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import os
import re
from dotenv import load_dotenv
from typing import Tuple
from groq import Groq
def get_api_key() -> str:
"""
Get the api key to the LLM
Returns:
str: The api key to be used to reach the LLM
"""
load_dotenv()
api_key = os.getenv('key')
return api_key
def get_response(prompt: str, api_key) -> Tuple[str, str]:
"""
Gets the response of the LLM on the provided prompt.
Args:
prompt (str): The prompt to be plugged in
Returns:
tuple:
str: Includes the thinking part of the LLM, showing its thought process.
str: The actual answer to your prompt
"""
client = Groq(api_key=api_key)
completion = client.chat.completions.create(
model="deepseek-r1-distill-llama-70b",
messages=[
{
'role': 'user',
'content': prompt
}
],
temperature=0.6,
max_completion_tokens=4096,
top_p=0.95,
stream=True,
stop=None,
)
chunks = []
for chunk in completion:
current_chunk = chunk.choices[0].delta.content or ""
chunks.append(current_chunk)
full_response = "".join(chunks)
# Splitting the text
thought_process_match = re.search(r"<think>\s*(.*?)\s*</think>", full_response, re.DOTALL)
thought_process = thought_process_match.group(1) if thought_process_match else ""
actual_response = re.sub(r"<think>.*?</think>\s*", "", full_response, flags=re.DOTALL)
return thought_process, actual_response |