File size: 17,166 Bytes
65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 c7a9ff7 65b5532 1ccd052 65b5532 1ccd052 65b5532 c7a9ff7 65b5532 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Data models for the SafeSpace Content Moderation Environment.
SafeSpace is an RL environment where an AI agent acts as a content moderator,
investigating reported posts and making structured moderation decisions.
"""
from typing import Any, Dict, List, Literal, Optional
from openenv.core.env_server.types import Action, Observation, State
from pydantic import BaseModel, ConfigDict, Field
ActionType = Literal[
"request_author_profile",
"request_author_violations",
"request_thread_context",
"request_community_rules",
"request_linked_content",
"request_similar_precedents",
"request_reporter_credibility",
"decide",
]
DecisionType = Literal["approve", "remove", "escalate", "warn"]
SeverityType = Literal["none", "low", "medium", "high", "critical"]
TriggerType = Literal["user_report", "auto_flag", "appeal", "proactive_audit"]
MediaType = Literal["text", "text+image", "text+link"]
DifficultyType = Literal["easy", "medium", "hard"]
# ============================================================================
# Supporting Models (nested in Observation)
# ============================================================================
class ContentItem(BaseModel):
"""A content item (post) that needs moderation review."""
post_id: str = Field(..., description="Unique identifier for the post")
text: str = Field(..., description="The text content of the post")
author_id: str = Field(..., description="Unique identifier of the author")
community: str = Field(
..., description="Community where the post was made (e.g., 'gaming', 'health')"
)
timestamp: str = Field(..., description="ISO timestamp when the post was created")
media_type: MediaType = Field(
..., description="Type of media: 'text', 'text+image', or 'text+link'"
)
media_description: Optional[str] = Field(
default=None, description="Text description of image/link if present"
)
class TriggerInfo(BaseModel):
"""How this content entered the moderation queue."""
trigger_type: TriggerType = Field(
...,
description="One of: 'user_report', 'auto_flag', 'appeal', 'proactive_audit'",
)
# For user_report
report_count: int = Field(default=0, description="Number of reports received")
report_categories: List[str] = Field(
default_factory=list, description="Categories selected by reporters"
)
sample_report_reason: Optional[str] = Field(
default=None, description="Example report reason from a user"
)
# For auto_flag
auto_flag_reason: Optional[str] = Field(
default=None, description="Why automated system flagged this content"
)
# For appeal
original_decision: Optional[str] = Field(
default=None, description="The original moderation decision being appealed"
)
appeal_text: Optional[str] = Field(
default=None, description="User's appeal message"
)
# For proactive_audit
audit_reason: Optional[str] = Field(
default=None, description="Why this content was selected for audit"
)
class GatheredContext(BaseModel):
"""Context gathered through investigation actions. Starts empty."""
author_profile: Optional[Dict[str, Any]] = Field(
default=None, description="Author's bio, account age, follower count"
)
author_violations: Optional[List[Dict[str, Any]]] = Field(
default=None, description="Author's past moderation violations"
)
thread_context: Optional[List[Dict[str, Any]]] = Field(
default=None, description="Full conversation thread"
)
community_rules: Optional[str] = Field(
default=None, description="Community-specific moderation guidelines"
)
linked_content_summary: Optional[str] = Field(
default=None, description="What the linked content contains"
)
similar_precedents: Optional[List[Dict[str, Any]]] = Field(
default=None, description="How similar posts were moderated before"
)
reporter_credibility: Optional[Dict[str, Any]] = Field(
default=None, description="Reporter's history of accurate vs false reports"
)
class BreakdownComponent(BaseModel):
"""Typed reward or grading component with room for structured details."""
model_config = ConfigDict(extra="allow")
score: Optional[float] = Field(default=None, description="Component score")
max: Optional[float] = Field(default=None, description="Maximum component score")
min: Optional[float] = Field(default=None, description="Minimum component score")
raw_score: Optional[float] = Field(
default=None, description="Raw component score before normalization"
)
raw_max: Optional[float] = Field(
default=None, description="Raw maximum component score before normalization"
)
raw_min: Optional[float] = Field(
default=None, description="Raw minimum component score before normalization"
)
weight: Optional[float] = Field(
default=None, description="Normalized weighting used by the task grade"
)
details: Dict[str, Any] = Field(
default_factory=dict,
description="Structured details for the component calculation",
)
class RewardBreakdown(BaseModel):
"""Typed reward breakdown returned on reset, intermediate, and terminal steps."""
