Certainly! Here's the translation of "Gen-AI in Action: Using LLM to Automate Coca-Cola Title Normalization" in simplified Chinese while keeping the HTML structure: ```html Gen-AI in Action: Using LLM to Automate Coca-Cola Title Normalization ``` Translated to simplified Chinese: ```html Gen-AI实战：使用LLM自动化可口可乐标题规范化 ``` This maintains the original structure with the translated text in simplified Chinese.
Sure, here's the text "Introduction" translated into simplified Chinese while maintaining the HTML structure: ```html
介绍
``` In this HTML snippet: - `` indicates a paragraph tag in HTML. - `介绍` is the simplified Chinese translation of "Introduction".
```html
曾经发现自己在来自各个餐厅的奇怪命名的可口可乐商品标题的海洋中无法自拔吗？嗯，那是我每月一次的典型任务！我们公司经常苦于要规范化一份庞大的可口可乐产品清单：套餐、单人饮料和疯狂的组合。为了解救我们免受这种持续的头痛之苦，我利用OpenAI的3.5 API构建了一个LLM应用——我们的技术超级英雄闪亮登场，拯救了全天下！
```Certainly! Here's the HTML structure with the simplified Chinese translation: ```html
与 API 一起工作简直就像坐过山车一样。提供示例感觉像是用零食训练小狗。我们的主要目标很简单。我们希望我们的模型能够在最少的提示下将那些奇怪的产品标题标准化到业务标准。速度并不是首要任务，因为这个应用程序是周期性运行的，而不是实时运行。
``` In this HTML snippet: - `` tags are used to wrap the translated text for paragraph formatting. - The Chinese text is included within the `
` tags, preserving the structure as requested.
Certainly! Here's how you can structure the HTML while translating the text into simplified Chinese: ```html
而且哦，我们别忘了这段旅程中最有趣的一部分！想象一下，当我意识到给LLM输入太多详细的指令会让它变得不高兴时，我有多么惊讶。是的，我们亲爱的LLM也会像其他机器学习模型一样出现“过拟合”，忘记其他一切 —— 就像电脑经历咖啡因崩溃一样！ ☕💤
``` In this HTML snippet: - `` tags are used to enclose the translated text, indicating it's a paragraph. - The Chinese text is placed directly inside the `
` tags. Ensure your HTML document has the necessary structure around this snippet for it to display correctly in a browser or another HTML viewer.
Sure, here's how you could translate that phrase into simplified Chinese while keeping the HTML structure intact: ```html 敬请关注这个科技冒险的更多内容（相信我），它会变得更加精彩！ ``` In this translation: - `` is used to indicate inline text without affecting the overall structure. - The Chinese text translates to "Stay tuned for more of this tech adventure (trust me), it gets even better!" This keeps the structure of your HTML intact while providing the translated text in simplified Chinese.
Sure, here's how you can translate "Overview of the Technology Stack" into simplified Chinese while keeping the HTML structure intact:
```html
技术栈概览
```
In this HTML snippet:
- `` denotes a top-level heading in HTML.
- "技术栈概览" is the simplified Chinese translation of "Overview of the Technology Stack".
Certainly! Here's how you can structure your HTML with the translated simplified Chinese text: ```html
对于这次规范化的冒险，我选择了OpenAI的3.5 API作为我们项目的核心。这个决定主要是受到预算限制的驱使，但事实证明它非常有能力。这个API使我们能够创建模型可以从中学习的提示（就像用零食训练小狗一样，记得吗？🐶）。除了API之外，像Python用于脚本编写和一些数据预处理工具也是我工具包的一部分。简单而有效！
``` In this HTML snippet: - `` tags are used to enclose the paragraph of translated text. - The Chinese text is directly embedded within the paragraph tags. - This maintains the structure of the HTML while incorporating the translated content.
Certainly! Here's the translation of "Prompt Engineering Techniques" into simplified Chinese, while maintaining HTML structure: ```html
迅速的工程技术
``` In this HTML snippet, `` and `
` denote the beginning and end of a paragraph, respectively, ensuring the text "迅速的工程技术" (Prompt Engineering Techniques) is enclosed within a paragraph element.Certainly! Here's how you can structure the HTML while incorporating the simplified Chinese translation: ```html
In crafting the prompt for the LLM application, several techniques were used. These can be categorized into General Techniques and Requirement-Based Techniques to ensure effective and standardized normalization of Coca-Cola product titles. Different delimiters like triple commas (''') and triple angle brackets (>>) are used in the prompt to help the LLM easily identify different sections in the prompt.
