基于知识图谱的医疗问答系统(dockerfile+docker-compose)

ops/2024/12/23 13:50:47/

文章目录

  • 一、搭建 Neo4j 图数据库
    • 1、方式选择
    • 2、Dockerfile+docker-compose部署neo4j容器
      • 2.1、更新 yum 镜像源
      • 2.2、安装 docker-ce 社区版
      • 2.3、配置镜像加速
      • 2.4、安装 Docker Compose
        • 2.4.1、下载 Docker Compose 二进制包
        • 2.4.2、设置可执行权限
        • 2.4.3、查看版本
      • 2.5、创建目录结构
      • 2.6、编写neo4j.conf配置文件
      • 2.7、编写 dockerfile 文件
      • 2.8、构建ne4j容器镜像
      • 2.9、编写docker-compose.yaml文件
      • 2.10、运行docker-compose
      • 2.11、浏览器登录 neo4j
  • 二、Neo4j 初始配置
    • 1、清空 Neo4j 数据库
  • 三、PyCharm 项目安装必备库
    • 1、py2neo 库
    • 2、pymongo 库
    • 3、lxml 库
  • 四、python 连接 Neo4j
    • 1、浏览器 browser 查看Neo4j 连接状态
    • 2、修改源文件中 Graph 连接格式
  • 五、PyCharm 导入医疗知识图谱
    • 1、读取文件
    • 2、建立节点
    • 3、创建知识图谱中心疾病的节点
    • 4、创建知识图谱实体节点类型schema
    • 5、创建实体关系边
    • 6、创建实体关联边
    • 7、导出数据
    • 8、程序主入口
      • 8.1、UnicodeDecodeError: 'gbk' codec can't decode byte 0xaf in position 81: illegal multibyte sequence
      • 8.2、修改代码:for data in open(self.data_path):
    • 9、运行结果
    • 10、优化导入数据时间
  • 六、PyCharm 实现问答系统
    • 1、问句类型分类脚本
    • 2、问句解析脚本
    • 3、问答程序脚本
    • 4、问答系统实现
      • 4.1、模型初始化
      • 4.2、问答主函数
      • 4.3、运行主入口
      • 4.4、运行结果

说在前面:参考刘焕勇老师在 Github 上开源的项目

GitHub地址:基于知识图谱的医疗问答系统

一、搭建 Neo4j 图数据库

1、方式选择

  • windows 使用 Neo4j Desktop (2024-12-09开始 Neo4j desktop 无法打开表现为三个/四个僵尸进程,查看本地日志会发现[403]无法获取到https://dist.neo4j.org/neo4j-desktop/win/latest.yml这个路径的资源。解决方案:断网打开 Neo4j Desktop / Neo4j Desktop 1.5.8 Launches Zombie Processes Only - Neo4j Graph Platform / Desktop - Neo4j Online Community)
  • 云环境 dockerfile + docker-compose (部署构建简单易懂无需专注 jdk 版本,优先考虑)
  • 最终理想化:kubernetes 部署 (符合主流技术导向,虽说部署较复杂且多坑但是企业级以及行业主导地位等因素使用 k8s 部署还是最佳实践)

首次部署优先采用 dockerfile + docker-compose

dockercomposeneo4j_15">2、Dockerfile+docker-compose部署neo4j容器

2.1、更新 yum 镜像源

rm -rf /etc/yum.repos.d/*
wget -O /etc/yum.repos.d/centos7.repo http://mirrors.aliyun.com/repo/Centos-7.repo
wget -O /etc/yum.repos.d/epel-7.repo http://mirrors.aliyun.com/repo/epel-7.repo
wget -O /etc/yum.repos.d/docker-ce.repo https://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo

dockerce__26">2.2、安装 docker-ce 社区版

yum install -y docker-ce

2.3、配置镜像加速

cat > /etc/docker/daemon.json << EOF
{"exec-opts": ["native.cgroupdriver=systemd"],"registry-mirrors": ["https://dockerhub.icu","https://hub.rat.dev","https://docker.wanpeng.top","https://doublezonline.cloud","https://docker.mrxn.net","https://docker.anyhub.us.kg","https://dislabaiot.xyz","https://docker.fxxk.dedyn.io"]
}
EOFsystemctl daemon-reload && systemctl restart docker && systemctl enable docker

