starmen1265
efddef98d3
- ParseInput extends StepInput with url field - ParseOutput extends StepOutput with platform, content_type, content_id, resolved_url, original_url - Tests for schema validation and inheritance Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
276 lines
10 KiB
Python
276 lines
10 KiB
Python
"""Core Pipeline Schemas
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定义工作流管道的核心 Schema，包括 StepInput、StepOutput、StepResult、
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StepStatus、StepRecord、WorkflowState 和 StepDefinition。
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使用 Pydantic v2 确保类型安全和自动验证。
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, Dict, Literal, Any, List
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from datetime import datetime
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from enum import Enum
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class StepStatus(str, Enum):
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"""步骤执行状态枚举"""
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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SKIPPED = "skipped"
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class StepInput(BaseModel):
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"""所有步骤输入的基类
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Attributes:
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content_id: 内容唯一标识，如 B站 BV号、知乎回答ID等
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platform: 平台名称，限定为 bilibili 或 zhihu
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"""
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content_id: str = Field(..., min_length=1, description="内容唯一标识")
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platform: Literal["bilibili", "zhihu"] = Field(..., description="平台名称")
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class StepOutput(BaseModel):
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"""所有步骤输出的基类
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Attributes:
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success: 步骤是否成功执行
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error_message: 错误信息，成功时为 None
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completed_at: 完成时间，默认为当前时间
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"""
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success: bool = Field(..., description="步骤是否成功")
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error_message: Optional[str] = Field(None, description="错误信息")
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completed_at: datetime = Field(default_factory=datetime.now, description="完成时间")
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class StepResult(BaseModel):
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"""步骤执行结果
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包含步骤的输入、输出和执行状态。
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Attributes:
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step_name: 步骤名称
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input_data: 步骤输入数据
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output_data: 步骤输出数据，可能为 None
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status: 步骤执行状态
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"""
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step_name: str = Field(..., description="步骤名称")
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input_data: StepInput = Field(..., description="步骤输入数据")
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output_data: Optional[StepOutput] = Field(None, description="步骤输出数据")
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status: StepStatus = Field(default=StepStatus.PENDING, description="步骤执行状态")
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class StepRecord(BaseModel):
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"""单个步骤的执行记录
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记录步骤执行的状态、时间和结果摘要。
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Attributes:
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step_name: 步骤名称
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status: 步骤执行状态
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started_at: 开始时间
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completed_at: 完成时间
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error_message: 错误信息
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output_summary: 输出摘要
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"""
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step_name: str = Field(..., description="步骤名称")
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status: StepStatus = Field(..., description="步骤执行状态")
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started_at: Optional[datetime] = Field(None, description="开始时间")
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completed_at: Optional[datetime] = Field(None, description="完成时间")
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error_message: Optional[str] = Field(None, description="错误信息")
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output_summary: Optional[str] = Field(None, description="输出摘要")
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class WorkflowState(BaseModel):
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"""工作流状态 - 存储在 workflow_state.json
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管理整个工作流的状态，包括内容信息、执行历史和步骤状态。
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Attributes:
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schema_version: Schema 版本号
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content_id: 内容唯一标识
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platform: 平台名称
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source_url: 原始链接
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created_at: 创建时间
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updated_at: 更新时间
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steps: 步骤执行记录字典
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"""
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schema_version: str = Field(default="1.0", description="Schema 版本号")
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content_id: str = Field(..., description="内容唯一标识")
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platform: str = Field(..., description="平台名称")
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source_url: str = Field(..., description="原始链接")
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created_at: datetime = Field(default_factory=datetime.now, description="创建时间")
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updated_at: datetime = Field(default_factory=datetime.now, description="更新时间")
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steps: Dict[str, StepRecord] = Field(default_factory=dict, description="步骤执行记录")
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def is_step_completed(self, step_name: str) -> bool:
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"""检查指定步骤是否已完成
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Args:
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step_name: 步骤名称
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Returns:
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如果步骤存在且状态为 COMPLETED 返回 True，否则返回 False
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"""
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record = self.steps.get(step_name)
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return record is not None and record.status == StepStatus.COMPLETED
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def mark_step_started(self, step_name: str) -> None:
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"""标记步骤开始执行
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Args:
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step_name: 步骤名称
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"""
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self.steps[step_name] = StepRecord(
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step_name=step_name,
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status=StepStatus.RUNNING,
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started_at=datetime.now()
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)
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self.updated_at = datetime.now()
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def mark_step_completed(self, step_name: str, output_summary: Optional[str] = None) -> None:
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"""标记步骤完成
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Args:
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step_name: 步骤名称
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output_summary: 输出摘要（可选）
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"""
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if step_name in self.steps:
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self.steps[step_name].status = StepStatus.COMPLETED
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self.steps[step_name].completed_at = datetime.now()
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self.steps[step_name].output_summary = output_summary
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else:
