initial commit

cross_eval/models.py

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+"""Data models for cross-eval pipeline."""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class AgentConfig:
+    """Definition of a single agent."""
+
+    name: str
+    command: str
+    args: list[str] = field(default_factory=list)
+    system_prompt: Optional[str] = None
+    reasoning_effort: Optional[str] = None
+    stdin_mode: bool = False
+
+
+@dataclass
+class StepConfig:
+    """One step in the pipeline."""
+
+    name: str
+    agent: str  # reference to agents key
+    role: str  # "generate" or "review"
+    prompt_template: str  # "default:<role>" or file path
+    output_key: str
+    verdict: bool = False
+    verdict_pattern: str = r"VERDICT:\s*PASS"
+    context_override: dict[str, str] = field(default_factory=dict)
+    parallel: bool = False  # Can run concurrently with adjacent parallel steps
+
+
+@dataclass
+class PhaseConfig:
+    """One phase in a multi-phase pipeline (e.g. review-fix)."""
+
+    name: str
+    steps: list[StepConfig] = field(default_factory=list)
+    max_iterations: int = 10
+    consecutive_pass: int = 1  # stop after N consecutive PASSes
+
+
+@dataclass
+class PipelineConfig:
+    """Full cross-eval configuration."""
+
+    output_dir: Path = field(default_factory=lambda: Path("output"))
+    max_iterations: int = 3
+    min_iterations: int = 1
+    verbose: bool = False
+    language: str = "en"  # "en" or "ko"
+    inputs: dict[str, Path | str] = field(default_factory=dict)
+    agents: dict[str, AgentConfig] = field(default_factory=dict)
+    coders: list[str] = field(default_factory=list)
+    reviewers: list[str] = field(default_factory=list)
+    seniors: list[str] = field(default_factory=list)
+    pipeline: list[StepConfig] = field(default_factory=list)
+    phases: list[PhaseConfig] = field(default_factory=list)
+    preset_name: str = "custom"
+    _config_path: Optional[Path] = field(default=None, repr=False)
+    _config_mtime: Optional[float] = field(default=None, repr=False)
+
+
+@dataclass
+class AgentResult:
+    """Result from an agent invocation."""
+
+    output: str
+    exit_code: int
+    agent_name: str
+    step_name: str
+    duration_seconds: float
+
+
+@dataclass
+class ReviewMetrics:
+    """Parsed metrics from a single review output."""
+
+    # Severity counts
+    critical: int = 0
+    major: int = 0
+    minor: int = 0
+
+    # Category counts
+    over_engineering: int = 0
+    omission: int = 0
+
+    # Assessment counts
+    confirmed: int = 0
+    dismissed: int = 0
+
+
+@dataclass
+class IterationResult:
+    """Results from a single iteration."""
+
+    iteration: int
+    step_results: dict[str, AgentResult] = field(default_factory=dict)
+    step_outputs: dict[str, str] = field(default_factory=dict)
+    verdict: Optional[str] = None
+    feedback: Optional[str] = None
+    phase_name: Optional[str] = None
+    repeated_aggregate_warning: Optional[str] = None
+    review_metrics: Optional[ReviewMetrics] = None
+
+
+@dataclass
+class PipelineResult:
+    """Results from the entire pipeline run."""
+
+    iterations: list[IterationResult] = field(default_factory=list)
+    final_verdict: str = "MAX_ITERATIONS_REACHED"
+    total_duration: float = 0.0
+    run_dir: Optional[Path] = None
+    repeated_aggregate_warnings: list[str] = field(default_factory=list)