2.4、安装 Docker Compose

Releases · docker/compose

2.4.1、下载 Docker Compose 二进制包
curl -L "https://github.com/docker/compose/releases/download/v2.5.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
  • -L: 是curl的一个选项,表示跟随重定向。如果下载链接是重定向的,这个选项会让curl自动跟踪到最后的目标地址。
  • "https://github.com/docker/compose/releases/download/v2.5.1/docker-compose-$(uname -s)-$(uname -m)": 这是Docker Compose的下载URL,其中v2.5.1指定了要下载的Docker Compose版本号。$(uname -s)$(uname -m) 是shell命令,分别返回当前系统的类型(如Linux)和机器的硬件架构(如x86_64),这样可以确保下载与当前系统架构相匹配的Docker Compose二进制文件。
  • -o /usr/local/bin/docker-compose: -o--output 指定了下载文件的保存位置及名称。这里,文件会被保存为 /usr/local/bin/docker-compose,这是Docker Compose常见的安装路径,将其放在此处可以使其在PATH环境变量中,从而可以直接在命令行中通过docker-compose命令调用。
2.4.2、设置可执行权限
chmod +x /usr/local/bin/docker-compose
2.4.3、查看版本
docker-compose -v

2.5、创建目录结构

mkdir -p neo4j-docker/{conf,data,import,logs} && touch neo4j-docker/conf/neo4j.confchown -R neo4j:neo4j ./{conf,data,import,logs}chmod 755 ./{conf,data,logs,import}tree -L 2 neo4j-docker
neo4j-docker
├── conf
│   └── neo4j.conf
├── data
├── import
└── logs

2.6、编写neo4j.conf配置文件

cat > /root/neo4j-docker/conf/neo4j.conf <<  EOF
server.directories.import=/var/lib/neo4j/import
server.memory.pagecache.size=512Mserver.default_listen_address=0.0.0.0
dbms.security.allow_csv_import_from_file_urls=true
server.directories.logs=/logs
EOF

dockerfile__111">2.7、编写 dockerfile 文件

dockerfile">cat > /root/neo4j-docker/Dockerfile << EOF
# 使用官方 Neo4j 最新版本镜像作为基础镜像
FROM neo4j:latest# 设置环境变量,仅用于配置 Neo4j 认证
ENV NEO4J_AUTH=neo4j/neo4jpassword# 拷贝本地的配置文件到容器中
COPY ./conf/neo4j.conf /var/lib/neo4j/conf/# 定义容器启动时执行的命令
CMD ["neo4j"]
EOF

2.8、构建ne4j容器镜像

# 命令位置需要与Dockerfile位置同级
docker build -t my_neo4j:v1 .

image-20241210102548272

dockercomposeyaml_138">2.9、编写docker-compose.yaml文件

有坑:neo4j 5.x 版本所需密码位数需要在 8 位以上

version: '3'
services:neo4j:build: .image: my_neo4j:v1container_name: neo4j_containerrestart: alwaysports:- "7474:7474"- "7687:7687"environment:- NEO4J_AUTH=neo4j/neo4jpasswordvolumes:- ./data:/data- ./logs:/logs- ./import:/var/lib/neo4j/import- ./conf:/var/lib/neo4j/confcommand: ["neo4j"]

dockercompose_163">2.10、运行docker-compose

docker-compose -f docker-compose.yaml up -d

2.11、浏览器登录 neo4j

http://192.168.112.30:7474# 输入用户名:neo4j
# 输入密码:neo4jpassword

二、Neo4j 初始配置

1、清空 Neo4j 数据库

MATCH (n) DETACH DELETE n

image-20241217225238581

三、PyCharm 项目安装必备库

1、py2neo 库

pip install py2neo
  • 简化 Neo4j 连接和查询

    • 连接到 Neo4jpy2neo 提供了简单易用的接口来连接到 Neo4j 数据库,支持 HTTP 和 Bolt 协议。
    • 执行 Cypher 查询py2neo 允许你直接执行 Cypher 查询(Neo4j 的图查询语言),并以 Python 对象的形式返回结果。
  • 创建和管理图数据

    • 创建节点和关系py2neo 提供了高级抽象,允许你像操作 Python 对象一样创建和管理 Neo4j 中的节点和关系。你可以使用 NodeRelationship 类来表示图中的实体,并将它们保存到数据库中。
    • 批量操作py2neo 支持批量创建节点和关系,提高性能,减少网络往返次数。

2、pymongo 库

pip install pymongo
  • 用于连接和操作 MongoDB 数据库,读取、处理并重新插入医疗数据。
  • 提供了高效的 CRUD 操作,支持批量数据处理。

3、lxml 库

pip install lxml
  • 用于解析存储在 MongoDB 中的 HTML 文档,提取有用的医疗检查信息(如疾病名称、描述等)。
  • 通过 XPath 提取数据,并进行必要的清理和格式化。

四、python 连接 Neo4j

1、浏览器 browser 查看Neo4j 连接状态

:server status

image-20241217231334624

记住 URL (不是传统意义上的 http://,以及默认的端口号7474)

