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2025-12-31 06:08:08 +00:00
parent 94b2140a2c
commit 178c563012
5 changed files with 712 additions and 117 deletions
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.env.backup
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# Taiji AI-PAD 环境变量配置
# 数据库配置
ASYNC_DATABASE_URL=postgresql+asyncpg://taiji:By%40123456.@taijipda.postgres.database.azure.com:5432/postgres
DATABASE_URL=postgresql://taiji:By%40123456.@taijipda.postgres.database.azure.com:5432/postgres?sslmode=require
# Redis配置 (暂未启用)
REDIS_URL=redis://redis:6379
# NATS消息队列配置
NATS_URL=nats://nats:4222
# LiteLLM网关配置
LITELLM_MASTER_KEY=sk-1234567890abcdef
LITELLM_URL=http://litellm-gateway:4000
# OpenRouter配置
OPENROUTER_API_KEY=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
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
# RapidAPI配置
RAPIDAPI_KEY=33902cc39dmsha572ec6ae920fb5p13c196jsn8a11209a7e67
RAPIDAPI_HOST=rapidapi.com
# JWT配置
JWT_SECRET_KEY=your-super-secret-jwt-key-change-this-in-production
JWT_ALGORITHM=HS256
JWT_EXPIRE_MINUTES=1440
# 应用配置
APP_ENV=development
LOG_LEVEL=INFO
.env.backup.20251227_095645
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# Taiji AI-PAD 环境变量配置
# 数据库配置
ASYNC_DATABASE_URL=postgresql+asyncpg://taiji:By%40123456.@taijipda.postgres.database.azure.com:5432/postgres
DATABASE_URL=postgresql://taiji:By%40123456.@taijipda.postgres.database.azure.com:5432/postgres?sslmode=require
# Redis配置 (暂未启用)
# REDIS_URL已禁用（无本地Redis）
REDIS_URL=
# NATS消息队列配置
NATS_URL=nats://nats:4222
# LiteLLM网关配置
LITELLM_MASTER_KEY=sk-1234567890abcdef
LITELLM_URL=http://litellm-gateway:4000
# OpenRouter配置
OPENROUTER_API_KEY=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
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
# RapidAPI配置
RAPIDAPI_KEY=33902cc39dmsha572ec6ae920fb5p13c196jsn8a11209a7e67
RAPIDAPI_HOST=rapidapi.com
# JWT配置
JWT_SECRET_KEY=your-super-secret-jwt-key-change-this-in-production
JWT_ALGORITHM=HS256
JWT_EXPIRE_MINUTES=1440
# 应用配置
APP_ENV=development
LOG_LEVEL=INFO
.env.example
-40
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# taiji-AI-PAD 环境变量配置模板
# 复制此文件为 .env 并填写实际的密钥值
# cp .env.example .env
# ========== 数据库配置 ==========
POSTGRES_DB=taiji_db
POSTGRES_USER=taiji_user
POSTGRES_PASSWORD=taiji_pass
# ========== LiteLLM 网关配置 ==========
LITELLM_MASTER_KEY=sk-taiji-master-key
# ========== OpenRouter 配置 ==========
OPENROUTER_API_KEY=your-openrouter-api-key-here
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
# ========== RapidAPI 配置 ==========
RAPIDAPI_KEY=your-rapidapi-key-here
RAPIDAPI_HOST=rapidapi.com
