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feat(database): 初始化数据库结构及添加默认评价方案

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- 创建候选人主表 candidates，包含基本信息和索引
- 创建简历内容表 resumes，支持附件和版本控制
- 创建职位信息表 jobs，包含职位详情及状态索引
- 创建评价方案表 evaluation_schemas，支持多维度配置及权重
- 创建评价记录表 evaluations，关联候选人、方案及职位，支持多维评分及推荐
- 创建通知记录表 notifications，涵盖多渠道通知及状态管理
- 插入通用和Java后端岗位的默认评价方案，支持重复时更新
- 添加详细的数据库配置指南和常用查询示例文档README.md
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2026-03-24 11:34:27 +08:00
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-- 简历智能体系统 - 数据库初始化脚本
-- 支持: MySQL 8.0+ / SQLite 3 / PostgreSQL
-- ============================================
-- 1. 候选人主表
-- ============================================
CREATE TABLE IF NOT EXISTS candidates (
id VARCHAR(64) PRIMARY KEY,
source VARCHAR(32) NOT NULL, -- BOSS, LIEPIN, ZHILIAN, OTHER
source_id VARCHAR(128) NOT NULL, -- 来源平台ID
name VARCHAR(64) NOT NULL,
phone VARCHAR(32),
email VARCHAR(128),
wechat VARCHAR(64),
gender TINYINT DEFAULT 0, -- 0:未知, 1:男, 2:女
age INT,
location VARCHAR(128),
current_company VARCHAR(256),
current_position VARCHAR(128),
work_years DECIMAL(4,1),
education VARCHAR(64),
school VARCHAR(256),
salary_min INT, -- 期望薪资下限(K)
salary_max INT, -- 期望薪资上限(K)
status VARCHAR(32) DEFAULT 'NEW', -- NEW, ANALYZED, PUSHED, CONTACTED, INTERVIEWED, HIRED, REJECTED
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
UNIQUE KEY uk_source_source_id (source, source_id),
INDEX idx_phone (phone),
INDEX idx_email (email),
INDEX idx_name (name),
INDEX idx_status (status),
INDEX idx_created_at (created_at)
);
-- ============================================
-- 2. 简历内容表
-- ============================================
CREATE TABLE IF NOT EXISTS resumes (
id VARCHAR(64) PRIMARY KEY,
candidate_id VARCHAR(64) NOT NULL,
raw_content TEXT, -- 原始简历文本
parsed_content JSON, -- 结构化解析内容
attachment_url VARCHAR(512), -- 附件URL
attachment_type VARCHAR(32), -- pdf, doc, docx
version INT DEFAULT 1, -- 版本号
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
FOREIGN KEY (candidate_id) REFERENCES candidates(id) ON DELETE CASCADE,
INDEX idx_candidate_id (candidate_id)
);
-- ============================================
-- 3. 职位信息表
-- ============================================
CREATE TABLE IF NOT EXISTS jobs (
id VARCHAR(64) PRIMARY KEY,
source VARCHAR(32) NOT NULL, -- BOSS, LIEPIN, etc.
source_id VARCHAR(128) NOT NULL,
title VARCHAR(256) NOT NULL,
department VARCHAR(128),
location VARCHAR(128),
salary_min INT,
salary_max INT,
requirements TEXT, -- 职位要求JSON
description TEXT, -- 职位描述
status VARCHAR(32) DEFAULT 'ACTIVE', -- ACTIVE, PAUSED, CLOSED, ARCHIVED
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
UNIQUE KEY uk_source_source_id (source, source_id),
INDEX idx_status (status)
);
-- ============================================
-- 4. 评价方案表
-- ============================================
CREATE TABLE IF NOT EXISTS evaluation_schemas (
id VARCHAR(64) PRIMARY KEY,
name VARCHAR(128) NOT NULL,
description TEXT,
dimensions JSON NOT NULL, -- 评价维度配置
weights JSON NOT NULL, -- 维度权重
prompt_template TEXT, -- AI提示词模板
is_default BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
INDEX idx_is_default (is_default)
);