2、修改源文件中 Graph 连接格式

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/medical.json')self.g = Graph("neo4j://192.168.112.30:7687", auth=("neo4j", "neo4jpassword"))

build_medicalgraph.pyanswer_search.py 两个原文件中的 self.g = Graph() 的连接格式都更改为上述代码中的格式。

五、PyCharm 导入医疗知识图谱

1、读取文件

# 读取文件def read_nodes(self):# 共7类节点drugs = [] # 药品foods = [] # 食物checks = [] # 检查departments = [] #科室producers = [] #药品大类diseases = [] #疾病symptoms = []#症状disease_infos = []#疾病信息# 构建节点实体关系rels_department = [] # 科室-科室关系rels_noteat = [] # 疾病-忌吃食物关系rels_doeat = [] # 疾病-宜吃食物关系rels_recommandeat = [] # 疾病-推荐吃食物关系rels_commonddrug = [] # 疾病-通用药品关系rels_recommanddrug = [] # 疾病-热门药品关系rels_check = [] # 疾病-检查关系rels_drug_producer = [] # 厂商-药物关系rels_symptom = [] #疾病症状关系rels_acompany = [] # 疾病并发关系rels_category = [] # 疾病与科室之间的关系count = 0for data in open(self.data_path, encoding='utf8', mode='r'):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

2、建立节点

# 建立节点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

3、创建知识图谱中心疾病的节点

# 创建知识图谱中心疾病的节点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

4、创建知识图谱实体节点类型schema

# 创建知识图谱实体节点类型schemadef 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

5、创建实体关系边

# 创建实体关系边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', '推荐食谱')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', '属于')self.create_relationship('Disease', 'Drug', rels_commonddrug, 'common_drug', '常用药品')self.create_relationship('Producer', 'Drug', rels_drug_producer, 'drugs_of', '生产药品')self.create_relationship('Disease', 'Drug', rels_recommanddrug, 'recommand_drug', '好评药品')self.create_relationship('Disease', 'Check', rels_check, 'need_check', '诊断检查')self.create_relationship('Disease', 'Symptom', rels_symptom, 'has_symptom', '症状')self.create_relationship('Disease', 'Disease', rels_acompany, 'acompany_with', '并发症')self.create_relationship('Disease', 'Department', rels_category, 'belongs_to', '所属科室')

6、创建实体关联边

# 创建实体关联边def create_relationship(self, start_node, end_node, edges, rel_type, rel_name):count = 0# 去重处理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

7、导出数据

# 导出数据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()return

8、程序主入口

if __name__ == '__main__':handler = MedicalGraph()print("step1:导入图谱节点中")handler.create_graphnodes()print("step2:导入图谱边中")      handler.create_graphrels()
# 创建知识节点和边(nodes + rels)
# handler.create_graphnodes()
# handler.create_graphrels()
快捷键:Ctrl + Shift + F10

8.1、UnicodeDecodeError: ‘gbk’ codec can’t decode byte 0xaf in position 81: illegal multibyte sequence

直接运行会报错:UnicodeDecodeError: ‘gbk’ codec can’t decode byte 0xaf in position 81: illegal multibyte sequence

8.2、修改代码:for data in open(self.data_path):

for data in open(self.data_path, encoding='utf8', mode='r'):
  • 需要确保文件的编码格式为 utf8
  • 打开文件模式为只读模式

