mirror of
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714 lines
21 KiB
Markdown
714 lines
21 KiB
Markdown
# Workflow 系统开发者文档
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本文档面向 LangBot 开发者，详细介绍 Workflow 系统的技术架构、核心组件和扩展方法。
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## 目录
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- [系统架构概述](#系统架构概述)
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- [目录结构](#目录结构)
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- [核心组件](#核心组件)
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- [后端模块](#后端模块)
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- [前端组件](#前端组件)
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- [数据库表结构](#数据库表结构)
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- [API 接口文档](#api-接口文档)
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- [如何添加新节点类型](#如何添加新节点类型)
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- [调试功能实现](#调试功能实现)
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---
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## 系统架构概述
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Workflow 系统采用前后端分离架构，主要包含以下层次：
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```
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┌─────────────────────────────────────────────────────────────┐
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│ 前端层 (React) │
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│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
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│ │ 可视化编辑器 │ 节点面板 │ 属性面板 │ 调试器 │ │
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│ │ ReactFlow │ NodePalette │ PropertyPanel│ Debugger │ │
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│ └─────────────┴──────────────┴──────────────┴───────────┘ │
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├─────────────────────────────────────────────────────────────┤
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│ API 层 (Quart) │
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│ ┌─────────────┬──────────────┬──────────────────────────┐ │
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│ │ Workflow API│ Debug API │ Node Types API │ │
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│ └─────────────┴──────────────┴──────────────────────────┘ │
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├─────────────────────────────────────────────────────────────┤
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│ 核心引擎层 (Python) │
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│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
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│ │ Executor │ Registry │ Node │ Entities │ │
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│ │ 执行引擎 │ 节点注册表 │ 节点基类 │ 数据结构 │ │
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│ └─────────────┴──────────────┴──────────────┴───────────┘ │
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├─────────────────────────────────────────────────────────────┤
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│ 存储层 (SQLAlchemy) │
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│ ┌─────────────┬──────────────┬──────────────────────────┐ │
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│ │ Workflow │ Executions │ Triggers │ │
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│ └─────────────┴──────────────┴──────────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## 目录结构
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### 后端代码结构
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```
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LangBot/src/langbot/pkg/
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├── workflow/ # Workflow 核心模块
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│ ├── __init__.py # 模块初始化，导出公共接口
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│ ├── entities.py # 数据实体定义
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│ ├── executor.py # 执行引擎
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│ ├── node.py # 节点基类和装饰器
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│ ├── registry.py # 节点类型注册表
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│ └── nodes/ # 内置节点实现
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│ ├── __init__.py # 注册所有内置节点
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│ ├── trigger.py # 触发节点
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│ ├── process.py # 处理节点
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│ ├── control.py # 控制节点
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│ └── action.py # 动作节点
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├── entity/persistence/
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│ └── workflow.py # 数据库模型
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├── api/http/
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│ ├── controller/groups/workflows/
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│ │ └── workflows.py # API 路由控制器
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│ └── service/
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│ └── workflow.py # 业务逻辑服务
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└── persistence/migrations/
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└── dbm026_workflow_tables.py # 数据库迁移
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```
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### 前端代码结构
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```
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LangBot/web/src/app/home/workflows/
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├── page.tsx # Workflow 列表页
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├── WorkflowDetailContent.tsx # 详情页内容
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├── store/
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│ └── useWorkflowStore.ts # Zustand 状态管理
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└── components/
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├── workflow-editor/ # 可视化编辑器
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│ ├── index.ts # 导出
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│ ├── WorkflowEditorComponent.tsx # 主编辑器组件
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│ ├── WorkflowNodeComponent.tsx # 自定义节点组件
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│ ├── NodePalette.tsx # 节点面板
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│ ├── PropertyPanel.tsx # 属性面板
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│ └── node-configs/ # 节点配置元数据
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│ ├── types.ts # 配置类型定义
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│ ├── trigger-configs.ts
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│ ├── ai-configs.ts
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│ ├── process-configs.ts
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│ ├── control-configs.ts
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│ ├── action-configs.ts
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│ ├── integration-configs.ts
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│ └── index.ts # 配置汇总
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├── workflow-debugger/ # 调试器组件
