WIN-JHFT4D3SIVT\caoxiaozhu
36c93a764f
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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LearningSignalType = Literal[
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"preference",
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"workflow",
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"decomposition",
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"tool_success",
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"correction",
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]
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class SessionRetrospective(BaseModel):
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retrospective_id: str | None = None
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user_id: str
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conversation_id: str
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request_message_id: str | None = None
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response_message_id: str | None = None
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query_text: str
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final_response: str | None = None
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summary: str
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task_type: str | None = None
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execution_mode: str | None = None
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primary_agent: str | None = None
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verification_status: str | None = None
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verification_summary: str | None = None
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used_skill_names: list[str] = Field(default_factory=list)
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evidence_refs: list[dict[str, Any]] = Field(default_factory=list)
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task_refs: list[dict[str, Any]] = Field(default_factory=list)
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event_refs: list[dict[str, Any]] = Field(default_factory=list)
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context_snapshot: dict[str, Any] = Field(default_factory=dict)
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learning_signals: list["LearningSignal"] = Field(default_factory=list)
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pattern_candidates: list["PatternCandidate"] = Field(default_factory=list)
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skill_candidates: list["SkillCandidate"] = Field(default_factory=list)
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learning_decision: "LearningDecision | None" = None
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outcome: Literal["completed", "partial", "failed"] = "completed"
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captured_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
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class LearningSignal(BaseModel):
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signal_type: LearningSignalType
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confidence: float = 0.0
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evidence_refs: list[dict[str, Any]] = Field(default_factory=list)
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explanation: str | None = None
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payload: dict[str, Any] = Field(default_factory=dict)
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class PatternCandidate(BaseModel):
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pattern_id: str
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pattern_type: str
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description: str
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confidence: float = 0.0
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evidence_refs: list[dict[str, Any]] = Field(default_factory=list)
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class SkillCandidate(BaseModel):
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candidate_id: str
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name: str
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summary: str
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candidate_type: Literal["workflow_skill", "preference_skill", "decomposition_skill"] = "workflow_skill"
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source_pattern_ids: list[str] = Field(default_factory=list)
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confidence: float = 0.0
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evidence_refs: list[dict[str, Any]] = Field(default_factory=list)
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recommended_status: Literal["candidate", "shadow"] = "candidate"
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class LearningDecision(BaseModel):
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decision: Literal["reinforce_memory", "create_candidate", "promote_skill", "defer", "reject"]
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explanation: str
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evidence_refs: list[dict[str, Any]] = Field(default_factory=list)
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metadata: dict[str, Any] = Field(default_factory=dict)
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