# ========== OpenAI 配置（可选）==========
# OPENAI_API_KEY=your-openai-api-key-here
# ========== Anthropic 配置（可选）==========
# ANTHROPIC_API_KEY=your-anthropic-api-key-here
# ========== Langfuse 配置（可选，用于监控）==========
# LANGFUSE_PUBLIC_KEY=your-langfuse-public-key
# LANGFUSE_SECRET_KEY=your-langfuse-secret-key
# LANGFUSE_HOST=https://cloud.langfuse.com
# ========== 其他服务配置 ==========
REDIS_URL=redis://redis:6379
NATS_URL=nats://nats:4222
DATABASE_URL=postgresql://taiji_user:taiji_pass@postgres:5432/taiji_db
# ========== AI Agent Manager 配置 ==========
# Kubernetes Agent Pod 管理服务
AGENT_MANAGER_URL=http://localhost:8000
AGENT_K8S_NAMESPACE=ai-agents
Docs/前后端调试接口说明/API-MCPServer服务.md
+338 -12
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@@ -20,15 +20,23 @@
6. [获取工具列表](#6-获取工具列表)
7. [Prometheus Metrics](#7-prometheus-metrics)
### K8s Agent 管理 API（新增）
8. [获取模板列表](#8-获取模板列表)
9. [获取模板详情](#9-获取模板详情)
10. [创建 K8s Agent](#10-创建-k8s-agent)
11. [删除 Agent](#11-删除-agent)
12. [获取 Agent 状态](#12-获取-agent-状态)
13. [获取 Agent 资源使用](#13-获取-agent-资源使用)
### MCP 监控 API
8. [获取系统性能指标](#8-获取系统性能指标)
9. [获取服务统计信息](#9-获取服务统计信息)
10. [获取性能趋势数据](#10-获取性能趋势数据)
11. [获取系统告警](#11-获取系统告警)
12. [获取监控仪表盘聚合](#12-获取监控仪表盘聚合)
14. [获取系统性能指标](#14-获取系统性能指标)
15. [获取服务统计信息](#15-获取服务统计信息)
16. [获取性能趋势数据](#16-获取性能趋势数据)
17. [获取系统告警](#17-获取系统告警)
18. [获取监控仪表盘聚合](#18-获取监控仪表盘聚合)
### WebSocket API
13. [MCP Protocol WebSocket](#13-mcp-protocol-websocket)
19. [MCP Protocol WebSocket](#19-mcp-protocol-websocket)
---
@@ -345,11 +353,329 @@ curl -X GET "http://localhost:8002/metrics"
---
## K8s Agent 管理 API（新增）
> **说明**: 这些接口用于管理 Kubernetes 中的 AI Agent Pod，通过 AI Agent Manager 服务实现。
### 8. 获取模板列表
**GET** `/agents/templates`
获取所有可用的 Agent 模板及其所需参数。**此接口无需认证**。
**请求示例**:
```bash
curl -X GET "http://localhost:8002/agents/templates"
```
**响应示例**:
```json
{
"templates": [
{
"template": "echo_agent",
"port": null,
"env_info": {}
},
{
"template": "jina_search_agent",
"port": 8080,
"env_info": {
"required": {
"JINA_API_KEY": "Jina API密钥，从 https://jina.ai/ 获取"
},
"optional": {
"SERVICE_PORT": "HTTP服务端口，默认8080",
"SERVICE_HOST": "HTTP服务监听地址，默认0.0.0.0"
}
}
},
{
"template": "mysql_agent",
"port": null,
"env_info": {
"required": {
"MYSQL_HOST": "MySQL数据库主机地址",
"MYSQL_USER": "MySQL用户名",
"MYSQL_PASSWORD": "MySQL密码",
"MYSQL_DATABASE": "MySQL数据库名",
"OPENAI_API_KEY": "OpenAI API密钥"
},
"optional": {
"MYSQL_PORT": "MySQL端口，默认3306"
}
}
}
],
"count": 7
}
```
**可用模板列表**:
| 模板名称 | 端口 | 说明 |
|---------|------|------|
| `echo_agent` | - | 简单回显 Agent，用于测试 |
| `chat_agent` | - | 聊天对话 Agent |
| `code_agent` | - | 代码生成 Agent |
| `search_agent` | - | 搜索 Agent |
| `jina_search_agent` | 8080 | Jina 网页内容抓取 Agent |
| `mysql_agent` | - | MySQL 数据库查询 Agent |
| `postgresql_agent` | - | PostgreSQL 数据库查询 Agent |
---
### 9. 获取模板详情
**GET** `/agents/templates/{template_name}`
获取指定模板的详细信息，包括所需环境变量。**此接口无需认证**。
**路径参数**:
- `template_name` (string, 必填): 模板名称
**请求示例**:
```bash
