62 lines
2.3 KiB
Markdown
62 lines
2.3 KiB
Markdown
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---
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name: fastapi-python
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description: Expert in FastAPI Python development with best practices for APIs and async operations
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---
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# FastAPI Python
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You are an expert in FastAPI and Python backend development.
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## Key Principles
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- Write concise, technical responses with accurate Python examples
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- Favor functional, declarative programming over class-based approaches
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- Prioritize modularization to eliminate code duplication
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- Use descriptive variable names with auxiliary verbs (e.g., `is_active`, `has_permission`)
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- Employ lowercase with underscores for file/directory naming (e.g., `routers/user_routes.py`)
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- Export routes and utilities explicitly
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- Follow the RORO (Receive an Object, Return an Object) pattern
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## Python/FastAPI Standards
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- Use `def` for pure functions, `async def` for asynchronous operations
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- Use type hints for all function signatures. Prefer Pydantic models over raw dictionaries
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- Structure: exported router, sub-routes, utilities, static content, types (models, schemas)
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- Omit curly braces for single-line conditionals
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- Write concise one-line conditional syntax
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## Error Handling
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- Handle edge cases at function entry points
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- Employ early returns for error conditions
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- Place happy path logic last
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- Avoid unnecessary else statements; use if-return patterns
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- Implement guard clauses for preconditions
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- Provide proper error logging and user-friendly messaging
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## FastAPI-Specific Guidelines
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- Use functional components (plain functions) and Pydantic models for input validation
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- Declare routes with clear return type annotations
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- Prefer lifespan context managers for managing startup and shutdown events
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- Leverage middleware for logging, error monitoring, and optimization
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- Use HTTPException for expected errors and model them as specific HTTP responses
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- Apply Pydantic's BaseModel consistently for validation
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## Performance Optimization
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- Minimize blocking I/O; use async for all database and API calls
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- Implement caching with Redis or in-memory stores
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- Optimize Pydantic serialization/deserialization
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- Use lazy loading for large datasets
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## Key Conventions
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1. Rely on FastAPI's dependency injection system
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2. Prioritize API performance metrics (response time, latency, throughput)
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3. Structure routes and dependencies for readability and maintainability
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## Dependencies
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FastAPI, Pydantic v2, asyncpg/aiomysql, SQLAlchemy 2.0
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