9、运行结果

image-20241217234528761

10、优化导入数据时间

import concurrent
import concurrent.futures
import json
import multiprocessing
import osfrom py2neo import Graph, Node, Subgraph
from tqdm import tqdmclass MedicalGraph:def __init__(self):passdef clear(self):self.g.run("MATCH (n) DETACH DELETE n")'''读取文件'''def read_nodes(self):# 共7类节点drugs = []  # 药品foods = []  # 食物checks = []  # 检查departments = []  # 科室producers = []  # 药品大类diseases = []  # 疾病symptoms = []  # 症状disease_infos = []  # 疾病信息# 构建节点实体关系rels_department = []  # 科室-科室关系rels_noteat = []  # 疾病-忌吃食物关系rels_doeat = []  # 疾病-宜吃食物关系rels_recommandeat = []  # 疾病-推荐吃食物关系rels_commonddrug = []  # 疾病-通用药品关系rels_recommanddrug = []  # 疾病-热门药品关系rels_check = []  # 疾病-检查关系rels_drug_producer = []  # 厂商-药物关系rels_symptom = []  # 疾病症状关系rels_acompany = []  # 疾病并发关系rels_category = []  # 疾病与科室之间的关系for data in open(self.data_path):disease_dict = {}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'''建立节点'''def create_node(self, label, nodes):batch_size = 1000batches = [list(nodes)[i:i + batch_size] for i in range(0, len(nodes), batch_size)]for batch in tqdm(batches, desc=f"Creating {label} Nodes", unit="batch"):batch_nodes = [Node(label, name=node_name) for node_name in batch]self.g.create(Subgraph(batch_nodes))'''创建知识图谱中心疾病的节点'''def create_diseases_nodes(self, disease_infos):batch_size = 1000batches = [disease_infos[i:i + batch_size] for i in range(0, len(disease_infos), batch_size)]for batch in tqdm(batches, desc="Importing Disease Nodes", unit="batch"):batch_nodes = [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']) for disease_dict in batch]self.g.create(Subgraph(batch_nodes))'''创建知识图谱实体节点类型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)self.create_node('Food', Foods)self.create_node('Check', Checks)self.create_node('Department', Departments)self.create_node('Producer', Producers)self.create_node('Symptom', Symptoms)'''创建实体关系边'''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', '推荐食谱')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', '属于')self.create_relationship('Disease', 'Drug', rels_commonddrug, 'common_drug', '常用药品')self.create_relationship('Producer', 'Drug', rels_drug_producer, 'drugs_of', '生产药品')self.create_relationship('Disease', 'Drug', rels_recommanddrug, 'recommand_drug', '好评药品')self.create_relationship('Disease', 'Check', rels_check, 'need_check', '诊断检查')self.create_relationship('Disease', 'Symptom', rels_symptom, 'has_symptom', '症状')self.create_relationship('Disease', 'Disease', rels_acompany, 'acompany_with', '并发症')self.create_relationship('Disease', 'Department', rels_category, 'belongs_to', '所属科室')'''创建实体关联边'''def create_relationship(self, start_node, end_node, edges, rel_type, rel_name):batch_size = 10000set_edges = set(['###'.join(edge) for edge in edges])batches = [list(set_edges)[i:i + batch_size] for i in range(0, len(set_edges), batch_size)]executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(multiprocessing.cpu_count(), 4))tasks = [lambda: (tx := self.g.begin(),[tx.run(f"MATCH (p:{start_node}), (q:{end_node}) "f"WHERE p.name='{p}' AND q.name='{q}' "f"CREATE (p)-[rel:{rel_type} {{name:'{rel_name}'}}]->(q)") for edge in batch for p, q in [edge.split('###')]],self.g.commit(tx)) for batch in tqdm(batches, desc=f"Creating {rel_type} Relationships", unit="batch")]executor.map(lambda task: task(), tasks)executor.shutdown()'''导出数据'''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()if __name__ == '__main__':handler = MedicalGraph()handler.clear()print("step1:导入图谱节点中")handler.create_graphnodes()print("step2:导入图谱边中")handler.create_graphrels()

六、PyCharm 实现问答系统

1、问句类型分类脚本

这里 加载多个特征词列表 处需要保证文件编码格式为 utf8

即添加内容:encoding=‘utf8’

