刘国栋
8f7f082c3d
- 重构项目目录结构，将功能模块移至 modules/ 目录 - 创建平台同步基础架构，包括发布器基类和 GitHub 发布器 - 新增 UI 状态管理模块 (modules/ui/state.py) 统一管理会话状态 - 更新依赖配置，添加平台同步所需依赖 (httpx, pyperclip) - 整理文档结构，将所有文档分类移至 docs/ 目录 - 添加 .cursorrules 文件定义项目开发规范 - 清理根目录重复文件，保持项目结构整洁
423 lines
16 KiB
Python
423 lines
16 KiB
Python
"""
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ROI 分析与成本优化模块
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用于计算 API 调用成本、分析 ROI、提供成本优化建议
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"""
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from typing import Dict, List, Optional, Tuple
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from datetime import datetime, timedelta
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import pandas as pd
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from collections import defaultdict
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class ROIAnalyzer:
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"""ROI 分析器"""
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def __init__(self, usd_to_cny_rate: float = 7.2):
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"""
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Args:
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usd_to_cny_rate: USD 到 CNY 的汇率（默认 7.2）
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"""
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self.usd_to_cny_rate = usd_to_cny_rate
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# 各平台 API 定价（每 1K tokens，USD）
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# 注意：这些是示例价格，实际价格可能不同，需要根据实际情况更新
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self.pricing_config = {
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"DeepSeek": {
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"input": 0.00014, # $0.14 per 1M tokens
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"output": 0.00028, # $0.28 per 1M tokens
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},
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"OpenAI (GPT)": {
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"gpt-4": {"input": 0.03, "output": 0.06},
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"gpt-4-turbo": {"input": 0.01, "output": 0.03},
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"gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
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},
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"Tongyi (通义千问)": {
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"qwen-plus": {"input": 0.002, "output": 0.008},
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"qwen-turbo": {"input": 0.0008, "output": 0.002},
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},
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"Groq": {
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"input": 0.0, # 免费
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"output": 0.0,
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},
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"Moonshot (Kimi)": {
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"moonshot-v1-8k": {"input": 0.012, "output": 0.012},
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"moonshot-v1-32k": {"input": 0.024, "output": 0.024},
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},
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"豆包（字节跳动）": {
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"doubao-pro-4k": {"input": 0.0008, "output": 0.002},
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"doubao-lite-4k": {"input": 0.0004, "output": 0.001},
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},
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"文心一言（百度）": {
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"ernie-4.0": {"input": 0.012, "output": 0.012},
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"ernie-3.5": {"input": 0.002, "output": 0.002},
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},
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}
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def calculate_cost(
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self,
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provider: str,
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model: str,
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input_tokens: int,
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output_tokens: int
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) -> Tuple[float, float]:
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"""
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计算 API 调用成本
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Args:
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provider: 提供商名称
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model: 模型名称
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input_tokens: 输入 token 数量
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output_tokens: 输出 token 数量
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Returns:
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(cost_usd, cost_cny) 元组
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"""
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cost_usd = 0.0
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# 获取定价配置
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pricing = self.pricing_config.get(provider, {})
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if not pricing:
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# 如果没有配置，返回 0
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return 0.0, 0.0
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# 处理不同的定价结构
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if "input" in pricing and "output" in pricing:
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# 统一定价（如 DeepSeek、Groq）
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input_price = pricing["input"]
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output_price = pricing["output"]
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elif model in pricing:
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# 按模型定价（如 OpenAI）
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model_pricing = pricing[model]
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input_price = model_pricing.get("input", 0.0)
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output_price = model_pricing.get("output", 0.0)
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else:
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# 默认使用第一个模型的定价
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if pricing:
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first_model = list(pricing.keys())[0]
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if isinstance(pricing[first_model], dict):
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input_price = pricing[first_model].get("input", 0.0)
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output_price = pricing[first_model].get("output", 0.0)
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else:
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input_price = pricing.get("input", 0.0)
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output_price = pricing.get("output", 0.0)
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else:
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input_price = 0.0
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output_price = 0.0
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# 计算成本（价格是每 1K tokens）
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cost_usd = (input_tokens / 1000.0 * input_price) + (output_tokens / 1000.0 * output_price)
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cost_cny = cost_usd * self.usd_to_cny_rate
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return cost_usd, cost_cny
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def analyze_costs(
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self,
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api_calls_df: pd.DataFrame,
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verify_results_df: Optional[pd.DataFrame] = None
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) -> Dict:
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"""
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分析成本数据
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Args:
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api_calls_df: API 调用记录 DataFrame
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verify_results_df: 验证结果 DataFrame（可选，用于 ROI 分析）
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Returns:
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成本分析结果字典
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"""
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if api_calls_df.empty:
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return {
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"total_cost_usd": 0.0,
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"total_cost_cny": 0.0,
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"total_tokens": 0,
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"total_calls": 0,
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"cost_by_operation": {},
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"cost_by_provider": {},
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"cost_by_keyword": {},
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"cost_by_platform": {},
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"daily_costs": [],
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"roi_analysis": {}
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}
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# 总成本
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total_cost_usd = api_calls_df["成本(USD)"].sum()