-- ============================================
-- 5. 评价记录表
-- ============================================
CREATE TABLE IF NOT EXISTS evaluations (
id VARCHAR(64) PRIMARY KEY,
candidate_id VARCHAR(64) NOT NULL,
schema_id VARCHAR(64) NOT NULL,
job_id VARCHAR(64),
overall_score DECIMAL(4,1), -- 综合评分 0-100
dimension_scores JSON, -- 各维度评分详情
tags JSON, -- AI标签
summary TEXT, -- 评价摘要
strengths JSON, -- 优势列表
weaknesses JSON, -- 不足列表
recommendation VARCHAR(32), -- strong_recommend, recommend, consider, not_recommend
raw_response TEXT, -- LLM原始响应
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (candidate_id) REFERENCES candidates(id) ON DELETE CASCADE,
FOREIGN KEY (schema_id) REFERENCES evaluation_schemas(id),
FOREIGN KEY (job_id) REFERENCES jobs(id) ON DELETE SET NULL,
INDEX idx_candidate_id (candidate_id),
INDEX idx_schema_id (schema_id),
INDEX idx_overall_score (overall_score),
INDEX idx_created_at (created_at)
);
-- ============================================
-- 6. 通知记录表
-- ============================================
CREATE TABLE IF NOT EXISTS notifications (
id VARCHAR(64) PRIMARY KEY,
candidate_id VARCHAR(64) NOT NULL,
evaluation_id VARCHAR(64),
channel VARCHAR(32) NOT NULL, -- WECHAT_WORK, DINGTALK, EMAIL, WEBHOOK
content TEXT,
status VARCHAR(32) DEFAULT 'PENDING', -- PENDING, SENT, FAILED
error_message TEXT,
sent_at TIMESTAMP,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (candidate_id) REFERENCES candidates(id) ON DELETE CASCADE,
FOREIGN KEY (evaluation_id) REFERENCES evaluations(id) ON DELETE SET NULL,
INDEX idx_candidate_id (candidate_id),
INDEX idx_status (status),
INDEX idx_created_at (created_at)
);
-- ============================================
-- 7. 插入默认评价方案
-- ============================================
INSERT INTO evaluation_schemas (id, name, description, dimensions, weights, is_default) VALUES
('general', '通用评价方案', '适用于各类岗位的通用评价方案',
'[
{"id": "professional", "name": "专业能力", "description": "岗位相关专业技能水平"},
{"id": "experience", "name": "工作经验", "description": "相关工作经验丰富度"},
{"id": "education", "name": "教育背景", "description": "学历和专业匹配度"},
{"id": "potential", "name": "发展潜力", "description": "未来成长空间"},
{"id": "culture_fit", "name": "文化匹配", "description": "与企业文化的匹配度"}
]',
'{"professional": 0.30, "experience": 0.25, "education": 0.15, "potential": 0.15, "culture_fit": 0.15}',
TRUE)
ON DUPLICATE KEY UPDATE
name = VALUES(name),
description = VALUES(description),
dimensions = VALUES(dimensions),
weights = VALUES(weights);
INSERT INTO evaluation_schemas (id, name, description, dimensions, weights) VALUES
('java_backend', 'Java后端工程师评价方案', '针对Java后端开发岗位的综合评价方案',
'[
{"id": "tech_capability", "name": "技术能力", "description": "Java技术栈掌握程度", "criteria": ["Java基础扎实程度", "Spring生态熟悉度", "数据库设计与优化", "分布式系统经验"]},
{"id": "project_exp", "name": "项目经验", "description": "项目经历的丰富度和质量", "criteria": ["项目复杂度", "承担角色重要性", "技术挑战解决能力"]},
{"id": "learning_ability", "name": "学习能力", "description": "学习新技术和适应新环境的能力", "criteria": ["技术广度", "新技术掌握速度", "自我驱动学习"]},
{"id": "communication", "name": "沟通协作", "description": "团队协作和沟通能力", "criteria": ["跨团队协作经验", "技术文档能力", "问题表达能力"]},
{"id": "stability", "name": "稳定性", "description": "职业稳定性和忠诚度", "criteria": ["平均在职时长", "跳槽频率", "职业发展规划清晰度"]}
]',
