作为在 AI 工程领域摸爬滚打 5 年的老兵，我见过太多团队在 Agent 开发中被「任务中断」「状态丢失」「账单超支」三个坑反复折磨。今天这篇文章，我用真实踩坑经历告诉你：为什么 HolySheep AI 是国内开发者跑 Agent 长任务的最优解，以及如何用它的 API 优雅地实现断点续跑、状态持久化和精准计费审计。

结论摘要

经过我在生产环境中的实际测试：

HolySheep vs 官方 API vs 国内竞品：核心参数对比

对比维度HolySheep AIOpenAI 官方Anthropic 官方某国内中转
汇率政策 ¥1 = $1（无损） ¥7.3 = $1 ¥7.3 = $1 ¥6.8-7.2 = $1
国内延迟 38-47ms 200-400ms（需代理） 300-500ms（需代理） 60-150ms
GPT-4.1 Output $8/MTok $15/MTok - $9-12/MTok
Claude Sonnet 4.5 Output $15/MTok - $18/MTok $16-20/MTok
Gemini 2.5 Flash Output $2.50/MTok - - $3-5/MTok
DeepSeek V3.2 Output $0.42/MTok - - $0.5-0.8/MTok
支付方式 微信/支付宝/银行卡 国际信用卡 国际信用卡 支付宝/微信
免费额度 注册即送 $5 新手 少量试用 极少或无
适合人群 国内企业/开发者首选 出海项目 出海项目 预算敏感型

适合谁与不适合谁

✅ 强烈推荐使用 HolySheep 的场景

❌ 不适合的场景

为什么选 HolySheep

我在多个项目中对比测试过市面主流方案，最终 HolySheep 脱颖而出的核心原因有三个：

1. 成本优势是实打实的

以我最近做的 RAG 知识库项目为例：

2. 延迟优势直接影响用户体验

Agent 应用对响应延迟极其敏感。我测试过同样的多轮对话场景：

3. 状态管理与断点续跑支持

这是今天文章的核心。HolySheep 的 streaming API 配合合理的状态管理设计，能实现真正的生产级长任务支持。

价格与回本测算

让我们用实际数字说话。以下是我在生产环境中的真实成本分析：

场景：电商智能客服 Agent

成本项官方 APIHolySheep节省
月 Token 消耗（input） 2000 万 2000 万 -
月 Token 消耗（output） 500 万 500 万 -
模型选型 Claude Sonnet 4.5 Claude Sonnet 4.5 -
Input 成本 $3/MTok → $60 $3/MTok → $60 $0
Output 成本 $18/MTok → $900 $15/MTok → $750 $150
汇率损失 ¥7.3/$1 → 额外 730% ¥1/$1 → 0% ¥6300+
月总成本（CNY） ¥7000+ ¥810 ¥6200+

回本周期：如果你的团队月 API 消费 >¥500，半年即可节省出一台 MacBook Pro。

核心实现：断点续跑与状态持久化

下面进入实战环节。我会展示如何在 HolySheep API 上构建可靠的长任务 Agent 系统。

方案一：基于消息历史的断点续跑

"""
HolySheep API - Agent 长任务断点续跑方案
核心思路：将对话历史持久化到 Redis/DB，支持任意中断点恢复
"""
import os
import json
import time
import hashlib
from datetime import datetime, timedelta
from typing import List, Dict, Optional, Any


导入 HolySheep SDK（如果使用第三方库）


pip install openai


import openai

class AgentStateManager:
    """Agent 状态管理器 - 支持断点续跑"""
    
    def __init__(
        self,
        api_key: str,
        base_url: str = "https://api.holysheep.ai/v1",
        storage_backend: str = "redis"  # "redis" | "sqlite" | "memory"
    ):
        self.client = openai.OpenAI(
            api_key=api_key,
            base_url=base_url
        )
        self.conversation_history: Dict[str, List[Dict]] = {}
        self.checkpoints: Dict[str, Dict] = {}
        
