113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
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from __future__ import annotations
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.db.base import Base
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from app.models.agent_feedback import AgentOperationFeedback
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from app.schemas.agent_feedback import (
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AgentFeedbackCreate,
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AgentFeedbackRead,
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AgentFeedbackSummaryRead,
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)
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LOW_RATING_MAX = 3
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class AgentFeedbackService:
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def __init__(self, db: Session) -> None:
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self.db = db
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def ensure_storage_ready(self) -> None:
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Base.metadata.create_all(bind=self.db.get_bind(), tables=[AgentOperationFeedback.__table__])
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def create_feedback(self, payload: AgentFeedbackCreate) -> AgentFeedbackRead:
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self.ensure_storage_ready()
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feedback = AgentOperationFeedback(
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run_id=payload.run_id,
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conversation_id=payload.conversation_id,
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user_id=payload.user_id,
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agent=payload.agent or "",
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source=payload.source or "",
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session_type=payload.session_type or "",
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operation_type=payload.operation_type or "assistant_round",
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operation_status=payload.operation_status or "",
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rating=int(payload.rating),
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reason=self._normalize_reason(payload.reason),
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context_json=self._normalize_context(payload.context_json),
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)
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self.db.add(feedback)
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self.db.commit()
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self.db.refresh(feedback)
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return AgentFeedbackRead.model_validate(feedback)
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def summarize_feedback(
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self,
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*,
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agent: str | None = None,
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session_type: str | None = None,
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limit: int = 200,
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) -> AgentFeedbackSummaryRead:
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self.ensure_storage_ready()
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stmt = select(AgentOperationFeedback).order_by(AgentOperationFeedback.created_at.desc()).limit(limit)
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if agent:
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stmt = stmt.where(AgentOperationFeedback.agent == agent)
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if session_type:
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stmt = stmt.where(AgentOperationFeedback.session_type == session_type)
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feedback_items = list(self.db.scalars(stmt).all())
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rating_distribution = {str(score): 0 for score in range(1, 6)}
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agents: dict[str, int] = {}
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session_types: dict[str, int] = {}
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low_feedback: list[dict[str, Any]] = []
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total_rating = 0
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for item in feedback_items:
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rating = max(1, min(int(item.rating or 0), 5))
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total_rating += rating
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rating_distribution[str(rating)] = rating_distribution.get(str(rating), 0) + 1
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if item.agent:
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agents[item.agent] = agents.get(item.agent, 0) + 1
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if item.session_type:
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session_types[item.session_type] = session_types.get(item.session_type, 0) + 1
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if rating <= LOW_RATING_MAX:
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low_feedback.append(
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{
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"feedback_id": item.feedback_id,
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"run_id": item.run_id,
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"conversation_id": item.conversation_id,
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"user_id": item.user_id,
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"agent": item.agent,
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"session_type": item.session_type,
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"rating": rating,
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"reason": item.reason,
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"created_at": item.created_at,
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}
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)
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total_feedback = len(feedback_items)
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average_rating = round(total_rating / total_feedback, 2) if total_feedback else 0.0
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return AgentFeedbackSummaryRead(
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window_limit=limit,
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total_feedback=total_feedback,
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average_rating=average_rating,
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low_rating_count=len(low_feedback),
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rating_distribution=rating_distribution,
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agents=agents,
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session_types=session_types,
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recent_low_feedback=low_feedback[:10],
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)
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@staticmethod
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def _normalize_reason(value: str | None) -> str | None:
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normalized = str(value or "").strip()
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return normalized[:1000] if normalized else None
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@staticmethod
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def _normalize_context(value: dict[str, Any] | None) -> dict[str, Any]:
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if not isinstance(value, dict):
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return {}
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return value
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