api_request_parallel_processor_universal_SEQUENTIAL.py

"""
API REQUEST PARALLEL PROCESSOR

Using the OpenAI API to process lots of text quickly takes some care.
If you trickle in a million API requests one by one, they'll take days to complete.
If you flood a million API requests in parallel, they'll exceed the rate limits and fail with errors.
To maximize throughput, parallel requests need to be throttled to stay under rate limits.

This script parallelizes requests to the OpenAI API while throttling to stay under rate limits.

Features:
- Streams requests from file, to avoid running out of memory for giant jobs
- Makes requests concurrently, to maximize throughput
- Throttles request and token usage, to stay under rate limits
- Retries failed requests up to {max_attempts} times, to avoid missing data
- Logs errors, to diagnose problems with requests

Example command to call script:
```
python examples/api_request_parallel_processor.py \
  --requests_filepath examples/data/example_requests_to_parallel_process.jsonl \
  --save_filepath examples/data/example_requests_to_parallel_process_results.jsonl \
  --request_url https://api.openai.com/v1/embeddings \
  --max_requests_per_minute 1500 \
  --max_tokens_per_minute 6250000 \
  --token_encoding_name cl100k_base \
  --max_attempts 5 \
  --logging_level 20
```

Inputs:
- requests_filepath : str
    - path to the file containing the requests to be processed
    - file should be a jsonl file, where each line is a json object with API parameters and an optional metadata field
    - e.g., {"model": "text-embedding-ada-002", "input": "embed me", "metadata": {"row_id": 1}}
    - as with all jsonl files, take care that newlines in the content are properly escaped (json.dumps does this automatically)
    - an example file is provided at examples/data/example_requests_to_parallel_process.jsonl
    - the code to generate the example file is appended to the bottom of this script
- save_filepath : str, optional
    - path to the file where the results will be saved
    - file will be a jsonl file, where each line is an array with the original request plus the API response
    - e.g., [{"model": "text-embedding-ada-002", "input": "embed me"}, {...}]
    - if omitted, results will be saved to {requests_filename}_results.jsonl
- request_url : str, optional
    - URL of the API endpoint to call
    - if omitted, will default to "https://api.openai.com/v1/embeddings"
- api_key : str, optional
    - API key to use
    - if omitted, the script will attempt to read it from an environment variable {os.getenv("OPENAI_API_KEY")}
- max_requests_per_minute : float, optional
    - target number of requests to make per minute (will make less if limited by tokens)
    - leave headroom by setting this to 50% or 75% of your limit
    - if requests are limiting you, try batching multiple embeddings or completions into one request
    - if omitted, will default to 1,500
- max_tokens_per_minute : float, optional
    - target number of tokens to use per minute (will use less if limited by requests)
    - leave headroom by setting this to 50% or 75% of your limit
    - if omitted, will default to 125,000
- token_encoding_name : str, optional
    - name of the token encoding used, as defined in the `tiktoken` package
    - if omitted, will default to "cl100k_base" (used by `text-embedding-ada-002`)
- max_attempts : int, optional
    - number of times to retry a failed request before giving up
    - if omitted, will default to 5
- logging_level : int, optional
    - level of logging to use; higher numbers will log fewer messages
    - 40 = ERROR; will log only when requests fail after all retries
    - 30 = WARNING; will log when requests his rate limits or other errors
    - 20 = INFO; will log when requests start and the status at finish
    - 10 = DEBUG; will log various things as the loop runs to see when they occur
    - if omitted, will default to 20 (INFO).

The script is structured as follows:
    - Imports
    - Define main()
        - Initialize things
        - In main loop:
            - Get next request if one is not already waiting for capacity
            - Update available token & request capacity
            - If enough capacity available, call API
            - The loop pauses if a rate limit error is hit
            - The loop breaks when no tasks remain
    - Define dataclasses
        - StatusTracker (stores script metadata counters; only one instance is created)
        - APIRequest (stores API inputs, outputs, metadata; one method to call API)
    - Define functions
        - api_endpoint_from_url (extracts API endpoint from request URL)
        - append_to_jsonl (writes to results file)
        - num_tokens_consumed_from_request (bigger function to infer token usage from request)
        - task_id_generator_function (yields 0, 1, 2, ...)
    - Run main()
"""

# imports
import aiohttp  # for making API calls concurrently
import argparse  # for running script from command line
import asyncio  # for running API calls concurrently
import json  # for saving results to a jsonl file
import logging  # for logging rate limit warnings and other messages
import os  # for reading API key
import re  # for matching endpoint from request URL
import tiktoken  # for counting tokens
import time  # for sleeping after rate limit is hit
from dataclasses import (
    dataclass,
    field,
)  # for storing API inputs, outputs, and metadata


def process_api_requests_from_file(
    vendor_name: str,
    requests_filepath: str,
    save_filepath: str,
    request_url: str,
    api_key: str,
    max_requests_per_minute: float,
    max_tokens_per_minute: float,
    token_encoding_name: str,
    max_attempts: int,
    logging_level: int,
):
    """Processes API requests sequentially."""
    # initialize logging
    logging.basicConfig(level=logging_level)
    logging.debug(f"Logging initialized at level {logging_level}")

