openai-async-client 0.2.0

OpenAI async API with client side timeout, retry with exponential backoff and connection reuse

OpenAI client with client timeout and parallel processing

Quick Install

pip install openai-async-client

🤔 What is this?

This library is aimed at assisting with OpenAI API usage by:

Support for client side timeouts with retry and backoff for completions.

Support for concurrent processing with pandas DataFrames.

Example of chat completion with client timeout of 1 second to connect and 10 seconds to read with a maximum of 3 retries.

import os
from httpx import Timeout
from openai_async_client import OpenAIAsync, ChatRequest, Message, SystemMessage

client = OpenAIAsync(api_key=os.environ['OPENAI_API_KEY'])

messages = [
    Message(
        role="user",
        content=f"Hello ChatGPT, Give a brief overview of the book Frankenstein by Mary Shelley.",
    )
]

response = client.chat_completion(request=ChatRequest(messages=messages),client_timeout=Timeout(1.0,read=10.0),retries=3)

Example of concurrent processing a DataFrame for chat completions with 4 concurrent connections.

import os
from httpx import Timeout
from openai_async_client import OpenAIAsync, ChatRequest, Message, SystemMessage
import uuid
import pandas as pd

[//]: # (Example DataFrame)
TEST_INPUTS = [
   "the open society and its enemies by Karl Popper",
   "Das Capital by Karl Marx",
   "Pride and Prejudice by Jane Austen",
   "Frankenstein by Mary Shelley",
   "Moby Dick by  Herman Melville",
]

records = [
   {"user_id": i, "book_id": str(uuid.uuid4())[:6], "book_name": s}
   for i, s in enumerate(TEST_INPUTS)
]
input_df = pd.DataFrame.from_records(records)


client = OpenAIAsync(api_key=os.environ['OPENAI_API_KEY'])

[//]: # (Define a mapping function from row to prompt)
def my_prompt_fn(r: pd.Series) -> ChatRequest:
   message = Message(
       role="user",
       content=f"Hello ChatGPT, Give a brief overview of the book {r.book_name}.",
   )

[//]: # (key Dict is mandatory since results order is NOT guaranteed!)
   key = {"user_id": r.user_id, "book_id": r.book_id}
   return ChatRequest(
       key=key,
       messages=[message],
       system=SystemMessage(content="Assistant is providing book reviews"),
   )

[//]: # (parallel process the DataFrame making up to 4 concurrent requests to OpenAI endpoint)
result_df = client.chat_completions(df=input_df, request_fn=my_prompt_fn,max_connections=4)

[//]: # (result_df columns contains 'openai_reply' and 'api_error' columns.

Default Response Extraction

By default, only the "assistant" message (or messages if n>1) would be returned, but you can implement a custom ResponseProcessor

class ResponseProcessor(Generic[R], Callable[..., R], ABC):
    @abstractmethod
    def __call__(self, json: str, *args: Any, **kwargs: Any) -> R:
        pass

Disclaimer

This repository has no connection whatsoever to OpenAI.