arch-data

Implement Pydantic data models and generate PostgreSQL schemas with pgvector integration.

Updated Feb 1, 2026

Search Tags:#postgresql #pydantic #python #data modeling #database schema #pgvector

Ready to run

npx skills add https://github.com/sabyunrepo/IaaS --skill arch-data

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arch-data
Source: https://github.com/sabyunrepo/IaaS/tree/main/.claude/skills/arch-data
Command: npx skills add https://github.com/sabyunrepo/IaaS --skill arch-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the implementation of data models, ensuring consistency between Pydantic models, PostgreSQL schemas, and vector database storage.

Core Features & Use Cases

Data Modeling: Define and implement data structures using Pydantic.
Database Schema Generation: Create corresponding PostgreSQL tables and columns.
Vector Database Integration: Set up vector storage with pgvector for embeddings.
Use Case: When developing a new feature that requires storing user profiles and interview questions, use this Skill to define the User and InterviewQuestion Pydantic models and generate the necessary PostgreSQL tables and pgvector schemas.

Quick Start

Implement the Pydantic and PostgreSQL models for the 'job' entity.

Frequently Asked Questions about arch-data

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

FAQPage Schema

How do I generate PostgreSQL schemas from Pydantic models?▼

You can generate PostgreSQL schemas from Pydantic models by defining Pydantic BaseModel structures and mapping them to SQLAlchemy ORM tables, automatically creating the corresponding database columns and constraints.

How does pgvector integration work with Pydantic and PostgreSQL?▼

Pgvector integration works by defining vector storage fields within your Pydantic data models and generating the corresponding PostgreSQL schema structures to store and manage embedding vectors directly in the database.

What is the best way to handle UUID and Enum states in PostgreSQL data modeling?▼

The best way to handle UUID and Enum states in PostgreSQL data modeling is to define them within Pydantic BaseModel fields, which seamlessly map to SQLAlchemy ORM column types for reliable database schema generation.

Can I use this approach to manage data migration strategies for PostgreSQL?▼

Yes, this approach supports data migration strategies by defining file layouts for models and ensuring consistent schema generation across Pydantic definitions and PostgreSQL database structures during updates.

Does this data modeling approach support both Pydantic and SQLAlchemy ORM mapping?▼

Yes, this data modeling approach fully supports SQLAlchemy ORM mapping by translating Pydantic BaseModel definitions into corresponding PostgreSQL tables and columns for unified database schema management.

What are the limitations of using Pydantic for PostgreSQL schema generation?▼

A limitation of using Pydantic for PostgreSQL schema generation is that it primarily focuses on data structures and pgvector integration, requiring manual orchestration for complex relational database migrations beyond basic file layout definitions.