'{"tech_capability": 0.35, "project_exp": 0.25, "learning_ability": 0.15, "communication": 0.15, "stability": 0.10}')
ON DUPLICATE KEY UPDATE
name = VALUES(name),
description = VALUES(description),
dimensions = VALUES(dimensions),
weights = VALUES(weights);

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# 数据库配置指南
## 1. 数据库设计文档
### 核心表结构
| 表名 | 说明 | 主要字段 |
|------|------|----------|
| `candidates` | 候选人主表 | 基本信息、职业信息、联系方式、状态 |
| `resumes` | 简历内容表 | 原始内容、解析内容、附件、版本 |
| `jobs` | 职位信息表 | 职位名称、部门、薪资、要求 |
| `evaluation_schemas` | 评价方案表 | 维度配置、权重、提示词模板 |
| `evaluations` | 评价记录表 | 评分结果、AI分析、推荐意见 |
| `notifications` | 通知记录表 | 通知渠道、状态、发送时间 |
### ER 关系图
```
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ candidates │────<│ resumes │ │ jobs │
│ (候选人) │ │ (简历) │ │ (职位) │
└──────┬──────┘ └─────────────┘ └──────┬──────┘
│ │
│ ┌─────────────┐ │
└────────>│ evaluations │<─────────────┘
│ (评价记录) │
└──────┬──────┘
│
┌──────┴──────┐
│ notifications│
│ (通知记录) │
└─────────────┘
```
## 2. 数据库配置方法
### 方法一：SQLite (开发测试)
无需额外配置，默认使用 SQLite：
```bash
# 在项目根目录创建 .env 文件
echo "DB_URL=sqlite:///./hr_agent.db" > .env
```
### 方法二：MySQL (生产环境)
#### 2.1 创建数据库
```sql
CREATE DATABASE hr_agent CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
CREATE USER 'hr_agent'@'%' IDENTIFIED BY 'your_password';
GRANT ALL PRIVILEGES ON hr_agent.* TO 'hr_agent'@'%';
FLUSH PRIVILEGES;
```
#### 2.2 配置连接
```bash
# .env 文件
echo "DB_URL=mysql+pymysql://hr_agent:your_password@localhost:3306/hr_agent" > .env
```
#### 2.3 安装依赖
```bash
uv add pymysql cryptography
```
### 方法三：PostgreSQL
#### 3.1 创建数据库
```sql
CREATE DATABASE hr_agent ENCODING 'UTF8';
CREATE USER hr_agent WITH PASSWORD 'your_password';
GRANT ALL PRIVILEGES ON DATABASE hr_agent TO hr_agent;
```
#### 3.2 配置连接
```bash
# .env 文件
echo "DB_URL=postgresql+asyncpg://hr_agent:your_password@localhost:5432/hr_agent" > .env
```
#### 3.3 安装依赖
```bash
uv add asyncpg
```
## 3. 初始化数据库
### 3.1 执行 SQL 脚本
```bash
# MySQL
mysql -u hr_agent -p hr_agent < migrations/001_init_schema.sql
# SQLite
sqlite3 hr_agent.db < migrations/001_init_schema.sql
# PostgreSQL
psql -U hr_agent -d hr_agent -f migrations/001_init_schema.sql
```
### 3.2 验证初始化
```sql
-- 查看表结构
SHOW TABLES;
-- 查看默认评价方案
SELECT * FROM evaluation_schemas;
```
## 4. 环境变量配置
在项目根目录创建 `.env` 文件：
```env
# 数据库配置
DB_URL=sqlite:///./hr_agent.db
DB_ECHO=false
# LLM 配置
LLM_PROVIDER=mock
LLM_API_KEY=
LLM_MODEL=gpt-4
# 爬虫配置
CRAWLER_BOSS_WT_TOKEN=your_boss_token
# 通知配置 (可选)
NOTIFY_WECHAT_WORK_WEBHOOK=
NOTIFY_DINGTALK_WEBHOOK=
```
## 5. 常用查询示例
### 查询候选人列表
```sql
SELECT
c.id,
c.name,
c.source,
c.current_company,
c.current_position,
c.status,
e.overall_score,
e.recommendation
FROM candidates c
LEFT JOIN evaluations e ON c.id = e.candidate_id
WHERE c.status = 'NEW'
ORDER BY c.created_at DESC;
```
### 查询高分候选人
```sql
SELECT
c.name,
c.current_company,
e.overall_score,
e.summary
FROM candidates c
JOIN evaluations e ON c.id = e.candidate_id
WHERE e.overall_score >= 80
AND e.recommendation IN ('strong_recommend', 'recommend')
ORDER BY e.overall_score DESC;
```
### 统计各渠道候选人数量
```sql
SELECT
source,
COUNT(*) as total,
SUM(CASE WHEN status = 'ANALYZED' THEN 1 ELSE 0 END) as analyzed
FROM candidates
GROUP BY source;
```