        # 模拟存储后端（实际使用 Redis/SQLite）
        self.storage = {}
        
    def create_session(self, session_id: Optional[str] = None) -> str:
        """创建新的会话，生成唯一 session_id"""
        if not session_id:
            session_id = hashlib.sha256(
                f"{time.time()}-{os.urandom(8)}".encode()
            ).hexdigest()[:16]
        
        self.conversation_history[session_id] = []
        self.checkpoints[session_id] = {
            "created_at": datetime.now().isoformat(),
            "last_update": datetime.now().isoformat(),
            "total_tokens": 0,
            "request_count": 0,
            "last_checkpoint": None
        }
        
        return session_id
    
    def add_message(self, session_id: str, role: str, content: str) -> None:
        """添加消息到历史"""
        if session_id not in self.conversation_history:
            self.create_session(session_id)
            
        message = {
            "role": role,  # "user" | "assistant" | "system"
            "content": content,
            "timestamp": datetime.now().isoformat()
        }
        self.conversation_history[session_id].append(message)
        self.checkpoints[session_id]["last_update"] = datetime.now().isoformat()
    
    def save_checkpoint(self, session_id: str) -> Dict[str, Any]:
        """保存检查点 - 用于断点续跑"""
        if session_id not in self.conversation_history:
            raise ValueError(f"Session {session_id} not found")
        
        checkpoint_data = {
            "session_id": session_id,
            "history_snapshot": self.conversation_history[session_id].copy(),
            "checkpoint_time": datetime.now().isoformat(),
            "message_count": len(self.conversation_history[session_id]),
            "total_tokens": self.checkpoints[session_id]["total_tokens"]
        }
        
        # 持久化到存储后端
        self.storage[f"checkpoint:{session_id}"] = json.dumps(checkpoint_data)
        self.checkpoints[session_id]["last_checkpoint"] = checkpoint_data["checkpoint_time"]
        
        return checkpoint_data
    
    def restore_from_checkpoint(self, session_id: str) -> bool:
        """从检查点恢复"""
        checkpoint_key = f"checkpoint:{session_id}"
        if checkpoint_key not in self.storage:
            return False
        
        checkpoint_data = json.loads(self.storage[checkpoint_key])
        self.conversation_history[session_id] = checkpoint_data["history_snapshot"]
        return True
    
    def chat(
        self,
        session_id: str,
        message: str,
        model: str = "claude-sonnet-4-20250514",
        max_tokens: int = 4096,
        temperature: float = 0.7,
        auto_checkpoint: bool = True,
        checkpoint_interval: int = 5
    ) -> Dict[str, Any]:
        """发送消息并获取响应，支持自动检查点"""
        
        # 添加用户消息
        self.add_message(session_id, "user", message)
        
        # 构建请求消息
        messages = self.conversation_history[session_id].copy()
        
        start_time = time.time()
        
        try:
            # 调用 HolySheep API
            response = self.client.chat.completions.create(
                model=model,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=False
            )
            
            latency_ms = (time.time() - start_time) * 1000
            
            # 提取响应
            assistant_message = response.choices[0].message.content
            usage = response.usage
            
            # 添加助手消息到历史
            self.add_message(session_id, "assistant", assistant_message)
            
            # 更新计费统计
            self.checkpoints[session_id]["total_tokens"] += (
                (usage.prompt_tokens or 0) + (usage.completion_tokens or 0)
            )
            self.checkpoints[session_id]["request_count"] += 1
            
            # 自动保存检查点
            if auto_checkpoint and \
               self.checkpoints[session_id]["request_count"] % checkpoint_interval == 0:
                self.save_checkpoint(session_id)
            
            return {
                "session_id": session_id,
                "response": assistant_message,
                "usage": {
                    "prompt_tokens": usage.prompt_tokens,
                    "completion_tokens": usage.completion_tokens,
                    "total_tokens": usage.prompt_tokens + usage.completion_tokens
                },
                "latency_ms": round(latency_ms, 2),
                "checkpoint_saved": (
                    self.checkpoints[session_id]["request_count"] % checkpoint_interval == 0
                )
            }
            
        except Exception as e:
            # 出错时保存检查点，便于排查和恢复
            self.save_checkpoint(session_id)
            raise
    
    def get_session_status(self, session_id: str) -> Dict[str, Any]:
        """获取会话状态"""
        if session_id not in self.checkpoints:
            return {"status": "not_found"}
        
        cp = self.checkpoints[session_id]
        return {
            "status": "active",
            "session_id": session_id,
            "message_count": len(self.conversation_history.get(session_id, [])),
            "total_tokens": cp["total_tokens"],
            "request_count": cp["request_count"],
            "last_update": cp["last_update"],
            "has_checkpoint": cp["last_checkpoint"] is not None
        }



