python continuous question and answer gpt through open api interface

1 first time question

response = openai.ChatCompletion.create(
    model=model_name,
    messages=[
        {"role": "system", "content": "精通python的专家"}, # 设定机器角色

    	{"role": "user", "content": "我想学习python,需要学习哪些知识"}	
    ]
)

2 first time answer

{
  "id": "c",
  "object": "chat.",
  "created": 1695121013,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\u5b66\u4e60Python\u9700\u8981\u638c\u63e1\u4ee5\u4e0b\u77e5\u8bc6\uff1a\n\n1. Python\u8bed\u6cd5\uff1a\u4e86\u89e3Python\u7684\u57fa\u672c\u8bed\u6cd5\u7ed3\u6784\uff0c\u5305\u62ec\u53d8\u91cf\u3001\u6570\u636e\u7c7b\u578b\u3001\u8868\u8fbe\u5f0f\u3001\u5faa\u73af\u548c\u6761\u4ef6\u8bed\u53e5\u7b49\u3002\n\n2. \u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\uff1a\u638c\u63e1\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5982\u5217\u8868\u3001\u5b57\u5178\u3001\u5143\u7ec4\u7b49\uff0c\u4ee5\u53ca\u76f8\u5e94\u7684\u64cd\u4f5c\u65b9\u6cd5\u3002\u540c\u65f6\uff0c\u4e86\u89e3\u5e38\u89c1\u7b97\u6cd5\u7684\u57fa\u672c\u539f\u7406\u548c\u5b9e\u73b0\u65b9\u5f0f\u3002\n\n3. \u51fd\u6570\u548c\u6a21\u5757\uff1a\u5b66\u4e60\u5982\u4f55\u5b9a\u4e49\u548c\u8c03\u7528\u51fd\u6570\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u548c\u521b\u5efa\u6a21\u5757\u3002\u638c\u63e1\u6a21\u5757\u548c\u5305\u7684\u6982\u5ff5\uff0c\u80fd\u591f\u4f7f\u7528\u6807\u51c6\u5e93\u548c\u7b2c\u4e09\u65b9\u5e93\u3002\n\n4. \u6587\u4ef6\u64cd\u4f5c\uff1a\u5b66\u4e60\u5982\u4f55\u8bfb\u5199\u6587\u4ef6\uff0c\u4ee5\u53ca\u5982\u4f55\u5904\u7406\u6587\u672c\u548cCSV\u6587\u4ef6\u7b49\u3002\n\n5. \u5f02\u5e38\u5904\u7406\uff1a\u4e86\u89e3\u5f02\u5e38\u7684\u6982\u5ff5\uff0c\u5b66\u4f1a\u5982\u4f55\u6355\u83b7\u548c\u5904\u7406\u5f02\u5e38\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u5065\u58ee\u6027\u3002\n\n6. \u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\uff1a\u638c\u63e1\u9762\u5411\u5bf9\u8c61\u7684\u57fa\u672c\u539f\u7406\u548c\u76f8\u5173\u6982\u5ff5\uff0c\u80fd\u591f\u5b9a\u4e49\u7c7b\u548c\u5bf9\u8c61\uff0c\u5e76\u4f7f\u7528\u7ee7\u627f\u3001\u5c01\u88c5\u548c\u591a\u6001\u7b49\u7279\u6027\u3002\n\n7. \u6570\u636e\u5e93\u7f16\u7a0b\uff1a\u5b66\u4e60\u5982\u4f55\u8fde\u63a5\u548c\u64cd\u4f5c\u6570\u636e\u5e93\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528SQL\u8bed\u53e5\u8fdb\u884c\u6570\u636e\u67e5\u8be2\u548c\u66f4\u65b0\u3002\n\n8. Web\u5f00\u53d1\uff1a\u4e86\u89e3Web\u5f00\u53d1\u7684\u57fa\u672c\u6982\u5ff5\u548c\u6280\u672f\uff0c\u5b66\u4e60\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884cWeb\u5f00\u53d1\uff0c\u638c\u63e1\u5e38\u7528\u7684Web\u6846\u67b6\u5982Django\u6216Flask\u7b49\u3002\n\n9. \u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\uff1a\u5b66\u4e60\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u53ef\u89c6\u5316\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u7b49\u65b9\u9762\u7684\u77e5\u8bc6\u3002\n\n10. \u8f6f\u4ef6\u5de5\u7a0b\u548c\u8c03\u8bd5\u6280\u5de7\uff1a\u4e86\u89e3\u8f6f\u4ef6\u5de5\u7a0b\u7684\u5e38\u7528\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u5982\u7248\u672c\u63a7\u5236\u3001\u6d4b\u8bd5\u548c\u8c03\u8bd5\u7b49\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u548c\u53ef\u6269\u5c55\u6027\u3002\n\n\u4ee5\u4e0a\u662f\u5b66\u4e60Python\u7684\u57fa\u672c\u77e5\u8bc6\u70b9\uff0c\u901a\u8fc7\u5b66\u4e60\u8fd9\u4e9b\u77e5\u8bc6\uff0c\u60a8\u5c06\u80fd\u591f\u7f16\u5199\u7b80\u5355\u5230\u590d\u6742\u7684Python\u7a0b\u5e8f\uff0c\u5e76\u4e14\u80fd\u591f\u5e94\u7528\u5230\u4e0d\u540c\u9886\u57df\u7684\u5f00\u53d1\u4e2d\u3002"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 36,
    "completion_tokens": 551,
    "total_tokens": 587
  }
}

