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@ -100,7 +100,11 @@ npm install
### Environment Variables
Environment variables play a critical role in configuring the behavior of the AI Log Monitoring System. They allow you to customize log directories, adjust token limits, and set up necessary credentials. Create a `.env` file in the root of your project and populate it with the following variables:
Environment variables play a critical role in configuring the behavior of the AI Log Monitoring System.
They allow you to customize log directories, adjust token limits, and set up necessary credentials.
Create a `.env` file in the root of your project and populate it with the following variables:
```bash
# General Configuration
@ -139,7 +143,9 @@ const LOG_DIRECTORY = '/your/custom/path/to/logs';
### Ignored IPs and Subnets
The system allows you to specify IP addresses and subnets that should be ignored during log monitoring. This is particularly useful for filtering out trusted sources or known harmless traffic (e.g., public DNS servers).
The system allows you to specify IP addresses and subnets that should be ignored during log monitoring.
This is particularly useful for filtering out trusted sources or known harmless traffic (e.g., public DNS servers).
- **Ignored IPs**: Directly listed IP addresses that should be skipped during processing.
- **Ignored Subnets**: Subnets specified in CIDR notation that represent ranges of IP addresses to be ignored.
@ -196,7 +202,9 @@ This server handles incoming log data, processes it with the AI model, and retur
The `ai_log.js` script uses the `Tail` module to monitor NGINX log files in real-time.
As new lines are added to the logs, the script reads and buffers them. The buffer size is configurable, allowing the system to batch-process logs before sending them to the backend.
As new lines are added to the logs, the script reads and buffers them.
The buffer size is configurable, allowing the system to batch-process logs before sending them to the backend.
**Key Features**:
@ -419,7 +427,9 @@ The system ensures that only necessary data is stored and processed, with an emp
## Performance Optimization
The AI Log Monitoring System is designed to be efficient and scalable, handling large volumes of log data with minimal overhead. However, some optimizations can further enhance performance, especially in high-traffic environments.
The AI Log Monitoring System is designed to be efficient and scalable, handling large volumes of log data with minimal overhead.
However, some optimizations can further enhance performance, especially in high-traffic environments.
### Managing Token Limits