修改用户个人资料识别逻辑
This commit is contained in:
zk
@@ -45,7 +45,7 @@ public class UserProfileService {
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private UserProfileCompetitionMapper competitionMapper;
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@Autowired
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private org.jiayunet.service.UserSkillTagMatchService userSkillTagMatchService;
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private org.jiayunet.service.UserProfileAnalyzeService userProfileAnalyzeService;
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// ==================== 主表 ====================
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@@ -78,7 +78,7 @@ public class UserProfileService {
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profile.setUpdateTime(now);
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userProfileMapper.insert(profile);
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}
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 教育经历 ====================
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@@ -114,7 +114,7 @@ public class UserProfileService {
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item.setUpdateTime(now);
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});
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educationMapper.batchInsert(list);
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 工作经历 ====================
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@@ -151,7 +151,7 @@ public class UserProfileService {
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item.setUpdateTime(now);
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});
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workMapper.batchInsert(list);
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 实习经历 ====================
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@@ -188,7 +188,7 @@ public class UserProfileService {
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item.setUpdateTime(now);
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});
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internshipMapper.batchInsert(list);
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 项目经历 ====================
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@@ -225,7 +225,7 @@ public class UserProfileService {
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item.setUpdateTime(now);
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});
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projectMapper.batchInsert(list);
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 竞赛经历 ====================
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@@ -262,7 +262,7 @@ public class UserProfileService {
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item.setUpdateTime(now);
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});
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competitionMapper.batchInsert(list);
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userSkillTagMatchService.matchUserSkillTags(userId);
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userProfileAnalyzeService.analyzeUserProfile(userId);
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}
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// ==================== 内部方法 ====================
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@@ -0,0 +1,341 @@
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package org.jiayunet.service;
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import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
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import com.baomidou.mybatisplus.core.toolkit.IdWorker;
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import com.fasterxml.jackson.databind.JsonNode;
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import lombok.extern.slf4j.Slf4j;
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import org.jiayunet.ai.AiChatAbility;
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import org.jiayunet.mapper.*;
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import org.jiayunet.pojo.po.*;
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import org.jiayunet.pojo.vo.UserHonorsVo;
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import org.jiayunet.tool.HttpTool;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.scheduling.annotation.Async;
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import org.springframework.stereotype.Service;
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import java.time.Instant;
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import java.util.*;
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import java.util.stream.Collectors;
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/**
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* 用户简历分析服务
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* <p>主要功能：用户保存简历后异步分析，提取学校等级、经历质量、专业、技能等维度数据</p>
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* <p>依赖：AiChatAbility（AI调用）、DictCacheService（专业分类缓存）</p>