3 Second question -> Send the first machine answer together

response = openai.ChatCompletion.create(
    model=model_name,
    messages=[
        {"role": "system", "content": "精通python的专家"}, # 设定机器角色

        {"role": "user", "content": "我想学习python,需要学习哪些知识"},

        {"role": "assistant","content":  "\u5b66\u4e60Python\u9700\u8981\u638c\u63e1\u4ee5\u4e0b\u77e5\u8bc6\uff1a\n\n1.\u57fa\u672c\u8bed\u6cd5\u548c\u8bed\u4e49\uff1a\u4e86\u89e3Python\u7684\u57fa\u672c\u8bed\u6cd5\u548c\u5e38\u7528\u8bed\u4e49\uff0c\u5305\u62ec\u53d8\u91cf\u3001\u6570\u636e\u7c7b\u578b\u3001\u8fd0\u7b97\u7b26\u3001\u6761\u4ef6\u8bed\u53e5\u3001\u5faa\u73af\u8bed\u53e5\u7b49\u3002\n\n2.\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\uff1a\u638c\u63e1\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5982\u5217\u8868\u3001\u5143\u7ec4\u3001\u5b57\u5178\u548c\u96c6\u5408\uff0c\u5e76\u719f\u6089\u5e38\u7528\u7684\u7b97\u6cd5\uff0c\u5982\u6392\u5e8f\u3001\u67e5\u627e\u7b49\u3002\n\n3.\u51fd\u6570\u548c\u6a21\u5757\uff1a\u5b66\u4e60\u5982\u4f55\u5b9a\u4e49\u548c\u4f7f\u7528\u51fd\u6570\uff0c\u4e86\u89e3\u6a21\u5757\u7684\u6982\u5ff5\u548c\u4f7f\u7528\u65b9\u6cd5\uff0c\u638c\u63e1\u5982\u4f55\u5bfc\u5165\u548c\u8c03\u7528\u6a21\u5757\u3002\n\n4.\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\uff1a\u7406\u89e3\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u7684\u6982\u5ff5\u548c\u539f\u5219\uff0c\u5b66\u4e60\u5982\u4f55\u5b9a\u4e49\u7c7b\u3001\u521b\u5efa\u5bf9\u8c61\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u7ee7\u627f\u548c\u591a\u6001\u3002\n\n5.\u6587\u4ef6\u64cd\u4f5c\uff1a\u5b66\u4e60\u5982\u4f55\u8bfb\u5199\u6587\u4ef6\uff0c\u5305\u62ec\u6587\u672c\u6587\u4ef6\u548c\u4e8c\u8fdb\u5236\u6587\u4ef6\u7684\u8bfb\u5199\u3002\n\n6.\u5f02\u5e38\u5904\u7406\uff1a\u638c\u63e1\u5982\u4f55\u5904\u7406\u7a0b\u5e8f\u8fd0\u884c\u4e2d\u7684\u5f02\u5e38\u60c5\u51b5\uff0c\u5305\u62ectry-except\u8bed\u53e5\u7684\u4f7f\u7528\u3002\n\n7.\u5e38\u7528\u5e93\u548c\u6846\u67b6\uff1a\u4e86\u89e3Python\u7684\u5e38\u7528\u5e93\u548c\u6846\u67b6\uff0c\u5982NumPy\u3001Pandas\u3001Matplotlib\u3001Django\u7b49\uff0c\u5b66\u4f1a\u4f7f\u7528\u5b83\u4eec\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002\n\n8.\u7f16\u7a0b\u5b9e\u8df5\uff1a\u901a\u8fc7\u5b9e\u9645\u9879\u76ee\u548c\u7ec3\u4e60\uff0c\u63d0\u9ad8\u7f16\u7a0b\u80fd\u529b\u548c\u5b9e\u8df5\u7ecf\u9a8c\u3002\n\n\u9664\u4e86\u4ee5\u4e0a\u51e0\u70b9\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u76f8\u5173\u7684\u4e66\u7c4d\u3001\u53c2\u4e0e\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u7684\u8bfe\u7a0b\u3001\u53c2\u4e0e\u7f16\u7a0b\u793e\u533a\u7b49\u65b9\u5f0f\u6765\u5b66\u4e60Python\u3002"},

