ai-server

Name: ai-server
Availability: InStock
Author: Opentrons

Standardize FastAPI service project structure and deployment workflows.

515|208|Updated Jul 6, 2015

Search Tags:#deployment #fastapi #uv #docker #settings #ai-server

Ready to run

npx skills add https://github.com/Opentrons/opentrons --skill ai-server

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-server
Source: https://github.com/Opentrons/opentrons/tree/main/.cursor/skills/ai-server
Command: npx skills add https://github.com/Opentrons/opentrons --skill ai-server

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Defines conventions, structure, and deployment practices for the opentrons-ai-server FastAPI service to ensure consistency across teams and environments.

Core Features & Use Cases

Project structure guidance for a standalone FastAPI service (api/, tests/, deploy workflows, and settings).
Dependency management and reproducible builds using uv, pyproject.toml, and uv.lock.
Configuration and settings handling via pydantic-settings with local .env and AWS Secrets Manager integration.
Docker-based deployment guidance and AWS ECS/Fargate deployment considerations.
CI/CD and testing guidance, including unit and live tests.

Quick Start

Set up the opentrons-ai-server project by installing dependencies with uv, then run the FastAPI service locally or in CI using the provided Make targets.

Frequently Asked Questions about ai-server

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

FAQPage Schema

How do I structure a standalone FastAPI service for consistent deployment across staging and prod?▼

Standardize your FastAPI service by enforcing a project structure with dedicated api/, tests/, and deploy directories. This approach ensures consistent configuration and deployment workflows across staging and prod environments.

What's the best way to manage dependencies for a FastAPI project using uv and pyproject.toml?▼

Manage FastAPI dependencies using uv with pyproject.toml and a uv.lock file. This guarantees reproducible builds across local development and CI environments by locking exact package versions.

How does pydantic-settings handle configuration for a FastAPI deployment with AWS Secrets Manager?▼

Pydantic-settings centralizes FastAPI configuration by loading local .env files during development and seamlessly integrating with AWS Secrets Manager for secure production deployments.

Can I use Docker and AWS ECS Fargate for deploying a FastAPI application?▼

Yes, you can deploy FastAPI applications using Docker containers and AWS ECS Fargate. This combination provides scalable, serverless deployment guidance specifically tailored for standalone AI services.

Does this FastAPI standardization include guidance for CI/CD and testing?▼

Yes, the FastAPI standardization includes comprehensive CI/CD guidance alongside testing protocols. It specifies running unit and live tests through provided Make targets to ensure service reliability.

When do I need centralized configuration for a FastAPI AI service?▼

You need centralized configuration when maintaining a standalone FastAPI AI service across multiple environments. Using pyproject.toml and pydantic-settings ensures settings remain consistent from local development to AWS production.