基于现有低代码平台，添加企业级功能、AI增强和高级集成能力。
1. 企业级工作流引擎
# workflow_engine.py
from typing import Dict, List, Any
from enum import Enum
from datetime import datetime
import asyncio
class NodeType(Enum):
START = "start"
APPROVAL = "approval"
ACTION = "action"
CONDITION = "condition"
END = "end"
class WorkflowNode:
def __init__(self, node_id: str, node_type: NodeType, config: Dict):
self.node_id = node_id
self.node_type = node_type
self.config = config
self.next_nodes = []
async def execute(self, context: Dict) -> Dict:
"""执行节点逻辑"""
if self.node_type == NodeType.START:
return {"status": "started", "next_nodes": self.next_nodes}
elif self.node_type == NodeType.APPROVAL:
return await self._handle_approval(context)
elif self.node_type == NodeType.ACTION:
return await self._handle_action(context)
elif self.node_type == NodeType.CONDITION:
return await self._handle_condition(context)
class WorkflowEngine:
def __init__(self):
self.workflows: Dict[str, Workflow] = {}
self.executions: Dict[str, WorkflowExecution] = {}
async def create_workflow(self, name: str, nodes: List[Dict]) -> str:
"""创建工作流"""
workflow_id = f"wf_{datetime.now().timestamp()}"
workflow = Workflow(workflow_id, name, nodes)
self.workflows[workflow_id] = workflow
return workflow_id
async def execute_workflow(self, workflow_id: str, initial_data: Dict) -> str:
"""执行工作流"""
execution_id = f"exec_{datetime.now().timestamp()}"
execution = WorkflowExecution(execution_id, workflow_id, initial_data)
self.executions[execution_id] = execution
# 异步执行工作流
asyncio.create_task(self._run_workflow(execution))
return execution_id
async def _run_workflow(self, execution: WorkflowExecution):
"""运行工作流实例"""
workflow = self.workflows[execution.workflow_id]
current_node = workflow.start_node
while current_node:
result = await current_node.execute(execution.context)
execution.history.append({
"node_id": current_node.node_id,
"timestamp": datetime.now(),
"result": result
})
if result.get("status") == "completed":
current_node = self._get_next_node(current_node, result)
else:
# 等待人工审批或其他异步操作
break
# 使用示例
engine = WorkflowEngine()
# 创建请假审批工作流
leave_workflow = await engine.create_workflow("请假审批", [
{
"type": "start",
"id": "start",
"next": "manager_approval"
},
{
"type": "approval",
"id": "manager_approval",
"approvers": ["manager@company.com"],
"next": "hr_approval"
},
{
"type": "approval",
"id": "hr_approval",
"approvers": ["hr@company.com"],
"next": "end"
}
])
# 执行工作流
execution_id = await engine.execute_workflow(leave_workflow, {
"applicant": "john@company.com",
"leave_days": 5,
"reason": "年假"
})2. AI增强的数据处理
# ai_data_processor.py
import pandas as pd
import openai
from typing import Dict, List, Any
import json
class AIDataProcessor:
def __init__(self):
self.client = openai.OpenAI(api_key="your-api-key")
async def smart_data_mapping(self, source_data: Dict, target_schema: Dict) -> Dict:
"""智能数据映射"""
prompt = f"""
根据源数据结构和目标schema，生成数据映射规则：
源数据:
{json.dumps(source_data, indent=2)}
目标Schema:
{json.dumps(target_schema, indent=2)}
请返回JSON格式的映射规则，包含字段对应关系和转换逻辑。
"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=1000
)
mapping_rules = self._extract_json(response.choices[0].message.content)
return await self._apply_mapping_rules(source_data, mapping_rules)
async def generate_data_transformations(self, data: pd.DataFrame,
objectives: List[str]) -> List[Dict]:
"""基于目标生成数据转换建议"""
prompt = f"""
分析以下数据，为达成目标建议数据转换操作：
数据样本:
{data.head().to_string()}
数据列: {list(data.columns)}
目标: {', '.join(objectives)}
建议的数据清洗和转换步骤：
"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=800
)
