Agent/examples/content_finder/tools/feedback.py

"""
反馈收集工具 - 收集运营反馈和内容表现数据
"""

from typing import Dict, Any, Optional
from datetime import datetime
from agent.tools import tool, ToolResult, ToolContext


@tool(description="记录运营人员对内容的反馈")
async def record_operator_feedback(
    content_id: str,
    rating: str,
    notes: str = "",
    operator_id: str = "default",
    ctx: ToolContext = None,
) -> ToolResult:
    """
    记录运营人员对内容的反馈

    Args:
        content_id: 内容ID
        rating: 评级（excellent/good/poor）
        notes: 备注说明
        operator_id: 运营人员ID
        ctx: 工具上下文
    """
    feedback = {
        "content_id": content_id,
        "rating": rating,
        "notes": notes,
        "operator_id": operator_id,
        "feedback_time": datetime.now().isoformat(),
    }

    # 保存反馈（伪代码）
    await _save_feedback_to_memory(feedback, ctx)

    return ToolResult(
        title="反馈已记录",
        output=f"已记录对内容 {content_id} 的反馈：{rating}",
        data=feedback,
    )


@tool(description="更新内容在平台的表现数据")
async def update_content_performance(
    content_id: str,
    platform_views: int = 0,
    platform_likes: int = 0,
    platform_shares: int = 0,
    internal_views: int = 0,
    internal_engagement: float = 0.0,
    conversion_rate: float = 0.0,
    ctx: ToolContext = None,
) -> ToolResult:
    """
    更新内容在平台的表现数据

    Args:
        content_id: 内容ID
        platform_views: 平台播放量
        platform_likes: 平台点赞数
        platform_shares: 平台分享数
        internal_views: 内部平台播放量
        internal_engagement: 内部互动率
        conversion_rate: 转化率
        ctx: 工具上下文
    """
    performance = {
        "content_id": content_id,
        "platform_views": platform_views,
        "platform_likes": platform_likes,
        "platform_shares": platform_shares,
        "internal_views": internal_views,
        "internal_engagement": internal_engagement,
        "conversion_rate": conversion_rate,
        "update_time": datetime.now().isoformat(),
    }

    # 保存表现数据（伪代码）
    await _save_performance_to_memory(performance, ctx)

    return ToolResult(
        title="表现数据已更新",
        output=f"已更新内容 {content_id} 的表现数据",
        data=performance,
    )


@tool(description="查询历史反馈和表现数据")
async def query_historical_data(
    content_id: Optional[str] = None,
    rating_filter: Optional[str] = None,
    limit: int = 50,
    ctx: ToolContext = None,
) -> ToolResult:
    """
    查询历史反馈和表现数据

    Args:
        content_id: 内容ID（可选，不指定则查询所有）
        rating_filter: 评级筛选（excellent/good/poor）
        limit: 返回数量限制
        ctx: 工具上下文
    """
    # 查询数据（伪代码）
    feedbacks = await _query_feedbacks_from_memory(
        content_id=content_id,
        rating_filter=rating_filter,
        limit=limit,
        ctx=ctx,
    )

    performances = await _query_performances_from_memory(
        content_id=content_id,
        limit=limit,
        ctx=ctx,
    )

    return ToolResult(
        title="历史数据查询完成",
        output=f"找到 {len(feedbacks)} 条反馈，{len(performances)} 条表现数据",
        data={
            "feedbacks": feedbacks,
            "performances": performances,
        },
    )


# ===== 私有辅助函数（伪代码）=====

async def _save_feedback_to_memory(feedback: Dict[str, Any], ctx: ToolContext) -> None:
    """保存反馈到记忆系统"""
    # 实际实现需要调用memory_store或knowledge系统
    pass


async def _save_performance_to_memory(performance: Dict[str, Any], ctx: ToolContext) -> None:
    """保存表现数据到记忆系统"""
    # 实际实现需要调用memory_store或knowledge系统
    pass


async def _query_feedbacks_from_memory(
    content_id: Optional[str],
    rating_filter: Optional[str],
    limit: int,
    ctx: ToolContext,
) -> list:
    """从记忆系统查询反馈"""
    # 实际实现需要调用memory_store或knowledge系统
    return []


async def _query_performances_from_memory(
    content_id: Optional[str],
    limit: int,
    ctx: ToolContext,
) -> list:
    """从记忆系统查询表现数据"""
    # 实际实现需要调用memory_store或knowledge系统
    return []