113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
"""Pipeline 数据模型"""
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from enum import Enum
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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from agentkit.orchestrator.retry import StepRetryPolicy
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class StageStatus(str, Enum):
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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 ReviewIssue(BaseModel):
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"""单条审查问题"""
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severity: Literal["critical", "major", "minor"] = Field(description="问题严重程度")
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category: Literal["logic_error", "security", "style", "test_failure", "architecture"] = Field(description="问题类别")
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description: str = Field(min_length=1, description="问题描述")
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location: str | None = Field(default=None, description="文件路径/行号")
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suggestion: str | None = Field(default=None, description="修复建议")
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class ReviewFeedback(BaseModel):
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"""Verifier 返回的结构化审查反馈"""
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passed: bool = Field(description="是否通过审查")
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issues: list[ReviewIssue] = Field(default_factory=list, description="问题列表")
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summary: str = Field(min_length=1, description="自然语言审查报告")
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score: float = Field(ge=0.0, le=1.0, description="质量评分 (0-1)")
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class AdversarialState(BaseModel):
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"""对抗轮次状态追踪"""
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current_round: int = Field(default=0, description="当前对抗轮次")
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max_rounds: int = Field(default=3, description="最大对抗轮次")
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feedback_history: list[ReviewFeedback] = Field(default_factory=list, description="反馈历史")
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last_feedback: ReviewFeedback | None = Field(default=None, description="最后一次反馈")
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class PipelineStage(BaseModel):
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name: str
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agent: str
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action: str
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depends_on: list[str] = []
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inputs: dict[str, Any] = {}
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outputs: list[str] = []
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timeout_seconds: int = 300
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retry_count: int = 0
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continue_on_failure: bool = False
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condition: str | None = None
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retry_policy: StepRetryPolicy | None = None
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compensate: str | None = None
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# 对抗闭环相关字段
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verifier: str | None = Field(default=None, description="Verifier Agent 名称，配置后启用对抗模式")
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max_adversarial_rounds: int = Field(default=3, description="最大对抗轮次")
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verifier_timeout_seconds: int = Field(default=120, description="Verifier Agent 独立超时时间（秒），避免与 Worker 共享 timeout_seconds")
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feedback_mode: Literal["structured+natural", "structured", "natural"] = Field(default="structured+natural", description="反馈模式")
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escalate_on_exhaust: str | None = Field(default=None, description="对抗轮次耗尽后的升级目标")
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model_config = {"arbitrary_types_allowed": True}
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class Pipeline(BaseModel):
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name: str
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version: str
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description: str
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stages: list[PipelineStage]
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variables: dict[str, Any] = {}
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class StageResult(BaseModel):
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stage_name: str
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status: StageStatus = StageStatus.PENDING
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output_data: dict[str, Any] | None = None
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error_message: str | None = None
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started_at: str | None = None
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completed_at: str | None = None
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class PipelineResult(BaseModel):
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pipeline_name: str
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status: StageStatus = StageStatus.PENDING
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stage_results: dict[str, StageResult] = {}
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variables: dict[str, Any] = {}
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error_message: str | None = None
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metadata: dict[str, Any] = {}
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class AdaptiveConfig(BaseModel):
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"""Configuration for adaptive pipeline execution with reflection-replanning."""
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enabled: bool = False
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max_reflections: int = 3
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reflection_model: str = "default"
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skip_stages: list[str] = []
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model_config = {"arbitrary_types_allowed": True}
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class ReflectionReport(BaseModel):
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"""Structured report from pipeline reflection analysis."""
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failure_type: str # input_error, resource_error, logic_error, timeout
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root_cause: str
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suggested_fix: str
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failed_stage: str
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reflection_number: int = 1
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