import os
import ahocorasickclass QuestionClassifier:def __init__(self):cur_dir = '/'.join(os.path.abspath(__file__).split('/')[:-1])# 特征词路径self.disease_path = os.path.join(cur_dir, 'dict/disease.txt')self.department_path = os.path.join(cur_dir, 'dict/department.txt')self.check_path = os.path.join(cur_dir, 'dict/check.txt')self.drug_path = os.path.join(cur_dir, 'dict/drug.txt')self.food_path = os.path.join(cur_dir, 'dict/food.txt')self.producer_path = os.path.join(cur_dir, 'dict/producer.txt')self.symptom_path = os.path.join(cur_dir, 'dict/symptom.txt')self.deny_path = os.path.join(cur_dir, 'dict/deny.txt')# 加载特征词self.disease_wds= [i.strip() for i in open(self.disease_path,encoding='utf8') if i.strip()]self.department_wds= [i.strip() for i in open(self.department_path,encoding='utf8') if i.strip()]self.check_wds= [i.strip() for i in open(self.check_path,encoding='utf8') if i.strip()]self.drug_wds= [i.strip() for i in open(self.drug_path,encoding='utf8') if i.strip()]self.food_wds= [i.strip() for i in open(self.food_path,encoding='utf8') if i.strip()]self.producer_wds= [i.strip() for i in open(self.producer_path,encoding='utf8') if i.strip()]self.symptom_wds= [i.strip() for i in open(self.symptom_path,encoding='utf8') if i.strip()]self.region_words = set(self.department_wds + self.disease_wds + self.check_wds + self.drug_wds + self.food_wds + self.producer_wds + self.symptom_wds)self.deny_words = [i.strip() for i in open(self.deny_path,encoding='utf8') if i.strip()]# 构造领域actreeself.region_tree = self.build_actree(list(self.region_words))# 构建词典self.wdtype_dict = self.build_wdtype_dict()# 问句疑问词self.symptom_qwds = ['症状', '表征', '现象', '症候', '表现']self.cause_qwds = ['原因','成因', '为什么', '怎么会', '怎样才', '咋样才', '怎样会', '如何会', '为啥', '为何', '如何才会', '怎么才会', '会导致', '会造成']self.acompany_qwds = ['并发症', '并发', '一起发生', '一并发生', '一起出现', '一并出现', '一同发生', '一同出现', '伴随发生', '伴随', '共现']self.food_qwds = ['饮食', '饮用', '吃', '食', '伙食', '膳食', '喝', '菜' ,'忌口', '补品', '保健品', '食谱', '菜谱', '食用', '食物','补品']self.drug_qwds = ['药', '药品', '用药', '胶囊', '口服液', '炎片']self.prevent_qwds = ['预防', '防范', '抵制', '抵御', '防止','躲避','逃避','避开','免得','逃开','避开','避掉','躲开','躲掉','绕开','怎样才能不', '怎么才能不', '咋样才能不','咋才能不', '如何才能不','怎样才不', '怎么才不', '咋样才不','咋才不', '如何才不','怎样才可以不', '怎么才可以不', '咋样才可以不', '咋才可以不', '如何可以不','怎样才可不', '怎么才可不', '咋样才可不', '咋才可不', '如何可不']self.lasttime_qwds = ['周期', '多久', '多长时间', '多少时间', '几天', '几年', '多少天', '多少小时', '几个小时', '多少年']self.cureway_qwds = ['怎么治疗', '如何医治', '怎么医治', '怎么治', '怎么医', '如何治', '医治方式', '疗法', '咋治', '怎么办', '咋办', '咋治']self.cureprob_qwds = ['多大概率能治好', '多大几率能治好', '治好希望大么', '几率', '几成', '比例', '可能性', '能治', '可治', '可以治', '可以医']self.easyget_qwds = ['易感人群', '容易感染', '易发人群', '什么人', '哪些人', '感染', '染上', '得上']self.check_qwds = ['检查', '检查项目', '查出', '检查', '测出', '试出']self.belong_qwds = ['属于什么科', '属于', '什么科', '科室']self.cure_qwds = ['治疗什么', '治啥', '治疗啥', '医治啥', '治愈啥', '主治啥', '主治什么', '有什么用', '有何用', '用处', '用途','有什么好处', '有什么益处', '有何益处', '用来', '用来做啥', '用来作甚', '需要', '要']print('model init finished ......')return'''分类主函数'''def classify(self, question):data = {}medical_dict = self.check_medical(question)if not medical_dict:return {}data['args'] = medical_dict#收集问句当中所涉及到的实体类型types = []for type_ in medical_dict.values():types += type_question_type = 'others'question_types = []# 症状if self.check_words(self.symptom_qwds, question) and ('disease' in types):question_type = 'disease_symptom'question_types.append(question_type)if self.check_words(self.symptom_qwds, question) and ('symptom' in types):question_type = 'symptom_disease'question_types.append(question_type)# 原因if self.check_words(self.cause_qwds, question) and ('disease' in types):question_type = 'disease_cause'question_types.append(question_type)# 并发症if self.check_words(self.acompany_qwds, question) and ('disease' in types):question_type = 'disease_acompany'question_types.append(question_type)# 推荐食品if self.check_words(self.food_qwds, question) and 'disease' in types:deny_status = self.check_words(self.deny_words, question)if deny_status:question_type = 'disease_not_food'else:question_type = 'disease_do_food'question_types.append(question_type)#已知食物找疾病if self.check_words(self.food_qwds+self.cure_qwds, question) and 'food' in types:deny_status = self.check_words(self.deny_words, question)if deny_status:question_type = 'food_not_disease'else:question_type = 'food_do_disease'question_types.append(question_type)# 推荐药品if self.check_words(self.drug_qwds, question) and 'disease' in types:question_type = 'disease_drug'question_types.append(question_type)# 药品治啥病if self.check_words(self.cure_qwds, question) and 'drug' in types:question_type = 'drug_disease'question_types.append(question_type)# 疾病接受检查项目if self.check_words(self.check_qwds, question) and 'disease' in types:question_type = 'disease_check'question_types.append(question_type)# 已知检查项目查相应疾病if self.check_words(self.check_qwds+self.cure_qwds, question) and 'check' in types:question_type = 'check_disease'question_types.append(question_type)# 症状防御if self.check_words(self.prevent_qwds, question) and 'disease' in types:question_type = 'disease_prevent'question_types.append(question_type)# 疾病医疗周期if self.check_words(self.lasttime_qwds, question) and 'disease' in types:question_type = 'disease_lasttime'question_types.append(question_type)# 疾病治疗方式if self.check_words(self.cureway_qwds, question) and 'disease' in types:question_type = 'disease_cureway'question_types.append(question_type)# 疾病治愈可能性if self.check_words(self.cureprob_qwds, question) and 'disease' in types:question_type = 'disease_cureprob'question_types.append(question_type)# 疾病易感染人群if self.check_words(self.easyget_qwds, question) and 'disease' in types :question_type = 'disease_easyget'question_types.append(question_type)# 若没有查到相关的外部查询信息,那么则将该疾病的描述信息返回if question_types == [] and 'disease' in types:question_types = ['disease_desc']# 若没有查到相关的外部查询信息,那么则将该疾病的描述信息返回if question_types == [] and 'symptom' in types:question_types = ['symptom_disease']# 将多个分类结果进行合并处理,组装成一个字典data['question_types'] = question_typesreturn data'''构造词对应的类型'''def build_wdtype_dict(self):wd_dict = dict()for wd in self.region_words:wd_dict[wd] = []if wd in self.disease_wds:wd_dict[wd].append('disease')if wd in self.department_wds:wd_dict[wd].append('department')if wd in self.check_wds:wd_dict[wd].append('check')if wd in self.drug_wds:wd_dict[wd].append('drug')if wd in self.food_wds:wd_dict[wd].append('food')if wd in self.symptom_wds:wd_dict[wd].append('symptom')if wd in self.producer_wds:wd_dict[wd].append('producer')return wd_dict'''构造actree,加速过滤'''def build_actree(self, wordlist):actree = ahocorasick.Automaton()for index, word in enumerate(wordlist):actree.add_word(word, (index, word))actree.make_automaton()return actree'''问句过滤'''def check_medical(self, question):region_wds = []for i in self.region_tree.iter(question):wd = i[1][1]region_wds.append(wd)stop_wds = []for wd1 in region_wds:for wd2 in region_wds:if wd1 in wd2 and wd1 != wd2:stop_wds.append(wd1)final_wds = [i for i in region_wds if i not in stop_wds]final_dict = {i:self.wdtype_dict.get(i) for i in final_wds}return final_dict'''基于特征词进行分类'''def check_words(self, wds, sent):for wd in wds:if wd in sent:return Truereturn Falseif __name__ == '__main__':handler = QuestionClassifier()while 1:question = input('input an question:')data = handler.classify(question)print(data)

