110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
import os
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import time #IWish
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import openai
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import asyncio
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# Configure standard logging
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import logging
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logging.basicConfig(level=logging.INFO, format='[%(asctime)s-%(levelname)s-%(module)s-%(lineno)d]- %(message)s')
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logger = logging.getLogger(__name__)
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_random_exponential,
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) # for exponential backoff
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async def test_openai_api_key(api_key: str) -> tuple[bool, str]:
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"""
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Test if the provided OpenAI API key is valid.
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Args:
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api_key (str): The OpenAI API key to test
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Returns:
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tuple[bool, str]: A tuple containing (is_valid, message)
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"""
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try:
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# Create OpenAI client with the provided key
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client = openai.OpenAI(api_key=api_key)
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# Try to list models as a simple API test
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models = client.models.list()
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# If we get here, the key is valid
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return True, "OpenAI API key is valid"
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except openai.AuthenticationError:
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return False, "Invalid OpenAI API key"
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except openai.RateLimitError:
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return False, "Rate limit exceeded. Please try again later."
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except Exception as e:
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return False, f"Error testing OpenAI API key: {str(e)}"
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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp, system_prompt):
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"""
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Wrapper function for OpenAI's ChatGPT completion.
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Args:
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prompt (str): The input text to generate completion for.
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model (str, optional): Model to be used for the completion. Defaults to "gpt-4o".
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temperature (float, optional): Controls randomness. Lower values make responses more deterministic. Defaults to 0.2.
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max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 4096
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top_p (float, optional): Controls diversity. Defaults to 0.9.
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n (int, optional): Number of completions to generate. Defaults to 1.
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Returns:
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str: The generated text completion.
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Raises:
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SystemExit: If an API error, connection error, or rate limit error occurs.
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"""
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# Wait for 10 seconds to comply with rate limits
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for _ in range(5):
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time.sleep(1)
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try:
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# Create variables to collect the stream of chunks
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collected_chunks = []
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collected_messages = []
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full_reply_content = None
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client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}],
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max_tokens=max_tokens,
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n=n,
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top_p=top_p,
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stream=True,
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frequency_penalty=fp
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# Additional parameters can be included here
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)
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# Iterate through the stream of events
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for chunk in response:
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collected_chunks.append(chunk) # save the event response
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chunk_message = chunk.choices[0].delta.content # extract the message
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collected_messages.append(chunk_message) # save the message
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print(chunk.choices[0].delta.content, end = "", flush = True)
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# Clean None in collected_messages
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collected_messages = [m for m in collected_messages if m is not None]
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full_reply_content = ''.join([m for m in collected_messages])
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return full_reply_content
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except openai.APIError as e:
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logger.error(f"OpenAI API Error: {e}")
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raise SystemExit from e
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except openai.APIConnectionError as e:
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logger.error(f"Failed to connect to OpenAI API: {e}")
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raise SystemExit from e
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except openai.RateLimitError as e:
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logger.error(f"Rate limit exceeded on OpenAI API request: {e}")
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raise SystemExit from e
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except Exception as err:
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logger.error(f"OpenAI error: {err}")
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raise SystemExit from e
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