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│ ├── index.ts
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│ └── WorkflowDebugger.tsx
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├── workflow-form/ # 表单组件
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│ └── WorkflowFormComponent.tsx
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└── workflow-executions/ # 执行历史组件
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└── WorkflowExecutionsTab.tsx
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```
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---
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## 核心组件
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### 后端模块
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#### 1. 执行引擎 (WorkflowExecutor)
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位置：[`executor.py`](../../src/langbot/pkg/workflow/executor.py)
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执行引擎负责工作流的实际执行，包括：
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- **拓扑排序**：确定节点执行顺序
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- **节点执行**：调用各节点的 execute 方法
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- **控制流处理**：处理条件分支、循环、并行执行
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- **错误处理**：支持重试机制
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```python
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class WorkflowExecutor:
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async def execute(
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self,
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workflow: WorkflowDefinition,
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context: ExecutionContext,
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start_node_id: Optional[str] = None
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) -> ExecutionContext:
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"""执行工作流"""
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# 1. 构建执行图
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# 2. 初始化节点状态
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# 3. 找到起始节点
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# 4. 按拓扑顺序执行
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```
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**调试执行器 (DebugWorkflowExecutor)**
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继承自 WorkflowExecutor，增加了调试支持：
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- 断点支持
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- 单步执行
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- 暂停/继续
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- 实时日志
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```python
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class DebugWorkflowExecutor(WorkflowExecutor):
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async def execute_debug(
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self,
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workflow: WorkflowDefinition,
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context: ExecutionContext,
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debug_state: DebugExecutionState,
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) -> ExecutionContext:
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"""调试模式执行"""
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```
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#### 2. 节点注册表 (NodeTypeRegistry)
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位置：[`registry.py`](../../src/langbot/pkg/workflow/registry.py)
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单例模式管理所有节点类型：
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```python
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class NodeTypeRegistry:
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_instance: Optional['NodeTypeRegistry'] = None
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def register(self, node_type: str, node_class: type[WorkflowNode]):
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"""注册节点类型"""
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def create_instance(self, node_type: str, node_id: str, config: dict) -> WorkflowNode:
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"""创建节点实例"""
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def list_all(self) -> list[dict]:
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"""获取所有节点类型的 Schema"""
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```
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#### 3. 节点基类 (WorkflowNode)
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位置：[`node.py`](../../src/langbot/pkg/workflow/node.py)
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所有节点必须继承此基类：
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```python
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class WorkflowNode(abc.ABC):
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# 节点元数据
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type_name: str = ""
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name: str = ""
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description: str = ""
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category: str = "misc"
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icon: str = ""
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# 端口定义
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inputs: list[NodePort] = []
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outputs: list[NodePort] = []
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# 配置 Schema
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config_schema: list[NodeConfig] = []
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@abc.abstractmethod
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async def execute(
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self,
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inputs: dict[str, Any],
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context: ExecutionContext
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) -> dict[str, Any]:
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"""执行节点逻辑"""
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pass
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```
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#### 4. 数据实体 (entities.py)
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主要数据结构：
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```python
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class WorkflowDefinition:
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"""工作流定义"""
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uuid: str
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name: str
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nodes: list[NodeDefinition]
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edges: list[EdgeDefinition]
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settings: WorkflowSettings
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class ExecutionContext:
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"""执行上下文"""