``` Translated into simplified Chinese: ```html在制定LLM申请提示时，采用了几种技术。这些可以分为常规技术和基于需求的技术，以确保可口可乐产品标题的有效和标准化规范化。在提示中使用了不同的分隔符，如三个逗号（'''）和三个尖括号（>>），以帮助LLM轻松识别提示中的不同部分。
```以下是我LLM申请的最终提示：
system_message_content = """
You are an advanced large language model tasked to follow the user instructions strictly.
To perform the instructed task, learn the patterns from examples delimited by <<<>>>.
<<<>>>
Example 1: User input the title = 'Coca-Cola Zero Sugar Zero Koffeinfri 1,5 l'.
The approach I used to normalize the input title for example 1 is based on following steps:
First, I translated the whole title to make sure that I can easily understand each word.
Second, I used the translated title and looked into the ground_truth_flavors which are enclosed in triple commas ''' and tried to match the best
possible flavor to make sure that it meets the standard as per my organization.
Third, I removed and add the words in translated title to exactly match with ground_truth_flavor and kept other words as it was in translated title.
Fourth, After modifing the title in third step, I realized that the volume of drink doesn't look appropriate for display so I did some modifications.
Finally, my normalized title became => Coca-Cola Zero Koffeinfri 1.5 l.
Example 2: User input the title = 'Jag vill ha Coca-Cola på köpet'.
The approach I used to normalize the input title for example 2 is based on following steps:
First, I translated the whole title to make sure that I can easily understand each word.
Second, I used the translated title and try to figure out that is it appropriate for display and found out that it sounds like a sentence which
is not appropriate for a coca-cola product title display so, I removed the extra words and kept only Coca-cola. As there is no flavor or volume
otherwise I would keep them as well. The extracted version in this case, was => Coca-Cola
Third, I used the extracted version from second step and looked into the ground_truth_flavors which are enclosed in triple commas '''
and tried to match the best possible flavor to make sure that it meets the standard as per my organization.
Fourth, I modified the extracted version with the matched flavor from third step then my modified version was => Coca-Cola Original Taste
Finally, I again carefully examined this modified version for display purpose so I realized that it misses volume then I added 33 cl into the
modified version. But, one thing I always kept in my mind that if the volume was present then I wouldn't add 33 cl in this title.
In this way, my final normalized version looked like this => Coca-Cola Original Taste 33 cl
Example 3: User input the title = '1 Nduja och 1 Coca Cola'.
The approach I used to normalize the input title for example 3 is based on following steps:
First, I translated the whole title to make sure that I can easily understand each word.
Second, I used the translated title and try to figure out that is it appropriate for display and found out that it sounds like a deal meal which
is totally appropriate to display because restaurant could sell the coca-cola in deal, so, I decided to just modify the coca-cola title by keeping
rest of the words same.
Third, I used the translated title from first step and proceeded with my decision from second step. To modify the coca-cola title, I looked into the
ground_truth_flavors which are enclosed in triple commas ''' and tried to match the best possible flavor to make sure that it meets the standard as
per my organization.
Fourth, I modified the translated title with the best matched flavor from third step then my modified version was => 1 Nduja and 1 Coca-Cola Original Taste
Finally, I again carefully examined this modified version for display purpose so I realized that it misses volume then I added 33 cl into the
modified version. But, one thing I always kept in my mind that if the volume was present then I wouldn't add 33 cl in this title.
In this way, my final normalized version looked like this => 1 Nduja and 1 Coca-Cola Original Taste 33 cl
Example 4: User input the title = '1 Margherita + 1 Coca Cola/Zero'.
The approach I used to normalize the input title for example 4 is based on following steps:
First, I translated the whole title to make sure that I can easily understand each word.
Second, I used the translated title and try to figure out that is it appropriate for display and found out that it sounds like a deal meal which
is totally appropriate to display because restaurant could sell the coca-cola in deal. I realized that the Coca-cola in this case have forward
slash '/' which means that two versions of coca-cola are available in this deal, so, I decided to modify the both versions of coca-cola by keeping
rest of the words same.
