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Data models for the SRE Incident Investigation Environment.
An agent receives realistic system telemetry (logs, metrics, alerts) and must
investigate, diagnose root cause, and submit a structured incident report.
"""
from typing import Any, Dict, List, Literal, Optional
from openenv.core.env_server.types import Action, Observation, State
from pydantic import Field
# ---------------------------------------------------------------------------
# Action
# ---------------------------------------------------------------------------
class SREAction(Action):
"""
An investigative action taken by the SRE agent.
The agent can:
- query_logs : filter logs by service/level/time
- query_metrics : fetch a named metric time-series
- query_alerts : list active / recent alerts
- annotate : add a free-text hypothesis note (no new data revealed)
- submit : submit the final incident report (ends episode)
"""
action_type: Literal[
"query_logs",
"query_metrics",
"query_alerts",
"annotate",
"submit",
] = Field(..., description="Type of investigative action")
# --- query_logs ---
service: Optional[str] = Field(
default=None,
description="Service name to filter logs (e.g. 'payment-service'). None = all services.",
)
log_level: Optional[Literal["DEBUG", "INFO", "WARN", "ERROR", "FATAL"]] = Field(
default=None, description="Minimum log level to return"
)
time_window_minutes: Optional[int] = Field(
default=30, description="How many minutes of logs to retrieve (max 120)"
)
log_query: Optional[str] = Field(
default=None,
description="Optional keyword to search within log messages",
)
# --- query_metrics ---
metric_name: Optional[str] = Field(
default=None,
description=(
"Metric to fetch. Available: error_rate, latency_p99, latency_p50, "
"cpu_usage, memory_usage, db_connections, request_rate, cache_hit_rate"
),
)
# --- annotate / submit ---
note: Optional[str] = Field(
default=None, description="Free-text annotation or hypothesis"
)
# --- submit fields ---
root_cause_service: Optional[str] = Field(
default=None, description="Service identified as root cause"
)
root_cause_type: Optional[
Literal[
"resource_exhaustion",
"dependency_failure",
"configuration_error",
"code_bug",
"data_corruption",
"network_partition",
"cascading_failure",
"traffic_spike",
]
] = Field(default=None, description="Category of root cause")
affected_services: Optional[List[str]] = Field(
default=None, description="List of services affected by the incident"
)
severity: Optional[Literal["P1", "P2", "P3", "P4"]] = Field(
default=None, description="Incident severity level"
)
recommended_action: Optional[str] = Field(
default=None,
description="Recommended remediation (free text, ≤500 chars)",
)
confidence: Optional[float] = Field(
default=None,
ge=0.0,
le=1.0,
description="Agent's confidence in diagnosis (0.0–1.0)",
)
# ---------------------------------------------------------------------------
# Observation
# ---------------------------------------------------------------------------
class LogEntry(State):
"""A single log line returned from a query."""
model_config = {"extra": "allow"}
timestamp: str = Field(description="ISO-8601 timestamp")
service: str = Field(description="Emitting service name")
level: str = Field(description="Log level")
message: str = Field(description="Log message body")
trace_id: Optional[str] = Field(default=None)
class MetricPoint(State):
"""A single time-series data point."""
model_config = {"extra": "allow"}
timestamp: str = Field(description="ISO-8601 timestamp")
value: float = Field(description="Metric value")
class AlertEntry(State):
"""An active or recently-fired alert."""
model_config = {"extra": "allow"}
alert_name: str
service: str
severity: str
fired_at: str
message: str
status: Literal["firing", "resolved"]
class SREObservation(Observation):
"""Observation returned after each SRE action."""
# What action was just taken
action_taken: str = Field(default="", description="Echo of the action type")
# Data returned by queries
logs: List[Dict[str, Any]] = Field(
default_factory=list, description="Log entries matching the query"
)
metrics: List[Dict[str, Any]] = Field(
default_factory=list, description="Metric time-series points"
)
metric_name: Optional[str] = Field(
default=None, description="Name of the metric that was queried"
)
alerts: List[Dict[str, Any]] = Field(
default_factory=list, description="Active/recent alerts"
)
# Feedback after annotation
annotation_accepted: bool = Field(default=False)
# Score feedback after submit
grader_score: Optional[float] = Field(
default=None,
description="Score 0.0–1.0 returned by the deterministic grader after submit",
)
grader_breakdown: Optional[Dict[str, Any]] = Field(
default=None,
description="Per-criterion breakdown of the grader score",
)
# General feedback message
message: str = Field(default="", description="Human-readable status message")
# Budget tracking
queries_remaining: int = Field(
default=10, description="Number of query actions remaining before forced submit"
)
# ---------------------------------------------------------------------------
# State
# ---------------------------------------------------------------------------
class SREState(State):
"""Internal environment state for an SRE episode."""
task_id: str = Field(default="", description="Identifier of the current task")
difficulty: str = Field(
default="easy", description="Task difficulty: easy | medium | hard"
)
step_count: int = Field(default=0)
queries_used: int = Field(default=0)
max_queries: int = Field(default=10)
annotations: List[str] = Field(default_factory=list)
submitted: bool = Field(default=False)
final_score: Optional[float] = Field(default=None)
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