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5

rules

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NEW

FastAPI .cursorrules prompt file guide

Code Style and Structure:

Follow PEP 8 guidelines for Python code style
Use meaningful variable and function names (snake_case for variables and functions, PascalCase for classes)
Keep functions small and focused on a single task
Use docstrings for modules, classes, and functions
Organize code into logical modules and packages
Use virtual environments for project isolation

Python-specific Conventions:

Prefer list comprehensions and generator expressions over map() and filter()
Use context managers (with statements) for resource management
Utilize decorators for cross-cutting concerns (e.g., logging, timing)
Leverage type hints for improved code readability and tooling support
Use f-strings for string formatting

Framework and API Development:

Use FastAPI as the primary framework for building high-performance APIs
Leverage FastAPI's automatic OpenAPI (Swagger) documentation
Utilize dependency injection for clean, testable code
Implement RESTful API design principles
Use Pydantic for data validation, serialization, and settings management
Define request and response models using Pydantic
Utilize Pydantic's BaseSettings for configuration management

Database and Data Modeling:

Use SQLModel, which combines SQLAlchemy core with Pydantic models
Leverage SQLModel's ability to use the same models for database tables and API schemas
Utilize SQLModel's async support for high-performance database operations
Implement database migrations using Alembic
Use connection pooling for efficient database connections
Implement proper indexing for frequently queried fields

Authentication and Security:

Implement JWT for stateless authentication
Use FastAPI's built-in security utilities
Use bcrypt for password hashing
Apply CORS policies using FastAPI's CORS middleware
Implement rate limiting for API endpoints (consider using FastAPI's dependencies for this)

Error Handling and Logging:

Use FastAPI's exception handlers for consistent error responses
Implement custom exception classes for application-specific errors
Use a logging framework (e.g., Python's built-in logging module)
Structure log messages for easy parsing and analysis

Frontend Development:

For server-side rendering, consider using FastAPI with Jinja2 templates
Implement responsive design using CSS frameworks (e.g., Bootstrap)
For single-page applications, use a JavaScript framework (e.g., React, Vue.js) that consumes your FastAPI backend

Testing:

Write unit tests using pytest
Implement integration tests for API endpoints using FastAPI's TestClient
Use factories (e.g., Factory Boy) for test data generation
Aim for high test coverage, especially for critical paths
Utilize Pydantic's parse_obj_as for easy test data creation

Asynchronous Processing:

Leverage FastAPI's asynchronous capabilities for concurrent processing
For background tasks, use FastAPI's background tasks feature or Celery for more complex scenarios
Implement message queues (e.g., RabbitMQ, Redis) for task management if needed

Deployment and DevOps:

Use Docker for containerization
Create optimized Dockerfiles for your FastAPI applications
Implement CI/CD pipelines (e.g., GitLab CI, GitHub Actions)
Use environment variables for configuration management
Utilize Pydantic's BaseSettings to load and validate environment variables
Implement health check endpoints for monitoring

Performance Optimization:

Leverage FastAPI's asynchronous capabilities for I/O-bound operations
Use caching mechanisms (e.g., Redis) for frequently accessed data
Implement database query optimization techniques with SQLModel
Profile code to identify and resolve bottlenecks

API Documentation:

Utilize FastAPI's automatic interactive API documentation (Swagger UI and ReDoc)
Keep API documentation up-to-date by maintaining accurate Pydantic models and FastAPI path operation functions

Version Control:

Use Git for version control
Implement feature branching and pull request workflows
Write meaningful commit messages

Dependencies Management

Use pip and requirements.txt for managing project dependencies
Consider using Poetry for more advanced dependency management

Remember to keep your code DRY (Don't Repeat Yourself), follow SOLID principles, and prioritize readability and maintainability in your Python full-stack development projects. Leverage the power of FastAPI, SQLModel, and Pydantic to create robust, type-safe, and high-performance applications.

stack:

FastAPI .cursorrules prompt file guide

Rohit Nair

6

NEW

Python OpenNEM Setup .cursorrules prompt file

You are an expert Python developer familiar with the OpenNEM project which you will be writing code for.

OpenNEM is an open source project to map and visualise the electricity system in Australia.

The libraries used in this project are:

FastAPI
Postgres v3.12
asyncpg
SqlAlchemy 2.0
Alembic for database migrations
The arq scheduler and task runner

In any code you generate:

Documentation should be written in Australian English
Write concise, technical responses with accurate Python
Use functional, declarative programming and avoid classes where possible
Write async methods in almost all cases
Include type definitions for method signatures and returns
Include docstrings for all methods
Methods that are private to a module or file should be preceeded with an underscore to keep them private
Documentation for the project is available in the docs/ folder
The database model are written in SqlAlchemy ORM and are stored in opennem/db/models/opennem.py
Every database change requires a new Alembic migration
All queries of small to medium size should utilize the SqlAlchemy ORM query style and in async
For sessions use the get_read_session and get_write_session methods from opennem.db where each is appropriate
OpenNEM settings are read from environment variables with some default specified in the schema at opennem/settings_schema.py
You can utilise the settings object by importing settings via from opennem import settings
prefer Pydantic models over dictionaries
methods should raise exceptions rather than retuning None and all exceptions should be caught and logged as errors
suggest more optimal or efficient methods to carry out functionality - including any possible additional libraries that would assist.
This application is deployed as a docker container where both the API and scheduler are run as separate docker processes
The FastAPI API is located at opennem.api.app and is run using uvicorn
The Arq scheduler and tasks queue is located at opennem.tasks.app and is run by calling that module