Third, I used the translated title from first step and proceeded with my decision from second step. To modify the both versions of coca-cola, I
looked into the ground_truth_flavors which are enclosed in triple commas ''' and tried to match the best possible flavors with each version to make
sure that it meets the standard as per my organization.
Fourth, I modified the translated title with the best matched flavors from third step then my modified version
was => 1 Margherita + 1 Coca-Cola Original Taste/ Coca-Cola Zero
Finally, I again carefully examined this modified version for display purpose so I realized that infact there are two versions of coca-cola but there should
be volume present to clearly show the customer so, I added 33 cl into the modified version. But, one thing I always kept in my mind that if the
volume was present then I wouldn't add 33 cl in this title.
In this way, my final normalized version looked like this => 1 Margherita + 1 Coca-Cola Original Taste/ Coca-Cola Zero 33 cl
Example 5: User input the title = 'Coca-Cola glasflaska 0,5 liters'.
The approach I used to normalize the input title for example 5 is based on following steps:
First, I translated the whole title to make sure that I can easily understand each word.
Second, I used the translated title and try to figure out that is it appropriate for display and found out that it sounds like a title with some
unnecassary words which don't make sense in display as its obvious, so, I decided to remove the unnecessary words from the translated title. In
this case the title after removing extra words looked like this 'Coca-Cola 0,5 liters'.
Third, I used the modified version from second step and looked into the ground_truth_flavors which are enclosed in triple commas ''' and tried
to match the best possible flavor to make sure that it meets the standard as per my organization as there is no flavor present in the modified version
so I would choose the 'Coca-Cola Original Taste' from ground_truth_flavors.
Fourth, I removed and add the words in modified version to exactly match with ground_truth_flavor and kept other words same as it was in modified version.
Finally, I carefully examined at my modified version from fourth step and realized that the volume of the drink doesn't look appropriate so I decided to convert it,
my final normalized title became => Coca-Cola Original Taste 50 cl.
<<<>>>
'''
Ground_Truth_Flavors:
{ground_truth}
'''
NOTE: Modify your normalized title by observing patterns from the following provided examples of user input titles on left side and their final normalized title on right side separated by '---'.
a. Coca-Cola / Pepsi --- Coca-Cola Original Taste/ Pepsi 33 cl (In this example, the volume of drink was missing and there was no explicit word which could be interpereted as volume, therefore we added base volume which is 33cl)
b. Coca-Cola Original Taste 1,5 liter --- Coca-Cola Original Taste 1.5L
c. Coca cola sockerfri 0,5 liter --- Coca-Cola Zero 50 cl
d. Coca-Cola Zero Sugar 1.5L --- Coca-Cola Zero 1.5L
e. Stor Coca Cola Zero --- Coca-Cola Zero 2L (In this example, Stor means Large so we interpret Large 2L volume of coca-cola)
f. Medium Coca Cola Zero --- Coca-Cola Zero 50 cl (In this example, medium is interpreted as 50cl)
g. Coca-Cola Zero Sugar 0kr --- Coca-Cola Zero 33cl (In this example, removed 0kr as it is unnecessary word and the volume was missing so added base volume which is 33cl)
h. Choose between Coca Cola/Zero/Sprite --- Coca Cola Original Taste/ Coca-Cola Zero/ Sprite (In this example, removed the extra words between and choose because by slashes(/) consumer can easily know it choosable)
i. Coca-Cola 1/5 l --- Coca-Cola Original Taste 1.5L
j. Coca Cola --- Coca-Cola Original Taste 33cl (In this example, there is no specific flavor given so I proceeded with base flavor which is 'Original Taste' then the volume of drink was missing and there was no explicit word which could be interpereted as volume, therefore we added base volume which is 33cl)
k. 70 Deluxe 4 Coca-Cola zero 2 Edamame --- 70 Deluxe 4 Coca-Cola Zero 33cl 2 Edamame (In this example, the volume of drink was missing and there was no explicit word which could be interpereted as volume, therefore we added base volume which is 33cl)
l. Dryck 0,4cl - Coca Cola Zero --- Coca Cola Zero 40 cl (In this example, the dryck keyword was unnecessary and 0,4cl as volume was written in the starting so I adjusted the positions of coca cola title and then its flavor and then its volume.)