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execution_id: str
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workflow_id: str
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status: ExecutionStatus
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variables: dict
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node_states: dict[str, NodeState]
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history: list[ExecutionStep]
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```
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### 前端组件
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#### 1. WorkflowEditorComponent
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主编辑器组件，基于 React Flow 实现：
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- **画布交互**：拖拽、缩放、平移
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- **节点连接**：自动验证端口类型
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- **撤销/重做**：基于历史记录栈
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- **复制/粘贴**：支持多选复制
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关键功能：
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```tsx
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function WorkflowEditorInner() {
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const { nodes, edges, onNodesChange, onEdgesChange, onConnect } = useWorkflowStore();
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// 拖放添加节点
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const onDrop = useCallback((event: React.DragEvent) => {
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const type = event.dataTransfer.getData('application/reactflow');
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const position = screenToFlowPosition({ x: event.clientX, y: event.clientY });
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addNode(type, position);
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}, []);
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// 复制粘贴
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const handleCopy = useCallback(() => { ... }, []);
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const handlePaste = useCallback(() => { ... }, []);
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}
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```
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#### 2. NodePalette
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节点面板组件，展示可用节点类型：
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```tsx
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function NodePalette() {
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// 按类别组织节点
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const categories = [
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{ id: 'trigger', name: '触发节点', icon: Zap },
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{ id: 'ai', name: 'AI 节点', icon: Brain },
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{ id: 'process', name: '处理节点', icon: Cpu },
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{ id: 'control', name: '控制节点', icon: GitBranch },
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{ id: 'action', name: '动作节点', icon: Send },
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{ id: 'integration', name: '集成节点', icon: Plug },
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];
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// 拖拽开始
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const onDragStart = (event: React.DragEvent, nodeType: string) => {
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event.dataTransfer.setData('application/reactflow', nodeType);
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};
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}
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```
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#### 3. PropertyPanel
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属性面板组件，动态渲染节点配置表单：
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```tsx
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function PropertyPanel() {
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const { selectedNodeId, nodes, updateNodeData } = useWorkflowStore();
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// 根据节点类型获取配置元数据
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const selectedNode = nodes.find(n => n.id === selectedNodeId);
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const nodeConfig = getNodeConfig(selectedNode?.data?.nodeType);
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// 动态渲染配置字段
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return (
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<div>
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{nodeConfig?.fields.map(field => (
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<ConfigField key={field.name} field={field} />
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))}
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</div>
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);
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}
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```
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#### 4. WorkflowDebugger
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调试器组件，支持实时调试：
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```tsx
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function WorkflowDebugger({ workflowUuid, workflow }) {
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const [debugState, setDebugState] = useState<DebugState>('idle');
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const [executionId, setExecutionId] = useState<string>('');
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const [logs, setLogs] = useState<ExecutionLog[]>([]);
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// 启动调试
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const startDebug = async () => {
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const result = await backendClient.post(
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`/api/v1/workflows/${workflowUuid}/debug/start`,
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{ context, variables, breakpoints }
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);
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setExecutionId(result.execution_id);
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};
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// 轮询状态
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useEffect(() => {
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if (debugState === 'running') {
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const interval = setInterval(fetchState, 500);
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return () => clearInterval(interval);
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}
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}, [debugState]);
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}
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```
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#### 5. useWorkflowStore
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Zustand 状态管理：