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* <p>使用表：bg_user_profile（主表，读取/更新）、bg_user_profile_*（5张子表，读取）、
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* bg_skill_tag（技能入库）、bg_user_profile_skill_tag_relation（关联表）</p>
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*
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* @author zk
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*/
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@Slf4j
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@Service
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public class UserProfileAnalyzeService {
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@Autowired
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private AiChatAbility aiChatAbility;
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@Autowired
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private DictCacheService dictCacheService;
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@Autowired
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private UserProfileMapper userProfileMapper;
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@Autowired
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private UserProfileEducationMapper educationMapper;
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@Autowired
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private UserProfileWorkMapper workMapper;
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@Autowired
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private UserProfileInternshipMapper internshipMapper;
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@Autowired
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private UserProfileProjectMapper projectMapper;
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@Autowired
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private UserProfileCompetitionMapper competitionMapper;
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@Autowired
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private SkillTagMapper skillTagMapper;
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@Autowired
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private UserProfileSkillTagRelationMapper relationMapper;
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/**
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* 异步分析用户简历
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* <p>1. 查询用户完整简历 2. 第一次AI综合分析 3. 第二次AI专业归一化 4. 第三次AI技能提取</p>
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*/
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@Async("userProfileAsyncExecutor")
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public void analyzeUserProfile(Long userId) {
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try {
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log.info("开始分析用户简历, userId={}", userId);
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// 1. 查询用户完整简历
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UserProfile profile = userProfileMapper.selectOne(new LambdaQueryWrapper<UserProfile>().eq(UserProfile::getUserId, userId));
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if (profile == null) {
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log.info("用户主表为空，清空技能标签, userId={}", userId);
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clearSkillRelations(userId);
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return;
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}
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List<UserProfileEducation> educationList = educationMapper.selectList(new LambdaQueryWrapper<UserProfileEducation>().eq(UserProfileEducation::getUserId, userId));
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List<UserProfileWork> workList = workMapper.selectList(new LambdaQueryWrapper<UserProfileWork>().eq(UserProfileWork::getUserId, userId));
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List<UserProfileInternship> internshipList = internshipMapper.selectList(new LambdaQueryWrapper<UserProfileInternship>().eq(UserProfileInternship::getUserId, userId));
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List<UserProfileProject> projectList = projectMapper.selectList(new LambdaQueryWrapper<UserProfileProject>().eq(UserProfileProject::getUserId, userId));
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List<UserProfileCompetition> competitionList = competitionMapper.selectList(new LambdaQueryWrapper<UserProfileCompetition>().eq(UserProfileCompetition::getUserId, userId));
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// 2. 数据有效性检查
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if (educationList.isEmpty() && workList.isEmpty() && internshipList.isEmpty() && projectList.isEmpty() && competitionList.isEmpty()) {
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log.info("用户所有子表为空，清空技能标签, userId={}", userId);
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clearSkillRelations(userId);
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return;
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}
