A Python-based service that processes transactions using Google Cloud Vertex AI. This service handles data processing with configurable logging and supports multiple input datasets.

• Features
• Prerequisites
• Installation
• Configuration
• Usage
• Project Structure
• Logging
• Troubleshooting

• Transaction processing with Vertex AI integration
• Configurable logging system
• YAML-based configuration management
• Support for multiple input datasets
• Automated result generation

• Python 3.8 or higher
• Google Cloud Platform account
• Vertex AI API enabled
• Valid GCP credentials

1. Clone the repository:

git clone

2. Install the required using poetry or pip:

poetry install

pip install -r requirements.txt

1. Configure your credentials:

   • Place your GCP service account key in credentials/credential.json
   • Update config.yaml with your project settings

2. Example config.yaml structure:

project_id: "your-project-id"
location: "us-central1"
model_id: "your-model-id"
input_datasets:
  - name: "dataset1"
logging:
  level: "INFO"
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  file: "logs.log"

Run the service using the following command:

python main.py

The service will:

1. Process input files from the dataset directory
2. Generate logs in the logs directory
3. Output results to the results directory

.
├── config.py # Configuration management
├── config.yaml # Configuration settings
├── credentials/credential.json # GCP credentials
├── dataset/ # Input data files
├── logs/ # Application logs
├── main.py # Application entry point
├── services/ # Core service modules
└── utils/ # Utility functions

• Logs are automatically generated in the logs directory
• Format: process_YYYYMMDD_HHMMSS.log
• Contains detailed processing information and error tracking

1. Credential Error

   Solution: Ensure credentials/credential.json is properly configured and has the required permissions
   

2. Configuration Error

   Solution: Verify config.yaml format and required fields
   

3. Processing Error

   Solution: Check input data format in dataset directory and logs for specific error messages