Скачать или смотреть Mock Requests for AI code | Pytest

Let's look at requests_mock : "requests-mock provides a building block to stub out the HTTP requests portions of your testing code."

✅ pip install requests-mock

example code (calls function from another file that you make)
https://gist.github.com/RGGH/5dd1af9c...

Using mock APIs for testing, like in this example, can be especially beneficial for AI projects that rely on external APIs or services that charge per request (e.g., OpenAI, AWS, Google Cloud). Here's how it can help:

Cost Savings: Many AI services charge for API usage based on the number of tokens (for text generation) or data processed. By using mock APIs during testing, you simulate these calls without actually consuming paid resources. This drastically reduces costs, especially during development when frequent testing is required.

Faster Iteration: Mocking API requests removes network delays, so you can rapidly test and refine your functions without waiting for real server responses. This is critical when you're trying to optimize model integration or logic in AI-driven applications.

Avoid Rate Limits: Many AI platforms enforce rate limits on API calls. Using a mock for tests helps you avoid exceeding these limits during development, while still ensuring your code behaves as expected.

Error Simulation: Mocking allows you to simulate various response scenarios, like API errors or timeouts, which helps you write robust AI applications that can gracefully handle failures or retry logic.
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