2、问句解析脚本

class QuestionPaser:'''构建实体节点'''def build_entitydict(self, args):entity_dict = {}for arg, types in args.items():for type in types:if type not in entity_dict:entity_dict[type] = [arg]else:entity_dict[type].append(arg)return entity_dict'''解析主函数'''def parser_main(self, res_classify):args = res_classify['args']entity_dict = self.build_entitydict(args)question_types = res_classify['question_types']sqls = []for question_type in question_types:sql_ = {}sql_['question_type'] = question_typesql = []if question_type == 'disease_symptom':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'symptom_disease':sql = self.sql_transfer(question_type, entity_dict.get('symptom'))elif question_type == 'disease_cause':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_acompany':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_not_food':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_do_food':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'food_not_disease':sql = self.sql_transfer(question_type, entity_dict.get('food'))elif question_type == 'food_do_disease':sql = self.sql_transfer(question_type, entity_dict.get('food'))elif question_type == 'disease_drug':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'drug_disease':sql = self.sql_transfer(question_type, entity_dict.get('drug'))elif question_type == 'disease_check':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'check_disease':sql = self.sql_transfer(question_type, entity_dict.get('check'))elif question_type == 'disease_prevent':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_lasttime':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_cureway':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_cureprob':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_easyget':sql = self.sql_transfer(question_type, entity_dict.get('disease'))elif question_type == 'disease_desc':sql = self.sql_transfer(question_type, entity_dict.get('disease'))if sql:sql_['sql'] = sqlsqls.append(sql_)return sqls'''针对不同的问题,分开进行处理'''def sql_transfer(self, question_type, entities):if not entities:return []# 查询语句sql = []# 查询疾病的原因if question_type == 'disease_cause':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cause".format(i) for i in entities]# 查询疾病的防御措施elif question_type == 'disease_prevent':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.prevent".format(i) for i in entities]# 查询疾病的持续时间elif question_type == 'disease_lasttime':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cure_lasttime".format(i) for i in entities]# 查询疾病的治愈概率elif question_type == 'disease_cureprob':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cured_prob".format(i) for i in entities]# 查询疾病的治疗方式elif question_type == 'disease_cureway':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cure_way".format(i) for i in entities]# 查询疾病的易发人群elif question_type == 'disease_easyget':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.easy_get".format(i) for i in entities]# 查询疾病的相关介绍elif question_type == 'disease_desc':sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.desc".format(i) for i in entities]# 查询疾病有哪些症状elif question_type == 'disease_symptom':sql = ["MATCH (m:Disease)-[r:has_symptom]->(n:Symptom) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]# 查询症状会导致哪些疾病elif question_type == 'symptom_disease':sql = ["MATCH (m:Disease)-[r:has_symptom]->(n:Symptom) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]# 查询疾病的并发症elif question_type == 'disease_acompany':sql1 = ["MATCH (m:Disease)-[r:acompany_with]->(n:Disease) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql2 = ["MATCH (m:Disease)-[r:acompany_with]->(n:Disease) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql = sql1 + sql2# 查询疾病的忌口elif question_type == 'disease_not_food':sql = ["MATCH (m:Disease)-[r:no_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]# 查询疾病建议吃的东西elif question_type == 'disease_do_food':sql1 = ["MATCH (m:Disease)-[r:do_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql2 = ["MATCH (m:Disease)-[r:recommand_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql = sql1 + sql2# 已知忌口查疾病elif question_type == 'food_not_disease':sql = ["MATCH (m:Disease)-[r:no_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]# 已知推荐查疾病elif question_type == 'food_do_disease':sql1 = ["MATCH (m:Disease)-[r:do_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql2 = ["MATCH (m:Disease)-[r:recommand_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql = sql1 + sql2# 查询疾病常用药品-药品别名记得扩充elif question_type == 'disease_drug':sql1 = ["MATCH (m:Disease)-[r:common_drug]->(n:Drug) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql2 = ["MATCH (m:Disease)-[r:recommand_drug]->(n:Drug) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql = sql1 + sql2# 已知药品查询能够治疗的疾病elif question_type == 'drug_disease':sql1 = ["MATCH (m:Disease)-[r:common_drug]->(n:Drug) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql2 = ["MATCH (m:Disease)-[r:recommand_drug]->(n:Drug) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]sql = sql1 + sql2# 查询疾病应该进行的检查elif question_type == 'disease_check':sql = ["MATCH (m:Disease)-[r:need_check]->(n:Check) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]# 已知检查查询疾病elif question_type == 'check_disease':sql = ["MATCH (m:Disease)-[r:need_check]->(n:Check) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]return sqlif __name__ == '__main__':handler = QuestionPaser()