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String profileJson = buildProfileJson(profile, educationList, workList, internshipList, projectList, competitionList);
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// 3. 第一次AI：简历综合分析（失败不影响后续）
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try {
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analyzeProfileDimensions(profile.getId(), profileJson);
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} catch (Exception e) {
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log.warn("简历综合分析失败, userId={}", userId, e);
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}
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// 4. 第二次AI：专业归一化（失败不影响后续）
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try {
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analyzeMajor(profile.getId(), profileJson);
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} catch (Exception e) {
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log.warn("专业归一化失败, userId={}", userId, e);
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}
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// 5. 第三次AI：技能提取
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try {
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extractSkillTags(userId, profileJson);
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} catch (Exception e) {
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log.warn("技能提取失败, userId={}", userId, e);
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}
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log.info("用户简历分析完成, userId={}", userId);
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} catch (Exception e) {
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log.error("用户简历分析异常, userId={}", userId, e);
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}
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}
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/**
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* 第一次AI：简历综合分析
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* <p>一次性识别学校等级、公司背书、经历时长、职责深度、量化产出、荣誉</p>
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*/
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private void analyzeProfileDimensions(Long profileId, String profileJson) {
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String systemPrompt = """
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你是一个简历分析助手。根据用户简历，分析以下维度并返回JSON。
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返回JSON格式：
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{
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"schoolRank": 1-4的数字(1=C9/985/QS前50 2=211/双一流/QS前200 3=普通一本/QS前500 4=其他，无教育经历则null),
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"companyPrestige": 1-4的数字(1=名企(世界500强/互联网大厂/知名国企/独角兽) 2=普通实习 3=仅校内活动 4=无经历，取最高档),
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"experienceDuration": 1-3的数字(1=实习≥3月 2=1-3月 3=≤1月，取最长的一段，无实习则null),
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"roleDepth": 1-3的数字(1=主导/创新(主导、核心负责、从0到1、架构设计) 2=执行/应用(负责、开发、实现、维护) 3=辅助/学习(协助、参与、了解)，取最高档),
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"outputQuality": 1-3的数字(1=有量化结果(含数字比率如提升20%) 2=有具体产出(明确产出物但无数字) 3=纯描述，取最高档),
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"honors": {"national":["具体奖项名"],"provincial":["具体奖项名"],"school":["具体奖项/证书名"],"paper":["具体论文名"]}
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}
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规则：
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1. 每个维度独立判断，某维度信息不足则设为null
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2. 多段经历取最高档（最优表现）
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3. honors中只填简历中明确提到的，没有则空数组
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4. 只返回JSON，不要其他内容
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""";
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String userMessage = "【用户简历】\n" + profileJson;
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String aiResponse = aiChatAbility.chat(systemPrompt, userMessage);
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String json = cleanAiResponse(aiResponse);
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try {
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JsonNode root = HttpTool.objectMapper.readTree(json);
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UserProfile update = new UserProfile();
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update.setId(profileId);
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update.setSchoolRank(root.path("schoolRank").isNull() ? null : root.path("schoolRank").asInt());
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update.setCompanyPrestige(root.path("companyPrestige").isNull() ? null : root.path("companyPrestige").asInt());
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update.setExperienceDuration(root.path("experienceDuration").isNull() ? null : root.path("experienceDuration").asInt());
