恢复误删除的文件
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chenshaohua
158
ruoyi-admin/src/main/resources/application-local.yml
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158
ruoyi-admin/src/main/resources/application-local.yml
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# 项目相关配置
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ruoyi:
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# 名称
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name: RuoYi
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# 版本
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version: 3.8.8
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# 版权年份
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copyrightYear: 2024
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# 文件路径 示例（ Windows配置D:/ruoyi/uploadPath，Linux配置 /home/ruoyi/uploadPath）
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profile: /Users/chenyanchang/logs/ruoyi/uploadPath
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# 获取ip地址开关
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addressEnabled: false
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# 验证码类型 math 数字计算 char 字符验证
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captchaType: math
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# 数据源配置
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spring:
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datasource:
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type: com.alibaba.druid.pool.DruidDataSource
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driverClassName: com.highgo.jdbc.Driver
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druid:
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# 主库数据源
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# master:
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# url: jdbc:highgo://124.243.245.42:5866/shz?useUnicode=true&characterEncoding=utf8¤tSchema=shz&stringtype=unspecified
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# #username: syssso
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# username: sysdba
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# password: ZKR2024@comzkr
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# 正式环境
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# master:
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# url: jdbc:highgo://39.98.44.136:6023/highgo?useUnicode=true&characterEncoding=utf8¤tSchema=shz&stringtype=unspecified
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# username: sysdba
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# password: Hello@2026
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# 测试环境
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master:
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url: jdbc:highgo://47.111.103.66:5866/highgo?useUnicode=true&characterEncoding=utf8¤tSchema=shz&stringtype=unspecified
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username: sysdba
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password: Hello@2026
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# 从库数据源
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slave:
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# 从数据源开关/默认关闭
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enabled: false
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url:
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username:
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password:
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# 初始连接数
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initialSize: 10
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# 最小连接池数量
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minIdle: 30
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# 最大连接池数量
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maxActive: 50
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# 配置获取连接等待超时的时间
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maxWait: 60000
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# 配置连接超时时间
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connectTimeout: 30000
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# 配置网络超时时间
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socketTimeout: 60000
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# 配置间隔多久才进行一次检测，检测需要关闭的空闲连接，单位是毫秒
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timeBetweenEvictionRunsMillis: 60000
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# 配置一个连接在池中最小生存的时间，单位是毫秒
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minEvictableIdleTimeMillis: 300000
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# 配置一个连接在池中最大生存的时间，单位是毫秒
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maxEvictableIdleTimeMillis: 900000
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# 配置检测连接是否有效
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validationQuery: SELECT version()
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testWhileIdle: true
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testOnBorrow: false
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testOnReturn: false
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webStatFilter:
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enabled: true
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statViewServlet:
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enabled: true
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# 设置白名单，不填则允许所有访问
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allow:
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url-pattern: /druid/*
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# 控制台管理用户名和密码
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login-username: ruoyi
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login-password: 123456
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filter:
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stat:
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enabled: true
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# 慢SQL记录
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log-slow-sql: true
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slow-sql-millis: 1000
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merge-sql: true
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wall:
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config:
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multi-statement-allow: true
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redis:
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# 本地
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#host: 127.0.0.1
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#port: 6379
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host: 124.243.245.42
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port: 5379
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# 数据库索引
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database: 0
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# 密码（本地通常无密码）
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password:
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# 连接超时时间
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timeout: 100s
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lettuce:
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pool:
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# 连接池中的最小空闲连接
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min-idle: 0
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# 连接池中的最大空闲连接
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max-idle: 8
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# 连接池的最大数据库连接数
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max-active: 8
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# #连接池最大阻塞等待时间（使用负值表示没有限制）
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max-wait: -1ms
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# easy-es
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easy-es:
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enable: true
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banner: false
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address: 124.243.245.42:9200
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global-config:
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process-index-mode: manual
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db-config:
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refresh-policy: immediate
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username: elastic
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password: zkr2024@@.com
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#ai
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chat:
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baseUrl: http://39.98.44.136:8082
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chatUrl: /v1/chat/completions
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chatDetailUrl: /core/chat/getPaginationRecords
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chatHistoryUrl: /core/chat/getHistories
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updateNameUrl: /core/chat/updateHistory
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stickChatUrl: /core/chat/updateHistory
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delChatUrl: /core/chat/delHistory
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delAllChatUrl: /core/chat/clearHistories
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guestUrl: /v1/chat/completions
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praiseUrl: /core/chat/feedback/updateUserFeedback
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appId: 67cd49095e947ae0ca7fadd8
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apiKey: fastgpt-qMl63276wPZvKAxEkW77bur0sSJpmuC6Ngg9lzyEjufLhsBAurjT55j
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model: qd-job-turbo
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audioText:
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asr: http://127.0.0.1:8000/asr/file
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tts: http://127.0.0.1:19527/synthesize
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#浪潮单点登录相关
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#互联网端：
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lc_web_auth:
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appId: cloud-out-2fb6330e9c0843e1a1424efda5d604c0
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appSecret: x14lueHbtLQL7Pz2G7gE4wcGCV6TDblO5xfeu9V2wGk=
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getTokenUrl: http://218.31.252.15:9081/prod-psout-api/auth/token
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getUserInfoUrl: http://218.31.252.15:9081/prod-psout-api/system/app/authorize/user/info
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editUserInfoUrl: http://218.31.252.15:9081/prod-psout-api/auth/user/edit
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#监管端：
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lc_cms_auth:
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appId: cloud-9793ee8a8c3d47b8871007ffc4128502
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appSecret: Yi+NACK70UPg8rFvsnnfBUq1wcLD4nm6ilC4II/4C4k=
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getTokenUrl: http://218.31.252.15:9081/prod-api/auth/token
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getUserInfoUrl: http://218.31.252.15:9081/prod-api/system/app/authorize/user/info
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resrtPwdUrl: http://218.31.252.15:9081/prod-api/ps/user/resetPwd/zkr
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changeStatusUrl: http://218.31.252.15:9081/prod-api/ps/user/change/status/zkr
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File diff suppressed because it is too large
Load Diff
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package com.ruoyi.cms.service.impl;
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import com.ruoyi.cms.mapper.JobMapper;
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import com.ruoyi.cms.service.IJobTitleSuggestService;
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import com.ruoyi.cms.util.JobTitleSuggestTokenizer;
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import com.ruoyi.common.core.redis.RedisCache;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import javax.annotation.PostConstruct;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.LinkedHashMap;
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import java.util.LinkedHashSet;
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import java.util.List;
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import java.util.Map;
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import java.util.Set;
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import java.util.TreeMap;
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import java.util.TreeSet;
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import java.util.UUID;
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import java.util.concurrent.TimeUnit;
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import java.util.stream.Collectors;
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/**
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* 岗位名称 IK 分词联想服务实现。
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*/
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@Service
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public class JobTitleSuggestServiceImpl implements IJobTitleSuggestService {
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private static final Logger logger = LoggerFactory.getLogger(JobTitleSuggestServiceImpl.class);
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/**
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* Redis Hash：field 是 IK 分词，value 是包含该分词的完整岗位名称列表。
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*/
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private static final String CACHE_KEY = "app:job:title:suggest";
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private static final String BUILDING_CACHE_KEY = CACHE_KEY + ":building";
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private static final String LOCK_KEY = CACHE_KEY + ":rebuild:lock";
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private static final int DEFAULT_LIMIT = 10;
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private static final int MAX_LIMIT = 20;
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private static final int LOCK_EXPIRE_MINUTES = 30;
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@Autowired
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private JobMapper jobMapper;
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@Autowired
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private RedisCache redisCache;
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/**
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* 确保服务启动后 Redis 中已有一份缓存；后续由 Quartz 定时任务周期性重建。
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*/
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@PostConstruct
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public void init() {
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rebuildCache();
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}
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@Override
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public List<String> suggest(String keyword, Integer limit) {
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String normalizedKeyword = JobTitleSuggestTokenizer.normalize(keyword);
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if (normalizedKeyword.isEmpty()) {
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return Collections.emptyList();
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}
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int resultLimit = normalizeLimit(limit);
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try {
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Map<String, Object> tokenTitles = redisCache.getCacheMap(CACHE_KEY);
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if (tokenTitles == null || tokenTitles.isEmpty()) {
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return Collections.emptyList();
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}
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// Hash field 只来自 IK 分词；这里仅对已有词项做前缀过滤，不生成新的前缀词。