        {"role": "user", "content": "我想具体点了解学习方面的,帮我详细的说明一下"}    
        ]
)

4 second answer

{
  "id": "chatcmpl-",
  "object": "chat.completion",
  "created": 1695121443,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\u5f53\u4f60\u51b3\u5b9a\u5b66\u4e60Python\u65f6\uff0c\u53ef\u4ee5\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u8fdb\u884c\u5b66\u4e60\uff1a\n\n1. \u5b66\u4e60\u57fa\u672c\u8bed\u6cd5\u548c\u8bed\u4e49\uff1a\u4f60\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfbPython\u5b98\u65b9\u6587\u6863\u3001\u6559\u7a0b\u548c\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u4e0a\u7684\u8bfe\u7a0b\u6765\u5b66\u4e60Python\u7684\u57fa\u672c\u8bed\u6cd5\u548c\u8bed\u4e49\u3002\u91cd\u70b9\u7406\u89e3\u53d8\u91cf\u3001\u6570\u636e\u7c7b\u578b\uff08\u5982\u6570\u5b57\u3001\u5b57\u7b26\u4e32\u548c\u5e03\u5c14\u503c\uff09\u3001\u8fd0\u7b97\u7b26\u548c\u8868\u8fbe\u5f0f\u3001\u6d41\u7a0b\u63a7\u5236\u8bed\u53e5\uff08\u5982if-else\u3001for\u5faa\u73af\u548cwhile\u5faa\u73af\uff09\u7b49\u3002\n\n2. \u719f\u6089\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\uff1a\u5b66\u4e60\u5982\u4f55\u4f7f\u7528Python\u5185\u7f6e\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5982\u5217\u8868\u3001\u5143\u7ec4\u3001\u5b57\u5178\u548c\u96c6\u5408\uff0c\u4ee5\u53ca\u5982\u4f55\u8fdb\u884c\u5e38\u89c1\u7684\u64cd\u4f5c\uff0c\u5982\u589e\u5220\u6539\u67e5\u3002\u6b64\u5916\uff0c\u638c\u63e1\u5e38\u7528\u7684\u7b97\u6cd5\uff0c\u5982\u6392\u5e8f\u3001\u67e5\u627e\u548c\u9012\u5f52\uff0c\u53ef\u4ee5\u901a\u8fc7\u4e66\u7c4d\u3001\u7b97\u6cd5\u6559\u7a0b\u548c\u5728\u7ebf\u8d44\u6e90\u8fdb\u884c\u5b66\u4e60\u3002\n\n3. \u7406\u89e3\u51fd\u6570\u548c\u6a21\u5757\uff1a\u5b66\u4e60\u5982\u4f55\u5b9a\u4e49\u548c\u8c03\u7528\u51fd\u6570\uff0c\u4e86\u89e3\u53c2\u6570\u548c\u8fd4\u56de\u503c\u7684\u4f7f\u7528\uff0c\u638c\u63e1\u51fd\u6570\u7684\u4f5c\u7528\u57df\u548c\u5c40\u90e8\u53d8\u91cf\u3002\u6b64\u5916\uff0c\u5b66\u4e60\u5982\u4f55\u4f7f\u7528\u6a21\u5757\uff0c\u4e86\u89e3\u5982\u4f55\u5bfc\u5165\u548c\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\uff0c\u53ef\u4ee5\u63d0\u9ad8\u5f00\u53d1\u6548\u7387\u3002\u53ef\u4ee5\u53c2\u8003\u5b98\u65b9\u6587\u6863\u548c\u76f8\u5173\u4e66\u7c4d\u3002\n\n4. \u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\uff1a\u7406\u89e3\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u7684\u6982\u5ff5\u548c\u539f\u5219\uff0c\u5b66\u4e60\u5982\u4f55\u5b9a\u4e49\u7c7b\u3001\u521b\u5efa\u5bf9\u8c61\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u7ee7\u627f\u548c\u591a\u6001\u3002\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