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1. 背景

AI智能体（工作流/多智能体）都以来大量的工具才能高效完成计算密集及结果确定的任务。知名的notebook/jupyterlab是不错的选择。

这里有个综合能力强且开源的python在线开发部署一体平台DataFlux-Func，同时具有python微服务开发/部署，定时任务调度，可弹性扩容，安全鉴权，虚拟路径等生产级能力，可以作为接口平台/数据处理平台，满足小规模的产品应用需求。开源。

github代码库：

https://github.com/GuanceCloud/dataflux-func.git
 

2.安装（docker-compose)

参考文档：

https://func.guance.com/#/doc-index

官方提供了自动安装（基于docker swarm）只用按文档执行bash命令就自动安装了。同时提供了helm用于k8s安装（生产级）。

因为需要window下安装，这里提供docker compose的安装方法。

2.1 下载镜像（先找个目录）

./download-portable.sh --arch=x86_64

会有下载几个镜像

图片中的docker-compose.yaml, 文件内容如下：

* (mini版，worker只有1个，如果需要扩容worker，可修改scale参数>1）

* 请修改：宿主机的共享目录，密码端口等，改成自己喜欢的。

version: '3.8'services:# MYSQL STARTmysql:image: pubrepo.jiagouyun.com/dataflux-func/mysql:5.7.26labels:- mysqllogging:driver: json-fileoptions:max-size: 1mmax-file: 10networks:- datafluxfuncvolumes:- "D:/dockerDFFVol/mysql:/var/lib/mysql"environment:MYSQL_ROOT_PASSWORD: 2fd9179461546968MYSQL_DATABASE: dataflux_funccommand: --tls-version=TLSv1.2 --innodb-large-prefix=on --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci --performance-schema=off --table-open-cache=400 --default-authentication-plugin=mysql_native_password# MYSQL END# REDIS STARTredis:image: pubrepo.jiagouyun.com/dataflux-func/redis:5.0.7labels:- redislogging:driver: json-fileoptions:max-size: 1mmax-file: 10networks:- datafluxfuncvolumes:- "D:/dockerDFFVol/redis:/data"command: --stop-writes-on-bgsave-error yes# REDIS END# WORKER STARTworker:image: pubrepo.jiagouyun.com/dataflux-func/dataflux-func:6.2.3labels:- workervolumes:- "D:/dockerDFFVol/data:/data"logging:driver: json-fileoptions:max-size: 10mmax-file: 10networks:- datafluxfuncenvironment:HOST_HOSTNAME: 0.0.0.0 # 修改了Swarm特有的变量引用方式command: ./run-worker.shscale: 1  # 替换了deploy.replicas# WORKER ENDbeat:image: pubrepo.jiagouyun.com/dataflux-func/dataflux-func:6.2.3labels:- beatvolumes:- "D:/dockerDFFVol/data:/data"logging:driver: json-fileoptions:max-size: 1mmax-file: 10networks:- datafluxfuncenvironment:HOST_HOSTNAME: 0.0.0.0 # 修改了Swarm特有的变量引用方式redis_host: rediscommand: ./run-beat.shserver:image: pubrepo.jiagouyun.com/dataflux-func/dataflux-func:6.2.3labels:- servervolumes:- "D:/dockerDFFVol/data:/data"logging:driver: json-fileoptions:max-size: 10mmax-file: 10networks:- datafluxfuncenvironment:HOST_HOSTNAME: 0.0.0.0 # 修改了Swarm特有的变量引用方式ports:- "9000:8088"command: ./run-server.shnetworks:datafluxfunc:driver: bridge  # 替换了overlay驱动

2.2 导入镜像启动服务初始化

docker load < dataflux-func.tar.gz
docker load < mysql.tar.gz
docker load < redis.tar.gz

导入后：

启动：

docker-compose -p dff_6_2_3 up -d

访问：http://localhost:9000，有个配置初始化页面。

* 这里补齐一些配置，创建use-config.yaml文件，如下。

SECRET: 4xKQ1iaLwMnC72Jn
MYSQL_HOST: mysql
MYSQL_PORT: 3306
MYSQL_USER: root
MYSQL_PASSWORD: root
MYSQL_DATABASE: dataflux_func
REDIS_HOST: redis
REDIS_PORT: 6379
REDIS_DATABASE: 5
REDIS_PASSWORD: ''
REDIS_USE_TLS: false
LOG_LEVEL: WARNING
LOG_FILE_FORMAT: text
WEB_BASE_URL: ''
DB_ENGINE: mysql
_IS_INSTALLED: true