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update.setRoleDepth(root.path("roleDepth").isNull() ? null : root.path("roleDepth").asInt());
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update.setOutputQuality(root.path("outputQuality").isNull() ? null : root.path("outputQuality").asInt());
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// 解析 honors
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JsonNode honorsNode = root.path("honors");
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if (!honorsNode.isMissingNode() && !honorsNode.isNull()) {
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UserHonorsVo honors = HttpTool.objectMapper.treeToValue(honorsNode, UserHonorsVo.class);
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update.setHonors(honors);
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}
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update.setUpdateTime(Instant.now());
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userProfileMapper.updateById(update);
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} catch (Exception e) {
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log.warn("简历综合分析AI返回解析失败: {}", json, e);
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}
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}
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/**
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* 第二次AI：专业归一化
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* <p>传入教育经历和三级专业分类列表，AI返回用户专业ID数组 → 更新 bg_user_profile.major_ids</p>
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*/
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private void analyzeMajor(Long profileId, String profileJson) {
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String systemPrompt = """
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你是一个专业归一化助手。根据用户的教育经历，从专业分类列表中匹配用户的专业。
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返回JSON格式：
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{
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"majorIds": [专业ID数组，从专业列表中选择最匹配的，可多个（如本科+硕士不同专业），无法判断则空数组]
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}
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规则：
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1. 只能从给定专业列表中选择ID
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2. 每段教育经历匹配一个最相关的专业
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3. 无教育经历或专业信息不足时返回空数组
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4. 只返回JSON，不要其他内容
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""";
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String userMessage = "【用户简历】\n" + profileJson + "\n\n【专业分类列表】\n" + dictCacheService.getMajorCategoryText();
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String aiResponse = aiChatAbility.chat(systemPrompt, userMessage);
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String json = cleanAiResponse(aiResponse);
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try {
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JsonNode root = HttpTool.objectMapper.readTree(json);
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List<Long> majorIds = new ArrayList<>();
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JsonNode idsNode = root.path("majorIds");
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if (idsNode.isArray()) {
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for (JsonNode node : idsNode) {
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long id = node.asLong(0);
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if (id > 0 && !majorIds.contains(id)) {
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majorIds.add(id);
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}
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}
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}
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UserProfile update = new UserProfile();
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update.setId(profileId);
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update.setMajorIds(majorIds.isEmpty() ? null : majorIds);
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update.setUpdateTime(Instant.now());
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userProfileMapper.updateById(update);
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} catch (Exception e) {
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log.warn("专业归一化AI返回解析失败: {}", json, e);
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}
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}
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/**
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* 第三次AI：技能提取
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* <p>自由提取用户技能，prompt与岗位侧保持一致，INSERT IGNORE入bg_skill_tag → 写关联表</p>
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*/
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private void extractSkillTags(Long userId, String profileJson) {
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String systemPrompt = """
