⚡ Live Playground: AI Dev Tool: PostgreSQL Vector Search Extension

```python
# AI Dev Tool: FastAPI Pydantic v2 Micro-SaaS Boilerplate
# Description: A production-ready starter boilerplate for building micro-SaaS applications with FastAPI and Pydantic v2.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

# Initialize the FastAPI application
app = FastAPI()

# Define the database connection
SQLALCHEMY_DATABASE_URL = "postgresql://user:password@host:port/dbname"
engine = create_engine(SQLALCHEMY_DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

# Define the base class for database models
Base = declarative_base()

# Define a sample database model
class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String)
    email = Column(String, unique=True)

# Create the database tables
Base.metadata.create_all(bind=engine)

# Define a Pydantic model for user data
class UserRequest(BaseModel):
    name

⚡ Live Code Sandbox: AI Dev Tool: PostgreSQL Vector Search Extension