openai-async-client 0.2.4

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.

Important Disclaimer:

This client is specifically designed to be used in a non-asyncio Python process to help accelerate workload processing. Internally, the client utilizes the asyncio.run() function to manage the event loop.

Please note that if you're already using async/await in your code, this client may not function properly without modifications. We encourage you to fork the repository and make any necessary changes to fit your specific use case with asyncio. We appreciate any contributions and feedback to improve the compatibility of this client with various asyncio implementations.

Chat Completion

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 AsyncCreate, Message, ChatCompletionRequest, SystemMessage, OpenAIParams
from openai_async_client.model import TextCompletionRequest, EndpointConfig

from openai_async_client import OpenAIAsync, ChatRequest, Message, SystemMessage

create = AsyncCreate(api_key=os.environ["OPENAI_API_KEY"])
messages = [
    Message(
        role="user",
        content=f"Hello ChatGPT, Give a brief overview of the Pride and Prejudice by Jane Austen.",
    )
]
response = create.completion(ChatCompletionRequest(prompt=messages),client_timeout=Timeout(1.0,read=10.0),retries=3)

Text Completion.

create = AsyncCreate()
response = create.completion(TextCompletionRequest(prompt=f"Hello DaVinci, Give a brief overview of Moby Dick by  Herman Melville."))

DataFrame processing

Example of parallel chat completions for a DataFrame with concurrent connections.

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)


create = AsyncCreate()

[//]: # (Define a mapping function from row to prompt)
def chat_prompt_fn(r: pd.Series) -> ChatCompletionRequest:
   message = Message(
       role="user",
       content=f"Hello ChatGPT, Give a brief overview of the book {r.book_name}.",
   )
   [//]: # (key Dict is mandatory since results are NOT returned in order)
   key = {"user_id": r.user_id, "book_id": r.book_id}
   return ChatCompletionRequest(
       key=key,
       prompt=[message],
       system=SystemMessage(content="Assistant is providing book reviews"),
       params=OpenAIParams(model="gpt-3.5-turbo", n=n)
   )

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

[//]: # (result_df columns are the input_df columns plus:
"openai_completion" - the completion/s (str/list),
"openai_id",
"openai_created",
"openai_prompt_tokens",
"openai_completion_tokens",
"openai_total_tokens",
"api_error" - http, openai, api error or pd.NA when everything is Okay.

Disclaimer

This repository is not affiliated with OpenAI.