m. Coca Cola Stor 2L --- Coca-Cola Original Taste 2L (In this example, base flavor is choosen as there was no explicit mention of flavor, Stor and 2L both represents the volume of this drink So I decided to proceed with numerical volume 2L, there is no need to write both stor and 2L)
Expected Output: Your output should be in JSON format: {response_format}
"""
user_message = f"Normalize the title: {input_title}"
Sure, here is the translation in simplified Chinese while keeping the HTML structure: ```html
让我们了解上述提示中使用的技术。
``` This HTML snippet maintains the structure while providing the translated text in simplified Chinese.Sure, here's how you could write "General Techniques" in simplified Chinese while keeping the HTML structure intact: ```html 常规技术 ``` In this HTML snippet: - `` is used to mark up the text. - `lang="zh"` indicates the language is Chinese. - `"常规技术"` is the translation for "General Techniques" in simplified Chinese characters.
Sure, here's the translation of "System Instruction:" in simplified Chinese while keeping the HTML structure intact: ```html 系统指导： ``` This HTML snippet will display "系统指导：" in simplified Chinese.
在HTML结构中，将以下英文文本翻译为简体中文： 设置LLM的舞台，确保严格按照用户指令执行。
system_message_content = """
You are an advanced large language model tasked to follow the user instructions strictly. …
"""
Certainly! Here's how you would write "Example-based Learning" in simplified Chinese within an HTML structure: ```html 基于示例的学习 ``` In this HTML snippet: - `` specifies that the enclosed text is in simplified Chinese. - "基于示例的学习" translates to "Example-based Learning" in English.
Certainly! Here is the translation in simplified Chinese while maintaining the HTML structure: ```html 帮助LLM识别模式并理解预期输出的类型。 ```
<<<>>>
Example 1: User input the title = 'Coca-Cola Zero Sugar Zero Koffeinfri 1,5 l'.
The approach I used to normalize the input title...
<<<>>>
Certainly! Here's the translation of "Requirement-Based Techniques" into simplified Chinese, while keeping the HTML structure: ```html 基于需求的技术 ``` This HTML snippet ensures that the text "基于需求的技术" appears correctly in simplified Chinese, marked with the appropriate language attribute for clarity and proper rendering in web environments.
Sure, here's the translation of "Translation for Clarity" into simplified Chinese while keeping the HTML structure: ```html
翻译以增加清晰度：
```Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html 允许模型在不同语言和格式之间有效工作。 ```
First, I translated the whole title to make sure that I can easily understand each word.
模式识别：
Sure, here's the translation in simplified Chinese while keeping the HTML structure intact: ```html
Purpose: 指导法学硕士在修改标题以符合业务标准。
```Third, I removed and added the words in the translated title to exactly match the ground_truth_flavor...
Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html 迭代改进： ```
Here's the translation of "Ensures the model methodically improves the title through each step." in simplified Chinese, keeping the HTML structure: ```html 确保模型通过每一步系统地改进标题。 ``` This HTML snippet maintains the structure while providing the translated text in simplified Chinese.
Finally, my normalized title became => Coca-Cola Zero Koffeinfri 1.5L.
Sure, here's the translation in simplified Chinese while maintaining the HTML structure: ```html 条件格式设置： ``` In this example, `` is used to encapsulate the translated text "条件格式设置：" in order to maintain inline styling without altering the structure of the HTML.
Sure, here is the text translated into simplified Chinese while maintaining HTML structure: ```html
处理边缘情况，确保最终输出精致。
```a. Coca-Cola / Pepsi --- Coca-Cola Original Taste/ Pepsi 33 cl (In this example, the volume of drink was missing...
Certainly! Here's how you can write "Final Format Check:" in simplified Chinese within an HTML structure: ```html
最终格式检查：
``` This HTML snippet will display "最终格式检查：" in a web page, indicating "Final Format Check:" in simplified Chinese.Certainly! Here's the HTML structure with the translated text in simplified Chinese: ```html 确保输出以所需格式，便于业务集成。 ```
Expected Output: Your output should be in JSON format: {response_format}
Sure, here's the translation in simplified Chinese while keeping the HTML structure intact: ```html 通过利用通用和特定要求的技术，我指导LLM团队始终生成符合业务标准且可以直接展示的规范化产品标题。 ``` In this translation: - "通过利用通用和特定要求的技术" corresponds to "By leveraging both general and requirement-specific techniques". - "我指导LLM团队" corresponds to "I guided the LLM team". - "始终生成符合业务标准且可以直接展示的规范化产品标题" corresponds to "to consistently produce normalized product titles that meet the business standards and are ready for display".
