Best practice for handling rate limits

[iserialize]

I have bit of code which i have added aiometer. What iam trying to understand is how best to optimize it to get the maximum performance. The API iam calling has a rate limit of 300 requests per minute hence setting the max_per_second to 5 (if i understand this correctly) should be within the limits however, i hit 429's even then. My code is below:

async def async_iterate_paginated_api(
    function: Callable[..., Awaitable[Any]], **kwargs: Any
) -> AsyncGenerator[List[Any], None]:
    """Return an async iterator over the results of any API."""
    page_number = 1
    response = await function(**kwargs, pageNumber=page_number)
    yield response.get("entities") 
    total_pages = response.get('pageCount')
    pages = [
        page
        for page in range(2, total_pages + 1)
    ]
    async def process(function: Callable[..., Awaitable[Any]], kwarg: Any, page: Any):
        response = await function(**kwarg,  pageNumber=page)
        return response.get('entities')

    async with aiometer.amap(functools.partial(process, function, kwargs), pages, max_per_second=5) as results:
        async for result in results:
            yield result

Any feedback would be helpful.

[iserialize]

With some testing, i think one of the issues may be that if i run the routine couple of times, the rate limit counter hasnt reset on the api hence subsequent runs will have less of a runway to run on. Any recommendations on how to best deal with this scenario?