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self.steps[step_name] = StepRecord(
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step_name=step_name,
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status=StepStatus.COMPLETED,
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completed_at=datetime.now(),
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output_summary=output_summary
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)
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self.updated_at = datetime.now()
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def mark_step_failed(self, step_name: str, error_message: str) -> None:
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"""标记步骤失败
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Args:
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step_name: 步骤名称
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error_message: 错误信息
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"""
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if step_name in self.steps:
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self.steps[step_name].status = StepStatus.FAILED
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self.steps[step_name].error_message = error_message
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self.steps[step_name].completed_at = datetime.now()
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else:
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self.steps[step_name] = StepRecord(
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step_name=step_name,
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status=StepStatus.FAILED,
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error_message=error_message,
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completed_at=datetime.now()
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)
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self.updated_at = datetime.now()
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class StepDefinition(BaseModel):
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"""步骤定义 - 描述步骤的前置条件、输出格式、成功标准
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Attributes:
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step_name: 步骤名称
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description: 步骤描述
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input_schema: 输入 Schema 类型
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output_schema: 输出 Schema 类型
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dependencies: 依赖的步骤列表
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"""
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step_name: str = Field(..., description="步骤名称")
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description: str = Field(..., description="步骤描述")
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input_schema: type[StepInput] = Field(..., description="输入 Schema 类型")
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output_schema: type[StepOutput] = Field(..., description="输出 Schema 类型")
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dependencies: list[str] = Field(default_factory=list, description="依赖的步骤列表")
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# =============================================================================
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# ASR Schemas (Phase 3)
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# =============================================================================
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class ASRInput(StepInput):
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"""ASR 步骤输入
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继承 StepInput 的基础字段，添加视频路径和内容类型。
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Attributes:
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video_path: 视频文件路径
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content_type: 内容类型，默认为 'video'
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"""
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video_path: str = Field(..., description="视频文件路径")
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content_type: str = Field(default="video", description="内容类型")
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class ASROutput(StepOutput):
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"""ASR 步骤输出
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继承 StepOutput 的基础字段，添加 ASR 特定字段。
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Attributes:
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tool: 使用的工具 (subtitle, whisper, whisper-diarization)
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has_subtitle: 是否有官方字幕
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output_files: 输出文件路径字典 {txt, srt, json}
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segments: 带时间戳的分段列表
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text: 完整文本
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duration_seconds: 音频时长
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speakers: 说话者列表（仅说话者分离时有值）
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"""
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tool: Literal["subtitle", "whisper", "whisper-diarization"] = Field(
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..., description="使用的工具"
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)
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has_subtitle: bool = Field(..., description="是否有官方字幕")
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output_files: Dict[str, str] = Field(
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default_factory=dict, description="输出文件路径 {txt, srt, json}"
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)
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segments: List[Dict[str, Any]] = Field(
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default_factory=list, description="带时间戳的分段列表"
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)
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text: str = Field(default="", description="完整文本")
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duration_seconds: float = Field(default=0.0, description="音频时长")
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speakers: Optional[List[str]] = Field(
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None, description="说话者列表（仅说话者分离时有值）"
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)
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# =============================================================================
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# Parse Schemas (Phase 04)
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# =============================================================================
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class ParseInput(StepInput):
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"""Parse 步骤输入
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继承 StepInput 的基础字段，添加 URL 字段。
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content_id 和 platform 在解析前是空的，解析后会被填充。
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Attributes:
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url: 要解析的 URL
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content_id: 解析后填充的内容 ID（初始为空）
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platform: 解析后填充的平台名称（初始为空）
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"""
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url: str = Field(..., min_length=1, description="URL to parse")
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content_id: str = Field(default="", description="Will be populated after parsing")
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platform: str = Field(default="", description="Will be populated after parsing")
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class ParseOutput(StepOutput):
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"""Parse 步骤输出
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继承 StepOutput 的基础字段，添加解析特定字段。
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Attributes:
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platform: 检测到的平台 (bilibili, zhihu)
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content_type: 内容类型 (video, article, answer, zhuanlan)
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content_id: 提取的内容 ID
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resolved_url: 短链解析后的完整 URL
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original_url: 用户提供的原始 URL
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"""
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platform: str = Field(..., description="Detected platform")
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content_type: str = Field(..., description="Content type (video, article, etc.)")
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content_id: str = Field(..., description="Extracted content ID")
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resolved_url: str = Field(..., description="Resolved URL after short link expansion")
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original_url: str = Field(..., description="Original URL provided by user")
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