u53c2\u8003\u4e66\u7c4d\u548c\u6848\u4f8b\u5b66\u4e60\uff0c\u540c\u65f6\u5b9e\u8df5\u4e2d\u4e5f\u4f1a\u6709\u66f4\u6df1\u5165\u7684\u7406\u89e3\u3002\n\n5. \u6587\u4ef6\u64cd\u4f5c\uff1a\u5b66\u4e60\u5982\u4f55\u8bfb\u5199\u6587\u672c\u6587\u4ef6\u548c\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u901a\u8fc7Python\u63d0\u4f9b\u7684\u5185\u7f6e\u51fd\u6570\u6765\u5b9e\u73b0\u3002\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u6587\u6863\u548c\u7ec3\u4e60\u6765\u638c\u63e1\u8fd9\u4e00\u90e8\u5206\u77e5\u8bc6\u3002\n\n6. \u5f02\u5e38\u5904\u7406\uff1a\u5b66\u4e60\u5982\u4f55\u4f7f\u7528try-except\u8bed\u53e5\u6765\u6355\u83b7\u548c\u5904\u7406\u5f02\u5e38\uff0c\u4f7f\u7a0b\u5e8f\u5728\u51fa\u73b0\u95ee\u9898\u65f6\u80fd\u591f\u6709\u4f18\u96c5\u7684\u5904\u7406\u65b9\u5f0f\u3002\u53ef\u4ee5\u53c2\u8003\u5b98\u65b9\u6587\u6863\u548c\u76f8\u5173\u6559\u7a0b\u8fdb\u884c\u5b66\u4e60\u3002\n\n7. \u638c\u63e1\u5e38\u7528\u5e93\u548c\u6846\u67b6\uff1aPython\u6709\u8bb8\u591a\u5e38\u7528\u7684\u7b2c\u4e09\u65b9\u5e93\u548c\u6846\u67b6\u53ef\u4f9b\u4f7f\u7528\uff0c\u5982NumPy\u3001Pandas\u3001Matplotlib\u3001Django\u7b49\u3002\u5b66\u4e60\u8fd9\u4e9b\u5e93\u548c\u6846\u67b6\u53ef\u4ee5\u63d0\u9ad8\u5f00\u53d1\u6548\u7387\u548c\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u7684\u80fd\u529b\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u9605\u8bfb\u5b98\u65b9\u6587\u6863\u3001\u53c2\u4e0e\u5728\u7ebf\u8bfe\u7a0b\u548c\u5b9e\u8df5\u9879\u76ee\u6765\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\u3002\n\n8. \u7f16\u7a0b\u5b9e\u8df5\uff1a\u901a\u8fc7\u5b8c\u6210\u5b9e\u9645\u9879\u76ee\u548c\u7ec3\u4e60\uff0c\u63d0\u9ad8\u4f60\u7684\u7f16\u7a0b\u80fd\u529b\u548c\u5b9e\u8df5\u7ecf\u9a8c\u3002\u53ef\u4ee5\u5229\u7528\u5f00\u6e90\u9879\u76ee\u3001\u5728\u7ebf\u7f16\u7a0b\u6311\u6218\u3001\u7ec3\u4e60\u9898\u7b49\u6765\u7ec3\u4e60\u548c\u5b9e\u8df5\u3002\n\n\u603b\u4e4b\uff0c\u5b66\u4e60Python\u9700\u8981\u7406\u8bba\u4e0e\u5b9e\u8df5\u76f8\u7ed3\u5408\uff0c\u9605\u8bfb\u6587\u6863\u3001\u4e66\u7c4d\u3001\u53c2\u4e0e\u8bfe\u7a0b\u548c\u5b9e\u8df5\u9879\u76ee\u90fd\u662f\u6709\u6548\u7684\u5b66\u4e60\u65b9\u6cd5\u3002\u6301\u7eed\u7ec3\u4e60\u548c\u5b9e\u8df5\u662f\u63d0\u9ad8\u7f16\u7a0b\u80fd\u529b\u7684\u5173\u952e\uff0c\u591a\u52a8\u624b\u5199\u4ee3\u7801\uff0c\u9010\u6e10\u638c\u63e1Python\u7f16\u7a0b\u7684\u6280\u80fd\u3002"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 473,
    "completion_tokens": 812,
    "total_tokens": 1285
  }
}

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Origin blog.csdn.net/tenc1239/article/details/133041414