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total_cost_cny = api_calls_df["成本(CNY)"].sum()
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total_tokens = api_calls_df["总Token"].sum()
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total_calls = len(api_calls_df)
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# 按操作类型统计
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cost_by_operation = {}
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if "操作类型" in api_calls_df.columns:
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operation_groups = api_calls_df.groupby("操作类型")
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for op_type, group in operation_groups:
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cost_by_operation[op_type] = {
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"cost_usd": group["成本(USD)"].sum(),
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"cost_cny": group["成本(CNY)"].sum(),
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"calls": len(group),
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"tokens": group["总Token"].sum()
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}
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# 按提供商统计
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cost_by_provider = {}
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if "提供商" in api_calls_df.columns:
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provider_groups = api_calls_df.groupby("提供商")
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for provider, group in provider_groups:
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cost_by_provider[provider] = {
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"cost_usd": group["成本(USD)"].sum(),
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"cost_cny": group["成本(CNY)"].sum(),
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"calls": len(group),
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"tokens": group["总Token"].sum()
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}
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# 按关键词统计
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cost_by_keyword = {}
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if "关键词" in api_calls_df.columns:
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keyword_groups = api_calls_df.groupby("关键词")
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for keyword, group in keyword_groups:
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if pd.notna(keyword) and keyword:
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cost_by_keyword[keyword] = {
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"cost_usd": group["成本(USD)"].sum(),
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"cost_cny": group["成本(CNY)"].sum(),
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"calls": len(group),
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"tokens": group["总Token"].sum()
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}
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# 按平台统计
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cost_by_platform = {}
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if "平台" in api_calls_df.columns:
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platform_groups = api_calls_df.groupby("平台")
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for platform, group in platform_groups:
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if pd.notna(platform) and platform:
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cost_by_platform[platform] = {
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"cost_usd": group["成本(USD)"].sum(),
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"cost_cny": group["成本(CNY)"].sum(),
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"calls": len(group),
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"tokens": group["总Token"].sum()
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}
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# 每日成本趋势
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daily_costs = []
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if "调用时间" in api_calls_df.columns:
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api_calls_df["日期"] = pd.to_datetime(api_calls_df["调用时间"]).dt.date
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daily_groups = api_calls_df.groupby("日期")
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for date, group in daily_groups:
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daily_costs.append({
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"date": date.isoformat() if isinstance(date, datetime.date) else str(date),
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"cost_usd": group["成本(USD)"].sum(),
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"cost_cny": group["成本(CNY)"].sum(),
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"calls": len(group),
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"tokens": group["总Token"].sum()
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})
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daily_costs.sort(key=lambda x: x["date"])
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# ROI 分析（如果有验证结果）
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roi_analysis = {}
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if verify_results_df is not None and not verify_results_df.empty:
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roi_analysis = self._calculate_roi(api_calls_df, verify_results_df)
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return {
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"total_cost_usd": total_cost_usd,
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"total_cost_cny": total_cost_cny,
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"total_tokens": int(total_tokens),
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"total_calls": total_calls,
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"cost_by_operation": cost_by_operation,
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"cost_by_provider": cost_by_provider,
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"cost_by_keyword": cost_by_keyword,
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"cost_by_platform": cost_by_platform,
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"daily_costs": daily_costs,
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"roi_analysis": roi_analysis
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}
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def _calculate_roi(
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self,
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api_calls_df: pd.DataFrame,
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verify_results_df: pd.DataFrame
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) -> Dict:
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"""
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计算 ROI（基于验证结果）
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Args:
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api_calls_df: API 调用记录
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verify_results_df: 验证结果
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Returns:
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ROI 分析结果
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"""
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# 计算总成本
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total_cost = api_calls_df["成本(CNY)"].sum()
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# 计算提及率提升（简化估算）
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# 这里假设每次提及的价值为某个固定值（需要根据实际情况调整）
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mention_value_per_mention = 10.0 # 每次提及的价值（CNY），可配置
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# 统计品牌提及次数
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brand_mentions = verify_results_df[verify_results_df["品牌"] == verify_results_df["品牌"].iloc[0] if len(verify_results_df) > 0 else ""]
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total_mentions = brand_mentions["提及次数"].sum() if "提及次数" in brand_mentions.columns else 0
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# 估算价值
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estimated_value = total_mentions * mention_value_per_mention
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# 计算 ROI
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roi_ratio = (estimated_value - total_cost) / total_cost * 100 if total_cost > 0 else 0
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roi_value = estimated_value - total_cost
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# 按关键词分析 ROI
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keyword_roi = {}
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if "关键词" in api_calls_df.columns and "问题" in verify_results_df.columns:
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# 合并数据
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keyword_costs = api_calls_df.groupby("关键词")["成本(CNY)"].sum()
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keyword_mentions = verify_results_df.groupby("问题")["提及次数"].sum()
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for keyword in keyword_costs.index:
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if pd.notna(keyword) and keyword:
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cost = keyword_costs[keyword]
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mentions = keyword_mentions.get(keyword, 0)
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value = mentions * mention_value_per_mention
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roi = (value - cost) / cost * 100 if cost > 0 else 0
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keyword_roi[keyword] = {