使用示例

if __name__ == "__main__":
    # 初始化 - 使用 HolySheep API Key
    api_key = "YOUR_HOLYSHEEP_API_KEY"  # 替换为你的 Key
    
    agent = AgentStateManager(
        api_key=api_key,
        base_url="https://api.holysheep.ai/v1"
    )
    
    # 创建会话
    session_id = agent.create_session()
    print(f"创建会话: {session_id}")
    
    # 模拟多轮对话
    messages = [
        "帮我分析一下最近的销售数据趋势",
        "环比增长了多少？",
        "有什么改进建议？"
    ]
    
    for msg in messages:
        result = agent.chat(session_id, msg)
        print(f"\n用户: {msg}")
        print(f"助手: {result['response'][:100]}...")
        print(f"Token: {result['usage']['total_tokens']}, 延迟: {result['latency_ms']}ms")
    
    # 查看会话状态
    status = agent.get_session_status(session_id)
    print(f"\n会话状态: {json.dumps(status, indent=2, ensure_ascii=False)}")

方案二：流式输出 + 增量保存（适合超长任务）

"""
HolySheep API - 流式输出 + 增量 Token 统计
适用于超长文本生成任务，如报告生成、代码批量处理等
"""
import os
import json
import time
import threading
from typing import Iterator, Dict, Any, Callable, Optional
from dataclasses import dataclass, field
from datetime import datetime

import openai

@dataclass
class StreamingStats:
    """流式响应统计"""
    prompt_tokens: int = 0
    completion_tokens: int = 0
    first_token_latency_ms: float = 0.0
    total_latency_ms: float = 0.0
    tokens_per_second: float = 0.0
    chunks_received: int = 0
    error: Optional[str] = None

class StreamingAgent:
    """流式 Agent - 提供实时 Token 统计和进度反馈"""
    
    def __init__(self, api_key: str):
        self.client = openai.OpenAI(
            api_key=api_key,
            base_url="https://api.holysheep.ai/v1"
        )
        self.request_log = []
        self.lock = threading.Lock()
    
    def stream_chat(
        self,
        messages: list,
        model: str = "gpt-4.1",
        max_tokens: int = 8192,
        temperature: float = 0.5,
        progress_callback: Optional[Callable[[str, StreamingStats], None]] = None
    ) -> tuple[str, StreamingStats]:
        """
        流式聊天，返回完整响应和统计信息
        
        Args:
            messages: 对话消息列表
            model: 模型选择
            max_tokens: 最大生成 Token 数
            temperature: 温度参数
            progress_callback: 进度回调函数，用于实时显示生成进度
        
        Returns:
            (完整响应文本, 统计信息)
        """
        stats = StreamingStats()
        full_response = []
        
        start_time = time.time()
        first_token_time = None
        
        try:
            # 发起流式请求到 HolySheep API
            stream = self.client.chat.completions.create(
                model=model,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=True  # 启用流式输出
            )
            
            # 处理流式响应
            for chunk in stream:
                current_time = time.time()
                
                # 记录首个 Token 延迟
                if first_token_time is None and chunk.choices:
                    if chunk.choices[0].delta.content:
                        first_token_time = current_time
                        stats.first_token_latency_ms = (
                            current_time - start_time
                        ) * 1000
                
                # 提取内容片段
                if chunk.choices and chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    full_response.append(content)
                    stats.chunks_received += 1
                    
                    # 调用进度回调（每 10 个 chunk 回调一次）
                    if progress_callback and stats.chunks_received % 10 == 0:
                        current_text = "".join(full_response)
                        progress_callback(current_text, stats)
            
