Examples#

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for i in range(len(reviews)):
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    prompt = f"""
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    你的任务是从电子商务网站上的产品评论中提取相关信息。
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    请对三个反引号之间的评论文本进行概括，最多20个词汇。
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    评论文本: ```{reviews[i]}```
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    """
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    response = get_completion(prompt)
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    print(f"评论{i+1}: ", response, "\n")

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prompt = f"""
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Identify the following items from the review text:
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- Sentiment (positive or negative)
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- Is the reviewer expressing anger? (true or false)
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- Item purchased by reviewer
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- Company that made the item
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The review is delimited with triple backticks. \
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Format your response as a JSON object with \
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"Sentiment", "Anger", "Item" and "Brand" as the keys.
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If the information isn't present, use "unknown" \
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as the value.
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Make your response as short as possible.
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Format the Anger value as a boolean.
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Review text: ```{lamp_review}```
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"""
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response = get_completion(prompt)
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print(response)

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prompt = f"""
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proofread and correct this review. Make it more compelling.
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Ensure it follows APA style guide and targets an advanced reader.
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Output in markdown format.
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Text: ```{text}```
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"""
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# 校对注：APA style guide是APA Style Guide是一套用于心理学和相关领域的研究论文写作和格式化的规则。
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# 它包括了文本的缩略版，旨在快速阅读，包括引用、解释和参考列表，
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# 其详细内容可参考：https://apastyle.apa.org/about-apa-style
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# 下一单元格内的汉化prompt内容由译者进行了本地化处理，仅供参考
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response = get_completion(prompt)
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display(Markdown(response))

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from tool import get_completion
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prompt = f"""
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你是一位客户服务的AI助手。
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你的任务是给一位重要客户发送邮件回复。
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根据客户通过“```”分隔的评价，生成回复以感谢客户的评价。提醒模型使用评价中的具体细节
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用简明而专业的语气写信。
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作为“AI客户代理”签署电子邮件。
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客户评论：
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```{review}```
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评论情感：{sentiment}
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"""
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response = get_completion(prompt)
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print(response)

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from openai import OpenAI
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# initialize client（建议在全局初始化一次）
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client = OpenAI()
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def get_completion(prompt, model="gpt-4o-mini"):
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    """单条 prompt 获取模型输出"""
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    response = client.chat.completions.create(
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        model=model,
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        messages=[{"role": "user", "content": prompt}],
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        temperature=0  # 控制模型输出的随机程度
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    )
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    return response.choices[0].message.content
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def get_completion_from_messages(messages, model="gpt-4o-mini", temperature=0):
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    """传入 messages 列表获取模型输出"""
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    response = client.chat.completions.create(
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        model=model,
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        messages=messages,
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        temperature=temperature
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    )
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    return response.choices[0].message.content

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messages =  [
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{'role':'system', 'content':'You are an assistant that speaks like Shakespeare.'},
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{'role':'user', 'content':'tell me a joke'},
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{'role':'assistant', 'content':'Why did the chicken cross the road'},
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{'role':'user', 'content':'I don\'t know'}  ]
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response = get_completion_from_messages(messages, temperature=1)
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print(response)

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messages =  [
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{'role':'system', 'content':'你是个友好的聊天机器人。'},
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{'role':'user', 'content':'Hi, 我是Isa'},
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{'role':'assistant', 'content': "Hi Isa! 很高兴认识你。今天有什么可以帮到你的吗?"},
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{'role':'user', 'content':'是的，你可以提醒我, 我的名字是什么?'}  ]
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response = get_completion_from_messages(messages, temperature=1)
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print(response)