3、问答程序脚本

from py2neo import Graphclass AnswerSearcher:def __init__(self):self.g = Graph("neo4j://192.168.112.30:7687", auth=("neo4j", "neo4jpassword"))self.num_limit = 20'''执行cypher查询,并返回相应结果'''def search_main(self, sqls):final_answers = []for sql_ in sqls:question_type = sql_['question_type']queries = sql_['sql']answers = []for query in queries:ress = self.g.run(query).data()answers += ressfinal_answer = self.answer_prettify(question_type, answers)if final_answer:final_answers.append(final_answer)return final_answers'''根据对应的qustion_type,调用相应的回复模板'''def answer_prettify(self, question_type, answers):final_answer = []if not answers:return ''if question_type == 'disease_symptom':desc = [i['n.name'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}的症状包括:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'symptom_disease':desc = [i['m.name'] for i in answers]subject = answers[0]['n.name']final_answer = '症状{0}可能染上的疾病有:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_cause':desc = [i['m.cause'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}可能的成因有:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_prevent':desc = [i['m.prevent'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}的预防措施包括:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_lasttime':desc = [i['m.cure_lasttime'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}治疗可能持续的周期为:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_cureway':desc = [';'.join(i['m.cure_way']) for i in answers]subject = answers[0]['m.name']final_answer = '{0}可以尝试如下治疗:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_cureprob':desc = [i['m.cured_prob'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}治愈的概率为(仅供参考):{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_easyget':desc = [i['m.easy_get'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}的易感人群包括:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_desc':desc = [i['m.desc'] for i in answers]subject = answers[0]['m.name']final_answer = '{0},熟悉一下:{1}'.format(subject,  ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_acompany':desc1 = [i['n.name'] for i in answers]desc2 = [i['m.name'] for i in answers]subject = answers[0]['m.name']desc = [i for i in desc1 + desc2 if i != subject]final_answer = '{0}的症状包括:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_not_food':desc = [i['n.name'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}忌食的食物包括有:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_do_food':do_desc = [i['n.name'] for i in answers if i['r.name'] == '宜吃']recommand_desc = [i['n.name'] for i in answers if i['r.name'] == '推荐食谱']subject = answers[0]['m.name']final_answer = '{0}宜食的食物包括有:{1}\n推荐食谱包括有:{2}'.format(subject, ';'.join(list(set(do_desc))[:self.num_limit]), ';'.join(list(set(recommand_desc))[:self.num_limit]))elif question_type == 'food_not_disease':desc = [i['m.name'] for i in answers]subject = answers[0]['n.name']final_answer = '患有{0}的人最好不要吃{1}'.format(';'.join(list(set(desc))[:self.num_limit]), subject)elif question_type == 'food_do_disease':desc = [i['m.name'] for i in answers]subject = answers[0]['n.name']final_answer = '患有{0}的人建议多试试{1}'.format(';'.join(list(set(desc))[:self.num_limit]), subject)elif question_type == 'disease_drug':desc = [i['n.name'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}通常的使用的药品包括:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'drug_disease':desc = [i['m.name'] for i in answers]subject = answers[0]['n.name']final_answer = '{0}主治的疾病有{1},可以试试'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'disease_check':desc = [i['n.name'] for i in answers]subject = answers[0]['m.name']final_answer = '{0}通常可以通过以下方式检查出来:{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))elif question_type == 'check_disease':desc = [i['m.name'] for i in answers]subject = answers[0]['n.name']final_answer = '通常可以通过{0}检查出来的疾病有{1}'.format(subject, ';'.join(list(set(desc))[:self.num_limit]))return final_answerif __name__ == '__main__':searcher = AnswerSearcher()