放到宿主机的共享目录下：

重启以下，访问：http://localhost:9000。点击初始化按钮，系统自动完成初始化。

2.3 正式登录

（用默认密码：admin/admin)

3. 微服务在线开发/部署

在线创建脚本，编辑，调试。语法高亮，好用，不亚于notebook。

没问题就点发布（这里比notebook还要好，发布后版本与编辑版本分离；编辑修改不影响在线的服务）

管理也去-同步API，创建一个公开的微服务API。选一下就行了。

用示例，系统给了调式的例子（post/get）都有，拷贝修改参数跑下。（生产环境，可以作为ping，验证服务状态）

4. 智能体调用工具

dify创建个工具：

我一般让deepseek等大模型帮忙生成，很多框架也有代码转定义的工具：

{"openapi": "3.1.0","info": {"title": "Addition Calculator","description": "A simple tool that adds two numbers together.","version": "v1.0.0"},"servers": [{"url": "http://host.docker.internal:5000"}],"paths": {"/plus": {"post": {"description": "Adds two numbers together and returns the sum","operationId": "AddNumbers","requestBody": {"required": true,"content": {"application/json": {"schema": {"type": "object","properties": {"x": {"type": "number","description": "First number to add","example": 5},"y": {"type": "number","description": "Second number to add","example": 3}},"required": ["x", "y"]}}}},"responses": {"200": {"description": "Successful addition","content": {"application/json": {"schema": {"type": "object","properties": {"result": {"type": "number","description": "The sum of x and y"}}}}}},"400": {"description": "Bad request","content": {"application/json": {"schema": {"type": "object","properties": {"error": {"type": "string","example": "Missing fields or invalid numbers"}}}}}},"500": {"description": "Calculation error","content": {"application/json": {"schema": {"type": "object","properties": {"error": {"type": "string","example": "Error during calculation"}}}}}}},"deprecated": false}}},"components": {"schemas": {"AdditionRequest": {"type": "object","properties": {"x": {"type": "number","description": "First number to add"},"y": {"type": "number","description": "Second number to add"}},"required": ["x", "y"]},"AdditionResponse": {"type": "object","properties": {"result": {"type": "number","description": "The sum of x and y"}}},"AdditionError": {"type": "object","properties": {"error": {"type": "string","description": "Error message"}}}}}
}