curl -X GET "http://localhost:8002/agents/templates/jina_search_agent"
```
**响应示例**:
```json
{
"template": "jina_search_agent",
"port": 8080,
"env_info": {
"required": {
"JINA_API_KEY": "Jina API密钥，从 https://jina.ai/ 获取"
},
"optional": {
"SERVICE_PORT": "HTTP服务端口，默认8080",
"SERVICE_HOST": "HTTP服务监听地址，默认0.0.0.0"
}
}
}
```
**错误响应**:
- `404`: 模板不存在
---
### 10. 创建 K8s Agent
**POST** `/agents`
创建新的 Agent，如果指定了 `template`，将在 Kubernetes 中创建对应的 Pod。
**请求头**:
```
Authorization: Bearer <token>
Content-Type: application/json
```
**请求体**:
```json
{
"name": "my-jina-agent",
"description": "Jina 搜索 Agent",
"template": "jina_search_agent",
"resource_config": {
"cpu_request": "100m",
"cpu_limit": "500m",
"memory_request": "128Mi",
"memory_limit": "512Mi",
"replicas": 1,
"env": {
"JINA_API_KEY": "your-jina-api-key"
}
}
}
```
**请求体参数**:
| 参数 | 类型 | 必填 | 说明 |
|------|------|------|------|
| `name` | string | 是 | Agent 名称 |
| `description` | string | 否 | Agent 描述 |
| `template` | string | 否 | 模板类型（见模板列表） |
| `resource_config` | object | 否 | K8s 资源配置 |
**resource_config 参数**:
| 参数 | 类型 | 默认值 | 说明 |
|------|------|--------|------|
| `cpu_request` | string | `100m` | CPU 请求量 |
| `cpu_limit` | string | `500m` | CPU 限制量 |
| `memory_request` | string | `128Mi` | 内存请求量 |
| `memory_limit` | string | `512Mi` | 内存限制量 |
| `replicas` | integer | 1 | 副本数量 |
| `env` | object | `{}` | 环境变量 |
**成功响应**:
```json
{
"id": "d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b",
"name": "my-jina-agent",
"description": "Jina 搜索 Agent",
"template": "jina_search_agent",
"pod_name": "my-jina-agent-d7b0a5c2",
"pod_ip": null,
"k8s_status": "Pending",
"service_port": 8080,
"access_url": null,
"cpu_request": "100m",
"cpu_limit": "500m",
"memory_request": "128Mi",
"memory_limit": "512Mi",
"status": "active",
"created_at": "2025-12-31T04:38:26Z"
}
```
**错误响应**:
- `400`: 无效的模板类型
- `401`: 未认证
- `402`: 账户余额不足
- `500`: K8s Pod 创建失败
---
### 11. 删除 Agent
**DELETE** `/agents/{agent_id}`
删除指定的 Agent。如果 Agent 有关联的 K8s Pod，也会一并删除。
**请求头**:
```
Authorization: Bearer <token>
```
**路径参数**:
- `agent_id` (string, 必填): Agent ID
**请求示例**:
```bash
curl -X DELETE "http://localhost:8002/agents/d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b" \
-H "Authorization: Bearer <token>"
```
**成功响应**:
```json
{
"status": "success",
"message": "Agent my-jina-agent 已删除"
}
```
**错误响应**:
- `401`: 未认证
- `403`: 无权限（非 Agent 所有者且非超级管理员）
- `404`: Agent 不存在
---
### 12. 获取 Agent 状态
**GET** `/agents/{agent_id}/status`
获取 Agent 的实时状态。如果 Agent 有关联的 K8s Pod，会从 Agent Manager 获取最新状态。
**路径参数**:
- `agent_id` (string, 必填): Agent ID
**请求示例**:
```bash
curl -X GET "http://localhost:8002/agents/d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b/status"
```
**响应示例（运行中）**:
```json
{
"id": "d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b",
