准备好的提示如下。这是我们的提示，基于找出相关问题想要表达的内容以及它属于哪个类别。在收到相关数值后，此提示查询第三方机构的API，并通过该机构返回类别数值，将其存储以供最终发送给机构的查询使用。

` tag with the translated text you need to display.

    def prompt_generator(self, question, table) -> str:
        prompt_category_detection = """You are a finance expert tasked with analyzing the following finance sentence and selecting the most relevant title from a specified table. Follow these steps:
    **Retrieve Relevant Data**: From the identified table, find the finance topic and finance category that best match the content of the sentence.
    **Finance Sentence:**
    "{question}"
    **Dictionary:**
    {table}
    **Instructions:**
    - Determine the correct table from the dictionary.
    - Use this table to find the finance topic and finance category values that are most relevant to the finance sentence
    - Ensure that the values retrieved are the best match to the content of the sentence.
    **Conclusion:**
    Provide the result in the following format, only return following information not add any other word or sentence in response, give answer only in JSON Object format, only return answer with the following format do not use different format:
    {{"category": "found_category", "id": category_id}}
    """
        return prompt_category_detection.format(question=question, table=table)

Sure, here is the translation of the English text into simplified Chinese, while keeping the HTML structure intact: ```html

调用类别服务如下，将响应存储在变量中以发送 POST 请求到主服务。

` tags are used to enclose the translated Chinese text, preserving the structure typical of HTML content.

def _parse_user_query(self, user_query: str) -> str:
        category_prompt = self.prompt_generator(question=user_query, table=self.categories)
        category_response = self.model.invoke(category_prompt)
        category_json = json.loads(category_response.content)
        print("founded values from LLM zero-shot-classification:", category_json)
        category_id = category_json["id"]
        return category_json.content

Certainly! Here's the HTML structure with the translated text in simplified Chinese: ```html

使用从第一个服务返回的类别，从问题中提取的起始日期和结束日期参数作为实体提取的输入，LLM 自动调用以下函数。在进行此调用时，相关函数在代码中没有其他地方被调用。

 def get_finance_advice(self, startdate, enddate) -> Dict:
        full_category_codes = ""
        for cin self.category_codes:
            full_category_codes += c["code"] + "-"
        base_url = '<<third-party-api-base-uri' + full_category_codes +'&startDate='+ startdate +'&endDate='+ enddate
        response = requests.get(base_url+'&type=json', headers=headers)
        print(response.content)
        return {
            "status_code": response.status_code,
            "content": response.content
        }

Sure, here is the translation of "Solution Architecture of the LLM based Financial Suggestion System" in simplified Chinese, keeping the HTML structure: ```html

LLM基于金融建议系统的解决方案架构

` denotes a level 1 heading in HTML, which is used here to represent the title. - The Chinese text inside the `

` tags translates to "LLM based Financial Suggestion System的解决方案架构". This HTML structure allows you to maintain both the content and the styling format when displaying the translated text on a webpage.

```html

生成AI支持的金融推荐系统的解决方案架构，从头到尾运行非常成功，具体如下：

1. 系统通过界面或API接收用户的查询，并通过SDK将其发送到后台的watsonx.ai环境。
2. watsonx.ai随后通过自己的基础模型库，使用Mistral-Large-2模型查询传入的查询，并确定第四步将使用哪个功能。
3. 然后，在第五步确定的功能内进行实体提取，并找到并发送给变量的开始和结束日期等基于时间的参数。
4. 在这里，大型语言模型使用零-shot分类功能来找到正确的功能，然后使用实体提取从以自然语言发送的查询句子中找到正确的参数。

```html

在接下来的步骤中，LLM访问嵌入在watsonx.ai中的目录，调用正确的函数并通过Python启动函数调用。在调用过程中需要进行另一个分类处理，以查找相关的目录信息。为此，我们通过SDK将gpt-4o-mini模型集成到watsonx.ai中，并利用其在表格数据上的分类能力。我们之所以在这里使用gpt-4o-mini模型，是因为该模型在表格数据的零样本分类等任务中比其他模型架构（如mistral和llama）更为成功，并且能始终给出相同的结果。第二个原因是展示watsonx.ai环境可以通过SDK在其自身系统中使用，通过访问不同云中或其他环境中的模型，而不仅仅是其自身模型服务器中的模型。

Sure, here's the HTML structure with the translated text in simplified Chinese: ```html

在做所有这些事情的过程中，我们在每一个步骤上充分利用了LangChain的功能。在LLM推理操作中对表格数据进行分类操作后（如2、3和4所示），现在使用watsonx.ai进行服务调用，使用所有获取的参数。

Certainly! Here is the translated text in simplified Chinese, maintaining the HTML structure: ```html

从服务返回的数值。在最后一步，它被发送回 watsonx.ai 中的 mistral-large-2 模型，并要求结合从机构接收的服务数据生成适当的问题答案。正如下面所示，这些是所有过程的逐步输出以及 watsonx.ai 生成的语言模型（LLM）答案。

