94 lines
4.4 KiB
Markdown
94 lines
4.4 KiB
Markdown
# ACH Server Media Import - Agent Instructions
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Guidelines and standards for the ACH Media Import project.
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## Project Overview
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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.
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## Technical Stack
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- **Language**: Python 3.8+
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- **Database**: PostgreSQL (via `psycopg2`)
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- **Cloud Storage**: AWS S3/S3-compatible storage (via `boto3`)
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- **Containerization**: Docker & Docker Compose
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- **Environment**: Managed via `.env` and `config.py`
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## Architecture & Modular Design
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The project uses a utility-based modular architecture orchestrated by `main.py`.
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- [main.py](main.py): Entry point and workflow orchestrator.
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- [s3_utils.py](s3_utils.py): S3 client operations and bucket listing.
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- [db_utils.py](db_utils.py): Database connectivity and SQL execution.
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- [validation_utils.py](validation_utils.py): Pattern matching and business logic validation.
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- [logging_config.py](logging_config.py): Centralized logging configuration.
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- [error_handler.py](error_handler.py): Error handling and notifications.
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- [email_utils.py](email_utils.py): SMTP integration for alerts.
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## Domain Logic: Inventory Codes
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The core validation revolves around "Inventory Codes" which MUST follow a strict 12-character format:
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- `^[VA][OC]-[A-Z0-9]{3}-\d{5}$`
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- Examples: `VA-C01-12345`, `OC-A99-67890`.
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- Files not matching this pattern in S3 are logged but skipped.
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## Development Workflows
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### Environment Setup
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- **Windows**: Use `. .venv\Scripts\Activate.ps1`
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- **Linux/macOS**: Use `source .venv/bin/activate`
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- **Dependency installation**: `pip install -r requirements.txt`
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### Local Execution
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- **Run script**: `python main.py`
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- **Verify Configuration**: Ensure `.env` is populated with `DB_`, `AWS_`, and `SMTP_` variables.
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### Docker Operations
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- **Build/Up**: `docker compose up -d --build`
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- **Logs**: `docker compose logs -f app`
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- **Stop**: `docker compose stop`
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## Coding Standards & Conventions
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### Logging
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- Use the custom logger from `logging_config.py`.
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- **Log Levels**: Use `logging.INFO`, `logging.WARNING`, and the custom `CUSTOM_ERROR_LEVEL` (35) via `error_handler.py`.
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- Logs are rotated and stored in the `logs/` directory.
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### Error Handling
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- Wrap critical operations that should trigger notifications in try-except blocks that call `error_handler.notify_error()`.
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- Avoid silent failures; ensure errors are logged to the appropriate file sync.
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### Configuration
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- Access settings exclusively via the `config.py` module's dictionaries: `db_config`, `aws_config`, `ach_config`.
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- Never hardcode credentials or endpoints.
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## Copilot / Agent Behavior
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This repository is used with an AI assistant. When interacting with the assistant, follow these principles:
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- **Do not modify code unless explicitly requested.** The assistant should not change files unless given a clear instruction to do so.
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- **Ask before acting.** If a change is needed, the assistant should describe the required modification and confirm before applying it.
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- **Prefer explanation over edits.** When debugging or answering questions, provide guidance and analysis rather than directly editing source files.
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- **Keep changes minimal.** If a code change is approved, apply the smallest possible edit that resolves the issue.
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## Code Style & Maintainability
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When generating or modifying code, prioritize **maintainability and clarity over optimization**.
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This is **development-stage code**, so it must remain easy to read, understand, and modify by humans.
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Guidelines:
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- Prefer **clear, explicit implementations** rather than clever or overly compact solutions.
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- Avoid **micro-optimizations** or complex patterns that reduce readability.
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- Do **not introduce obscure algorithms or creative tricks** that make the code difficult to understand.
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- Write code that a developer unfamiliar with the project can quickly follow.
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- Use **meaningful variable and function names**.
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- Add **thoughtful comments** explaining non-obvious logic, assumptions, and decisions.
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- Favor **simple and conventional approaches** instead of experimental or highly abstract ones.
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- Maintain a **consistent structure and formatting**.
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The goal is **clean, maintainable, well-documented code**, not maximum performance or cleverness.
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## Related Files
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- [query-sql.md](query-sql.md): Reference for database schema and SQL logic.
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- [requirements.txt](requirements.txt): Project dependencies.
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- [docker-compose.yml](docker-compose.yml): Deployment configuration.
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