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你是一个技能提取助手。根据用户简历，提取该用户具备的核心专业能力和工具技能。
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返回JSON数组格式，如：["java", "spring boot", "mysql", "redis"]
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规则：
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1. 统一使用小写字母
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2. 尽量简短，使用业界通用缩写（如 js 而非 javascript，k8s 而非 kubernetes）
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3. 提取范围包括：技术栈、专业领域知识、行业工具、专业资质能力等
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4. 不提取纯软技能（如沟通能力、团队协作、学习能力、积极主动）
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5. 如果简历中完全没有专业能力体现，返回空数组 []
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6. 最多15个，按熟练度排序
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7. 只返回JSON数组，不要其他内容
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示例1（技术岗）：
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输入：3年Java开发经验，熟悉Spring Boot、MySQL、Redis
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输出：["java", "spring boot", "mysql", "redis"]
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示例2（财务岗）：
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输入：负责费用管理与审核，月度经营利润分析，会计学专业
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输出：["财务管理", "会计核算", "经营分析", "费用审核"]
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示例3（科研岗）：
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输入：开展生物合成相关研究，发酵工程方向博士
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输出：["生物合成", "发酵工程"]
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示例4（无专业能力）：
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输入：具备较强的沟通能力和创新意识，积极主动
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输出：[]
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""";
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String userMessage = "【用户简历】\n" + profileJson;
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String aiResponse = aiChatAbility.chat(systemPrompt, userMessage);
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String json = cleanAiResponse(aiResponse);
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try {
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JsonNode arrayNode = HttpTool.objectMapper.readTree(json);
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if (!arrayNode.isArray() || arrayNode.isEmpty()) {
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clearSkillRelations(userId);
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return;
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}
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List<Long> skillTagIds = new ArrayList<>();
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for (JsonNode node : arrayNode) {
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String skillName = node.asText("").trim().toLowerCase();
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if (skillName.isBlank() || skillName.length() > 50) {
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continue;
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}
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Long tagId = findOrCreateSkillTag(skillName);
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if (tagId != null && !skillTagIds.contains(tagId)) {
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skillTagIds.add(tagId);
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}
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}
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replaceSkillRelations(userId, skillTagIds);
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} catch (Exception e) {
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log.warn("技能提取AI返回解析失败: {}", json, e);
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}
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}
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/**
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* 查找或创建技能标签（依靠数据库唯一索引保证并发安全）
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*/
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private Long findOrCreateSkillTag(String name) {
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skillTagMapper.insertIgnore(IdWorker.getId(), name, Instant.now());
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SkillTag tag = skillTagMapper.selectOne(new LambdaQueryWrapper<SkillTag>().eq(SkillTag::getName, name).last("LIMIT 1"));
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return tag != null ? tag.getId() : null;
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}
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/** 构建用户简历JSON */
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private String buildProfileJson(UserProfile profile, List<UserProfileEducation> educationList,
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List<UserProfileWork> workList, List<UserProfileInternship> internshipList,
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List<UserProfileProject> projectList, List<UserProfileCompetition> competitionList) {
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Map<String, Object> data = new HashMap<>();
