Introduction This article will teach you how to run evaluations using any LLM model without succumbing to the dreaded "OpenAI Rate Limit" exception. We’d start by: Defining what rate-limit means and using an analogy Understanding what OpenAI rate-limits are Explaining how the Rate-Limit Process was developed Explaining the Code Implementation Summarizing the strategy used in the Code Implementation What is Rate-limiting (and Analogy explanation)? So far, Cloudflare explanation is the best I have seen: Rate-limiting is a strategy for limiting network traffic. It puts a cap on how often someone can repeat an action within a certain time-frame - for instance, trying to log in to an account. To put it simply, imagine being a mother of four kids who all love honey. Last time, the honey ran out sooner than expected. Now, you've set a timer to count up to ten thousand and given each child a turn to have some honey. The timer represents the rate-limit, as it enforces a specific wait time before they can have more honey. Having explained the concept, let's understand OpenAI rate-limits and discuss how I implemented a rate-limit logic to manage OpenAI's R/TPM (request/token per minute) using Python. Understanding OpenAI Rate Limits OpenAI has set certain restrictions on the number of requests one can make for its AI models within a minute. These limitations are different for each AI model provided by OpenAI. For the free version: For the gpt-3.5-turbo model, users can make 40,000 token requests per day or 3 API calls per minute. For the tier 1: For the gpt-3.5-turbo model, users are allowed 60,000 token requests or 3,500 API calls per minute. For the gpt-4 model, the limit is 10,000 token requests or 500 API calls per minute. For the gpt-4-turbo-preview, users can make 150,000 token requests or 500 API calls per minute. For the gpt-4-vision-preview, users can make 10,000 token requests and/or 500 API calls per minute. See the for more information about other tiers rate-limits.. docs The reason for these restrictions includes: Ensuring that the services run smoothly and responsively, as complex tasks like the ones performed by the AI models require substantial resources. Managing the demand across all users and making sure that the available infrastructure, such as their servers and GPUs, can handle the requests without being overloaded. Preparing for surges in usage and maintaining efficient operation under high demand. These limitations are expected to stay consistent for the foreseeable future. Explaining The Rate-limit Process The process (see image below) involves enabling users to run LLM evaluations from the UI and configure rate-limit parameters for their LLM apps without needing to write the logic themselves. This is achieved through a function that prepares and invokes the batch. Each call in the batch invokes the function, which in turn invokes the final function ( ) with the retry mechanism. run_with_retry invoke_app I'm confident you can write the code-logic in any language of your choice after having a look at the above process. Regardless, I'll show you how I did mine. For more background and context, I primarily work as a backend software engineer at Agenta. is an open-source end-to-end LLM developer platform that provides you with the tools for prompt engineering and management, ⚖️ evaluation, human annotation, and 🚀 deployment. All without imposing any restrictions on your choice of framework, library, or model. allows developers and product teams to collaborate in building production-grade LLM-powered applications in less time. Agenta Agenta We wanted to give users the ability to configure their LLM evaluations rate-limiting configuration from the UI so they can bypass their LLM provider rate-limiting exception. Looking at the process diagram, the first thing to implement is the logic for preparing and invoking the batch (of LLM calls). Validating the rate limit configuration and using a data validation model to define the LLM run rate limit is important. The model below handles the parameter required for the batch invoke to function. rate_limit_config from pydantic import BaseModel, Field


class LLMRunRateLimit(BaseModel):
    batch_size: int = Field(default=10)
    max_retries: int = Field(default=3)
    retry_delay: int = Field(default=3)
    delay_between_batches: int = Field(default=5) The function takes the following parameters: batch_invoke uri: The URL of your LLM application. testset_data: The testset data that your LLM application needs to process. parameters: The parameters for your LLM application. rate_limit_config: The rate limit configuration (as seen in the above interface to create new evaluation). async def batch_invoke(
    uri: str, testset_data: List[Dict], parameters: Dict, rate_limit_config: Dict
) -> List[AppOutput]:
    """
    Invokes the LLm app in batches, processing the testset data.

    Args:
        uri (str): The URI of the LLm app.
        testset_data (List[Dict]): The testset data to be processed.
        parameters (Dict): The parameters for the LLm app.
        rate_limit_config (Dict): The rate limit configuration.