    # infer API endpoint and construct request header
    api_endpoint = api_endpoint_from_url(request_url, vendor_name)
    request_header = None
    if vendor_name == "openai":
        request_header = {"Authorization": f"Bearer {api_key}"}
    elif vendor_name == "anthropic":
        request_header = {
            "x-api-key": api_key,
            "anthropic-version": "2023-06-01",
            "content-type": "application/json",
        }
    elif vendor_name == "meta" or vendor_name == "google":
        request_header = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {api_key}",
        }
    else:
        print("Error. Invalid Model Input. Exiting")

    # initialize trackers
    task_id_generator = task_id_generator_function()
    status_tracker = StatusTracker()

    # process requests sequentially
    with open(requests_filepath) as file, requests.Session() as session:
        for line in file:
            request_json = json.loads(line)
            request = APIRequest(
                task_id=next(task_id_generator),
                request_json=request_json,
                token_consumption=0,
                attempts_left=max_attempts,
                metadata=request_json.pop("metadata", None),
            )
            status_tracker.num_tasks_started += 1
            logging.debug(f"Processing request {request.task_id}: {request}")

            while request.attempts_left > 0:
                error = None
                try:
                    response = session.post(
                        url=request_url,
                        headers=request_header,
                        json=request.request_json,
                    ).json()
                    if "error" in response:
                        logging.warning(
                            f"Request {request.task_id} failed with error {response['error']}"
                        )
                        status_tracker.num_api_errors += 1
                        error = response
                        if "Rate limit" in response["error"].get("message", ""):
                            status_tracker.num_rate_limit_errors += 1
                            status_tracker.num_api_errors -= 1
                except Exception as e:
                    logging.warning(f"Request {request.task_id} failed with Exception {e}")
                    status_tracker.num_other_errors += 1
                    error = e

                if error:
                    request.result.append(error)
                    request.attempts_left -= 1
                    if request.attempts_left == 0:
                        logging.error(
                            f"Request {request.request_json} failed after all attempts. Saving errors: {request.result}"
                        )
                        data = (
                            [request.request_json, [str(e) for e in request.result], request.metadata]
                            if request.metadata
                            else [request.request_json, [str(e) for e in request.result]]
                        )
                        append_to_jsonl(data, save_filepath)
                        status_tracker.num_tasks_failed += 1
                else:
                    data = (
                        [request.request_json, response, request.metadata]
                        if request.metadata
                        else [request.request_json, response]
                    )
                    append_to_jsonl(data, save_filepath)
                    status_tracker.num_tasks_succeeded += 1
                    logging.debug(f"Request {request.task_id} saved to {save_filepath}")
                    break

    # after finishing, log final status
    logging.info(f"Sequential processing complete. Results saved to {save_filepath}")
    if status_tracker.num_tasks_failed > 0:
        logging.warning(
            f"{status_tracker.num_tasks_failed} / {status_tracker.num_tasks_started} requests failed. Errors logged to {save_filepath}."
        )
    if status_tracker.num_rate_limit_errors > 0:
        logging.warning(
            f"{status_tracker.num_rate_limit_errors} rate limit errors received. Consider running at a lower rate."
        )
# dataclasses


@dataclass
class StatusTracker:
    """Stores metadata about the script's progress. Only one instance is created."""

    num_tasks_started: int = 0
    num_tasks_in_progress: int = 0  # script ends when this reaches 0
    num_tasks_succeeded: int = 0
    num_tasks_failed: int = 0
    num_rate_limit_errors: int = 0
    num_api_errors: int = 0  # excluding rate limit errors, counted above
    num_other_errors: int = 0
    time_of_last_rate_limit_error: int = 0  # used to cool off after hitting rate limits


@dataclass
class APIRequest:
    """Stores an API request's inputs, outputs, and other metadata. Contains a method to make an API call."""

    task_id: int
    request_json: dict
    token_consumption: int
    attempts_left: int
    metadata: dict
    result: list = field(default_factory=list)

    async def call_api(
        self,
        session: aiohttp.ClientSession,
        request_url: str,
        request_header: dict,
        retry_queue: asyncio.Queue,
        save_filepath: str,
        status_tracker: StatusTracker,
    ):
        """Calls the OpenAI API and saves results."""
        logging.info(f"Starting request #{self.task_id}")
        error = None
        try:
            async with session.post(
                url=request_url, headers=request_header, json=self.request_json
            ) as response:
                response = await response.json()
            if "error" in response:
                logging.warning(
                    f"Request {self.task_id} failed with error {response['error']}"
                )
                status_tracker.num_api_errors += 1
                error = response
                if "Rate limit" in response["error"].get("message", ""):
                    status_tracker.time_of_last_rate_limit_error = time.time()
                    status_tracker.num_rate_limit_errors += 1
                    status_tracker.num_api_errors -= (
                        1  # rate limit errors are counted separately
                    )