model_config = ConfigDict(extra="allow")
reward_type: str = Field(default="unknown", description="Reward breakdown category")
total: float = Field(default=0.0, description="Total reward for this step")
raw_total: Optional[float] = Field(
default=None, description="Raw total reward for this step before normalization"
)
score: Optional[float] = Field(
default=None, description="Normalized score for simple cases"
)
raw_score: Optional[float] = Field(
default=None, description="Raw score for simple cases before normalization"
)
requested_score: Optional[float] = Field(
default=None, description="Normalized uncapped score requested by the reward rule"
)
raw_requested_score: Optional[float] = Field(
default=None,
description="Raw uncapped score requested by the reward rule before normalization",
)
applied_score: Optional[float] = Field(
default=None, description="Normalized score applied after caps or bounds"
)
raw_applied_score: Optional[float] = Field(
default=None,
description="Raw score applied after caps or bounds before normalization",
)
step_total: Optional[float] = Field(
default=None, description="Normalized combined step reward in multi-part terminal cases"
)
raw_step_total: Optional[float] = Field(
default=None,
description="Raw combined step reward in multi-part terminal cases before normalization",
)
trajectory_total: Optional[float] = Field(
default=None, description="Normalized cumulative trajectory shaping reward"
)
raw_trajectory_total: Optional[float] = Field(
default=None,
description="Raw cumulative trajectory shaping reward before normalization",
)
episode_total: Optional[float] = Field(
default=None, description="Normalized running episode reward after this step"
)
raw_episode_total: Optional[float] = Field(
default=None,
description="Raw running episode reward after this step before normalization",
)
cumulative_total: Optional[float] = Field(
default=None,
description="Normalized episode reward total after terminal application",
)
raw_cumulative_total: Optional[float] = Field(
default=None,
description="Raw episode reward total after terminal application before normalization",
)
theoretical_terminal_max: Optional[float] = Field(
default=None, description="Normalized maximum possible terminal reward"
)
theoretical_terminal_min: Optional[float] = Field(
default=None, description="Normalized minimum possible terminal reward"
)
raw_theoretical_terminal_max: Optional[float] = Field(
default=None,
description="Raw maximum possible terminal reward before normalization",
)
raw_theoretical_terminal_min: Optional[float] = Field(
default=None,
description="Raw minimum possible terminal reward before normalization",
)
context_field: Optional[str] = Field(
default=None, description="Context source involved in the reward"
)
context_needed: List[str] = Field(
default_factory=list, description="Ground-truth context sources needed"
)
is_needed: Optional[bool] = Field(
default=None, description="Whether the requested context was useful"
)
retrieved: Optional[bool] = Field(
default=None, description="Whether the context source had retrievable data"
)
reason: Optional[str] = Field(default=None, description="Machine-readable reason")
action_type: Optional[str] = Field(
default=None, description="Action type involved in the reward"
)
trajectory_cap: Optional[float] = Field(
default=None, description="Trajectory reward cap in effect"
)
decision: Optional[BreakdownComponent] = Field(
default=None, description="Decision-scoring component"
)
factor: Optional[BreakdownComponent] = Field(
default=None, description="Factor overlap component"
)
efficiency: Optional[BreakdownComponent] = Field(
default=None, description="Efficiency component"
)
calibration: Optional[BreakdownComponent] = Field(
default=None, description="Calibration component"
)
trajectory: Optional[Dict[str, Any]] = Field(
default=None,
description="Nested trajectory reward payload for no-decision terminal cases",
)
no_decision: Optional[Dict[str, Any]] = Field(
default=None,
description="Nested no-decision penalty payload when the budget is exhausted",
)
last_terminal_breakdown: Optional[Dict[str, Any]] = Field(
default=None,
description="Previous terminal reward payload when guarding completed episodes",
)
class TaskGradeBreakdown(BaseModel):
"""Typed normalized grader breakdown returned on terminal steps."""
model_config = ConfigDict(extra="allow")
decision: Optional[BreakdownComponent] = Field(
default=None, description="Decision grading component"
)
factor_overlap: Optional[BreakdownComponent] = Field(
default=None, description="Factor-overlap grading component"
)
efficiency: Optional[BreakdownComponent] = Field(
default=None, description="Efficiency grading component"
)
calibration: Optional[BreakdownComponent] = Field(
default=None, description="Calibration grading component"
)
total: float = Field(default=0.0, description="Normalized task grade in the open interval (0, 1)")
# ============================================================================
# Core OpenEnv Models
# ============================================================================
class ModerationAction(Action):
"""
Action to be executed in the SafeSpace environment.