5. 数据开发平台

可以安装你想要的库：

让大模型开发个同步数据库表任务工具方法：

*（方法写的很强大，而且不需要改，实验中密码记错了只改了密码；代码写在最后）

*  可以写更多跨库SQL到表的工具；同库select insert工具等。

配置一个定时调度（点点点...）

验证：

先删除表

等1分钟：

可以查看日志：

附录

数据同步代码参考

MySQLSync工具类完全由大模型写完，运行无问题。

将其转化为定时任务，只用写个DFF的方法引用工具类的方法就好了。

import mysql.connector
from mysql.connector import errorcode
import time
from threading import Lockclass MySQLSync:def __init__(self, source_config, target_config):self.source_config = source_configself.target_config = target_configself.lock = Lock()def _get_connection(self, config):"""创建数据库连接池"""try:return mysql.connector.connect(host=config['host'],port=config['port'],user=config['user'],password=config['password'],database=config['database'],pool_name=f"{config['database']}_pool",pool_size=3)except mysql.connector.Error as err:if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:print("用户名或密码错误")elif err.errno == errorcode.ER_BAD_DB_ERROR:print("数据库不存在")else:print(f"连接错误: {err}")raisedef full_sync(self, table_name):"""全量同步表数据"""try:with self.lock:source_conn = self._get_connection(self.source_config)target_conn = self._get_connection(self.target_config)source_cursor = source_conn.cursor(dictionary=True)target_cursor = target_conn.cursor()# 获取源表结构source_cursor.execute(f"SHOW CREATE TABLE {table_name}")create_table_sql = source_cursor.fetchone()['Create Table']# 在目标库创建表（如果不存在）target_cursor.execute(f"DROP TABLE IF EXISTS {table_name}")target_cursor.execute(create_table_sql)# 分页查询源数据page_size = 1000offset = 0total_rows = 0while True:source_cursor.execute(f"SELECT * FROM {table_name} LIMIT {offset}, {page_size}")rows = source_cursor.fetchall()if not rows:break# 构建批量插入语句columns = list(rows[0].keys())placeholders = ['%s'] * len(columns)insert_sql = f"""INSERT INTO {table_name} ({','.join(columns)})VALUES ({','.join(placeholders)})"""# 执行批量插入target_cursor.executemany(insert_sql,[tuple(row.values()) for row in rows])total_rows += len(rows)offset += page_sizeprint(f"已同步 {total_rows} 行数据...")target_conn.commit()print(f"全量同步完成，共同步 {total_rows} 行数据")except Exception as e:print(f"同步失败: {str(e)}")if 'target_conn' in locals():target_conn.rollback()finally:for conn in [source_conn, target_conn]:if conn and conn.is_connected():conn.close()def incremental_sync(self, table_name):"""增量同步（基于binlog）"""try:# 使用canal-python客户端监听binlogfrom canal.client import Clientfrom canal.protocol import EntryProtocol_pb2client = Client()client.connect(host=self.source_config['host'],port=11111  # canal服务端端口)client.check_valid(username=self.source_config['user'].encode(),password=self.source_config['password'].encode())client.subscribe(client_id=b'1001',destination=b'example',filter=bytes(f'{self.source_config["database"]}\\.{table_name}', 'utf-8'))print(f"开始监听 {table_name} 表的变更...")while True:message = client.get(100)entries = message['entries']for entry in entries:if entry.entryType in [EntryProtocol_pb2.EntryType.TRANSACTIONBEGIN,EntryProtocol_pb2.EntryType.TRANSACTIONEND]:continuerow_change = EntryProtocol_pb2.RowChange()row_change.MergeFromString(entry.storeValue)event_type = row_change.eventTypeheader = entry.headerwith self.lock:target_conn = self._get_connection(self.target_config)target_cursor = target_conn.cursor()try:for row in row_change.rowDatas:if event_type == EntryProtocol_pb2.EventType.INSERT:columns = [col.name for col in row.afterColumns]values = [col.value for col in row.afterColumns]placeholders = ['%s'] * len(columns)insert_sql = f"""INSERT INTO {table_name} ({','.join(columns)})VALUES ({','.join(placeholders)})"""target_cursor.execute(insert_sql, values)elif event_type == EntryProtocol_pb2.EventType.UPDATE:set_clause = []where_clause = []params = []for col in row.afterColumns:set_clause.append(f"{col.name}=%s")params.append(col.value)if col.updated:where_clause.append(f"{col.name}=%s")params.append(row.beforeColumns[int(col.index)].value)update_sql = f"""UPDATE {table_name}SET {','.join(set_clause)}WHERE {' AND '.join(where_clause)}"""target_cursor.execute(update_sql, params)elif event_type == EntryProtocol_pb2.EventType.DELETE:where_clause = []params = []for col in row.beforeColumns:where_clause.append(f"{col.name}=%s")params.append(col.value)delete_sql = f"""DELETE FROM {table_name}WHERE {' AND '.join(where_clause)}"""target_cursor.execute(delete_sql, params)target_conn.commit()print(f"已处理 {len(row_change.rowDatas)} 条变更记录")except Exception as e:target_conn.rollback()print(f"处理变更失败: {str(e)}")finally:if target_conn and target_conn.is_connected():target_conn.close()time.sleep(0.1)except KeyboardInterrupt:print("停止监听")except Exception as e:print(f"增量同步异常: {str(e)}")@DFF.API('同步表任务', category='bigdata', tags=['bigdata', 'simple'], cache_result=300, timeout=3600)
def syncTable(tbl_name):'''两数相加输入参数 x, y 均为数字类型，返回结果为两者之和'''# 配置源库和目标库source_config = {'host': 'host.docker.internal','port': 33306,'user': 'readonly_user','password': '改我','database': 'e01'}target_config = {'host': 'host.docker.internal','port': 33306,'user': 'root','password': '改我','database': 'e02'}sync = MySQLSync(source_config, target_config)# 执行全量同步sync.full_sync(tbl_name)# 测试函数不需要装饰器
def test_plus():syncTable('region')