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data.put("name", profile.getName());
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data.put("skills", profile.getSkills());
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data.put("certificates", profile.getCertificates());
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data.put("education", educationList);
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data.put("work", workList);
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data.put("internship", internshipList);
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data.put("project", projectList);
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data.put("competition", competitionList);
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try {
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return HttpTool.objectMapper.writeValueAsString(data);
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} catch (Exception e) {
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log.error("用户资料序列化失败", e);
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return "{}";
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}
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}
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/** 清空用户技能标签关联 */
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private void clearSkillRelations(Long userId) {
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relationMapper.delete(new LambdaQueryWrapper<UserProfileSkillTagRelation>().eq(UserProfileSkillTagRelation::getUserId, userId));
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}
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||||
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/** 全量替换用户技能标签关联 */
|
||||
private void replaceSkillRelations(Long userId, List<Long> skillTagIds) {
|
||||
relationMapper.delete(new LambdaQueryWrapper<UserProfileSkillTagRelation>().eq(UserProfileSkillTagRelation::getUserId, userId));
|
||||
if (skillTagIds.isEmpty()) {
|
||||
return;
|
||||
}
|
||||
Instant now = Instant.now();
|
||||
List<UserProfileSkillTagRelation> relations = skillTagIds.stream().map(tagId -> {
|
||||
UserProfileSkillTagRelation relation = new UserProfileSkillTagRelation();
|
||||
relation.setUserId(userId);
|
||||
relation.setSkillTagId(tagId);
|
||||
relation.setCreateTime(now);
|
||||
return relation;
|
||||
}).collect(Collectors.toList());
|
||||
relationMapper.batchInsert(relations);
|
||||
}
|
||||
|
||||
/** 清理AI返回的markdown代码块和控制字符 */
|
||||
private String cleanAiResponse(String response) {
|
||||
String json = response.trim();
|
||||
if (json.startsWith("```")) {
|
||||
json = json.replaceAll("^```\\w*\\n?", "").replaceAll("\\n?```$", "").trim();
|
||||
}
|
||||
json = json.replaceAll("[\\x00-\\x08\\x0B\\x0C\\x0E-\\x1F]", "");
|
||||
return json;
|
||||
}
|
||||
}
|
||||
@@ -1,324 +0,0 @@
|
||||
package org.jiayunet.service;
|
||||
|
||||
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
|
||||
import com.fasterxml.jackson.databind.JsonNode;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.jiayunet.ai.AiChatAbility;
|
||||
import org.jiayunet.mapper.*;
|
||||
import org.jiayunet.pojo.po.*;
|
||||
import org.jiayunet.tool.HttpTool;
|
||||
import org.springframework.beans.factory.annotation.Autowired;
|
||||
import org.springframework.scheduling.annotation.Async;
|
||||
import org.springframework.stereotype.Service;
|
||||
|
||||
import java.time.Instant;
|
||||
import java.util.*;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* 用户技能标签匹配服务
|
||||
* <p>主要功能：根据用户个人资料，AI识别匹配的技能标签</p>
|
||||
* <p>依赖：AiChatAbility（AI调用）</p>
|
||||
* <p>使用表：bg_user_profile（主表）、bg_user_profile_*（5张子表）、
|
||||
* bg_job_category（岗位分类）、bg_skill_tag（技能标签）、
|
||||
* bg_user_profile_skill_tag_relation（关联表）</p>
|
||||
*
|
||||
* @author zk
|
||||
*/
|
||||
@Slf4j
|
||||
@Service
|
||||
public class UserSkillTagMatchService {
|
||||
|
||||
@Autowired
|
||||
private AiChatAbility aiChatAbility;
|
||||
|
||||
@Autowired
|
||||
private UserProfileMapper userProfileMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileEducationMapper educationMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileWorkMapper workMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileInternshipMapper internshipMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileProjectMapper projectMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileCompetitionMapper competitionMapper;
|
||||
|
||||
@Autowired
|
||||
private JobCategoryMapper jobCategoryMapper;
|
||||
|
||||
@Autowired
|
||||
private SkillTagMapper skillTagMapper;
|
||||
|
||||
@Autowired
|
||||
private UserProfileSkillTagRelationMapper relationMapper;
|
||||
|
||||
/**
|
||||
* 异步匹配用户技能标签
|
||||
* <p>1. 查询用户完整个人资料 2. 第一次AI调用识别二级分类 3. 查询三级分类
|
||||
* 4. 查询候选技能标签 5. 第二次AI调用匹配技能标签 6. 全量替换关联表</p>
|
||||
*/
|
||||
@Async("userProfileAsyncExecutor")
|
||||
public void matchUserSkillTags(Long userId) {
|
||||
try {
|
||||
log.info("开始匹配用户技能标签, userId={}", userId);
|
||||
|
||||
// 1. 查询用户完整个人资料
|
||||