4、问答系统实现

4.1、模型初始化

from answer_search import *
from question_classifier import *
from question_parser import *class ChatBotGraph:def __init__(self):self.classifier = QuestionClassifier()self.parser = QuestionPaser()self.searcher = AnswerSearcher()

4.2、问答主函数

    def chat_main(self, sent):answer = '您好,我是医药智能助理,希望可以帮到您。如果没答上来,可联系https://liuhuanyong.github.io/。祝您身体棒棒!'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)

4.3、运行主入口

运行 chatbot_graph.py 文件

if __name__ == '__main__':handler = ChatBotGraph()while 1:question = input('用户:')answer = handler.chat_main(question)print('医药智能助理:', answer)

4.4、运行结果

image-20241218113923894


http://www.ppmy.cn/ops/144313.html

相关文章

国产 HighGo 数据库企业版安装与配置指南

国产 HighGo 数据库企业版安装与配置指南 1. 下载安装包 访问 HighGo 官方网站&#xff08;https://www.highgo.com/&#xff09;&#xff0c;选择并下载企业版安装包。 2. 上传安装包到服务器 将下载的安装包上传至服务器&#xff0c;并执行以下命令&#xff1a; [rootmas…

最大似然检测在通信解调中的应用

最大似然检测&#xff08;Maximum Likelihood Detection&#xff0c;MLD&#xff09;&#xff0c;也称为最大似然序列估计&#xff08;Maximum Likelihood Sequence Estimation&#xff0c;MLSE&#xff09;&#xff0c;是一种在通信系统中广泛应用的解调方法。其核心思想是在给…

使用NodeJs 实现图片转PPT

序言 帮朋友下载网络资源。最后转化为PPT 网页是这样的 下载图片 需要使用nodejs来下载图片 安装需要的库 npm install axios执行下面的JS const fs require(fs); const path require(path); const axios require(axios); const { URL } require(url); const readlin…

从零开始学前端之HTML(三)

提示&#xff1a;文章写完后&#xff0c;目录可以自动生成&#xff0c;如何生成可参考右边的帮助文档 文章目录 HTML CSS 内联样式内部样式表外部样式表 HTML图像HTML 表格HTML列表HTML区块HTML表单HTML框架 HTML CSS 内联样式- 在HTML元素中使用"style" 属性 内部…

驾驶证识别API-JavaScript驾驶证ocr接口集成-场景解析

随着数字化转型的加速和人工智能技术的进步&#xff0c;驾驶证识别技术正逐渐成为众多行业优化服务流程、提升用户体验的关键工具&#xff0c;它不仅仅是一个简单的信息提取过程&#xff0c;更体现了现代信息技术与传统交通管理融合的新趋势。 通过集成驾驶证识别技术&#xff…

【java面向对象编程】第七弹----Object类、类变量与类方法

笔上得来终觉浅,绝知此事要躬行 &#x1f525; 个人主页&#xff1a;星云爱编程 &#x1f525; 所属专栏&#xff1a;javase &#x1f337;追光的人&#xff0c;终会万丈光芒 &#x1f389;欢迎大家点赞&#x1f44d;评论&#x1f4dd;收藏⭐文章 目录 一、Object类 1.1equa…

聚观早报 | 百度回应进军短剧;iPad Air将升级OLED

聚观早报每日整理最值得关注的行业重点事件&#xff0c;帮助大家及时了解最新行业动态&#xff0c;每日读报&#xff0c;就读聚观365资讯简报。 整理丨Cutie 12月18日消息 百度回应进军短剧 iPad Air将升级OLED 三星Galax S25 Ultra配色细节 一加Ace 5系列存储规格 小米…