return self._parse_transformation_suggestions(response.choices[0].message.content)
async def automated_data_quality_check(self, data: pd.DataFrame) -> Dict:
"""自动化数据质量检查"""
quality_report = {
"completeness": self._check_completeness(data),
"consistency": self._check_consistency(data),
"accuracy": await self._check_accuracy(data),
"anomalies": await self._detect_anomalies(data)
}
# 使用AI生成改进建议
improvement_suggestions = await self._generate_quality_improvements(quality_report)
quality_report["improvement_suggestions"] = improvement_suggestions
return quality_report
class SmartETL:
"""智能ETL处理器"""
def __init__(self):
self.ai_processor = AIDataProcessor()
async def process_data_pipeline(self, source_config: Dict,
transformation_rules: List[Dict],
target_config: Dict) -> Dict:
"""处理数据管道"""
# 提取数据
source_data = await self._extract_data(source_config)
# 使用AI优化转换规则
optimized_rules = await self.ai_processor.optimize_transformations(
source_data, transformation_rules
)
# 转换数据
transformed_data = await self._transform_data(source_data, optimized_rules)
# 加载数据
load_result = await self._load_data(transformed_data, target_config)
return {
"records_processed": len(transformed_data),
"processing_time": load_result["duration"],
"quality_metrics": load_result["quality_metrics"]
}3. 企业集成连接器
# enterprise_connectors.py
from typing import Dict, List
import aiohttp
import asyncio
class ConnectorManager:
def __init__(self):
self.connectors = {
"salesforce": SalesforceConnector(),
"slack": SlackConnector(),
"google_sheets": GoogleSheetsConnector(),
"database": DatabaseConnector(),
"api": APIConnector()
}
async def create_integration(self, source: str, target: str,
mapping_rules: Dict) -> str:
"""创建系统集成"""
integration_id = f"int_{datetime.now().timestamp()}"
integration = {
"id": integration_id,
"source": source,
"target": target,
"mapping_rules": mapping_rules,
"status": "active",
"created_at": datetime.now()
}
# 启动集成任务
asyncio.create_task(self._run_integration(integration))
return integration_id
async def _run_integration(self, integration: Dict):
"""运行集成任务"""
source_connector = self.connectors[integration["source"]]
target_connector = self.connectors[integration["target"]]
while integration["status"] == "active":
try:
# 从源系统获取数据
source_data = await source_connector.fetch_data()
# 转换数据
transformed_data = await self._transform_data(
source_data, integration["mapping_rules"]
)
# 推送到目标系统
await target_connector.push_data(transformed_data)
# 等待下一次同步
await asyncio.sleep(300) # 5分钟
except Exception as e:
print(f"集成错误: {e}")
await asyncio.sleep(60) # 错误后等待1分钟
class SalesforceConnector:
"""Salesforce连接器"""
async def fetch_data(self, query: str = None) -> List[Dict]:
"""从Salesforce获取数据"""
async with aiohttp.ClientSession() as session:
async with session.get(
"https://your-salesforce-instance.com/services/data/v50.0/query",
headers={"Authorization": "Bearer your-token"},
params={"q": query or "SELECT Id, Name FROM Account"}
) as response:
data = await response.json()
return data.get("records", [])
async def push_data(self, data: List[Dict]):
"""推送数据到Salesforce"""
async with aiohttp.ClientSession() as session:
async with session.post(
"https://your-salesforce-instance.com/services/data/v50.0/composite/sobjects",
headers={"Authorization": "Bearer your-token"},
json={"records": data}
) as response:
return await response.json()
class SlackConnector:
"""Slack连接器"""
async def send_message(self, channel: str, message: str,
attachments: List[Dict] = None):
"""发送Slack消息"""
payload = {
"channel": channel,
"text": message,