UserProfile profile = userProfileMapper.selectOne(new LambdaQueryWrapper<UserProfile>().eq(UserProfile::getUserId, userId));
|
||||
if (profile == null) {
|
||||
log.info("用户主表为空，清空技能标签, userId={}", userId);
|
||||
clearRelations(userId);
|
||||
return;
|
||||
}
|
||||
|
||||
List<UserProfileEducation> educationList = educationMapper.selectList(new LambdaQueryWrapper<UserProfileEducation>().eq(UserProfileEducation::getUserId, userId));
|
||||
List<UserProfileWork> workList = workMapper.selectList(new LambdaQueryWrapper<UserProfileWork>().eq(UserProfileWork::getUserId, userId));
|
||||
List<UserProfileInternship> internshipList = internshipMapper.selectList(new LambdaQueryWrapper<UserProfileInternship>().eq(UserProfileInternship::getUserId, userId));
|
||||
List<UserProfileProject> projectList = projectMapper.selectList(new LambdaQueryWrapper<UserProfileProject>().eq(UserProfileProject::getUserId, userId));
|
||||
List<UserProfileCompetition> competitionList = competitionMapper.selectList(new LambdaQueryWrapper<UserProfileCompetition>().eq(UserProfileCompetition::getUserId, userId));
|
||||
|
||||
// 2. 数据有效性检查
|
||||
if (educationList.isEmpty() && workList.isEmpty() && internshipList.isEmpty() && projectList.isEmpty() && competitionList.isEmpty()) {
|
||||
log.info("用户所有子表为空，清空技能标签, userId={}", userId);
|
||||
clearRelations(userId);
|
||||
return;
|
||||
}
|
||||
|
||||
// 3. 第一次 AI 调用（识别二级分类）
|
||||
List<Long> categoryIds = identifyCategories(profile, educationList, workList, internshipList, projectList, competitionList);
|
||||
if (categoryIds.isEmpty()) {
|
||||
log.info("AI未识别出岗位分类，清空技能标签, userId={}", userId);
|
||||
clearRelations(userId);
|
||||
return;
|
||||
}
|
||||
|
||||
// 4. 查询三级分类（技能标签挂在三级分类下）
|
||||
List<JobCategory> level3Categories = jobCategoryMapper.selectList(new LambdaQueryWrapper<JobCategory>().eq(JobCategory::getLevel, 3).in(JobCategory::getParentId, categoryIds));
|
||||
if (level3Categories.isEmpty()) {
|
||||
log.info("二级分类下无三级分类，清空技能标签, userId={}", userId);
|
||||
clearRelations(userId);
|
||||
return;
|
||||
}
|
||||
|
||||
// 5. 查询候选技能标签（使用三级分类ID）
|
||||
List<Long> level3CategoryIds = level3Categories.stream().map(JobCategory::getId).collect(Collectors.toList());
|
||||
List<SkillTag> candidateTags = skillTagMapper.selectList(new LambdaQueryWrapper<SkillTag>().in(SkillTag::getCategoryId, level3CategoryIds));
|
||||
if (candidateTags.isEmpty()) {
|
||||
log.info("候选技能标签为空，清空技能标签, userId={}", userId);
|
||||
clearRelations(userId);
|
||||
return;
|
||||
}
|
||||
|
||||
// 6. 第二次 AI 调用（匹配技能标签）
|
||||
List<Long> matchedTagIds = matchSkillTags(profile, educationList, workList, internshipList, projectList, competitionList, candidateTags);
|
||||
|
||||
// 7. 全量替换关联表
|
||||
replaceRelations(userId, matchedTagIds);
|
||||
|
||||
log.info("用户技能标签匹配完成, userId={}, 匹配{}个标签", userId, matchedTagIds.size());
|
||||
|
||||
} catch (Exception e) {
|
||||
log.error("用户技能标签匹配异常, userId={}", userId, e);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 第一次 AI 调用：识别用户所属的二级岗位分类
|
||||
*/
|
||||
private List<Long> identifyCategories(UserProfile profile, List<UserProfileEducation> educationList,
|
||||
List<UserProfileWork> workList, List<UserProfileInternship> internshipList,
|
||||
List<UserProfileProject> projectList, List<UserProfileCompetition> competitionList) {
|
||||
// 构建用户资料 JSON
|
||||
Map<String, Object> profileData = new HashMap<>();
|
||||
profileData.put("name", profile.getName());
|
||||
profileData.put("workYears", profile.getWorkYears());
|
||||
profileData.put("education", educationList);
|
||||
profileData.put("work", workList);
|
||||
profileData.put("internship", internshipList);
|
||||
profileData.put("project", projectList);
|
||||
profileData.put("competition", competitionList);
|
||||
|
||||
String profileJson;
|
||||
try {
|
||||
profileJson = HttpTool.objectMapper.writeValueAsString(profileData);
|
||||
} catch (Exception e) {
|
||||
log.error("用户资料序列化失败", e);
|
||||
return List.of();
|
||||
}
|
||||
|
||||
// 查询所有二级分类
|
||||
List<JobCategory> allCategories = jobCategoryMapper.selectList(new LambdaQueryWrapper<JobCategory>().eq(JobCategory::getLevel, 2));
|
||||
|
||||
// 构建分类列表文本（带一级分类名称）
|
||||
Map<Long, String> parentNameMap = new HashMap<>();
|
||||
List<JobCategory> parentCategories = jobCategoryMapper.selectList(new LambdaQueryWrapper<JobCategory>().eq(JobCategory::getLevel, 1));
|
||||
for (JobCategory parent : parentCategories) {
|
||||
parentNameMap.put(parent.getId(), parent.getName());
|
||||
}
|
||||
|
||||
StringBuilder categoryText = new StringBuilder();
|
||||
for (JobCategory category : allCategories) {
|
||||
String parentName = parentNameMap.getOrDefault(category.getParentId(), "未知");
|
||||
categoryText.append(String.format("ID: %d, 名称: %s > %s\n", category.getId(), parentName, category.getName()));
|
||||
}
|
||||
|
||||
// 构建 Prompt
|
||||
String systemPrompt = """
|
||||
任务：根据用户个人资料，选择最匹配的二级岗位分类。
|
||||
|
||||
规则：
|
||||
1. 选择 1-10 个最匹配的二级分类（优先核心岗位，按匹配度排序）
|
||||
2. 信息不足无法判断时返回空数组 []
|
||||
3. 只返回纯数字数组 JSON，不要任何其他文字
|
||||
|
||||
返回格式示例：
|
||||
- 有分类：[23, 45, 67]
|
||||
- 无法判断：[]
|
||||
""";
|
||||
|
||||