    Returns:
        List[AppOutput]: The list of app outputs after running all batches.
    """

    batch_size = rate_limit_config[
        "batch_size"
    ]  # Number of testset to make in each batch
    max_retries = rate_limit_config[
        "max_retries"
    ]  # Maximum number of times to retry the failed llm call
    retry_delay = rate_limit_config[
        "retry_delay"
    ]  # Delay before retrying the failed llm call (in seconds)
    delay_between_batches = rate_limit_config[
        "delay_between_batches"
    ]  # Delay between batches (in seconds)

    list_of_app_outputs: List[AppOutput] = []  # Outputs after running all batches
    openapi_parameters = await get_parameters_from_openapi(uri + "/openapi.json")

    async def run_batch(start_idx: int):
        print(f"Preparing {start_idx} batch...")
        end_idx = min(start_idx + batch_size, len(testset_data))
        for index in range(start_idx, end_idx):
            try:
                batch_output: AppOutput = await run_with_retry(
                    uri,
                    testset_data[index],
                    parameters,
                    max_retries,
                    retry_delay,
                    openapi_parameters,
                )
                list_of_app_outputs.append(batch_output)
                print(f"Adding outputs to batch {start_idx}")
            except Exception as exc:
                import traceback
                traceback.print_exc()
                print(
                    f"Error processing batch[{start_idx}]:[{end_idx}] ==> {str(exc)}"
                )

        # Schedule the next batch with a delay
        next_batch_start_idx = end_idx
        if next_batch_start_idx < len(testset_data):
            await asyncio.sleep(delay_between_batches)
            await run_batch(next_batch_start_idx)

    # Start the first batch
    await run_batch(0)
    return list_of_app_outputs After preparing and invoking the batch, the next step involves executing the logic. This custom implementation includes rate-limiting functionality and manages the invocation of the llm app, retrying after the set delay is reached. Exponential backoff, a technique that retries an operation with an exponentially increasing wait-time, is employed until a maximum retry count is reached. run_with_retry async def run_with_retry(
    uri: str,
    input_data: Any,
    parameters: Dict,
    max_retry_count: int,
    retry_delay: int,
    openapi_parameters: List[Dict],
) -> AppOutput:
    """
    Runs the specified app with retry mechanism.

    Args:
        uri (str): The URI of the app.
        input_data (Any): The input data for the app.
        parameters (Dict): The parameters for the app.
        max_retry_count (int): The maximum number of retries.
        retry_delay (int): The delay between retries in seconds.
        openapi_parameters (List[Dict]): The OpenAPI parameters for the app.

    Returns:
        AppOutput: The output of the app.

    """

    retries = 0
    last_exception = None
    while retries < max_retry_count:
        try:
            result = await invoke_app(uri, input_data, parameters, openapi_parameters)
            return result
        except (httpx.TimeoutException, httpx.ConnectTimeout, httpx.ConnectError) as e:
            last_exception = e
            print(f"Error in evaluation. Retrying in {retry_delay} seconds:", e)
            await asyncio.sleep(retry_delay)
            retries += 1

    # If max retries reached, return the last exception
    return AppOutput(output=None, status=str(last_exception)) The Use of : It’s important to handle an exception even after it has used up its max retries. This way, you allow all the data that you are trying to process to run, and then you can determine what failed and what passed. AppOutput The final step is invoking the app, using the of the LLM app to determine how to invoke it with a single datapoint. openapi_parameters The make_payload function should not concern you. It constructs the payload for invoking the LLM app based on its parameters. OpenAPI async def invoke_app(
    uri: str, datapoint: Any, parameters: Dict, openapi_parameters: List[Dict]
) -> AppOutput:
    """
    Invokes an app for one datapoint using the openapi_parameters to determine
    how to invoke the app.

    Args:
        uri (str): The URI of the app to invoke.
        datapoint (Any): The data to be sent to the app.
        parameters (Dict): The parameters required by the app taken from the db.
        openapi_parameters (List[Dict]): The OpenAPI parameters of the app.

    Returns:
        AppOutput: The output of the app.

    Raises:
        httpx.HTTPError: If the POST request fails.
    """

    url = f"{uri}/generate"
    payload = await make_payload(datapoint, parameters, openapi_parameters)

    async with httpx.AsyncClient() as client:
        try:
            logger.debug(f"Invoking app {uri} with payload {payload}")
            response = await client.post(
                url, json=payload, timeout=httpx.Timeout(timeout=5, read=None, write=5)
            )
            response.raise_for_status()

            llm_app_response = response.json()
            app_output = (
                llm_app_response["message"]
                if isinstance(llm_app_response, dict)
                else llm_app_response
            )
            return AppOutput(output=app_output, status="success")
        except:
            return AppOutput(output="Error", status="error") And that rounds up the process. Summary The backoff exponential strategy in the code works as follows: The batch_invoke function splits the testset data into smaller batches with a configurable size. Each batch is processed sequentially. Batch Processing: Within each batch, each data point is processed by the function. This function attempts to invoke the app for the data point. If the invocation fails due to specific network errors (timeouts, connection issues), the function retries with a delay. This delay is initially set to a configurable value ( ) and is doubled for each subsequent retry attempt within the same batch. Individual Invokes with Retry: run_with_retry retry_delay This approach helps avoid overloading the app server with repeated requests after a failure. It gives the server time to recover and allows the queue of pending requests to clear before retrying. The strategy also includes a configurable maximum number of retries per data point to prevent infinite loops. A delay between batches ( ) is also included to avoid exceeding rate limits set by the app server. delay_between_batches I hope this summarizes everything that you have learnt in today’s article. Please let me know if you have any questions!

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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