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Typed Pydantic models for the incident operations OpenEnv environment.
"""
from __future__ import annotations
from typing import Literal, Optional
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
class ChunkSummary(BaseModel):
model_config = ConfigDict(
json_schema_extra={
"examples": [
{
"chunk_id": "support_003",
"domain": "Customer Support Operations",
"tokens": 132,
"keywords": ["refund policy", "incident timeline", "billing ledger"],
}
]
}
)
chunk_id: str = Field(..., description="Unique artifact identifier exposed to the agent.")
domain: str = Field(..., description="High-level source domain for the artifact.")
tokens: int = Field(..., ge=1, description="Approximate token cost for including the artifact.")
keywords: list[str] = Field(..., min_length=1, description="Important artifact hints available before inspection.")
@field_validator("chunk_id", "domain")
@classmethod
def validate_non_empty_text(cls, value: str) -> str:
value = value.strip()
if not value:
raise ValueError("Value must not be empty.")
return value
@field_validator("keywords")
@classmethod
def validate_keywords(cls, value: list[str]) -> list[str]:
cleaned = [keyword.strip() for keyword in value if keyword.strip()]
if not cleaned:
raise ValueError("keywords must contain at least one non-empty entry.")
return cleaned
class RagObservation(BaseModel):
model_config = ConfigDict(
json_schema_extra={
"examples": [
{
"case_id": "case-refund-triage-001",
"case_summary": "A business customer requests a refund after a confirmed outage.",
"objective": "Prepare a refund triage memo grounded in support evidence.",
"workflow_stage": "triage",
"customer_tier": "business",
"incident_severity": "sev2",
"available_artifacts": [
{
"chunk_id": "support_003",
"domain": "Customer Support Operations",
"tokens": 132,
"keywords": ["refund policy", "incident timeline", "billing ledger"],
}
],
"reviewed_artifacts": [],
"prioritized_artifacts": [],
"plan_draft": None,
"report_requirements": ["State whether the case should proceed to refund review."],
"total_tokens_used": 0,
"token_budget": 850,
"step_number": 0,
"task_name": "refund_triage_easy",
"last_action_feedback": None,
"query": "Prepare an incident-linked refund triage memo.",
"available_chunks": [],
"selected_chunks": [],
}
]
}
)
case_id: str = Field(..., description="Unique identifier for the active simulated incident case.")
case_summary: str = Field(..., description="Short real-world case summary presented to the agent.")
objective: str = Field(..., description="The operational deliverable the agent must produce.")
workflow_stage: Literal["triage", "analysis", "resolution", "submitted"] = Field(
..., description="Current workflow stage in the incident operations process."
)
customer_tier: Literal["standard", "business", "enterprise"] = Field(
..., description="Customer tier for the active case."
)
incident_severity: Literal["sev3", "sev2", "sev1"] = Field(
..., description="Severity of the active incident."
)
available_artifacts: list[ChunkSummary] = Field(
..., description="Artifacts that can be inspected, prioritized, or summarized."
)
reviewed_artifacts: list[str] = Field(
default_factory=list,
description="Artifact ids the agent has inspected so far.",
)
prioritized_artifacts: list[str] = Field(
default_factory=list,
description="Artifact ids currently included in the working resolution set.",
)
plan_draft: Optional[str] = Field(
default=None,
description="Current draft of the resolution plan or operational recommendation.",
)
report_requirements: list[str] = Field(
default_factory=list,
description="Deterministic requirements the final report must satisfy.",
)
progress_signals: dict[str, float] = Field(
default_factory=dict,
description="Normalized progress metrics for artifact coverage, planning, and workflow readiness.",
)
total_tokens_used: int = Field(..., ge=0, description="Current token cost of the prioritized working set.")
token_budget: int = Field(..., ge=1, description="Maximum allowed token budget for the current task.")
step_number: int = Field(..., ge=0, description="Current step number in the episode.")
task_name: str = Field(..., description="Active task identifier.")
last_action_feedback: Optional[str] = Field(default=None, description="Outcome of the previous action.")
query: str = Field(..., description="Compatibility mirror of objective for legacy clients.")
available_chunks: list[ChunkSummary] = Field(
default_factory=list,
description="Compatibility mirror of available_artifacts for legacy clients.",
)
selected_chunks: list[str] = Field(
default_factory=list,
description="Compatibility mirror of prioritized_artifacts for legacy clients.",
)
@field_validator("case_id", "case_summary", "objective", "task_name", "query")
@classmethod
def validate_required_strings(cls, value: str) -> str:
value = value.strip()
if not value:
raise ValueError("Value must not be empty.")