            # 记录总延迟
            stats.total_latency_ms = (time.time() - start_time) * 1000
            
            # 获取 Token 统计（需要在请求后查询）
            # 注意：实际项目中建议在 create 时设置 stream_options
            # 此处简化处理
            
        except Exception as e:
            stats.error = str(e)
            raise
        
        # 计算速率
        if stats.total_latency_ms > 0:
            total_chars = len("".join(full_response))
            # 粗略估算 Token 数（中文约 1.5 Token/字，英文约 4 Token/词）
            est_tokens = total_chars * 0.75
            stats.tokens_per_second = (est_tokens / stats.total_latency_ms) * 1000
        
        return "".join(full_response), stats
    
    def batch_process(
        self,
        tasks: list,
        model: str = "gpt-4.1",
        max_tokens: int = 2048,
        delay_between_requests: float = 0.1
    ) -> list:
        """
        批量处理任务，支持断点续跑
        返回每个任务的 (结果, 统计) 元组列表
        """
        results = []
        total_cost = 0.0
        
        # Token 价格表（单位：$/MTok）- 来自 HolySheep 2026 价格
        price_table = {
            "gpt-4.1": {"input": 2.0, "output": 8.0},
            "claude-sonnet-4-20250514": {"input": 3.0, "output": 15.0},
            "gemini-2.5-flash": {"input": 0.125, "output": 2.50},
            "deepseek-v3.2": {"input": 0.1, "output": 0.42}
        }
        
        for i, task in enumerate(tasks):
            print(f"\n[{i+1}/{len(tasks)}] 处理任务...")
            
            messages = [{"role": "user", "content": task}]
            
            try:
                response, stats = self.stream_chat(
                    messages=messages,
                    model=model,
                    max_tokens=max_tokens,
                    progress_callback=lambda t, s: print(
                        f"  生成中... {s.chunks_received} chunks, "
                        f"延迟: {s.first_token_latency_ms:.0f}ms"
                    )
                )
                
                # 计算成本
                task_cost = 0
                if model in price_table:
                    # 估算实际 Token 数
                    est_input = len(task) * 0.75 / 1_000_000
                    est_output = len(response) * 0.75 / 1_000_000
                    task_cost = (
                        est_input * price_table[model]["input"] +
                        est_output * price_table[model]["output"]
                    )
                    total_cost += task_cost
                
                results.append({
                    "task_index": i,
                    "task": task[:100] + "..." if len(task) > 100 else task,
                    "response": response,
                    "stats": stats,
                    "estimated_cost_usd": round(task_cost, 4),
                    "success": True
                })
                
                print(f"  ✓ 完成 (耗时: {stats.total_latency_ms:.0f}ms, "
                      f"成本: ${task_cost:.4f})")
                
            except Exception as e:
                results.append({
                    "task_index": i,
                    "task": task,
                    "error": str(e),
                    "success": False
                })
                print(f"  ✗ 失败: {e}")
            
            # 请求间隔（避免限流）
            if i < len(tasks) - 1:
                time.sleep(delay_between_requests)
        
        # 打印汇总
        success_count = sum(1 for r in results if r.get("success"))
        print(f"\n{'='*50}")
        print(f"批量处理完成: {success_count}/{len(tasks)} 成功")
        print(f"总预估成本: ${total_cost:.4f}")
        print(f"平均延迟: {sum(r['stats'].total_latency_ms for r in results if r.get('success')) / max(success_count, 1):.0f}ms")
        
        return results



使用示例

if __name__ == "__main__":
    api_key = "YOUR_HOLYSHEEP_API_KEY"
    
    agent = StreamingAgent(api_key)
    
    # 示例任务列表
    sample_tasks = [
        "用 Python 写一个快速排序算法，包含详细注释",
        "解释什么是 RESTful API，设计原则有哪些",
        "对比 MySQL 和 PostgreSQL 的优劣适用场景",
        "写一个 Docker Compose 配置，启动 Nginx + Flask 应用",
        "介绍 React Hooks 的核心概念和使用场景"
    ]
    
    # 批量处理
    results = agent.batch_process(
        tasks=sample_tasks,
        model="deepseek-v3.2",  # 使用 DeepSeek V3.2，性价比最高
        max_tokens=2048
    )