Investigation actions (cost 1 action each):
- request_author_profile
- request_author_violations
- request_thread_context
- request_community_rules
- request_linked_content
- request_similar_precedents
- request_reporter_credibility
Terminal action:
- decide (requires decision fields)
"""
action_type: ActionType = Field(
...,
description=(
"One of: 'request_author_profile', 'request_author_violations', "
"'request_thread_context', 'request_community_rules', "
"'request_linked_content', 'request_similar_precedents', "
"'request_reporter_credibility', 'decide'"
),
)
# === Decision fields (required ONLY when action_type == "decide") ===
decision: Optional[DecisionType] = Field(
default=None,
description="One of: 'approve', 'remove', 'escalate', 'warn'",
)
primary_violation: Optional[str] = Field(
default=None,
description="Policy section ID (e.g., '1.0', '2.1', '3.1') or 'none'",
)
severity: Optional[SeverityType] = Field(
default=None,
description="One of: 'none', 'low', 'medium', 'high', 'critical'",
)
confidence: Optional[float] = Field(
default=None,
ge=0.0,
le=1.0,
description="Agent's confidence in the decision (0.0 to 1.0)",
)
key_factors: Optional[List[str]] = Field(
default=None,
description="Selected factors from the FACTOR_LIST that influenced the decision",
)
class ModerationObservation(Observation):
"""
Observation returned from the SafeSpace environment.
Contains the content to moderate, trigger information, gathered context,
platform policy, and episode progress.
"""
# Content and trigger info
content_item: Optional[ContentItem] = Field(
default=None, description="The content item being moderated"
)
trigger_info: Optional[TriggerInfo] = Field(
default=None, description="How this content entered the moderation queue"
)
# Investigation results (populated as agent gathers context)
gathered_context: GatheredContext = Field(
default_factory=GatheredContext,
description="Context gathered through investigation actions",
)
# Policy and factors
platform_policy: str = Field(
default="", description="The platform's content moderation policy document"
)
available_factors: List[str] = Field(
default_factory=list,
description="List of factors the agent can cite in its decision",
)
# Episode progress
actions_taken: int = Field(
default=0, description="Number of actions taken this episode"
)
max_actions: int = Field(
default=8, description="Maximum actions allowed per episode"
)
action_history: List[str] = Field(
default_factory=list, description="List of actions taken so far"
)
feedback: str = Field(
default="", description="Feedback message from the last action"
)
error_code: Optional[str] = Field(
default=None,
description="Structured error code for invalid or rejected actions",
)
# Reward breakdown (populated after terminal decision)
reward_breakdown: Optional[RewardBreakdown] = Field(
default=None,
description="Breakdown of reward components for the last step",
)
task_grade: Optional[float] = Field(
default=None,
ge=0.0,
le=1.0,
description="Deterministic normalized task grade for the current episode, strictly between 0 and 1 on terminal steps",
)
grade_breakdown: Optional[TaskGradeBreakdown] = Field(
default=None,
description="Breakdown of normalized task-grade components",
)
class ModerationState(State):
"""
State of the SafeSpace environment.
Tracks episode metadata and progress.
"""
# Override base State fields
episode_id: Optional[str] = Field(
default=None, description="Unique identifier for this episode"
)
step_count: int = Field(default=0, description="Number of steps taken")
# Episode identification
scenario_id: Optional[str] = Field(
default=None, description="Current scenario ID"
)
task_id: Optional[str] = Field(
default=None, description="Task ID used to load this scenario"
)
difficulty: Optional[DifficultyType] = Field(
default=None, description="Scenario difficulty: easy, medium, or hard"
)
trigger_type: Optional[TriggerType] = Field(
default=None, description="How this content entered the moderation queue"
)
# SafeSpace-specific public progress fields
actions_taken: int = Field(
default=0, description="Number of investigation actions taken"
)
max_actions: int = Field(
default=8, description="Maximum actions allowed per episode"
)
context_requested: List[str] = Field(
default_factory=list, description="List of context types requested"
)
decision_made: bool = Field(
default=False, description="Whether a terminal decision has been made"
)
episode_reward: float = Field(
default=0.0, description="Normalized total reward for episode"
)
raw_episode_reward: float = Field(
default=0.0, description="Raw total reward for episode before normalization"
)
done: bool = Field(default=False, description="Whether the episode is terminal")
last_error_code: Optional[str] = Field(
default=None,
description="Structured error code from the most recent rejected action",
)
|