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"""
Configuração de logging estruturado
"""
import logging
import sys
import json
from datetime import datetime
from typing import Dict, Any, Optional
from pathlib import Path
class StructuredFormatter(logging.Formatter):
"""Formatter para logs estruturados em JSON"""
def format(self, record: logging.LogRecord) -> str:
"""
Formata log record como JSON estruturado
Args:
record: Registro de log
Returns:
String JSON formatada
"""
log_data = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"level": record.levelname,
"logger": record.name,
"message": record.getMessage(),
"module": record.module,
"function": record.funcName,
"line": record.lineno
}
# Adiciona informações extras se existirem
if hasattr(record, "extra_data"):
log_data["extra"] = record.extra_data
# Adiciona informação de exceção se houver
if record.exc_info:
log_data["exception"] = self.formatException(record.exc_info)
return json.dumps(log_data, ensure_ascii=False)
class HumanReadableFormatter(logging.Formatter):
"""Formatter para logs legíveis por humanos"""
def __init__(self):
super().__init__(
fmt="%(asctime)s | %(levelname)-8s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S"
)
def setup_logger(
name: str,
level: str = "INFO",
log_file: Optional[str] = None,
structured: bool = False
) -> logging.Logger:
"""
Configura logger com formatação customizada
Args:
name: Nome do logger
level: Nível de log (DEBUG, INFO, WARNING, ERROR, CRITICAL)
log_file: Caminho do arquivo de log (opcional)
structured: Se True, usa formato JSON estruturado
Returns:
Logger configurado
"""
logger = logging.getLogger(name)
logger.setLevel(getattr(logging, level.upper()))
# Remove handlers existentes para evitar duplicação
logger.handlers.clear()
# Handler para console
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(getattr(logging, level.upper()))
if structured:
console_handler.setFormatter(StructuredFormatter())
else:
console_handler.setFormatter(HumanReadableFormatter())
logger.addHandler(console_handler)
# Handler para arquivo se especificado
if log_file:
log_path = Path(log_file)
log_path.parent.mkdir(parents=True, exist_ok=True)
file_handler = logging.FileHandler(log_file, encoding="utf-8")
file_handler.setLevel(getattr(logging, level.upper()))
if structured:
file_handler.setFormatter(StructuredFormatter())
else:
file_handler.setFormatter(HumanReadableFormatter())
logger.addHandler(file_handler)
return logger
def log_with_context(
logger: logging.Logger,
level: str,
message: str,
**kwargs
) -> None:
"""
Loga mensagem com contexto adicional
Args:
logger: Logger a usar
level: Nível do log
message: Mensagem principal
**kwargs: Contexto adicional (session_id, user_id, etc)
"""
extra_record = type('obj', (object,), {'extra_data': kwargs})()
log_func = getattr(logger, level.lower())
log_func(message, extra={"extra_data": kwargs})
class PerformanceLogger:
"""Logger especializado para métricas de performance"""
def __init__(self, logger: logging.Logger):
self.logger = logger
self.metrics: Dict[str, list] = {}
def log_metric(
self,
operation: str,
duration_ms: float,
metadata: Optional[Dict[str, Any]] = None
) -> None:
"""
Registra métrica de performance
Args:
operation: Nome da operação
duration_ms: Duração em milissegundos
metadata: Informações adicionais
"""
metric_data = {
"operation": operation,
"duration_ms": duration_ms,
"timestamp": datetime.utcnow().isoformat() + "Z"
}
if metadata:
metric_data.update(metadata)
self.logger.info(
f"Performance: {operation} completed in {duration_ms:.2f}ms",
extra={"extra_data": metric_data}
)
# Armazena em memória para análise
if operation not in self.metrics:
self.metrics[operation] = []
self.metrics[operation].append(duration_ms)
def get_stats(self, operation: Optional[str] = None) -> Dict[str, Any]:
"""
Retorna estatísticas de performance
Args:
operation: Operação específica (None = todas)
Returns:
Dicionário com estatísticas
"""
if operation:
if operation not in self.metrics:
return {}
durations = self.metrics[operation]
return {
"operation": operation,
"count": len(durations),
"avg_ms": sum(durations) / len(durations),
"min_ms": min(durations),
"max_ms": max(durations),
"total_ms": sum(durations)
}
# Retorna stats de todas operações
stats = {}
for op, durations in self.metrics.items():
stats[op] = {
"count": len(durations),
"avg_ms": sum(durations) / len(durations),
"min_ms": min(durations),
"max_ms": max(durations),
"total_ms": sum(durations)
}
return stats
def clear_metrics(self) -> None:
"""Limpa todas as métricas armazenadas"""
self.metrics.clear()
# Instâncias globais de logger
app_logger = setup_logger("rag_template", level="INFO")
db_logger = setup_logger("rag_template.database", level="INFO")
llm_logger = setup_logger("rag_template.llm", level="INFO")
embedding_logger = setup_logger("rag_template.embeddings", level="INFO")
# Logger de performance
perf_logger = PerformanceLogger(setup_logger("rag_template.performance", level="INFO"))
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