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| import os | |
| import time | |
| from openai import OpenAI | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| client = OpenAI( | |
| api_key=os.getenv("GROQ_API_KEY"), | |
| base_url="https://api.groq.com/openai/v1", | |
| ) | |
| MODEL = "llama-3.1-8b-instant" | |
| SYSTEM_PROMPT = ( | |
| "You are a programming tutor specializing in Data Structures, Algorithms, " | |
| "and Web Development. Answer questions clearly and concisely." | |
| ) | |
| def ask_baseline(question: str) -> dict: | |
| start = time.time() | |
| response = client.chat.completions.create( | |
| model=MODEL, | |
| messages=[ | |
| {"role": "system", "content": SYSTEM_PROMPT}, | |
| {"role": "user", "content": question}, | |
| ], | |
| max_tokens=300, | |
| temperature=0.3, | |
| ) | |
| elapsed = round(time.time() - start, 2) | |
| answer = response.choices[0].message.content.strip() | |
| return { | |
| "system": "Baseline", | |
| "question": question, | |
| "answer": answer, | |
| "response_time": elapsed, | |
| } | |
| if __name__ == "__main__": | |
| test_q = "What is binary search?" | |
| print(f"Question: {test_q}\n") | |
| result = ask_baseline(test_q) | |
| print(f"Answer:\n{result['answer']}") | |
| print(f"\nResponse time: {result['response_time']}s") | |