Are testing patterns included?

Yes, it includes boilerplate and configuration for pytest-asyncio, using an in-memory database and AsyncClient for robust integration testing.

Does this skill support vector databases?

Yes, it includes specific implementation patterns for pgvector to facilitate semantic and vector-based search within a PostgreSQL database.

How does it handle database sessions?

The skill provides a standardized dependency injection pattern for managing asynchronous SQLAlchemy sessions, ensuring clean resource management and testing.

What tech stack does this skill focus on?

It is optimized for Python 3.11+, FastAPI, SQLAlchemy 2.0 (Async), Pydantic v2, and PostgreSQL with pgvector.

FastAPI Backend Architect

Name: FastAPI Backend Architect
Author: fghaffar

byfghaffar

API Development

Streamlines the development of production-ready FastAPI backends using asynchronous patterns and PostgreSQL.

About

This skill provides a comprehensive framework and set of best practices for building scalable backend applications within the FastAPI ecosystem. It enforces modern architectural patterns, including asynchronous database interactions with SQLAlchemy 2.0, robust data validation via Pydantic v2, and advanced vector search capabilities with pgvector. By offering standardized templates for project structure, service layers, and dependency injection, it helps developers maintain consistency, improve code quality, and rapidly deploy high-performance APIs.

Key Features

Standardized project structure with clear separation between routers, services, and repositories
Integrated error handling middleware with Sentry monitoring and structured logging
0 GitHub stars
Comprehensive Pydantic v2 schema patterns for request validation and response serialization
Asynchronous database patterns using SQLAlchemy 2.0 and PostgreSQL pgvector integration
Pre-configured testing suite using pytest, pytest-asyncio, and httpx

Use Cases

Implementing semantic search features using pgvector and embedding models
Scaffolding a new production-grade FastAPI project with async database support
Migrating legacy Python APIs to modern Pydantic v2 and SQLAlchemy 2.0 standards

About

Key Features

Standardized project structure with clear separation between routers, services, and repositories
Integrated error handling middleware with Sentry monitoring and structured logging
0 GitHub stars
Comprehensive Pydantic v2 schema patterns for request validation and response serialization
Asynchronous database patterns using SQLAlchemy 2.0 and PostgreSQL pgvector integration
Pre-configured testing suite using pytest, pytest-asyncio, and httpx

Use Cases

Implementing semantic search features using pgvector and embedding models
Scaffolding a new production-grade FastAPI project with async database support
Migrating legacy Python APIs to modern Pydantic v2 and SQLAlchemy 2.0 standards