优化代码格式和异常处理
- 修复异常处理链，使用from语法保留原始异常 - 格式化代码以符合项目规范 - 优化导入模块的顺序 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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春河晴
@@ -9,17 +9,18 @@ from ...common.database import db
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logger = get_module_logger("llm_statistics")
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class LLMStatistics:
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def __init__(self, output_file: str = "llm_statistics.txt"):
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"""初始化LLM统计类
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Args:
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output_file: 统计结果输出文件路径
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"""
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self.output_file = output_file
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self.running = False
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self.stats_thread = None
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def start(self):
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"""启动统计线程"""
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if not self.running:
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@@ -27,16 +28,16 @@ class LLMStatistics:
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self.stats_thread = threading.Thread(target=self._stats_loop)
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self.stats_thread.daemon = True
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self.stats_thread.start()
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def stop(self):
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"""停止统计线程"""
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self.running = False
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if self.stats_thread:
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self.stats_thread.join()
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def _collect_statistics_for_period(self, start_time: datetime) -> Dict[str, Any]:
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"""收集指定时间段的LLM请求统计数据
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Args:
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start_time: 统计开始时间
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"""
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@@ -51,28 +52,26 @@ class LLMStatistics:
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"costs_by_user": defaultdict(float),
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"costs_by_type": defaultdict(float),
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"costs_by_model": defaultdict(float),
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#新增token统计字段
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# 新增token统计字段
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"tokens_by_type": defaultdict(int),
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"tokens_by_user": defaultdict(int),
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"tokens_by_model": defaultdict(int),
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}
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cursor = db.llm_usage.find({
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"timestamp": {"$gte": start_time}
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})
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cursor = db.llm_usage.find({"timestamp": {"$gte": start_time}})
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total_requests = 0
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for doc in cursor:
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stats["total_requests"] += 1
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request_type = doc.get("request_type", "unknown")
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user_id = str(doc.get("user_id", "unknown"))
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model_name = doc.get("model_name", "unknown")
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stats["requests_by_type"][request_type] += 1
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stats["requests_by_user"][user_id] += 1
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stats["requests_by_model"][model_name] += 1
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prompt_tokens = doc.get("prompt_tokens", 0)
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completion_tokens = doc.get("completion_tokens", 0)
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total_tokens = prompt_tokens + completion_tokens # 根据数据库字段调整
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@@ -80,112 +79,107 @@ class LLMStatistics:
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stats["tokens_by_user"][user_id] += total_tokens
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stats["tokens_by_model"][model_name] += total_tokens
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stats["total_tokens"] += total_tokens
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cost = doc.get("cost", 0.0)
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stats["total_cost"] += cost
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stats["costs_by_user"][user_id] += cost
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stats["costs_by_type"][request_type] += cost
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stats["costs_by_model"][model_name] += cost
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total_requests += 1
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if total_requests > 0:
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stats["average_tokens"] = stats["total_tokens"] / total_requests
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return stats
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def _collect_all_statistics(self) -> Dict[str, Dict[str, Any]]:
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"""收集所有时间范围的统计数据"""
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now = datetime.now()
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return {
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"all_time": self._collect_statistics_for_period(datetime.min),
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"last_7_days": self._collect_statistics_for_period(now - timedelta(days=7)),
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"last_24_hours": self._collect_statistics_for_period(now - timedelta(days=1)),
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"last_hour": self._collect_statistics_for_period(now - timedelta(hours=1))
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"last_hour": self._collect_statistics_for_period(now - timedelta(hours=1)),
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}
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def _format_stats_section(self, stats: Dict[str, Any], title: str) -> str:
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"""格式化统计部分的输出"""
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output = []
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output.append("\n"+"-" * 84)
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output.append("\n" + "-" * 84)
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output.append(f"{title}")
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output.append("-" * 84)
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output.append(f"总请求数: {stats['total_requests']}")
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if stats['total_requests'] > 0:
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if stats["total_requests"] > 0:
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output.append(f"总Token数: {stats['total_tokens']}")
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output.append(f"总花费: {stats['total_cost']:.4f}¥\n")
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data_fmt = "{:<32} {:>10} {:>14} {:>13.4f} ¥"
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# 按模型统计
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output.append("按模型统计:")
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output.append(("模型名称 调用次数 Token总量 累计花费"))
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for model_name, count in sorted(stats["requests_by_model"].items()):
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tokens = stats["tokens_by_model"][model_name]
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cost = stats["costs_by_model"][model_name]
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output.append(data_fmt.format(
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model_name[:32] + ".." if len(model_name) > 32 else model_name,
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count,
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tokens,
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cost
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))
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output.append(
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data_fmt.format(model_name[:32] + ".." if len(model_name) > 32 else model_name, count, tokens, cost)
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)
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output.append("")
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# 按请求类型统计
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output.append("按请求类型统计:")
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output.append(("模型名称 调用次数 Token总量 累计花费"))
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for req_type, count in sorted(stats["requests_by_type"].items()):
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tokens = stats["tokens_by_type"][req_type]
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cost = stats["costs_by_type"][req_type]
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output.append(data_fmt.format(
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req_type[:22] + ".." if len(req_type) > 24 else req_type,
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count,
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tokens,
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cost
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))
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output.append(
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data_fmt.format(req_type[:22] + ".." if len(req_type) > 24 else req_type, count, tokens, cost)
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)
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output.append("")
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# 修正用户统计列宽
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output.append("按用户统计:")
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output.append(("模型名称 调用次数 Token总量 累计花费"))
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for user_id, count in sorted(stats["requests_by_user"].items()):
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tokens = stats["tokens_by_user"][user_id]
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cost = stats["costs_by_user"][user_id]
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output.append(data_fmt.format(
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user_id[:22], # 不再添加省略号，保持原始ID
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count,
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tokens,
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cost
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))
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output.append(
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data_fmt.format(
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user_id[:22], # 不再添加省略号，保持原始ID
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count,
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tokens,
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cost,
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)
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)
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return "\n".join(output)
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def _save_statistics(self, all_stats: Dict[str, Dict[str, Any]]):
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"""将统计结果保存到文件"""
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current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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output = []
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output.append(f"LLM请求统计报告 (生成时间: {current_time})")
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# 添加各个时间段的统计
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sections = [
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("所有时间统计", "all_time"),
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("最近7天统计", "last_7_days"),
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("最近24小时统计", "last_24_hours"),
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("最近1小时统计", "last_hour")
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("最近1小时统计", "last_hour"),
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]
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for title, key in sections:
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output.append(self._format_stats_section(all_stats[key], title))
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# 写入文件
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with open(self.output_file, "w", encoding="utf-8") as f:
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f.write("\n".join(output))
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def _stats_loop(self):
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"""统计循环，每1分钟运行一次"""
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while self.running:
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@@ -194,7 +188,7 @@ class LLMStatistics:
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self._save_statistics(all_stats)
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except Exception:
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logger.exception("统计数据处理失败")
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# 等待1分钟
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for _ in range(60):
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if not self.running:
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