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"cost": cost,
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"mentions": int(mentions),
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"value": value,
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"roi": roi
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}
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return {
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"total_cost": total_cost,
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"total_mentions": int(total_mentions),
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"estimated_value": estimated_value,
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"roi_ratio": roi_ratio,
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"roi_value": roi_value,
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"mention_value_per_mention": mention_value_per_mention,
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"keyword_roi": keyword_roi
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}
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def get_optimization_suggestions(self, cost_analysis: Dict) -> List[Dict]:
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"""
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获取成本优化建议
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Args:
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cost_analysis: 成本分析结果
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Returns:
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优化建议列表
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"""
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suggestions = []
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total_cost = cost_analysis.get("total_cost_cny", 0.0)
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cost_by_provider = cost_analysis.get("cost_by_provider", {})
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cost_by_keyword = cost_analysis.get("cost_by_keyword", {})
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cost_by_operation = cost_analysis.get("cost_by_operation", {})
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# 检查是否有高成本提供商
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if cost_by_provider:
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sorted_providers = sorted(
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cost_by_provider.items(),
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key=lambda x: x[1]["cost_cny"],
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reverse=True
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)
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top_provider = sorted_providers[0]
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if top_provider[1]["cost_cny"] > total_cost * 0.5:
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suggestions.append({
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"type": "provider",
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"priority": "高",
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"title": f"考虑使用更便宜的提供商替代 {top_provider[0]}",
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"description": f"{top_provider[0]} 占总成本的 {top_provider[1]['cost_cny']/total_cost*100:.1f}%，考虑使用更经济的替代方案",
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"savings_estimate": top_provider[1]["cost_cny"] * 0.3 # 估算可节省30%
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})
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# 检查是否有低 ROI 关键词
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roi_analysis = cost_analysis.get("roi_analysis", {})
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keyword_roi = roi_analysis.get("keyword_roi", {})
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if keyword_roi:
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low_roi_keywords = [
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(kw, data) for kw, data in keyword_roi.items()
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if data.get("roi", 0) < 0
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]
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if low_roi_keywords:
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suggestions.append({
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"type": "keyword",
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"priority": "中",
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"title": f"发现 {len(low_roi_keywords)} 个负 ROI 关键词",
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"description": "这些关键词的成本高于产生的价值，建议暂停或优化",
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"keywords": [kw for kw, _ in low_roi_keywords[:5]]
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})
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# 检查操作类型分布
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if cost_by_operation:
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verify_cost = cost_by_operation.get("验证", {}).get("cost_cny", 0.0)
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generate_cost = cost_by_operation.get("生成", {}).get("cost_cny", 0.0)
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if verify_cost > total_cost * 0.7:
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suggestions.append({
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"type": "operation",
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"priority": "中",
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"title": "验证成本占比过高",
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"description": f"验证操作占总成本的 {verify_cost/total_cost*100:.1f}%，建议减少验证频率或使用更便宜的验证模型",
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"savings_estimate": verify_cost * 0.2
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})
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# 如果没有建议，添加通用建议
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if not suggestions:
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suggestions.append({
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"type": "general",
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"priority": "低",
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"title": "成本控制良好",
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"description": "当前成本结构合理，继续保持"
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})
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return suggestions
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def estimate_future_cost(
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self,
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api_calls_df: pd.DataFrame,
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days: int = 30
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) -> Dict:
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"""
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估算未来成本
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Args:
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api_calls_df: 历史 API 调用记录
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days: 预测天数
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Returns:
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未来成本估算
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"""
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if api_calls_df.empty:
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return {
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"estimated_daily_cost_cny": 0.0,
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"estimated_total_cost_cny": 0.0,
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"confidence": "低"
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}
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# 计算日均成本
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if "调用时间" in api_calls_df.columns:
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api_calls_df["日期"] = pd.to_datetime(api_calls_df["调用时间"]).dt.date
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daily_costs = api_calls_df.groupby("日期")["成本(CNY)"].sum()
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if len(daily_costs) > 0:
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avg_daily_cost = daily_costs.mean()
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estimated_total = avg_daily_cost * days
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# 计算置信度（基于数据点数量）
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confidence = "高" if len(daily_costs) >= 7 else ("中" if len(daily_costs) >= 3 else "低")
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return {
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"estimated_daily_cost_cny": float(avg_daily_cost),
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"estimated_total_cost_cny": float(estimated_total),
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"confidence": confidence,
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"data_points": len(daily_costs)
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}
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# 如果没有日期数据，使用总成本估算
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total_cost = api_calls_df["成本(CNY)"].sum()
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# 假设数据覆盖最近7天
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avg_daily = total_cost / 7.0 if total_cost > 0 else 0.0
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return {
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"estimated_daily_cost_cny": avg_daily,
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"estimated_total_cost_cny": avg_daily * days,
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"confidence": "低",
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"data_points": 0
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}
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