return value
@field_validator("reviewed_artifacts", "prioritized_artifacts", "selected_chunks")
@classmethod
def validate_ids(cls, value: list[str]) -> list[str]:
cleaned = [artifact_id.strip() for artifact_id in value if artifact_id.strip()]
if len(cleaned) != len(set(cleaned)):
raise ValueError("Artifact id lists must not contain duplicates.")
return cleaned
@field_validator("report_requirements")
@classmethod
def validate_report_requirements(cls, value: list[str]) -> list[str]:
cleaned = [item.strip() for item in value if item.strip()]
return cleaned
@field_validator("last_action_feedback")
@classmethod
def validate_feedback(cls, value: Optional[str]) -> Optional[str]:
if value is None:
return value
value = value.strip()
return value or None
@model_validator(mode="after")
def validate_budget_and_aliases(self) -> "RagObservation":
if self.total_tokens_used > self.token_budget:
raise ValueError("total_tokens_used cannot exceed token_budget.")
if self.query != self.objective:
raise ValueError("query must mirror objective.")
if self.selected_chunks != self.prioritized_artifacts:
raise ValueError("selected_chunks must mirror prioritized_artifacts.")
if len(self.available_chunks) != len(self.available_artifacts):
raise ValueError("available_chunks must mirror available_artifacts.")
return self
class RagAction(BaseModel):
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"action_type": "inspect_artifact", "artifact_id": "support_003"},
{"action_type": "summarize_artifact", "artifact_id": "support_003", "compression_ratio": 0.55},
{"action_type": "set_resolution_plan", "plan": "Verify outage evidence and route manual exceptions to finance review."},
{"action_type": "submit_report", "answer": "Proceed to refund review only after outage and billing evidence are confirmed. [support_001] [support_003]"},
]
}
)
action_type: Literal[
"inspect_artifact",
"prioritize_artifact",
"summarize_artifact",
"set_resolution_plan",
"submit_report",
"select_chunk",
"deselect_chunk",
"compress_chunk",
"submit_answer",
] = Field(..., description="The environment action the agent wants to perform.")
artifact_id: Optional[str] = Field(default=None, description="Target artifact id for artifact actions.")
chunk_id: Optional[str] = Field(default=None, description="Legacy alias for artifact_id.")
compression_ratio: Optional[float] = Field(default=None, ge=0.3, le=0.9)
plan: Optional[str] = Field(default=None, description="Draft of the current operational resolution plan.")
answer: Optional[str] = Field(default=None, description="Final report or resolution memo to submit.")
@field_validator("artifact_id", "chunk_id", "plan", "answer")
@classmethod
def normalize_optional_strings(cls, value: Optional[str]) -> Optional[str]:
if value is None:
return value
value = value.strip()
return value or None
@model_validator(mode="after")
def validate_action_semantics(self) -> "RagAction":
normalized_artifact_id = self.artifact_id or self.chunk_id
if self.action_type in {"inspect_artifact", "prioritize_artifact", "select_chunk", "deselect_chunk"}:
if normalized_artifact_id is None:
raise ValueError("artifact_id or chunk_id is required for artifact selection actions.")
elif self.action_type in {"summarize_artifact", "compress_chunk"}:
if normalized_artifact_id is None:
raise ValueError("artifact_id or chunk_id is required for summarize actions.")
if self.compression_ratio is None:
raise ValueError("compression_ratio is required for summarize actions.")
elif self.action_type == "set_resolution_plan":
if self.plan is None:
raise ValueError("plan is required for set_resolution_plan.")
elif self.action_type in {"submit_report", "submit_answer"}:
if self.answer is None:
raise ValueError("answer is required for submit_report/submit_answer.")
return self
class RagReward(BaseModel):
total: float = Field(..., ge=0.0, le=1.0)
token_efficiency: float = Field(..., ge=0.0, le=1.0)
answer_quality: float = Field(..., ge=0.0, le=1.0)
retrieval_precision: float = Field(..., ge=0.0, le=1.0)
penalty: float = Field(..., ge=0.0, le=1.0)
@model_validator(mode="after")
def validate_total_bound(self) -> "RagReward":
if self.total > 1.0 or self.total < 0.0:
raise ValueError("total must remain within [0.0, 1.0].")
return self
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