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```typescript
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interface WorkflowState {
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nodes: WorkflowNode[];
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edges: WorkflowEdge[];
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selectedNodeId: string | null;
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history: HistoryEntry[];
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historyIndex: number;
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isDirty: boolean;
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// Actions
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addNode: (type: string, position: XYPosition) => void;
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updateNodeData: (nodeId: string, data: Partial<NodeData>) => void;
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deleteNode: (nodeId: string) => void;
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undo: () => void;
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redo: () => void;
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}
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export const useWorkflowStore = create<WorkflowState>((set, get) => ({
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// ... state and actions
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}));
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```
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---
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## 数据库表结构
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### workflows 表
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```sql
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CREATE TABLE workflows (
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uuid VARCHAR(255) PRIMARY KEY,
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name VARCHAR(255) NOT NULL,
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description TEXT,
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emoji VARCHAR(10) DEFAULT '🔄',
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version INTEGER DEFAULT 1,
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is_enabled BOOLEAN DEFAULT TRUE,
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definition JSON NOT NULL, -- 节点和边定义
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global_config JSON DEFAULT '{}', -- 全局配置
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extensions_preferences JSON, -- 插件和 MCP 配置
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created_at TIMESTAMP,
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updated_at TIMESTAMP
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);
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```
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### workflow_versions 表
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```sql
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CREATE TABLE workflow_versions (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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workflow_uuid VARCHAR(255) NOT NULL,
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version INTEGER NOT NULL,
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definition JSON NOT NULL,
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global_config JSON DEFAULT '{}',
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created_at TIMESTAMP,
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created_by VARCHAR(255),
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UNIQUE(workflow_uuid, version)
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);
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```
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### workflow_executions 表
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```sql
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CREATE TABLE workflow_executions (
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uuid VARCHAR(255) PRIMARY KEY,
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workflow_uuid VARCHAR(255) NOT NULL,
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workflow_version INTEGER NOT NULL,
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status VARCHAR(20) NOT NULL, -- pending/running/completed/failed/cancelled
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trigger_type VARCHAR(50),
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trigger_data JSON,
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variables JSON,
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start_time TIMESTAMP,
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end_time TIMESTAMP,
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error TEXT,
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created_at TIMESTAMP
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);
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```
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### workflow_node_executions 表
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```sql
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CREATE TABLE workflow_node_executions (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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execution_uuid VARCHAR(255) NOT NULL,
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node_id VARCHAR(100) NOT NULL,
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node_type VARCHAR(50) NOT NULL,
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status VARCHAR(20) NOT NULL,
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inputs JSON,
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outputs JSON,
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start_time TIMESTAMP,
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end_time TIMESTAMP,
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error TEXT,
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retry_count INTEGER DEFAULT 0
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);
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```
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### workflow_triggers 表
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```sql
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CREATE TABLE workflow_triggers (
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uuid VARCHAR(255) PRIMARY KEY,
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workflow_uuid VARCHAR(255) NOT NULL,
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type VARCHAR(50) NOT NULL, -- message/cron/event/webhook
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config JSON NOT NULL,
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is_enabled BOOLEAN DEFAULT TRUE,
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priority INTEGER DEFAULT 0,
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created_at TIMESTAMP,
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updated_at TIMESTAMP
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);
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```
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---