方案三：Token 计费审计系统

"""
HolySheep API - Token 计费审计系统
实现精确到每个请求的 Token 消耗追踪和成本分析
"""
import os
import json
import sqlite3
import time
import hashlib
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass, asdict
from contextlib import contextmanager
import threading

@dataclass
class TokenUsage:
    """Token 使用记录"""
    request_id: str
    session_id: str
    model: str
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int
    latency_ms: float
    timestamp: str
    cost_usd: float
    cost_cny: float
    success: bool
    error_message: Optional[str] = None

class BillingAuditor:
    """
    Token 计费审计器
    提供精确到每个请求的计费追踪、报表生成、异常检测
    """
    
    # 2026 年最新价格表（$/MTok）
    PRICE_TABLE = {
        # GPT 系列
        "gpt-4.1": {"input": 2.0, "output": 8.0, "currency": "USD"},
        "gpt-4-turbo": {"input": 10.0, "output": 30.0, "currency": "USD"},
        "gpt-3.5-turbo": {"input": 0.5, "output": 1.5, "currency": "USD"},
        
        # Claude 系列
        "claude-opus-4-5": {"input": 15.0, "output": 75.0, "currency": "USD"},
        "claude-sonnet-4-20250514": {"input": 3.0, "output": 15.0, "currency": "USD"},
        "claude-haiku-3.5": {"input": 0.8, "output": 4.0, "currency": "USD"},
        
        # Gemini 系列
        "gemini-2.5-flash": {"input": 0.125, "output": 2.50, "currency": "USD"},
        "gemini-2.0-pro": {"input": 1.25, "output": 10.0, "currency": "USD"},
        
        # DeepSeek 系列（性价比最高）
        "deepseek-v3.2": {"input": 0.1, "output": 0.42, "currency": "USD"},
        "deepseek-coder": {"input": 0.14, "output": 0.28, "currency": "USD"},
        
        # Qwen 系列
        "qwen-turbo": {"input": 0.5, "output": 1.5, "currency": "USD"},
        "qwen-plus": {"input": 0.8, "output": 4.0, "currency": "USD"},
    }
    
    def __init__(self, db_path: str = "billing_audit.db"):
        self.db_path = db_path
        self._init_database()
        self._lock = threading.Lock()
        
        # HolySheep 汇率政策
        self.usd_to_cny_rate = 1.0  # ¥1 = $1，无损兑换
        self.exchange_loss_rate_official = 7.3  # 官方汇率
    
    def _init_database(self):
        """初始化 SQLite 数据库"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS token_usage (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                request_id TEXT UNIQUE NOT NULL,
                session_id TEXT,
                model TEXT NOT NULL,
                prompt_tokens INTEGER,
                completion_tokens INTEGER,
                total_tokens INTEGER,
                latency_ms REAL,
                timestamp TEXT,
                cost_usd REAL,
                cost_cny REAL,
                success INTEGER,
                error_message TEXT
            )
        """)
        
        cursor.execute("""
            CREATE INDEX IF NOT EXISTS idx_session ON token_usage(session_id)
        """)
        
        cursor.execute("""
            CREATE INDEX IF NOT EXISTS idx_timestamp ON token_usage(timestamp)
        """)
        
        cursor.execute("""
            CREATE INDEX IF NOT EXISTS idx_model ON token_usage(model)
        """)
        
        conn.commit()
        conn.close()
    
    def _generate_request_id(self) -> str:
        """生成唯一请求 ID"""
        return hashlib.sha256(
            f"{time.time()}-{threading.current_thread().ident}-{os.urandom(4)}".encode()
        ).hexdigest()[:24]
    
    def _calculate_cost(self, model: str, prompt_tokens: int, 
                       completion_tokens: int) -> Tuple[float, float]:
        """计算成本（USD 和 CNY）"""
        if model not in self.PRICE_TABLE:
            # 未知模型使用默认价格
            price_per_mtok = 10.0
        else:
            prices = self.PRICE_TABLE[model]
            price_per_mtok = prices["output"]  # 主要按 output 计费
        
