# ACH Server Media Import - Agent Instructions

Guidelines and standards for the ACH Media Import project.

## Project Overview
This project is a Python-based utility that imports media files from an S3-compatible bucket into a PostgreSQL database, enforcing specific naming conventions and metadata validation.

## Technical Stack
- **Language**: Python 3.8+
- **Database**: PostgreSQL (via `psycopg2`)
- **Cloud Storage**: AWS S3/S3-compatible storage (via `boto3`)
- **Containerization**: Docker & Docker Compose
- **Environment**: Managed via `.env` and `config.py`

## Architecture & Modular Design
The project uses a utility-based modular architecture orchestrated by `main.py`.
- [main.py](main.py): Entry point and workflow orchestrator.
- [s3_utils.py](s3_utils.py): S3 client operations and bucket listing.
- [db_utils.py](db_utils.py): Database connectivity and SQL execution.
- [validation_utils.py](validation_utils.py): Pattern matching and business logic validation.
- [logging_config.py](logging_config.py): Centralized logging configuration.
- [error_handler.py](error_handler.py): Error handling and notifications.
- [email_utils.py](email_utils.py): SMTP integration for alerts.

## Domain Logic: Inventory Codes
The core validation revolves around "Inventory Codes" which MUST follow a strict 12-character format:
- `^[VA][OC]-[A-Z0-9]{3}-\d{5}$`
- Examples: `VA-C01-12345`, `OC-A99-67890`.
- Files not matching this pattern in S3 are logged but skipped.

## Development Workflows

### Environment Setup
- **Windows**: Use `. .venv\Scripts\Activate.ps1`
- **Linux/macOS**: Use `source .venv/bin/activate`
- **Dependency installation**: `pip install -r requirements.txt`

### Local Execution
- **Run script**: `python main.py`
- **Verify Configuration**: Ensure `.env` is populated with `DB_`, `AWS_`, and `SMTP_` variables.

### Docker Operations
- **Build/Up**: `docker compose up -d --build`
- **Logs**: `docker compose logs -f app`
- **Stop**: `docker compose stop`

## Coding Standards & Conventions

### Logging
- Use the custom logger from `logging_config.py`.
- **Log Levels**: Use `logging.INFO`, `logging.WARNING`, and the custom `CUSTOM_ERROR_LEVEL` (35) via `error_handler.py`.
- Logs are rotated and stored in the `logs/` directory.

### Error Handling
- Wrap critical operations that should trigger notifications in try-except blocks that call `error_handler.notify_error()`.
- Avoid silent failures; ensure errors are logged to the appropriate file sync.

### Configuration
- Access settings exclusively via the `config.py` module's dictionaries: `db_config`, `aws_config`, `ach_config`.
- Never hardcode credentials or endpoints.

## Copilot / Agent Behavior

This repository is used with an AI assistant. When interacting with the assistant, follow these principles:

- **Do not modify code unless explicitly requested.** The assistant should not change files unless given a clear instruction to do so.
- **Ask before acting.** If a change is needed, the assistant should describe the required modification and confirm before applying it.
- **Prefer explanation over edits.** When debugging or answering questions, provide guidance and analysis rather than directly editing source files.
- **Keep changes minimal.** If a code change is approved, apply the smallest possible edit that resolves the issue.

## Code Style & Maintainability

When generating or modifying code, prioritize **maintainability and clarity over optimization**.

This is **development-stage code**, so it must remain easy to read, understand, and modify by humans.

Guidelines:

- Prefer **clear, explicit implementations** rather than clever or overly compact solutions.
- Avoid **micro-optimizations** or complex patterns that reduce readability.
- Do **not introduce obscure algorithms or creative tricks** that make the code difficult to understand.
- Write code that a developer unfamiliar with the project can quickly follow.
- Use **meaningful variable and function names**.
- Add **thoughtful comments** explaining non-obvious logic, assumptions, and decisions.
- Favor **simple and conventional approaches** instead of experimental or highly abstract ones.
- Maintain a **consistent structure and formatting**.

The goal is **clean, maintainable, well-documented code**, not maximum performance or cleverness.

## Related Files
- [query-sql.md](query-sql.md): Reference for database schema and SQL logic.
- [requirements.txt](requirements.txt): Project dependencies.
- [docker-compose.yml](docker-compose.yml): Deployment configuration.