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## API 接口文档
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### Workflow CRUD
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| 方法 | 路径 | 描述 |
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|-----|------|------|
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| GET | `/api/v1/workflows` | 获取工作流列表 |
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| POST | `/api/v1/workflows` | 创建工作流 |
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| GET | `/api/v1/workflows/:uuid` | 获取单个工作流 |
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| PUT | `/api/v1/workflows/:uuid` | 更新工作流 |
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| DELETE | `/api/v1/workflows/:uuid` | 删除工作流 |
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| POST | `/api/v1/workflows/:uuid/copy` | 复制工作流 |
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### 执行相关
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| 方法 | 路径 | 描述 |
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|-----|------|------|
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| POST | `/api/v1/workflows/:uuid/execute` | 手动执行工作流 |
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| GET | `/api/v1/workflows/:uuid/executions` | 获取执行记录 |
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### 版本管理
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| 方法 | 路径 | 描述 |
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|-----|------|------|
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| GET | `/api/v1/workflows/:uuid/versions` | 获取版本列表 |
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| POST | `/api/v1/workflows/:uuid/rollback/:version` | 回滚到指定版本 |
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### 调试 API
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| 方法 | 路径 | 描述 |
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|-----|------|------|
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| POST | `/api/v1/workflows/:uuid/debug/start` | 启动调试 |
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| POST | `/api/v1/workflows/:uuid/debug/:exec_id/pause` | 暂停执行 |
|
||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/resume` | 继续执行 |
|
||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/stop` | 停止执行 |
|
||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/step` | 单步执行 |
|
||
| GET | `/api/v1/workflows/:uuid/debug/:exec_id/state` | 获取调试状态 |
|
||
|
||
### 节点类型
|
||
|
||
| 方法 | 路径 | 描述 |
|
||
|-----|------|------|
|
||
| GET | `/api/v1/workflows/_/node-types` | 获取所有节点类型 |
|
||
| GET | `/api/v1/workflows/_/node-types/categories` | 按类别获取节点类型 |
|
||
|
||
---
|
||
|
||
## 如何添加新节点类型
|
||
|
||
### 步骤 1：创建节点类
|
||
|
||
在 `LangBot/src/langbot/pkg/workflow/nodes/` 下创建或修改文件：
|
||
|
||
```python
|
||
from ..node import WorkflowNode, NodePort, NodeConfig, workflow_node
|
||
from ..entities import ExecutionContext
|
||
|
||
@workflow_node('my_custom_node')
|
||
class MyCustomNode(WorkflowNode):
|
||
"""自定义节点"""
|
||
|
||
# 元数据
|
||
type_name = 'my_custom_node'
|
||
name = '我的自定义节点'
|
||
description = '这是一个自定义节点'
|
||
category = 'process' # trigger/process/control/action/integration
|
||
icon = '🔧'
|
||
|
||
# 输入端口
|
||
inputs = [
|
||
NodePort(name='input', type='string', description='输入数据', required=True),
|
||
]
|
||
|
||
# 输出端口
|
||
outputs = [
|
||
NodePort(name='output', type='string', description='输出数据'),
|
||
]
|
||
|
||
# 配置字段
|
||
config_schema = [
|
||
NodeConfig(
|
||
name='option',
|
||
type='select',
|
||
required=True,
|
||
options=['选项A', '选项B'],
|
||
description='选择一个选项'
|
||
),
|
||
NodeConfig(
|
||
name='value',
|
||
type='string',
|
||
required=False,
|
||
default='默认值',
|
||
description='配置值'
|
||
),
|
||
]
|
||
|
||
async def execute(
|
||
self,
|
||
inputs: dict[str, Any],
|
||
context: ExecutionContext
|
||
) -> dict[str, Any]:
|
||
"""执行节点逻辑"""
|
||
input_data = inputs.get('input', '')
|
||
option = self.get_config('option')
|
||
value = self.get_config('value', '')
|
||
|
||
# 处理逻辑
|
||
result = f"处理: {input_data} with {option} and {value}"
|
||
|
||
return {'output': result}
|
||
```
|
||
|
||
### 步骤 2：注册节点
|
||
|
||
在 `LangBot/src/langbot/pkg/workflow/nodes/__init__.py` 中导入：
|
||
|
||
```python
|
||
from .process import (
|
||
CodeExecutorNode,
|
||
HttpRequestNode,
|
||
DataTransformNode,
|
||
MyCustomNode, # 添加新节点
|
||
)
|
||
```
|
||
|
||
### 步骤 3：添加前端配置
|
||
|
||
在 `LangBot/web/src/app/home/workflows/components/workflow-editor/node-configs/` 目录下添加配置：
|
||
|
||
```typescript
|
||
// process-configs.ts
|
||
export const processNodeConfigs: NodeConfigMap = {
|
||
// ... 其他配置
|
||
|
||
my_custom_node: {
|
||
type: 'my_custom_node',
|
||
label: 'workflows.nodes.myCustomNode',
|
||
description: 'workflows.nodes.myCustomNodeDesc',
|
||
icon: 'Wrench',
|
||
category: 'process',
|
||
fields: [
|
||
{
|
||
name: 'option',
|
||
type: 'select',
|
||
label: 'workflows.fields.option',
|
||
required: true,
|
||
options: [
|
||
{ value: '选项A', label: '选项 A' },
|
||
{ value: '选项B', label: '选项 B' },
|
||
],
|
||
},
|
||
{
|
||
name: 'value',
|
||
type: 'string',
|
||
label: 'workflows.fields.value',
|
||
required: false,
|
||
defaultValue: '默认值',
|
||
},
|
||
],
|
||
},
|
||
};
|
||
```
|
||
|
||
### 步骤 4：添加国际化
|
||
|
||
在 `LangBot/web/src/i18n/locales/` 中添加翻译：
|
||
|
||
```typescript
|
||
// zh-Hans.ts
|
||
workflows: {
|
||
nodes: {
|
||
myCustomNode: '我的自定义节点',
|
||
myCustomNodeDesc: '这是一个自定义节点',
|
||
},
|
||
fields: {
|
||
option: '选项',
|
||
value: '值',
|
||
},
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 调试功能实现
|
||
|
||
### 后端调试状态管理
|
||
|
||
```python
|
||
class DebugExecutionState:
|
||
"""调试执行状态"""
|
||
|
||
def __init__(self, execution_id: str, breakpoints: list[str] = None):
|
||
self.execution_id = execution_id
|
||
self.status: str = 'running'
|
||
self.is_paused: bool = False
|
||
self.is_stopped: bool = False
|
||
self.breakpoints: set[str] = set(breakpoints or [])
|
||
self.logs: list[ExecutionLog] = []
|
||
self._pause_event = asyncio.Event()
|
||
|
||
def pause(self):
|
||
"""暂停执行"""
|
||
self.is_paused = True
|
||
self._pause_event.clear()
|
||
|
||
def resume(self):
|
||
"""继续执行"""
|
||
self.is_paused = False
|
||
self._pause_event.set()
|
||
|
||
async def wait_if_paused(self):
|
||
"""如果暂停则等待"""
|
||
if self.is_paused:
|
||
await self._pause_event.wait()
|
||
```
|
||
|
||
### 前端调试流程
|
||
|
||
1. **设置断点**：点击节点设置断点
|
||
2. **启动调试**：调用 `/debug/start` 启动调试执行
|
||
3. **轮询状态**：定期调用 `/debug/:id/state` 获取状态
|
||
4. **控制执行**：调用 pause/resume/step/stop 控制执行
|
||
5. **查看日志**：实时显示执行日志和节点状态
|
||
|
||
```typescript
|
||
// 调试状态轮询
|
||
const fetchDebugState = async () => {
|
||
const state = await backendClient.get(
|
||
`/api/v1/workflows/${workflowUuid}/debug/${executionId}/state`
|
||
);
|
||
|
||
// 更新节点状态
|
||
setNodeStates(state.node_states);
|
||
|
||
// 追加新日志
|
||
if (state.new_logs.length > 0) {
|
||
setLogs(prev => [...prev, ...state.new_logs]);
|
||
}
|
||
|
||
// 检查完成状态
|
||
if (state.status === 'completed' || state.status === 'error') {
|
||
setDebugState('idle');
|
||
}
|
||
};
|
||
```
|
||
|
||
---
|
||
|
||
## 扩展阅读
|
||
|
||
- [Workflow 功能设计文档](../../../plans/langbot-workflow-design.md)
|
||
- [用户使用指南](../user-guide/workflow-guide.md)
|
||
- [API 认证文档](../API_KEY_AUTH.md)
|