12 KiB
12 KiB
phase, plan, type, wave, depends_on, files_modified, autonomous, requirements, user_setup, must_haves
| phase | plan | type | wave | depends_on | files_modified | autonomous | requirements | user_setup | must_haves | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 01-foundation | 02 | tdd | 1 |
|
true |
|
|
Purpose: 建立统一的数据格式规范，所有后续步骤和状态管理都基于这些 Schema Output: kb_pipeline/schemas.py + 完整的单元测试
<execution_context> @/home/starmen1265/.claude/get-shit-done/workflows/execute-plan.md @/home/starmen1265/.claude/get-shit-done/templates/summary.md </execution_context>
@.planning/PROJECT.md @.planning/ROADMAP.md @.planning/STATE.mdFrom CONTEXT.md decisions:
- Schema 格式: Pydantic 模型 — 自带验证和序列化，类型安全
- 验证范围: 验证输入和输出 — 步骤执行前验证输入，执行后验证输出
- Schema 定义位置: 集中定义 — 所有步骤的 Schema 集中在一个模块
From RESEARCH.md Pattern 2: Pydantic Schema Registry:
from pydantic import BaseModel, Field
from typing import Optional, Literal
from datetime import datetime
class StepInput(BaseModel):
"""所有步骤输入的基类"""
content_id: str = Field(..., description="内容唯一标识，如 BV号")
platform: Literal["bilibili", "zhihu"] = Field(..., description="平台名称")
class StepOutput(BaseModel):
"""所有步骤输出的基类"""
success: bool = Field(..., description="步骤是否成功")
error_message: Optional[str] = Field(None, description="错误信息")
completed_at: datetime = Field(default_factory=datetime.now)
2. 创建 kb_pipeline/__init__.py:
```python
"""KB Pipeline - 工作流管道基础设施"""
from .schemas import (
StepInput, StepOutput, StepResult,
StepStatus, StepRecord, WorkflowState, StepDefinition
)
__all__ = [...]
```
3. 创建 tests/test_pipeline/__init__.py (空文件)
4. 创建 tests/test_pipeline/conftest.py:
- 添加通用 fixtures (tmp_content_dir, sample_workflow_state)
5. 创建 tests/test_pipeline/test_schemas.py，包含测试类:
- TestStepInput: 测试必填字段和验证
- TestStepOutput: 测试输出字段和默认值
- TestStepStatus: 测试枚举值
- TestStepRecord: 测试步骤记录
- TestWorkflowState: 测试工作流状态
- TestSchemaValidation: 测试 Pydantic 验证
```python
from pydantic import BaseModel, Field
from typing import Optional, Dict, Literal, Any
from datetime import datetime
from enum import Enum
class StepStatus(str, Enum):
"""步骤执行状态"""
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
SKIPPED = "skipped"
class StepInput(BaseModel):
"""所有步骤输入的基类"""
content_id: str = Field(..., min_length=1, description="内容唯一标识")
platform: Literal["bilibili", "zhihu"] = Field(..., description="平台名称")
class StepOutput(BaseModel):
"""所有步骤输出的基类"""
success: bool = Field(..., description="步骤是否成功")
error_message: Optional[str] = Field(None, description="错误信息")
completed_at: datetime = Field(default_factory=datetime.now)
class StepResult(BaseModel):
"""步骤执行结果"""
step_name: str
input_data: StepInput
output_data: Optional[StepOutput] = None
status: StepStatus = StepStatus.PENDING
class StepRecord(BaseModel):
"""单个步骤的执行记录"""
step_name: str
status: StepStatus
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
error_message: Optional[str] = None
output_summary: Optional[str] = None
class WorkflowState(BaseModel):
"""工作流状态 - 存储在 workflow_state.json"""
schema_version: str = Field(default="1.0")
content_id: str = Field(..., description="内容唯一标识")
platform: str = Field(..., description="平台名称")
source_url: str = Field(..., description="原始链接")
created_at: datetime = Field(default_factory=datetime.now)
updated_at: datetime = Field(default_factory=datetime.now)
steps: Dict[str, StepRecord] = Field(default_factory=dict)
def is_step_completed(self, step_name: str) -> bool:
record = self.steps.get(step_name)
return record is not None and record.status == StepStatus.COMPLETED
def mark_step_started(self, step_name: str) -> None:
self.steps[step_name] = StepRecord(
step_name=step_name,
status=StepStatus.RUNNING,
started_at=datetime.now()
)
self.updated_at = datetime.now()
def mark_step_completed(self, step_name: str, output_summary: str = None) -> None:
if step_name in self.steps:
self.steps[step_name].status = StepStatus.COMPLETED
self.steps[step_name].completed_at = datetime.now()
self.steps[step_name].output_summary = output_summary
else:
self.steps[step_name] = StepRecord(
step_name=step_name,
status=StepStatus.COMPLETED,
completed_at=datetime.now(),
output_summary=output_summary
)
self.updated_at = datetime.now()
def mark_step_failed(self, step_name: str, error_message: str) -> None:
if step_name in self.steps:
self.steps[step_name].status = StepStatus.FAILED
self.steps[step_name].error_message = error_message
self.steps[step_name].completed_at = datetime.now()
else:
self.steps[step_name] = StepRecord(
step_name=step_name,
status=StepStatus.FAILED,
error_message=error_message,
completed_at=datetime.now()
)
self.updated_at = datetime.now()
class StepDefinition(BaseModel):
"""步骤定义 - 描述步骤的前置条件、输出格式、成功标准"""
step_name: str
description: str
input_schema: type[StepInput]
output_schema: type[StepOutput]
dependencies: list[str] = Field(default_factory=list)
```
注意: WorkflowState 的方法实现先写在 schemas.py 中，后续会考虑移到 state.py
2. 验证所有 Schema 支持:
- model_dump() -> dict
- model_dump_json() -> str
- model_validate(data) -> instance
3. 确保 WorkflowState 的 steps 字典正确序列化/反序列化
<success_criteria>
- 所有核心 Schema 定义完成
- Pydantic 验证正常工作
- JSON 序列化/反序列化正常
- 测试覆盖率 > 80% </success_criteria>