adaptai / projects /ui /DeepCode /utils /simple_llm_logger.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
超简化LLM响应日志记录器
专注于记录LLM回复的核心内容,配置简单易用
"""
import json
import os
import yaml
from datetime import datetime
from pathlib import Path
from typing import Dict, Any
class SimpleLLMLogger:
"""超简化的LLM响应日志记录器"""
def __init__(self, config_path: str = "mcp_agent.config.yaml"):
"""
初始化日志记录器
Args:
config_path: 配置文件路径
"""
self.config = self._load_config(config_path)
self.llm_config = self.config.get("llm_logger", {})
# 如果禁用则直接返回
if not self.llm_config.get("enabled", True):
self.enabled = False
return
self.enabled = True
self._setup_logger()
def _load_config(self, config_path: str) -> Dict[str, Any]:
"""加载配置文件"""
try:
with open(config_path, "r", encoding="utf-8") as f:
return yaml.safe_load(f)
except Exception as e:
print(f"⚠️ 配置文件加载失败: {e},使用默认配置")
return self._get_default_config()
def _get_default_config(self) -> Dict[str, Any]:
"""获取默认配置"""
return {
"llm_logger": {
"enabled": True,
"output_format": "json",
"log_level": "basic",
"log_directory": "logs/llm_responses",
"filename_pattern": "llm_responses_{timestamp}.jsonl",
"include_models": ["claude-sonnet-4", "gpt-4", "o3-mini"],
"min_response_length": 50,
}
}
def _setup_logger(self):
"""设置日志记录器"""
log_dir = self.llm_config.get("log_directory", "logs/llm_responses")
# 创建日志目录
Path(log_dir).mkdir(parents=True, exist_ok=True)
# 生成日志文件名
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename_pattern = self.llm_config.get(
"filename_pattern", "llm_responses_{timestamp}.jsonl"
)
self.log_file = os.path.join(
log_dir, filename_pattern.format(timestamp=timestamp)
)
print(f"📝 LLM响应日志: {self.log_file}")
def log_response(self, content: str, model: str = "", agent: str = "", **kwargs):
"""
记录LLM响应 - 简化版本
Args:
content: LLM响应内容
model: 模型名称
agent: Agent名称
**kwargs: 其他可选信息
"""
if not self.enabled:
return
# 检查是否应该记录
if not self._should_log(content, model):
return
# 构建日志记录
log_entry = self._build_entry(content, model, agent, kwargs)
# 写入日志
self._write_log(log_entry)
# 控制台显示
self._console_log(content, model, agent)
def _should_log(self, content: str, model: str) -> bool:
"""检查是否应该记录"""
# 检查长度
min_length = self.llm_config.get("min_response_length", 50)
if len(content) < min_length:
return False
# 检查模型
include_models = self.llm_config.get("include_models", [])
if include_models and not any(m in model for m in include_models):
return False
return True
def _build_entry(self, content: str, model: str, agent: str, extra: Dict) -> Dict:
"""构建日志条目"""
log_level = self.llm_config.get("log_level", "basic")
if log_level == "basic":
# 基础级别:只记录核心内容
return {
"timestamp": datetime.now().isoformat(),
"content": content,
"model": model,
}
else:
# 详细级别:包含更多信息
entry = {
"timestamp": datetime.now().isoformat(),
"content": content,
"model": model,
"agent": agent,
}
# 添加额外信息
if "token_usage" in extra:
entry["tokens"] = extra["token_usage"]
if "session_id" in extra:
entry["session"] = extra["session_id"]
return entry
def _write_log(self, entry: Dict):
"""写入日志文件"""
output_format = self.llm_config.get("output_format", "json")
try:
with open(self.log_file, "a", encoding="utf-8") as f:
if output_format == "json":
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
elif output_format == "text":
timestamp = entry.get("timestamp", "")
model = entry.get("model", "")
content = entry.get("content", "")
f.write(f"[{timestamp}] {model}: {content}\n\n")
elif output_format == "markdown":
timestamp = entry.get("timestamp", "")
model = entry.get("model", "")
content = entry.get("content", "")
f.write(f"**{timestamp}** | {model}\n\n{content}\n\n---\n\n")
except Exception as e:
print(f"⚠️ 写入日志失败: {e}")
def _console_log(self, content: str, model: str, agent: str):
"""控制台简要显示"""
preview = content[:80] + "..." if len(content) > 80 else content
print(f"🤖 {model} ({agent}): {preview}")
# 全局实例
_global_logger = None
def get_llm_logger() -> SimpleLLMLogger:
"""获取全局LLM日志记录器实例"""
global _global_logger
if _global_logger is None:
_global_logger = SimpleLLMLogger()
return _global_logger
def log_llm_response(content: str, model: str = "", agent: str = "", **kwargs):
"""便捷函数:记录LLM响应"""
logger = get_llm_logger()
logger.log_response(content, model, agent, **kwargs)
# 示例使用
if __name__ == "__main__":
# 测试日志记录
log_llm_response(
content="这是一个测试的LLM响应内容,用于验证简化日志记录器的功能是否正常工作。",
model="claude-sonnet-4-20250514",
agent="TestAgent",
)
print("✅ 简化LLM日志测试完成")