如何使您的补全输出与新的 seed 参数保持一致

!pip install --upgrade openai # Switch to the latest version of OpenAI (1.3.3 at time of writing)

import openai
import asyncio
from IPython.display import display, HTML

from utils.embeddings_utils import (
    get_embedding,
    distances_from_embeddings
)

GPT_MODEL = "gpt-3.5-turbo-1106"

async def get_chat_response(
    system_message: str, user_request: str, seed: int = None, temperature: float = 0.7
):
    try:
        messages = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_request},
        ]

        response = openai.chat.completions.create(
            model=GPT_MODEL,
            messages=messages,
            seed=seed,
            max_tokens=200,
            temperature=temperature,
        )

        response_content = response.choices[0].message.content
        system_fingerprint = response.system_fingerprint
        prompt_tokens = response.usage.prompt_tokens
        completion_tokens = response.usage.total_tokens - response.usage.prompt_tokens

        table = f"""
        <table>
        <tr><th>Response</th><td>{response_content}</td></tr>
        <tr><th>System Fingerprint</th><td>{system_fingerprint}</td></tr>
        <tr><th>Number of prompt tokens</th><td>{prompt_tokens}</td></tr>
        <tr><th>Number of completion tokens</th><td>{completion_tokens}</td></tr>
        </table>
        """
        display(HTML(table))

        return response_content
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

def calculate_average_distance(responses):
    """
    This function calculates the average distance between the embeddings of the responses.
    The distance between embeddings is a measure of how similar the responses are.
    """
    # Calculate embeddings for each response
    response_embeddings = [get_embedding(response) for response in responses]

    # Compute distances between the first response and the rest
    distances = distances_from_embeddings(response_embeddings[0], response_embeddings[1:])

    # Calculate the average distance
    average_distance = sum(distances) / len(distances)

    # Return the average distance
    return average_distance