"name": "my-jina-agent",
"status": "active",
"k8s_status": "Running",
"pod_name": "my-jina-agent-d7b0a5c2",
"pod_ip": "10.244.1.107",
"node": "aks-nodepool1-12345678-vmss000000",
"service_port": 8080,
"access_url": "http://10.244.1.107:8080",
"endpoints": {
"root": "http://10.244.1.107:8080/",
"health": "http://10.244.1.107:8080/health"
},
"cpu_request": "100m",
"cpu_limit": "500m",
"memory_request": "128Mi",
"memory_limit": "512Mi",
"created_at": "2025-12-31T04:38:26Z",
"pod_created_at": "2025-12-31T04:38:26Z",
"conditions": [
{
"type": "Ready",
"status": "True",
"reason": null
},
{
"type": "ContainersReady",
"status": "True",
"reason": null
}
]
}
```
**K8s 状态值说明**:
| 状态 | 说明 |
|------|------|
| `Pending` | Pod 已被接受，但容器尚未创建 |
| `Running` | Pod 已绑定到节点，所有容器已创建 |
| `Succeeded` | Pod 中所有容器已成功终止 |
| `Failed` | Pod 中所有容器已终止，至少一个容器失败 |
| `Unknown` | 无法获取 Pod 状态 |
---
### 13. 获取 Agent 资源使用
**GET** `/agents/{agent_id}/metrics`
获取 Agent 的 CPU 和内存资源配置信息。
**路径参数**:
- `agent_id` (string, 必填): Agent ID
**请求示例**:
```bash
curl -X GET "http://localhost:8002/agents/d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b/metrics"
```
**响应示例**:
```json
{
"id": "d7b0a5c2-5f6a-4c27-9ef9-8d51b94f7a1b",
"name": "my-jina-agent",
"requests": {
"cpu": "100m",
"memory": "128Mi"
},
"limits": {
"cpu": "500m",
"memory": "512Mi"
}
}
```
---
## MCP 监控 API
**基础URL**: `http://localhost:8002/api/v1/monitoring`
### 8. 获取系统性能指标
### 14. 获取系统性能指标
**GET** `/api/v1/monitoring/metrics`
@@ -389,7 +715,7 @@ curl -X GET "http://localhost:8002/api/v1/monitoring/metrics"
---
### 9. 获取服务统计信息
### 15. 获取服务统计信息
**GET** `/api/v1/monitoring/stats`
@@ -427,7 +753,7 @@ curl -X GET "http://localhost:8002/api/v1/monitoring/stats?service=all"
---
### 10. 获取性能趋势数据
### 16. 获取性能趋势数据
**GET** `/api/v1/monitoring/trends`
@@ -462,7 +788,7 @@ curl -X GET "http://localhost:8002/api/v1/monitoring/trends?metric=executions&pe
---
### 11. 获取系统告警
### 17. 获取系统告警
**GET** `/api/v1/monitoring/alerts`
@@ -494,7 +820,7 @@ curl -X GET "http://localhost:8002/api/v1/monitoring/alerts?severity=warning"
---
### 12. 获取监控仪表盘聚合
### 18. 获取监控仪表盘聚合
**GET** `/api/v1/monitoring/dashboard`
@@ -529,7 +855,7 @@ curl -X GET "http://localhost:8002/api/v1/monitoring/dashboard"
## WebSocket API
### 13. MCP Protocol WebSocket
### 19. MCP Protocol WebSocket
**WebSocket URL**: `ws://localhost:8002/ws/{agent_name_or_id}`
scripts/copy_database.py
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@@ -0,0 +1,374 @@
#!/usr/bin/env python3
"""
将 taiji 数据库的表结构和数据复制到 postgres 数据库
使用方法:
python scripts/copy_database.py
注意: 需要安装 psycopg2-binary
pip install psycopg2-binary
"""
import psycopg2
from urllib.parse import quote_plus
import sys
# 数据库连接配置
DB_HOST = "taijipda.postgres.database.azure.com"
DB_USER = "taiji"
DB_PASSWORD = "By@123456."
