ai_logstash

A versatile logging package that supports both synchronous and asynchronous logging in Python, with Logstash integration for centralized log management and monitoring.

Features

• Supports both synchronous and asynchronous logging
• Integrates seamlessly with Logstash
• Masks sensitive information automatically
• Provides decorators for automatic exception logging
• Includes environment variables and local variables in logs
• Compatible with various Python applications, including FastAPI and Aiogram

Installation

Using pipenv (recommended)

[packages]
pipenv install ai_logstash

Using pip

pip install ai_logstash

Usage

Synchronous Logging

from ai_logstash import SyncAiLogger, sync_log_exception

logger = SyncAiLogger(
    'my-service',
    project_name="work_wallet",
    masked_variables_names=["PRIVATE", "password", "api_key"],
    logstash_host="logstash.example.com",
    logstash_port=5000,
    container_tag="1.0",
    environment="production"
)

@sync_log_exception(logger)
def my_function():
    logger.info("Starting my_function")
    # Your code here

Asynchronous Logging (e.g., for FastAPI or Aiogram)

from ai_logstash import AsyncAiLogger, create_async_error_handler

logger = AsyncAiLogger(
    'my-async-service',
    project_name="work_wallet",
    masked_variables_names=["PRIVATE", "password", "api_key"],
    logstash_host="logstash.example.com",
    logstash_port=5000,
    container_tag="1.0",
    environment="production"
)

errors_handler = create_async_error_handler(logger)

@errors_handler
async def my_async_function():
    await logger.info("Starting my_async_function")
    # Your async code here

Configuration

• service_name: Name of your service
• project_name: Name of your project (work_wallet, payout_bot, etc.)
• masked_variables_names: List of sensitive variable names to be masked
• logstash_host: Hostname of your Logstash server
• logstash_port: Port of your Logstash server
• container_tag: Tag for container version
• environment: Deployment environment (e.g., "production", "development")
• log_level: Logging level (default: "INFO")

Benefits

• Centralized logging with Logstash integration
• Automatic masking of sensitive information
• Easy integration with various Python frameworks
• Supports both synchronous and asynchronous applications
• Includes detailed error information and environment variables