"attachments": attachments or []
}
async with aiohttp.ClientSession() as session:
async with session.post(
"https://slack.com/api/chat.postMessage",
headers={"Authorization": "Bearer xoxb-your-token"},
json=payload
) as response:
return await response.json()4. 高级AI功能集成
# ai_enhancements.py
import openai
from typing import Dict, List
import json
class AIEnhancements:
def __init__(self):
self.client = openai.OpenAI(api_key="your-api-key")
async def generate_business_rules(self, business_description: str) -> List[Dict]:
"""从业务描述生成业务规则"""
prompt = f"""
根据以下业务需求，生成具体的业务规则：
业务描述: {business_description}
请返回JSON格式的业务规则列表，每个规则包含：
- 规则名称
- 条件
- 动作
- 优先级
"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=1500
)
return self._extract_json(response.choices[0].message.content)
async def optimize_workflow(self, current_workflow: Dict,
performance_metrics: Dict) -> Dict:
"""优化工作流性能"""
prompt = f"""
分析以下工作流，基于性能指标提出优化建议：
当前工作流:
{json.dumps(current_workflow, indent=2)}
性能指标:
{json.dumps(performance_metrics, indent=2)}
优化建议应包括：
1. 瓶颈识别
2. 并行化机会
3. 自动化建议
4. 预计性能提升
"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=1200
)
return self._parse_optimization_suggestions(response.choices[0].message.content)
async def generate_documentation(self, app_design: Dict,
audience: str = "technical") -> str:
"""自动生成应用文档"""
prompt = f"""
根据以下应用设计，生成{audience}文档：
应用设计:
{json.dumps(app_design, indent=2)}
文档应包括：
- 系统架构
- 功能说明
- 使用指南
- API文档（如适用）
"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
max_tokens=2000
)
return response.choices[0].message.content
class NaturalLanguageQuery:
"""自然语言查询处理器"""
def __init__(self):
self.ai_enhancements = AIEnhancements()
async def process_query(self, natural_language_query: str,
data_sources: List[Dict]) -> Dict:
"""处理自然语言查询"""
# 使用AI解析查询意图
parsed_query = await self.ai_enhancements.parse_query_intent(
natural_language_query, data_sources
)
# 生成SQL或API查询
generated_query = await self._generate_technical_query(parsed_query)
# 执行查询
query_result = await self._execute_query(generated_query, data_sources)
# 使用AI解释结果
interpreted_result = await self.ai_enhancements.interpret_results(
query_result, natural_language_query
)
return {
"original_query": natural_language_query,
"generated_query": generated_query,
"data": query_result,
"interpretation": interpreted_result
}5. 企业级管理界面
# admin_dashboard.py
from flask import Blueprint, render_template, jsonify, request
from typing import Dict, List
import json
admin_bp = Blueprint('admin', __name__, url_prefix='/admin')
@admin_bp.route('/')
def admin_dashboard():
"""管理员仪表板"""
return render_template('admin_dashboard.html')
@admin_bp.route('/api/usage-metrics')
def get_usage_metrics():
"""获取使用量指标"""
metrics = {
"active_users": 150,
"apps_created": 45,
"workflows_running": 12,
"data_processes": 28,
"api_calls": 1250
}
return jsonify(metrics)
@admin_bp.route('/api/integrations')
def get_integrations():
"""获取集成状态"""
integrations = [
{
"name": "Salesforce CRM",
"status": "active",
"last_sync": "2024-01-15 10:30:00",
"success_rate": 98.5
},
{
"name": "Slack通知",
"status": "active",
"last_sync": "2024-01-15 10:29:00",
"success_rate": 99.2
}
]
return jsonify(integrations)
@admin_bp.route('/api/performance')
def get_performance_metrics():
"""获取性能指标"""
performance = {
"response_time": {"avg": 125, "p95": 230},
"throughput": {"requests_per_second": 45},
"error_rate": 0.5,
"uptime": 99.8
}
return jsonify(performance)这个增强版AI低代码平台提供：
- 企业级工作流 - 复杂的业务流程自动化
- 智能数据处理 - AI驱动的数据映射和转换
- 系统集成 - 预构建的企业系统连接器
- AI增强功能 - 自然语言查询、自动优化
- 管理监控 - 使用量跟踪、性能监控
- 安全合规 - 企业级安全控制和审计
适合中大型企业部署使用。