DB_PORT = 5432
# 源数据库和目标数据库
SOURCE_DB = "taiji"
TARGET_DB = "postgres"
def get_connection(database):
"""获取数据库连接"""
return psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
user=DB_USER,
password=DB_PASSWORD,
database=database,
sslmode="require"
)
def get_all_tables(conn):
"""获取所有用户表"""
cursor = conn.cursor()
cursor.execute("""
SELECT table_name
FROM information_schema.tables
WHERE table_schema = 'public'
AND table_type = 'BASE TABLE'
ORDER BY table_name
""")
tables = [row[0] for row in cursor.fetchall()]
cursor.close()
return tables
def get_table_ddl(conn, table_name):
"""获取表的 DDL 语句"""
cursor = conn.cursor()
# 获取列定义
cursor.execute("""
SELECT
column_name,
data_type,
character_maximum_length,
numeric_precision,
numeric_scale,
is_nullable,
column_default,
udt_name
FROM information_schema.columns
WHERE table_schema = 'public' AND table_name = %s
ORDER BY ordinal_position
""", (table_name,))
columns = cursor.fetchall()
if not columns:
cursor.close()
return None
# 构建列定义
column_defs = []
for col in columns:
col_name, data_type, char_max_len, num_precision, num_scale, is_nullable, col_default, udt_name = col
# 处理数据类型
if data_type == 'character varying':
if char_max_len:
type_str = f"VARCHAR({char_max_len})"
else:
type_str = "VARCHAR"
elif data_type == 'character':
type_str = f"CHAR({char_max_len})" if char_max_len else "CHAR"
elif data_type == 'numeric':
if num_precision and num_scale:
type_str = f"NUMERIC({num_precision},{num_scale})"
elif num_precision:
type_str = f"NUMERIC({num_precision})"
else:
type_str = "NUMERIC"
elif data_type == 'ARRAY':
type_str = f"{udt_name.lstrip('_')}[]"
elif data_type == 'USER-DEFINED':
type_str = udt_name
else:
type_str = data_type.upper()
# 构建列定义
col_def = f' "{col_name}" {type_str}'
if is_nullable == 'NO':
col_def += " NOT NULL"
if col_default:
col_def += f" DEFAULT {col_default}"
column_defs.append(col_def)
# 获取主键约束
cursor.execute("""
SELECT kcu.column_name
FROM information_schema.table_constraints tc
JOIN information_schema.key_column_usage kcu
ON tc.constraint_name = kcu.constraint_name
AND tc.table_schema = kcu.table_schema
WHERE tc.constraint_type = 'PRIMARY KEY'
AND tc.table_schema = 'public'
AND tc.table_name = %s
ORDER BY kcu.ordinal_position
""", (table_name,))
pk_columns = [row[0] for row in cursor.fetchall()]
if pk_columns:
pk_def = f' PRIMARY KEY ("{"\", \"".join(pk_columns)}")'
column_defs.append(pk_def)
ddl = f'CREATE TABLE IF NOT EXISTS "{table_name}" (\n'
ddl += ",\n".join(column_defs)
ddl += "\n);"
cursor.close()
return ddl
def get_indexes(conn, table_name):
"""获取表的索引"""
cursor = conn.cursor()
cursor.execute("""
SELECT indexdef
FROM pg_indexes
WHERE schemaname = 'public'
AND tablename = %s
AND indexname NOT LIKE '%%_pkey'
""", (table_name,))
indexes = [row[0] for row in cursor.fetchall()]
cursor.close()
return indexes
def get_sequences(conn):
"""获取所有序列"""
cursor = conn.cursor()
cursor.execute("""
SELECT sequence_name
FROM information_schema.sequences
WHERE sequence_schema = 'public'
""")
sequences = [row[0] for row in cursor.fetchall()]
cursor.close()
return sequences
def get_sequence_value(conn, sequence_name):
"""获取序列当前值"""
cursor = conn.cursor()
try:
cursor.execute(f'SELECT last_value FROM "{sequence_name}"')
value = cursor.fetchone()[0]
except:
value = 1
cursor.close()
return value
def copy_table_data(source_conn, target_conn, table_name):
"""复制表数据"""
source_cursor = source_conn.cursor()
target_cursor = target_conn.cursor()
