基于知识图谱的医疗问答系统（dockerfile+docker-compose）
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
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
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"
]
}
EOF
systemctl daemon-reload && systemctl restart docker && systemctl enable docker
2.4、安装 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。$(uname -s)和$(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或--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.conf
chown -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=512M
server.default_listen_address=0.0.0.0
dbms.security.allow_csv_import_from_file_urls=true
server.directories.logs=/logs
EOF
2.7、编写 dockerfile 文件
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 .
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:v1
container_name: neo4j_container
restart: always
ports:
- "7474:7474"
- "7687:7687"
environment:
- NEO4J_AUTH=neo4j/neo4jpassword
volumes:
- ./data:/data
- ./logs:/logs
- ./import:/var/lib/neo4j/import
- ./conf:/var/lib/neo4j/conf
command: ["neo4j"]
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
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 Neo4j：
py2neo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。
- 连接到 Neo4j：
-
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Node和Relationship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。
- 创建节点和关系：
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
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,Node
class 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.py和answer_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 = 0
for data in open(self.data_path, encoding='utf8', mode='r'):
disease_dict = {}
count += 1
print(count)
data_json = json.loads(data)
disease = data_json['name']
disease_dict['name'] = disease
diseases.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_department
departments += cure_department
if '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_drug
if 'recommand_drug' in data_json:
recommand_drug = data_json['recommand_drug']
drugs += recommand_drug
for 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_eat
do_eat = data_json['do_eat']
for _do in do_eat:
rels_doeat.append([disease, _do])
foods += do_eat
recommand_eat = data_json['recommand_eat']
for _recommand in recommand_eat:
rels_recommandeat.append([disease, _recommand])
foods += recommand_eat
if 'check' in data_json:
check = data_json['check']
for _check in check:
rels_check.append([disease, _check])
checks += check
if '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 += producer
disease_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 = 0
for node_name in nodes:
node = Node(label, name=node_name)
self.g.create(node)
count += 1
print(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 = 0
for 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 += 1
print(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
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 += 1
print(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
10、优化导入数据时间
import concurrent
import concurrent.futures
import json
import multiprocessing
import os
from py2neo import Graph, Node, Subgraph
from tqdm import tqdm
class MedicalGraph:
def __init__(self):
pass
def 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'] = disease
diseases.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_department
departments += cure_department
if '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_drug
if 'recommand_drug' in data_json:
recommand_drug = data_json['recommand_drug']
drugs += recommand_drug
for 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_eat
do_eat = data_json['do_eat']
for _do in do_eat:
rels_doeat.append([disease, _do])
foods += do_eat
recommand_eat = data_json['recommand_eat']
for _recommand in recommand_eat:
rels_recommandeat.append([disease, _recommand])
foods += recommand_eat
if 'check' in data_json:
check = data_json['check']
for _check in check:
rels_check.append([disease, _check])
checks += check
if '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 += producer
disease_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 = 1000
batches = [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 = 1000
batches = [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 = 10000
set_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 ahocorasick
class 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actree
self.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_types
return 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 True
return False
if __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_type
sql = []
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'] = sql
sqls.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 sql
if __name__ == '__main__':
handler = QuestionPaser()
3、问答程序脚本
from py2neo import Graph
class 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 += ress
final_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_answer
if __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 answer
res_sql = self.parser.parser_main(res_classify)
final_answers = self.searcher.search_main(res_sql)
if not final_answers:
return answer
else:
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