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List<Map.Entry<String, Object>> matchedTokens = tokenTitles.entrySet().stream()
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.filter(entry -> entry.getKey() != null)
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.filter(entry -> entry.getKey().startsWith(normalizedKeyword))
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.sorted(Map.Entry.comparingByKey())
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.collect(Collectors.toList());
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Set<String> result = new LinkedHashSet<>();
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for (Map.Entry<String, Object> entry : matchedTokens) {
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if (result.size() >= resultLimit) {
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break;
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}
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// 先返回匹配到的 IK 分词本身。
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result.add(entry.getKey());
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// 再返回包含该 IK 分词的完整岗位名称。
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appendTitles(result, entry.getValue(), resultLimit);
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}
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return new ArrayList<>(result).subList(0, Math.min(result.size(), resultLimit));
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} catch (Exception e) {
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// 联想属于非核心能力，Redis 异常时返回空列表，不影响岗位主搜索。
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logger.warn("查询岗位名称 IK 分词缓存失败，keyword={}", keyword, e);
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return Collections.emptyList();
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}
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}
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@Override
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public void rebuildCache() {
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String lockValue = UUID.randomUUID().toString();
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boolean lockAcquired = false;
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try {
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lockAcquired = redisCache.setIfAbsent(
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LOCK_KEY, lockValue, LOCK_EXPIRE_MINUTES, TimeUnit.MINUTES);
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if (!lockAcquired) {
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logger.info("其他节点正在重建岗位名称 IK 分词缓存，本节点跳过");
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return;
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}
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List<String> sourceList = jobMapper.selectValidJobTitles();
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Map<String, List<String>> cacheData = buildTokenTitles(sourceList);
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// 先写临时 Hash，再原子替换正式 Hash，避免接口读到半成品。
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redisCache.deleteObject(BUILDING_CACHE_KEY);
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if (cacheData.isEmpty()) {
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redisCache.deleteObject(CACHE_KEY);
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} else {
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redisCache.setCacheMap(BUILDING_CACHE_KEY, cacheData);
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redisCache.redisTemplate.rename(BUILDING_CACHE_KEY, CACHE_KEY);
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}
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logger.info("岗位名称 IK 分词缓存重建完成，有效岗位名称{}个，IK 词{}个",
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sourceList == null ? 0 : sourceList.size(), cacheData.size());
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} catch (Exception e) {
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logger.error("重建岗位名称 IK 分词缓存失败", e);
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} finally {
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if (lockAcquired) {
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redisCache.deleteObject(LOCK_KEY);
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}
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}
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}
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private Map<String, List<String>> buildTokenTitles(List<String> sourceList) {
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Map<String, Set<String>> tokenTitles = new TreeMap<>();
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if (sourceList == null) {
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return Collections.emptyMap();
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}
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for (String title : sourceList) {
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String displayTitle = normalizeDisplayTitle(title);
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if (displayTitle.isEmpty()) {
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continue;
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}
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Set<String> tokens = JobTitleSuggestTokenizer.tokenize(displayTitle);
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for (String token : tokens) {
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tokenTitles.computeIfAbsent(token, key -> new TreeSet<>()).add(displayTitle);
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}
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}
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Map<String, List<String>> result = new LinkedHashMap<>();
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for (Map.Entry<String, Set<String>> entry : tokenTitles.entrySet()) {
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result.put(entry.getKey(), new ArrayList<>(entry.getValue()));
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}
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return result;
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}
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private void appendTitles(Set<String> result, Object cachedTitles, int resultLimit) {
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if (!(cachedTitles instanceof Iterable)) {
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return;
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}
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for (Object cachedTitle : (Iterable<?>) cachedTitles) {
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if (cachedTitle == null || result.size() >= resultLimit) {
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break;
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}
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String title = String.valueOf(cachedTitle);
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if (!title.isEmpty()) {
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result.add(title);
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}
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}
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}
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private String normalizeDisplayTitle(String title) {
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if (title == null) {
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return "";
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}
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return title.trim().replaceAll("\\s+", " ");
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}
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private int normalizeLimit(Integer limit) {
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if (limit == null) {
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return DEFAULT_LIMIT;
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}
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return Math.max(1, Math.min(limit, MAX_LIMIT));
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}
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}
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