在 Python 中创建 Stripe 测试数据

我们一直在致力于一门新的 AI 数据课程，向您展示如何通过将电子商务数据从 Stripe 移动到在 Supabase 上运行的 PGVector 来构建 AI 聊天机器人，通过 Airbyte PGVector 连接器创建 OpenAI 嵌入，使用 OpenAI 客户端库将自然语言支持添加到应用程序中。这是我们的许多客户正在实施的一种非常常见的“智能数据堆栈”应用程序模式。源和目的地可能会发生变化，但模式（数据源 > 移动数据并创建嵌入 > 支持矢量的数据存储 > 使用 OpenAI 的 Web 应用程序）保持不变。

由于我们正在开发旨在让人们上手的课程，因此我们希望让设置尽可能简单。其中很大一部分是在 Stripe 中创建足够的测试数据，这样就有一个合理的数据集供聊天机器人与之交互。如果您以前使用过 Stripe，您就会知道他们有一个很棒的沙盒，您可以在其中进行试验。唯一的问题是它没有预先加载示例数据。

您可以通过 CLI 设备命令加载一些示例数据集。但是，对于我们的使用来说，这些不符合需要。我们想要一个更大的数据集，并且由于这些材料将在网上和研讨会中使用，因此要求学习者在他们的本地计算机上安装一些东西，比如 CLI，会让你面临一大堆复杂的处理。您永远不知道用户正在运行什么操作系统版本，他们是否拥有正确的安装权限等等。我已经被烧伤太多次了，无法走这条路。

值得庆幸的是，Stripe 还拥有出色的 API 和出色的 Python 客户端，这意味着我们可以快速创建协作笔记本，供学习者运行和插入我们想要的数据。

通过 !pip install stripe 安装 stripe 库并使用 Google Collab 密钥传递测试密钥后，我们必须为客户和产品设置一些随机名称。目标是插入随机集合的客户、不同价格的产品和购买情况。这样，当我们向聊天机器人询问诸如“谁购买的商品最便宜？他们支付了多少钱？他们买了什么？”之类的问题时，就会这样。有足够的数据。

import stripe
import random
from google.colab import userdata


stripe.api_key = userdata.get('STRIPE_TEST_KEY')


# Sample data for generating random names
first_names = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Hank", "Ivy", "Jack", "Quinton", "Akriti", "Justin", "Marcos"]
last_names = ["Smith", "Johnson", "Williams", "Jones", "Brown", "Davis", "Miller", "Wilson", "Moore", "Taylor", "Wall", "Chau", "Keswani", "Marx"]
# Sample clothing product names
clothing_names = [
    "T-Shirt", "Jeans", "Jacket", "Sweater", "Hoodie",
    "Shorts", "Dress", "Blouse", "Skirt", "Pants",
    "Shoes", "Sandals", "Sneakers", "Socks", "Hat",
    "Scarf", "Gloves", "Coat", "Belt", "Tie",
    "Tank Top", "Cardigan", "Overalls", "Tracksuit", "Polo Shirt",
    "Cargo Pants", "Capris", "Dungarees", "Boots", "Cufflinks",
    "Raincoat", "Peacoat", "Blazer", "Slippers", "Underwear",
    "Leggings", "Windbreaker", "Tracksuit Bottoms", "Beanie", "Bikini"
]
# List of random colors
colors = [
    "Red", "Blue", "Green", "Yellow", "Black", "White", "Gray",
    "Pink", "Purple", "Orange", "Brown", "Teal", "Navy", "Maroon",
    "Gold", "Silver", "Beige", "Lavender", "Turquoise", "Coral"
]

接下来，是时候为我们需要的 Stripe 中的每种数据类型添加函数了。

# Function to create sample customers with random names
def create_customers(count=5):
    customers = []
    for _ in range(count):
        first_name = random.choice(first_names)
        last_name = random.choice(last_names)
        name = f"{first_name} {last_name}"
        email = f"{first_name.lower()}.{last_name.lower()}@example.com"

        customer = stripe.Customer.create(
            name=name,
            email=email,
            description="Sample customer for testing"
        )
        customers.append(customer)
        print(f"Created Customer: {customer['name']} (ID: {customer['id']})")
    return customers

# Function to create sample products with random clothing names and colors
def create_products(count=3):
    products = []
    for _ in range(count):
        color = random.choice(colors)
        product_name = random.choice(clothing_names)
        full_name = f"{color} {product_name}"
        product = stripe.Product.create(
            name=full_name,
            description=f"This is a {color.lower()} {product_name.lower()}"
        )
        products.append(product)
        print(f"Created Product: {product['name']} (ID: {product['id']})")
    return products

# Function to create prices for the products with random unit_amount
def create_prices(products, min_price=500, max_price=5000):
    prices = []
    for product in products:
        unit_amount = random.randint(min_price, max_price)  # Random amount in cents
        price = stripe.Price.create(
            unit_amount=unit_amount,
            currency="usd",
            product=product['id']
        )
        prices.append(price)
        print(f"Created Price: ${unit_amount / 100:.2f} for Product {product['name']} (ID: {price['id']})")
    return prices

# Function to create random purchases for each customer
def create_purchases(customers, prices, max_purchases_per_customer=5):
    purchases = []
    for customer in customers:
        num_purchases = random.randint(1, max_purchases_per_customer)  # Random number of purchases per customer
        for _ in range(num_purchases):
            price = random.choice(prices)  # Randomly select a product's price
            purchase = stripe.PaymentIntent.create(
                amount=price['unit_amount'],  # Amount in cents
                currency=price['currency'],
                customer=customer['id'],
                payment_method_types=["card"],  # Simulate card payment
                description=f"Purchase of {price['product']} by {customer['name']}"
            )
            purchases.append(purchase)
            print(f"Created Purchase for Customer {customer['name']} (Amount: ${price['unit_amount'] / 100:.2f})")
    return purchases

剩下的就是运行脚本并指定我们需要多少数据。

# Main function to create sample data
def main():
    print("Creating sample customers with random names...")
    customers = create_customers(count=20)

    print("\nCreating sample products with random clothing names and colors...")
    products = create_products(count=30)

    print("\nCreating prices for products with random amounts...")
    prices = create_prices(products, min_price=500, max_price=5000)

    print("\nCreating random purchases for each customer...")
    purchases = create_purchases(customers, prices, max_purchases_per_customer=10)

    print("\nSample data creation complete!")
    print(f"Created {len(customers)} customers, {len(products)} products, and {len(purchases)} purchases.")

if __name__ == "__main__":
    main()

将数据加载到我们的 Stripe Sandbox 中后，通过使用 Connector Builder 将 API 端点映射到每种数据类型的流并设置同步作业，将其连接到 Airbyte 只需几分钟。

问题解决了！我们的 Collab Python 脚本对于学习者来说非常容易将测试数据插入到 Stripe 中。希望对其他进行类似测试的人有所帮助。

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