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c532e4a 5b3340c c532e4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | """Pydantic request/response models for the FlakeForge REST API."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
# ── Health & Info ────────────────────────────────────────────────────────────
class HealthResponse(BaseModel):
status: str = "ok"
version: str = "0.1.0"
uptime_seconds: float = 0.0
environment_ready: bool = True
# True if HF token is present in env (HUGGING_FACE_TOKEN / HF_TOKEN / …); used for challenge LLM
challenge_llm_configured: bool = False
class ProjectInfo(BaseModel):
name: str = "FlakeForge"
version: str = "0.1.0"
description: str = "RL Agent for Flaky Test Repair"
root_cause_categories: List[str] = Field(default_factory=list)
total_test_repos: int = 0
max_steps_per_episode: int = 8
reward_signals: List[str] = Field(default_factory=list)
# ── Repos ────────────────────────────────────────────────────────────────────
class RepoInfo(BaseModel):
name: str
path: str
category: str = "unknown"
test_identifier: str = ""
has_manifest: bool = False
manifest: Optional[Dict[str, Any]] = None
class RepoListResponse(BaseModel):
repos: List[RepoInfo]
total: int
# ── Episode ──────────────────────────────────────────────────────────────────
class EpisodeStartRequest(BaseModel):
repo_path: str = ""
test_identifier: str = ""
max_steps: int = 8
num_runs: int = 10
class EpisodeStartResponse(BaseModel):
episode_id: str
status: str = "initialized"
observation: Dict[str, Any] = Field(default_factory=dict)
baseline_pass_rate: float = 0.0
env_type: str = "unknown"
should_train: bool = True
class EpisodeStepRequest(BaseModel):
raw_response: str = ""
think_text: str = ""
patch_text: str = ""
predicted_category: str = "unknown"
predicted_confidence: float = 0.5
class StepResult(BaseModel):
step: int
action: str = ""
category: str = "unknown"
confidence: float = 0.0
reward: float = 0.0
reward_breakdown: Dict[str, float] = Field(default_factory=dict)
pass_rate_before: float = 0.0
pass_rate_after: float = 0.0
patch_applied: bool = False
patch_files: List[str] = Field(default_factory=list)
think_summary: str = ""
done: bool = False
done_reason: str = ""
class EpisodeStepResponse(BaseModel):
step_result: StepResult
observation: Dict[str, Any] = Field(default_factory=dict)
class RunEpisodeRequest(BaseModel):
repo_path: str = ""
test_identifier: str = ""
max_steps: int = 8
num_runs: int = 10
backend: str = "nvidia"
class RunEpisodeResponse(BaseModel):
episode_id: str
status: str = "completed"
steps: List[StepResult] = Field(default_factory=list)
total_reward: float = 0.0
final_pass_rate: float = 0.0
baseline_pass_rate: float = 0.0
done_reason: str = ""
causal_graph: Optional[Dict[str, Any]] = None
class EpisodeStatusResponse(BaseModel):
episode_id: str
status: str = "idle"
current_step: int = 0
max_steps: int = 8
pass_rate: float = 0.0
total_reward: float = 0.0
done: bool = False
# ── Challenge ────────────────────────────────────────────────────────────────
class ChallengeRequest(BaseModel):
code: str
test_code: str = ""
preset: str = ""
class ChallengeAnalysis(BaseModel):
detected_category: str = "unknown"
confidence: float = 0.0
root_cause_file: str = ""
root_cause_function: str = ""
causal_chain: List[str] = Field(default_factory=list)
infrastructure_sensitive: bool = False
suggested_fix: str = ""
patch_diff: str = ""
explanation: str = ""
estimated_reward: float = 0.0
class ChallengeResponse(BaseModel):
status: str = "analyzed"
analysis: ChallengeAnalysis
# ── Training ─────────────────────────────────────────────────────────────────
class TrainingStats(BaseModel):
total_episodes: int = 0
avg_reward: float = 0.0
fix_rate: float = 0.0
avg_steps_to_fix: float = 0.0
category_breakdown: Dict[str, int] = Field(default_factory=dict)
reward_history: List[float] = Field(default_factory=list)
baseline_history: List[float] = Field(default_factory=list)
class TrainingStatsResponse(BaseModel):
stats: TrainingStats
# ── WebSocket messages ───────────────────────────────────────────────────────
class WSMessage(BaseModel):
type: str
data: Dict[str, Any] = Field(default_factory=dict)
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