String userMessage = "【用户个人资料】\n" + profileJson + "\n\n【二级岗位分类列表（格式：一级分类 > 二级分类）】\n" + categoryText;
|
||||
|
||||
try {
|
||||
String aiResponse = aiChatAbility.chat(systemPrompt, userMessage);
|
||||
return parseIdArray(aiResponse, allCategories.stream().map(JobCategory::getId).collect(Collectors.toSet()));
|
||||
} catch (Exception e) {
|
||||
log.error("AI识别岗位分类失败", e);
|
||||
return List.of();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 第二次 AI 调用：从候选标签中匹配用户技能标签
|
||||
*/
|
||||
private List<Long> matchSkillTags(UserProfile profile, List<UserProfileEducation> educationList,
|
||||
List<UserProfileWork> workList, List<UserProfileInternship> internshipList,
|
||||
List<UserProfileProject> projectList, List<UserProfileCompetition> competitionList,
|
||||
List<SkillTag> candidateTags) {
|
||||
// 构建用户资料 JSON
|
||||
Map<String, Object> profileData = new HashMap<>();
|
||||
profileData.put("name", profile.getName());
|
||||
profileData.put("workYears", profile.getWorkYears());
|
||||
profileData.put("education", educationList);
|
||||
profileData.put("work", workList);
|
||||
profileData.put("internship", internshipList);
|
||||
profileData.put("project", projectList);
|
||||
profileData.put("competition", competitionList);
|
||||
|
||||
String profileJson;
|
||||
try {
|
||||
profileJson = HttpTool.objectMapper.writeValueAsString(profileData);
|
||||
} catch (Exception e) {
|
||||
log.error("用户资料序列化失败", e);
|
||||
return List.of();
|
||||
}
|
||||
|
||||
// 构建候选标签文本（按分类分组）
|
||||
Map<Long, String> categoryNameMap = new HashMap<>();
|
||||
Set<Long> categoryIds = candidateTags.stream().map(SkillTag::getCategoryId).collect(Collectors.toSet());
|
||||
List<JobCategory> categories = jobCategoryMapper.selectList(new LambdaQueryWrapper<JobCategory>().in(JobCategory::getId, categoryIds));
|
||||
for (JobCategory category : categories) {
|
||||
categoryNameMap.put(category.getId(), category.getName());
|
||||
}
|
||||
|
||||
StringBuilder tagText = new StringBuilder();
|
||||
Map<Long, List<SkillTag>> groupedTags = candidateTags.stream().collect(Collectors.groupingBy(SkillTag::getCategoryId));
|
||||
for (Map.Entry<Long, List<SkillTag>> entry : groupedTags.entrySet()) {
|
||||
String categoryName = categoryNameMap.getOrDefault(entry.getKey(), "未知");
|
||||
tagText.append("【").append(categoryName).append("】\n");
|
||||
for (SkillTag tag : entry.getValue()) {
|
||||
tagText.append(String.format("ID: %d, 名称: %s\n", tag.getId(), tag.getName()));
|
||||
}
|
||||
tagText.append("\n");
|
||||
}
|
||||
|
||||
// 构建 Prompt
|
||||
String systemPrompt = """
|
||||
任务：根据用户个人资料，从候选技能标签中选出匹配的标签。
|
||||
|
||||
规则：
|
||||
1. 只选择用户明确具备或可推断的技能
|
||||
2. 无法匹配返回空数组 []
|
||||
3. 只返回纯数字数组 JSON，不要任何其他文字
|
||||
|
||||
返回格式示例：
|
||||
- 有匹配：[12, 34, 56]
|
||||
- 无匹配：[]
|
||||
""";
|
||||
|
||||
String userMessage = "【用户个人资料】\n" + profileJson + "\n\n【候选技能标签（按分类分组）】\n" + tagText;
|
||||
|
||||
try {
|
||||
String aiResponse = aiChatAbility.chat(systemPrompt, userMessage);
|
||||
return parseIdArray(aiResponse, candidateTags.stream().map(SkillTag::getId).collect(Collectors.toSet()));
|
||||
} catch (Exception e) {
|
||||
log.error("AI匹配技能标签失败", e);
|
||||
return List.of();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 解析 AI 返回的 ID 数组
|
||||
*/
|
||||
private List<Long> parseIdArray(String aiResponse, Set<Long> validIds) {
|
||||
String json = aiResponse.trim();
|
||||
if (json.startsWith("```")) {
|
||||
json = json.replaceAll("^```\\w*\\n?", "").replaceAll("\\n?```$", "").trim();
|
||||
}
|
||||
|
||||
try {
|
||||
JsonNode arrayNode = HttpTool.objectMapper.readTree(json);
|
||||
List<Long> result = new ArrayList<>();
|
||||
if (arrayNode.isArray()) {
|
||||
for (JsonNode node : arrayNode) {
|
||||
long id = node.asLong(0);
|
||||
if (id > 0 && validIds.contains(id) && !result.contains(id)) {
|
||||
result.add(id);
|
||||
}
|
||||
}
|
||||
}
|
||||
return result;
|
||||
} catch (Exception e) {
|
||||
log.warn("AI返回解析失败: {}", json, e);
|
||||
return List.of();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 清空用户技能标签关联
|
||||
*/
|
||||
public void clearRelations(Long userId) {
|
||||
relationMapper.delete(new LambdaQueryWrapper<UserProfileSkillTagRelation>().eq(UserProfileSkillTagRelation::getUserId, userId));
|
||||
}
|
||||
|
||||
/**
|
||||
* 全量替换用户技能标签关联
|
||||
*/
|
||||
|
||||
public void replaceRelations(Long userId, List<Long> skillTagIds) {
|
||||
// 先删除
|
||||
relationMapper.delete(new LambdaQueryWrapper<UserProfileSkillTagRelation>().eq(UserProfileSkillTagRelation::getUserId, userId));
|
||||
|
||||
// 结果为空则只删除
|
||||
if (skillTagIds.isEmpty()) {
|
||||
return;
|
||||
}
|
||||
|
||||
// 批量插入
|
||||
Instant now = Instant.now();
|
||||
List<UserProfileSkillTagRelation> relations = skillTagIds.stream().map(tagId -> {
|
||||
UserProfileSkillTagRelation relation = new UserProfileSkillTagRelation();
|
||||
relation.setUserId(userId);
|
||||
relation.setSkillTagId(tagId);
|
||||
relation.setCreateTime(now);
|
||||
return relation;
|
||||
}).collect(Collectors.toList());
|
||||
|
||||
relationMapper.batchInsert(relations);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user