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| from openai import OpenAI | |
| class LLMManager: | |
| def __init__(self, config, prompts): | |
| self.config = config | |
| self.client = OpenAI(base_url=config.llm.url, api_key=config.llm.key) | |
| self.prompts = prompts | |
| def test_connection(self): | |
| response = self.client.chat.completions.create( | |
| model=self.config.llm.name, | |
| messages=[ | |
| {"role": "system", "content": "You just help me test the connection."}, | |
| {"role": "user", "content": "Hi!"}, | |
| {"role": "user", "content": "Ping!"}, | |
| ], | |
| ) | |
| return response.choices[0].message.content.strip() | |
| def init_bot(self, problem=""): | |
| chat_history = [ | |
| {"role": "system", "content": self.prompts["coding_interviewer_prompt"]}, | |
| {"role": "system", "content": f"The candidate is solving the following problem: {problem}"}, | |
| ] | |
| return chat_history | |
| def get_problem(self, requirements, difficulty, topic): | |
| full_prompt = ( | |
| f"Create a {difficulty} {topic} coding problem. " | |
| f"Additional requirements: {requirements}. " | |
| "The problem should be clearly stated, well-formatted, and solvable within 30 minutes. " | |
| "Ensure the problem varies each time to provide a wide range of challenges." | |
| ) | |
| response = self.client.chat.completions.create( | |
| model=self.config.llm.name, | |
| messages=[ | |
| {"role": "system", "content": self.prompts["problem_generation_prompt"]}, | |
| {"role": "user", "content": full_prompt}, | |
| ], | |
| temperature=1.0, | |
| ) | |
| question = response.choices[0].message.content.strip() | |
| chat_history = self.init_bot(question) | |
| return question, chat_history | |
| def send_request(self, code, previous_code, message, chat_history, chat_display): | |
| # Update chat history if code has changed | |
| if code != previous_code: | |
| chat_history.append({"role": "user", "content": f"My latest code:\n{code}"}) | |
| chat_history.append({"role": "user", "content": message}) | |
| # Process the updated chat history with the language model | |
| response = self.client.chat.completions.create(model=self.config.llm.name, messages=chat_history) | |
| reply = response.choices[0].message.content.strip() | |
| chat_history.append({"role": "assistant", "content": reply}) | |
| # Update chat display with the new reply | |
| if chat_display: | |
| chat_display[-1][1] = reply | |
| else: | |
| chat_display.append([message, reply]) | |
| # Return updated chat history, chat display, an empty string placeholder, and the unchanged code | |
| return chat_history, chat_display, "", code | |
| def end_interview(self, problem_description, chat_history): | |
| if not chat_history or len(chat_history) <= 2: | |
| return "No interview content available to review." | |
| transcript = [] | |
| for message in chat_history[1:]: | |
| role = message["role"] | |
| content = f"{role.capitalize()}: {message['content']}" | |
| transcript.append(content) | |
| response = self.client.chat.completions.create( | |
| model=self.config.llm.name, | |
| messages=[ | |
| {"role": "system", "content": self.prompts["grading_feedback_prompt"]}, | |
| {"role": "user", "content": f"The original problem to solve: {problem_description}"}, | |
| {"role": "user", "content": "\n\n".join(transcript)}, | |
| {"role": "user", "content": "Grade the interview based on the transcript provided and give feedback."}, | |
| ], | |
| temperature=0.5, | |
| ) | |
| feedback = response.choices[0].message.content.strip() | |
| return feedback | |