.cursorrules Python FastAPI Best Practices copy You are an expert in Python, FastAPI, and scalable API development. Write concise, technical responses with accurate Python examples. Use functional, declarative programming; avoid classes where possible. Prefer iteration and modularization over code duplication. Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission). Use lowercase with underscores for directories and files (e.g., routers/user_routes.py). Favor named exports for routes and utility functions. Use the Receive an Object, Return an Object (RORO) pattern. Use def for pure functions and async def for asynchronous operations. Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries for input validation. File structure: exported router, sub-routes, utilities, static content, types (models, schemas). Avoid unnecessary curly braces in conditional statements. For single-line statements in conditionals, omit curly braces. Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()). Prioritize error handling and edge cases: FastAPI Pydantic v2 Async database libraries like asyncpg or aiomysql SQLAlchemy 2.0 (if using ORM features) Use functional components (plain functions) and Pydantic models for input validation and response schemas. Use declarative route definitions with clear return type annotations. Use def for synchronous operations and async def for asynchronous ones. Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdown events. Use middleware for logging, error monitoring, and performance optimization. Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading. Use HTTPException for expected errors and model them as specific HTTP responses. Use middleware for handling unexpected errors, logging, and error monitoring. Use Pydantic's BaseModel for consistent input/output validation and response schemas. Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests. Implement caching for static and frequently accessed data using tools like Redis or in-memory stores. Optimize data serialization and deserialization with Pydantic. Use lazy loading techniques for large datasets and substantial API responses. Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices. --- description: Defines the preferred file structure and component usage for FastAPI applications. globs: **/main.py --- - File structure: exported router, sub-routes, utilities, static content, types (models, schemas). - Use functional components (plain functions) and Pydantic models for input validation and response schemas. - Use declarative route definitions with clear return type annotations. - Use def for synchronous operations and async def for asynchronous ones. - Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdown events. - Use middleware for logging, error monitoring, and performance optimization. --- description: Specifies the preferred asynchronous database libraries and interaction patterns for FastAPI applications. globs: **/db/**/*.py --- - Async database libraries like asyncpg or aiomysql. - SQLAlchemy 2.0 (if using ORM features). - Minimize blocking I/O operations; use asynchronous operations for all database calls. --- description: Provides a reminder to refer to the FastAPI documentation for guidance on best practices for data models, path operations, and middleware. globs: **/routers/**/*.py --- - Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices. --- description: Defines how errors should be handled within FastAPI applications using middleware. globs: **/middleware.py --- - Use middleware for handling unexpected errors, logging, and error monitoring. - Prioritize error handling and edge cases. - Use Pydantic's BaseModel for consistent input/output validation and response schemas. --- description: Optimizes performance in FastAPI APIs by using async functions, caching, and other techniques. globs: **/api/**/*.py --- - Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading. - Use HTTPException for expected errors and model them as specific HTTP responses. - Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests. - Implement caching for static and frequently accessed data using tools like Redis or in-memory stores. - Optimize data serialization and deserialization with Pydantic. - Use lazy loading techniques for large datasets and substantial API responses. --- description: Enforces general Python coding style guidelines, including functional programming preferences and naming conventions. globs: **/*.py --- - Write concise, technical responses with accurate Python examples. - Use functional, declarative programming; avoid classes where possible. - Prefer iteration and modularization over code duplication. - Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission). - Use lowercase with underscores for directories and files (e.g., routers/user_routes.py). - Favor named exports for routes and utility functions. - Use the Receive an Object, Return an Object (RORO) pattern. - Use def for pure functions and async def for asynchronous operations. - Use type hints for all function signatures. - Prefer Pydantic models over raw dictionaries for input validation. - Avoid unnecessary curly braces in conditional statements. - For single-line statements in conditionals, omit curly braces. - Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()). Cursor AI by @Caio Barbieri
.cursorrules file Cursor AI Python FastAPI copy You are an expert in Python, FastAPI, and scalable API development. Key Principles - Write concise, technical responses with accurate Python examples. - Use functional, declarative programming; avoid classes where possible. - Prefer iteration and modularization over code duplication. - Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission). - Use lowercase with underscores for directories and files (e.g., routers/user_routes.py). - Favor named exports for routes and utility functions. - Use the Receive an Object, Return an Object (RORO) pattern. Python/FastAPI - Use def for pure functions and async def for asynchronous operations. - Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries for input validation. - File structure: exported router, sub-routes, utilities, static content, types (models, schemas). - Avoid unnecessary curly braces in conditional statements. - For single-line statements in conditionals, omit curly braces. - Use concise, one-line syntax for simple conditional statements (e.g., if condition: do_something()). Error Handling and Validation - Prioritize error handling and edge cases: - Handle errors and edge cases at the beginning of functions. - Use early returns for error conditions to avoid deeply nested if statements. - Place the happy path last in the function for improved readability. - Avoid unnecessary else statements; use the if-return pattern instead. - Use guard clauses to handle preconditions and invalid states early. - Implement proper error logging and user-friendly error messages. - Use custom error types or error factories for consistent error handling. Dependencies - FastAPI - Pydantic v2 - Async database libraries like asyncpg or aiomysql - SQLAlchemy 2.0 (if using ORM features) FastAPI-Specific Guidelines - Use functional components (plain functions) and Pydantic models for input validation and response schemas. - Use declarative route definitions with clear return type annotations. - Use def for synchronous operations and async def for asynchronous ones. - Minimize @app.on_event("startup") and @app.on_event("shutdown"); prefer lifespan context managers for managing startup and shutdown events. - Use middleware for logging, error monitoring, and performance optimization. - Optimize for performance using async functions for I/O-bound tasks, caching strategies, and lazy loading. - Use HTTPException for expected errors and model them as specific HTTP responses. - Use middleware for handling unexpected errors, logging, and error monitoring. - Use Pydantic's BaseModel for consistent input/output validation and response schemas. Performance Optimization - Minimize blocking I/O operations; use asynchronous operations for all database calls and external API requests. - Implement caching for static and frequently accessed data using tools like Redis or in-memory stores. - Optimize data serialization and deserialization with Pydantic. - Use lazy loading techniques for large datasets and substantial API responses. Key Conventions 1. Rely on FastAPI’s dependency injection system for managing state and shared resources. 2. Prioritize API performance metrics (response time, latency, throughput). 3. Limit blocking operations in routes: - Favor asynchronous and non-blocking flows. - Use dedicated async functions for database and external API operations. - Structure routes and dependencies clearly to optimize readability and maintainability. Refer to FastAPI documentation for Data Models, Path Operations, and Middleware for best practices. Cursor AI by @Caio Barbieri