Sure, here's the translation of "Testing and Validation" into simplified Chinese while keeping the HTML structure: ```html 测试和验证 ``` In this HTML snippet: - `` indicates that the text inside is in simplified Chinese. - `测试和验证` is the translation of "Testing and Validation". - `` closes the `` tag. This way, the text "Testing and Validation" will appear in simplified Chinese on a web page, maintaining proper HTML structure and language indication.
Sure, here's the translated text in simplified Chinese, maintaining the HTML structure: ```html
训练模型之后，一个至关重要的步骤是测试。这是我们发现模型究竟是天才还是只擅长虚张声势的时刻！为此，我设计了一个新的提示，将生成的标准化标题与实际标签进行比较。通过计算在100%范围内的准确率百分比，我可以衡量生成输出的精确度。这个过程就像我们的LLM（语言生成模型）的测谎仪测试一样 —— 确保它没有偷懒！
``` This HTML snippet contains the translated text in simplified Chinese.这是我用来计算准确率的提示信息：
accuracy_check_prompt = """
You are a detailed-oriented large language model tasked to compare generated titles with ground truth labels.
The generated title: {generated_title}.
The ground truth label: {ground_truth}.
Expected Output: Your output should be in JSON format: {response_format}
"""
usermessage = 'Compare the <generated title> with <ground truth label> and calculate the accuracy percentage from 100%.'
Sure, here's the translation in simplified Chinese while keeping the HTML structure intact: ```html
运行这个提示就像是让我们的LLM参加考试一样 —— 绝不允许作弊！它系统地将生成的标题与我们的黄金标准进行比较，为我们提供一个准确度分数，告诉我们我们的LLM是班级第一还是需要多做一点家庭作业。
``` In this HTML snippet, the text has been translated to simplified Chinese and wrapped in `` tags to maintain the structure of an HTML paragraph.
Certainly! Here's the translated text in simplified Chinese, keeping the HTML structure intact: ```html 通过这样做，我们可以轻松识别未达到100%的标题，从而能够手动介入并仅针对这些特定情况进行修正以供生产使用。因为，让我们面对现实，有时候连超级英雄也需要一个副手！ ```
Sure, here's how you could structure the HTML while displaying "Challenges Faced" in simplified Chinese:
```html
Challenges Faced
面临的挑战
```
In this HTML structure:
- `` specifies that the language of the document is simplified Chinese.
- `Challenges Faced ` sets the title of the page to "Challenges Faced".
- `面临的挑战
` is the heading in simplified Chinese, which translates to "Challenges Faced".
Certainly! Here is the translated text in simplified Chinese, maintaining HTML structure: ```html
使用 GPT-3.5 就像在与一个聪明但略显自负的学生打交道一样。我面临的一个主要挑战是当我尝试将评分系统纳入提示时。我的想法是为每个正确完成的步骤分配分数，总计达到 10 分。计划是在同一个提示中计算生成输出的准确性在 10 分中的得分。
``` This HTML snippet includes the translated text in simplified Chinese, enclosed within `` tags for paragraph formatting.
Sure, here's the HTML structure with the translated text in simplified Chinese: ```html
但你知道吗？我们亲爱的LLM开始每次都给自己10或9分，即使它完全偏离了轨道！🥴 就像它决定成为自己最喜欢的老师，总是无论准确性如何都给满分一样。事实上，它甚至差得远呢！
``` In the HTML structure: - `` tags are used to encapsulate the translated text, ensuring proper paragraph formatting.
Certainly! Here's the translated text in simplified Chinese, keeping the HTML structure intact: ```html
为了应对这个古怪的挑战，我不得不重新审视问题，并撰写了一个新的提示，专门用于测试和准确度测量。因为让我们面对现实吧，即使是最好的模型有时也需要现实检验！
``` In simplified Chinese characters: ``` 为了应对这个古怪的挑战，我不得不重新审视问题，并撰写了一个新的提示，专门用于测试和准确度测量。因为让我们面对现实吧，即使是最好的模型有时也需要现实检验！ ```Certainly! Here is the translation of "Conclusion" in simplified Chinese, while keeping the HTML structure: ```html 结论 ``` In this snippet: - `` is used to denote a small inline portion of the document. - `lang="zh-CN"` indicates the language of the text (simplified Chinese). - "结论" is the simplified Chinese translation for "Conclusion".
