug', rels_recommanddrug, 'recommand_drug', '好評藥品')self.create_relationship('Disease', 'Check', rels_check, 'need_check', '診斷檢查')self.create_relationship('Disease', 'Symptom', rels_symptom, 'has_symptom', '癥狀')self.create_relationship('Disease', 'Disease', rels_acompany, 'acompany_with', '并發癥')self.create_relationship('Disease', 'Department', rels_category, 'belongs_to', '所屬科室')'''創建實體關聯邊'''def create_relationship(self, start_node, end_node, edges, rel_type, rel_name):count = 0# 去重處理set_edges = []for edge in edges:set_edges.append('###'.join(edge))all = len(set(set_edges))for edge in set(set_edges):edge = edge.split('###')p = edge[0]q = edge[1]query = "match(p:%s),(q:%s) where p.name='%s'and q.name='%s' create (p)-[rel:%s{name:'%s'}]->(q)" % (start_node, end_node, p, q, rel_type, rel_name)try:self.g.run(query)count += 1print(rel_type, count, all)except Exception as e:print(e)return'''導出數據'''def export_data(self):Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos, rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug, rels_symptom, rels_acompany, rels_category = self.read_nodes()f_drug = open('drug.txt', 'w+')f_food = open('food.txt', 'w+')f_check = open('check.txt', 'w+')f_department = open('department.txt', 'w+')f_producer = open('producer.txt', 'w+')f_symptom = open('symptoms.txt', 'w+')f_disease = open('disease.txt', 'w+')f_drug.write('\n'.join(list(Drugs)))f_food.write('\n'.join(list(Foods)))f_check.write('\n'.join(list(Checks)))f_department.write('\n'.join(list(Departments)))f_producer.write('\n'.join(list(Producers)))f_symptom.write('\n'.join(list(Symptoms)))f_disease.write('\n'.join(list(Diseases)))f_drug.close()f_food.close()f_check.close()f_department.close()f_producer.close()f_symptom.close()f_disease.close()returnif __name__ == '__main__':handler = MedicalGraph()#handler.export_data()handler.create_graphnodes()handler.create_graphrels()

三、問答機器人對話

?

from question_classifier import *
from question_parser import *
from answer_search import *'''問答類'''
class ChatBotGraph:def __init__(self):self.classifier = QuestionClassifier()self.parser = QuestionPaser()self.searcher = AnswerSearcher()def chat_main(self, sent):answer = '沒能理解您的問題，我數據量有限。。。能不能問的標準點'res_classify = self.classifier.classify(sent)if not res_classify:return answerres_sql = self.parser.parser_main(res_classify)final_answers = self.searcher.search_main(res_sql)if not final_answers:return answerelse:return '\n'.join(final_answers)if __name__ == '__main__':handler = ChatBotGraph()while 1:question = input('咨詢:')answer = handler.chat_main(question)print('客服機器人:', answer)