        # 计算成本（转换为 MToken 单位）
        cost_usd = (completion_tokens / 1_000_000) * price_per_mtok
        cost_cny = cost_usd * self.usd_to_cny_rate
        
        return round(cost_usd, 6), round(cost_cny, 6)
    
    def record_usage(
        self,
        model: str,
        prompt_tokens: int,
        completion_tokens: int,
        latency_ms: float,
        session_id: Optional[str] = None,
        success: bool = True,
        error_message: Optional[str] = None
    ) -> TokenUsage:
        """记录单次 Token 使用"""
        request_id = self._generate_request_id()
        total_tokens = prompt_tokens + completion_tokens
        cost_usd, cost_cny = self._calculate_cost(model, prompt_tokens, completion_tokens)
        
        usage = TokenUsage(
            request_id=request_id,
            session_id=session_id or "default",
            model=model,
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            total_tokens=total_tokens,
            latency_ms=latency_ms,
            timestamp=datetime.now().isoformat(),
            cost_usd=cost_usd,
            cost_cny=cost_cny,
            success=success,
            error_message=error_message
        )
        
        # 写入数据库
        with self._lock:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            
            cursor.execute("""
                INSERT INTO token_usage 
                (request_id, session_id, model, prompt_tokens, completion_tokens,
                 total_tokens, latency_ms, timestamp, cost_usd, cost_cny, 
                 success, error_message)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            """, (
                usage.request_id, usage.session_id, usage.model,
                usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,
                usage.latency_ms, usage.timestamp, usage.cost_usd, usage.cost_cny,
                1 if usage.success else 0, usage.error_message
            ))
            
            conn.commit()
            conn.close()
        
        return usage
    
    def get_session_report(self, session_id: str) -> Dict[str, Any]:
        """生成指定会话的计费报表"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        # 汇总统计
        cursor.execute("""
            SELECT 
                COUNT(*) as total_requests,
                SUM(prompt_tokens) as total_prompt,
                SUM(completion_tokens) as total_completion,
                SUM(total_tokens) as total_tokens,
                SUM(cost_usd) as total_cost_usd,
                SUM(cost_cny) as total_cost_cny,
                AVG(latency_ms) as avg_latency,
                MIN(timestamp) as start_time,
                MAX(timestamp) as end_time
            FROM token_usage
            WHERE session_id = ? AND success = 1
        """, (session_id,))
        
        row = cursor.fetchone()
        
        report = {
            "session_id": session_id,
            "total_requests": row[0] or 0,
            "total_prompt_tokens": row[1] or 0,
            "total_completion_tokens": row[2] or 0,
            "total_tokens": row[3] or 0,
            "total_cost_usd": round(row[4] or 0, 6),
            "total_cost_cny": round(row[5] or 0, 6),
            "avg_latency_ms": round(row[6] or 0, 2),
            "start_time": row[7],
            "end_time": row[8]
        }
        
        # 按模型分组
        cursor.execute("""
            SELECT model, COUNT(*), SUM(total_tokens), SUM(cost_usd)
            FROM token_usage
            WHERE session_id = ? AND success = 1
            GROUP BY model
        """, (session_id,))
        
        report["by_model"] = [
            {
                "model": m, 
                "requests": c, 
                "tokens": t, 
                "cost_usd": round(co, 6)
            }
            for m, c, t, co in cursor.fetchall()
        ]
        
        conn.close()
        return report
    
    def get_daily_report(self, days: int = 7) -> Dict[str, Any]:
        """生成每日计费报表"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("""
            SELECT 
                DATE(timestamp) as date,
                COUNT(*) as requests,
                SUM(total_tokens) as tokens,
                SUM(cost_usd) as cost_usd,
                SUM(cost_cny) as cost_cny,
                AVG(latency_ms) as avg_latency
            FROM token_usage
            WHERE success = 1
            GROUP BY DATE(timestamp)
            ORDER BY date DESC
            LIMIT ?
        """, (days,))
        
        daily_stats = [
            {
                "date": row[0],
                "requests": row[1],
                "tokens": row[2] or 0,
                "cost_usd": round(row[3] or 0, 6),
                "cost_cny": round(row[4]

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