140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
import os
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import time
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import logging
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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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)
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import openai
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import asyncio
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# Configure standard 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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@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
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def deepseek_text_response(prompt, model, temperature, max_tokens, top_p, n, system_prompt):
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"""
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Wrapper function for DeepSeek's text generation.
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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 "deepseek-chat".
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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(10):
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time.sleep(1)
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try:
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client = DeepSeek(api_key=os.getenv('DEEPSEEK_API_KEY'), base_url="https://api.deepseek.com")
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response = client.reasoning.create(
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model=model,
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context=system_prompt,
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query=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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temperature=temperature
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)
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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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# 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.result # extract the message
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collected_messages.append(chunk_message) # save the message
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print(chunk.result, 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 Exception as err:
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logger.error(f"DeepSeek error: {err}")
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raise SystemExit from err
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async def test_deepseek_api_key(api_key: str) -> tuple[bool, str]:
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"""
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Test if the provided DeepSeek API key is valid.
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Args:
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api_key (str): The DeepSeek 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 DeepSeek base URL
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client = openai.OpenAI(
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api_key=api_key,
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base_url="https://api.deepseek.com/v1"
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)
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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, "DeepSeek API key is valid"
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except openai.AuthenticationError:
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return False, "Invalid DeepSeek 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 DeepSeek API key: {str(e)}"
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def deepseek_text_gen(prompt, model="deepseek-chat", temperature=0.7, max_tokens=2048):
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"""
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Generate text using DeepSeek's API.
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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 use. Defaults to "deepseek-chat"
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temperature (float, optional): Controls randomness. Defaults to 0.7
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max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 2048
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Returns:
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str: The generated text completion
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"""
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try:
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# Create OpenAI client with DeepSeek base URL
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client = openai.OpenAI(
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api_key=os.getenv('DEEPSEEK_API_KEY'),
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base_url="https://api.deepseek.com/v1"
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)
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# Generate chat completion
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response = client.chat.completions.create(
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model=model,
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messages=[{
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"role": "user",
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"content": prompt
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}],
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temperature=temperature,
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max_tokens=max_tokens
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)
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# Return the generated text
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return response.choices[0].message.content
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except Exception as e:
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logger.error(f"Error in DeepSeek text generation: {e}")
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return str(e)
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