        except (
            Exception
        ) as e:  # catching naked exceptions is bad practice, but in this case we'll log & save them
            logging.warning(f"Request {self.task_id} failed with Exception {e}")
            status_tracker.num_other_errors += 1
            error = e
        if error:
            self.result.append(error)
            if self.attempts_left:
                retry_queue.put_nowait(self)
            else:
                logging.error(
                    f"Request {self.request_json} failed after all attempts. Saving errors: {self.result}"
                )
                data = (
                    [self.request_json, [str(e) for e in self.result], self.metadata]
                    if self.metadata
                    else [self.request_json, [str(e) for e in self.result]]
                )
                append_to_jsonl(data, save_filepath)
                status_tracker.num_tasks_in_progress -= 1
                status_tracker.num_tasks_failed += 1
        else:
            data = (
                [self.request_json, response, self.metadata]
                if self.metadata
                else [self.request_json, response]
            )
            append_to_jsonl(data, save_filepath)
            status_tracker.num_tasks_in_progress -= 1
            status_tracker.num_tasks_succeeded += 1
            logging.debug(f"Request {self.task_id} saved to {save_filepath}")


# functions


def api_endpoint_from_url(request_url, vendor_name):
    """Extract the API endpoint from the request URL."""
    match=None
    if vendor_name=="openai":
        match = re.search("^https://[^/]+/v\\d+/(.+)$", request_url)
    elif vendor_name=="anthropic":
        match = re.search(r"^https://[^/]+/v1/(.+)$", request_url)
    elif vendor_name == "meta" or vendor_name == "google":
        match = re.search(r"^https://[^/]+/api/v1/(.+)$", request_url)
    else:
        print("Error. Invalid Model Input. Exiting")
        # exit()
    if match is None:
        # for Azure OpenAI deployment urls
        match = re.search(r"^https://[^/]+/openai/deployments/[^/]+/(.+?)(\?|$)", request_url)
    return match[1]


def append_to_jsonl(data, filename: str) -> None:
    """Append a json payload to the end of a jsonl file."""
    json_string = json.dumps(data)
    with open(filename, "a") as f:
        f.write(json_string + "\n")



def task_id_generator_function():
    """Generate integers 0, 1, 2, and so on."""
    task_id = 0
    while True:
        yield task_id
        task_id += 1


# run script


if __name__ == "__main__":
    # parse command line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument("--vendor_name", default=None)
    parser.add_argument("--requests_filepath")
    parser.add_argument("--save_filepath", default=None)
    parser.add_argument("--request_url", default=None)
    parser.add_argument("--api_key", default=None)
    parser.add_argument("--max_requests_per_minute", type=int, default=3_000 * 0.5)
    parser.add_argument("--max_tokens_per_minute", type=int, default=250_000 * 0.5)
    parser.add_argument("--token_encoding_name", default="cl100k_base")
    parser.add_argument("--max_attempts", type=int, default=5)
    parser.add_argument("--logging_level", default=logging.INFO)
    
    args = parser.parse_args()
    if args.vendor_name=="openai":
        args.api_key=os.getenv("OPENAI_API_KEY")
        args.request_url="https://api.openai.com/v1/chat/completions"
    elif args.vendor_name=="anthropic":
        args.api_key=os.getenv("ANTHROPIC_API_KEY")
        args.request_url="https://api.anthropic.com/v1/messages"
    elif args.vendor_name == "meta" or args.vendor_name == "google" :
        args.api_key = os.getenv("OPENROUTER_API_KEY")
        args.request_url = "https://openrouter.ai/api/v1/chat/completions"
    else:
        print("Error. Invalid Model Input. Exiting")
        # exit()
    if args.save_filepath is None:
        args.save_filepath = args.requests_filepath.replace(".jsonl", "_results.jsonl")

    # run script
    asyncio.run(
        process_api_requests_from_file(
            vendor_name=args.vendor_name,
            requests_filepath=args.requests_filepath,
            save_filepath=args.save_filepath,
            request_url=args.request_url,
            api_key=args.api_key,
            max_requests_per_minute=float(args.max_requests_per_minute),
            max_tokens_per_minute=float(args.max_tokens_per_minute),
            token_encoding_name=args.token_encoding_name,
            max_attempts=int(args.max_attempts),
            logging_level=int(args.logging_level),
        )
    )


"""
APPENDIX

The example requests file at openai-cookbook/examples/data/example_requests_to_parallel_process.jsonl contains 10,000 requests to text-embedding-ada-002.

It was generated with the following code:

```python
import json

filename = "data/example_requests_to_parallel_process.jsonl"
n_requests = 10_000
jobs = [{"model": "text-embedding-ada-002", "input": str(x) + "\n"} for x in range(n_requests)]
with open(filename, "w") as f:
    for job in jobs:
        json_string = json.dumps(job)
        f.write(json_string + "\n")
```

As with all jsonl files, take care that newlines in the content are properly escaped (json.dumps does this automatically).
"""