# 获取列名
source_cursor.execute("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = 'public' AND table_name = %s
ORDER BY ordinal_position
""", (table_name,))
columns = [row[0] for row in source_cursor.fetchall()]
if not columns:
source_cursor.close()
target_cursor.close()
return 0
# 获取数据
columns_str = ', '.join([f'"{c}"' for c in columns])
source_cursor.execute(f'SELECT {columns_str} FROM "{table_name}"')
rows = source_cursor.fetchall()
if not rows:
source_cursor.close()
target_cursor.close()
return 0
# 插入数据
placeholders = ', '.join(['%s'] * len(columns))
insert_sql = f'INSERT INTO "{table_name}" ({columns_str}) VALUES ({placeholders}) ON CONFLICT DO NOTHING'
for row in rows:
try:
target_cursor.execute(insert_sql, row)
except Exception as e:
print(f" 警告: 插入数据失败 - {e}")
target_conn.commit()
source_cursor.close()
target_cursor.close()
return len(rows)
def drop_all_tables(conn):
"""删除目标数据库中的所有表"""
cursor = conn.cursor()
# 获取所有表
cursor.execute("""
SELECT table_name
FROM information_schema.tables
WHERE table_schema = 'public'
AND table_type = 'BASE TABLE'
""")
tables = [row[0] for row in cursor.fetchall()]
if tables:
# 禁用外键检查并删除所有表
for table in tables:
try:
cursor.execute(f'DROP TABLE IF EXISTS "{table}" CASCADE')
print(f" ✓ 已删除表: {table}")
except Exception as e:
print(f" ✗ 删除表 {table} 失败: {e}")
conn.commit()
cursor.close()
return len(tables)
def main():
print("=" * 60)
print("PostgreSQL 数据库复制工具")
print(f"源数据库: {SOURCE_DB}")
print(f"目标数据库: {TARGET_DB}")
print("=" * 60)
# 连接源数据库
print("\n[1] 连接源数据库...")
try:
source_conn = get_connection(SOURCE_DB)
print(f" ✓ 成功连接到 {SOURCE_DB}")
except Exception as e:
print(f" ✗ 连接失败: {e}")
sys.exit(1)
# 连接目标数据库
print("\n[2] 连接目标数据库...")
try:
target_conn = get_connection(TARGET_DB)
print(f" ✓ 成功连接到 {TARGET_DB}")
except Exception as e:
print(f" ✗ 连接失败: {e}")
source_conn.close()
sys.exit(1)
# 删除目标数据库中的旧表
print("\n[3] 清理目标数据库旧表...")
dropped_count = drop_all_tables(target_conn)
print(f" 共删除 {dropped_count} 个旧表")
# 获取所有表
print("\n[4] 获取源数据库表列表...")
tables = get_all_tables(source_conn)
print(f" 找到 {len(tables)} 个表:")
for t in tables:
print(f" - {t}")
# 复制表结构
print("\n[5] 复制表结构...")
target_cursor = target_conn.cursor()
for table in tables:
print(f" 处理表: {table}")
# 获取并执行 DDL
ddl = get_table_ddl(source_conn, table)
if ddl:
try:
target_cursor.execute(ddl)
target_conn.commit()
print(f" ✓ 表结构已创建")
except Exception as e:
target_conn.rollback()
if "already exists" in str(e):
print(f" ○ 表已存在，跳过创建")
else:
print(f" ✗ 创建失败: {e}")
# 获取并创建索引
indexes = get_indexes(source_conn, table)
for idx in indexes:
try:
# 修改索引名以避免冲突
target_cursor.execute(idx)
target_conn.commit()
print(f" ✓ 索引已创建")
except Exception as e:
target_conn.rollback()
if "already exists" in str(e):
print(f" ○ 索引已存在，跳过")
else:
print(f" ✗ 索引创建失败: {e}")
target_cursor.close()
# 复制数据
print("\n[6] 复制表数据...")
for table in tables:
print(f" 复制表: {table}")
try:
count = copy_table_data(source_conn, target_conn, table)
print(f" ✓ 已复制 {count} 行数据")
except Exception as e:
print(f" ✗ 复制失败: {e}")
# 更新序列
print("\n[7] 同步序列值...")
sequences = get_sequences(source_conn)
target_cursor = target_conn.cursor()
for seq in sequences:
try:
value = get_sequence_value(source_conn, seq)
target_cursor.execute(f'SELECT setval(\'{seq}\', {value}, true)')
target_conn.commit()
print(f" ✓ 序列 {seq} 设置为 {value}")
except Exception as e:
target_conn.rollback()
print(f" ✗ 序列 {seq} 同步失败: {e}")
target_cursor.close()
# 关闭连接
source_conn.close()
target_conn.close()
print("\n" + "=" * 60)
print("数据库复制完成!")
print("=" * 60)
if __name__ == "__main__":
main()