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Benidrissa
BenidrissaCOMMUNITY
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fastapi-service

Build a FastAPI backend with async SQLAlchemy, Pydantic V2, and Celery tasks.

Updated Mar 30, 2026
Search Tags:#fastapi#backend#scaffolding#celery#pydantic-v2#async-sqlalchemy#rag-pipelines
One-click install
npx skills add https://github.com/Benidrissa/etutor-digital-ph --skill fastapi-service-benidrissa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fastapi-service
Source: https://github.com/Benidrissa/etutor-digital-ph/tree/main/.claude/skills/fastapi-service
Command: npx skills add https://github.com/Benidrissa/etutor-digital-ph --skill fastapi-service-benidrissa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds a production-grade FastAPI backend for Sira that can be extended with endpoints, domain models, services, RAG pipelines, and Celery tasks to power a scalable learning platform.

Core Features & Use Cases

  • 3-layer architecture (Backend → AI/RAG) ensures clean separation of concerns for scalable AI-enhanced learning experiences.
  • Async SQLAlchemy, Pydantic V2, Local Auth (TOTP MFA) provides type-safe, secure backend services with strong validation.
  • Mobile-first API design, Celery tasks, and Redis caching support high-load environments and asynchronous processing.
  • Use Case: Rapidly scaffold a production-ready backend for a bilingual, mobile-first learning platform.

Quick Start

Use this Skill to scaffold and boot the FastAPI backend for Sira by configuring environment variables and running the generated app template.

Frequently Asked Questions about fastapi-service

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I scaffold a FastAPI backend with async SQLAlchemy and Pydantic V2?▼

You can scaffold a production-grade FastAPI backend using async SQLAlchemy and Pydantic V2 by configuring environment variables and running the generated app template to establish a type-safe, secure foundation.

What is the best way to structure a FastAPI backend with RAG pipelines and Celery tasks?▼

The best way to structure a FastAPI backend with RAG pipelines and Celery tasks is using a 3-layer architecture, ensuring clean separation of concerns between the backend and AI/RAG orchestration for scalable processing.

How do I set up Celery tasks and Redis caching for a mobile-first FastAPI API?▼

You set up Celery tasks and Redis caching within a mobile-first FastAPI API design to handle asynchronous processing and high-load environments, ensuring scalable background task execution.

Does this FastAPI scaffolding support bilingual platform requirements and Claude-based RAG orchestration?▼

Yes, this FastAPI scaffolding supports bilingual platform requirements and Claude-based RAG orchestration, applying domain models and services tailored for a scalable, mobile-first learning platform.

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