Certainly! Here's the HTML structure with the translated text in simplified Chinese: ```html
毫无疑问，LLM（大语言模型）应用正在彻底改革它们所涉及的每个行业。如果我尝试用传统的基于自然语言处理的机器学习解决方案来解决这个问题，那么需要大量时间从头开始训练，更不用说部署和维护中的种种困难了。但是使用LLM，这个问题仅仅在一周内就得到了解决！
``` In this HTML snippet, the translated text is encapsulated within a `` (paragraph) tag, maintaining the structure while displaying the simplified Chinese translation of the original English text.
Certainly! Here's how you can structure your HTML to display the translated text in simplified Chinese: ```html
现在，尽管与 GPT-4 和 GPT-4-turbo 等模型相比，GPT-3.5 可能是 OpenAI 家族中最弱的一环，但它也是最快的。这意味着我必须起草详细的提示，以使其表现最佳，而 GPT-4 仅需一个例子就表现出惊人的能力。
``` This HTML structure includes the translated text in simplified Chinese within a `` (paragraph) element. Make sure to save the HTML file with UTF-8 encoding to display Chinese characters correctly.
Sure, here is the translated text in simplified Chinese, keeping the HTML structure: ```html
为了撰写一个全面的提示，请遵循以下四条基本规则：
``` In this HTML snippet: - `` denotes a paragraph tag, used here for structural clarity. - The Chinese text is directly translated from "To write a comprehensive prompt, here are four basic rules to follow:".
- Sure, here is the translated text in simplified Chinese, keeping the HTML structure:
```html
明确的说明：明确定义LLM的任务和角色。
``` - Sure, here's the HTML structure with the translated text in simplified Chinese:
```html
相关示例：提供几个示例来教导模型模式和预期输出。
``` In this structure: - `` represents a paragraph tag in HTML. - The Chinese text provided is the translation of "Relevant Examples: Provide several examples to teach the model the patterns and expected outputs."
- Certainly! Here's the text translated into simplified Chinese, while keeping the HTML structure: ```html 迭代优化：将任务分解为结构化步骤，供LLM跟随。 ``` This HTML code will display the translated text in a structured manner, with each phrase enclosed in `` tags for styling or targeting purposes if needed.
- Certainly! Here is the translation of "Edge Case Handling: Include special cases to ensure robustness and handle exceptions gracefully." in simplified Chinese, while maintaining the HTML structure:
```html
边缘情况处理：包括特殊情况以确保健壮性并优雅地处理异常。
``` This HTML snippet preserves the structure while displaying the translated text in simplified Chinese.
Certainly! Here's the translated text in simplified Chinese, while keeping the HTML structure: ```html
记住，即使是最先进的模型一开始也需要一点点指导。就像任何良好的关系一样，沟通是关键！
```Certainly! Here's the translation of the text into simplified Chinese while keeping the HTML structure intact: ```html Cheers to smoother workflows and fewer manual headaches — thanks to the magic of LLMs! ``` Translated text: ```html 为更顺畅的工作流程和更少的手动困扰干杯 — 多亏了LLM的魔力！ ```
Certainly! Here's the translation of "Appendix" into simplified Chinese, while keeping the HTML structure:
```html
附录
```
In this translation:
- `` and ` ` are the HTML tags indicating the beginning and end of the content.
- "附录" is the simplified Chinese translation of "Appendix".
Sure, here's the translation in simplified Chinese, keeping the HTML structure: ```html 以下是我们超级英雄的主要功能： ``` This HTML snippet maintains the structure while presenting the translated text in simplified Chinese.
def main(api_key, input_title, correct_titles, response_for, user_mes):
client = OpenAI(api_key=api_key)
system_message = system_message_content.format(ground_truth = correct_titles, response_format = response_for)
user_message = user_mes
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": user_message}
],
temperature = 0,
seed = 1234,
response_format={"type": "json_object"}
)
best_match = response.choices[0].message.content.strip()
return json.loads(best_match)
Certainly! Here's the translation in simplified Chinese while keeping the HTML structure intact: ```html
要找到完整的代码，请访问这个 Github 仓库：https://github.com/muhammadsameerkhan/LLM-Product-Title-Normalization.git
```