从服务返回的数值。在最后一步，它被发送回 watsonx.ai 中的 mistral-large-2 模型，并要求结合从机构接收的服务数据生成适当的问题答案。正如下面所示，这些是所有过程的逐步输出以及 watsonx.ai 生成的语言模型（LLM）答案。

```html

最后，通过执行文本生成来完成整个过程。通过向语言生成模型发送5个单独的推理请求，满足了从头到尾流程中三种不同任务（零-shot分类、实体提取、文本生成）的需求，使用了两种不同的模型。

Sure, here's the text translated into simplified Chinese while maintaining HTML structure: ```html 你可以将系统和模型的输出解释如下。 ``` This HTML structure ensures the text is presented correctly within a web context, where necessary.

Certainly! Here's the translation of "Test Results:" in simplified Chinese, while keeping the HTML structure intact: ```html

测试结果：

To keep the HTML structure intact while translating the text into simplified Chinese, you can use the following markup: ```html Question 1: What is the highest level of the British Pound against the Turkish Lira in May 2024? ``` And the translated text in simplified Chinese: ```html Question 1: 2024年5月英镑对土耳其里拉的最高水平是多少？ ``` This HTML snippet will ensure that the question is presented in both English and simplified Chinese, maintaining the structure and allowing for language-specific formatting or styling if needed.

Certainly! Here is the translated text in simplified Chinese while keeping the HTML structure: ```html 分类结果来自LLM分类和实体提取： ``` This HTML snippet maintains the structure while presenting the translated text in simplified Chinese.

{'category': 'Exchange Rates', 'id': 35}
[{'name': '(GBP) British Pound (Buying Foreign Currency Price)', 'code': 'EN-POUND-BUY'}, {'name': '(GBP) English Pound (Foreign Currency Sale)', 'code': 'EN-POUND-SALE'}]

Sure, here's the translation of the text into simplified Chinese while keeping the HTML structure: ```html

函数参数，LLM使用实体提取功能获取这些参数：

` tags are used for paragraph structure. - The Chinese text is enclosed within these tags to maintain the structure of the original sentence while providing the translation.

{'startdate': '01-05-2024', 'enddate': '31-05-2024'}

Certainly! Here's the HTML structure with the translated text in simplified Chinese: ```html

Certainly! Here's how you could structure the HTML to display the translated text in simplified Chinese: ```html

Answer from Watsonx.ai = “2024年5月，英镑对土耳其里拉的最高水平为41,2021土耳其里拉”

Certainly! Here's how you could structure your HTML while incorporating the simplified Chinese translation: ```html

` tags for proper paragraph formatting. - The Chinese translation is also enclosed in `

` tags, making it visually separated and properly structured within the HTML document.

Certainly! Here's how you can write "Categorization Results from LLM classification and entity extraction:" in simplified Chinese while keeping the HTML structure intact: ```html LLM分类和实体提取的分类结果： ``` This HTML code uses the `lang="zh-CN"` attribute to specify the language as simplified Chinese.

{'category': 'International Investment Position', 'id': 36}
{'name': 'Stocks and Debt Securities in the Portfolio of Persons Resident Abroad', 'code': 'stock_and_debt_external_citizens'}
[{'name': 'Banks overseas banks deposits', 'code': 'bank_overseas_deposits'}, {'name': 'Banks Foreign Exchange Deposit Acounts', 'code': 'Bank_Foreign_Exchange_Deposits'}]

Sure, here's the translation of the text into simplified Chinese while keeping the HTML structure intact: ```html

函数参数，LLM 使用实体提取功能获取这些参数：

` denotes a paragraph tag. - `函数参数，LLM 使用实体提取功能获取这些参数：` is the translated text. This ensures the text is displayed as a paragraph in HTML while presenting the translated Chinese content.

{'startdate': '01-01-2024', 'enddate': '31-12-2024'}

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html

LLM 文本生成结果，此步骤中，LLM 使用 API 响应体数值。

Sure, here's the text translated into simplified Chinese, while maintaining HTML structure: ```html Answer from Watsonx.ai = “The annual change rate of Stocks and Debt Securities in the Portfolio of Non-Residents is 5.7%.” ``` Translated into simplified Chinese: ```html Answer from Watsonx.ai = “非居民组合中股票和债务证券的年变动率为5.7%。” ``` This HTML structure ensures the content is clearly identified and correctly formatted for display.

Certainly! Here's the text translated into simplified Chinese while keeping the HTML structure intact: ```html Question 3: 在2023年，总的借记卡和信用卡支出中，航空公司和住宿行业占多少百分比？ ``` This HTML structure preserves the text formatting and includes the translated content in simplified Chinese.

Sure, here's how you could write that in simplified Chinese within an HTML structure: ```html

{'category': 'Bank and Credit Card Sectoral Expenditure Statistics', 'id': 77}
{'name': 'Debit Card and Credit Card Spending Amount', 'code': 'debit_credit_sp'}
[{'name': 'Airlines', 'code': 'trx-airlines-fnc'}, {'name': 'Accommodation', 'code': 'trx-accom-fnc'}]

Sure, here's the translation of "Function Parameters, LLM get these parameters with entity extraction features:" in simplified Chinese while keeping the HTML structure: ```html 功能参数，LLM 使用实体提取功能获取这些参数： ```

{'startdate': '01-01-2023', 'enddate': '31-12-2023'}

Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html LLM 文本生成结果，本步骤中，LLM 使用 API 响应体数值 ```

Certainly! Here's how you can structure the HTML while translating the text to simplified Chinese: ```html

Answer from Watsonx.ai = “2023年，10.5%的借记卡和信用卡支出用于航空公司和住宿行业。总支出金额为199,384,100土耳其里拉。”

` tags are used to enclose the translated text, indicating a paragraph. - The translated Chinese text is placed within double quotes for clarity. Make sure to replace the quotation marks with appropriate HTML entities (`"`) if needed, depending on the HTML context where you're using this snippet.

To translate the provided English text into simplified Chinese while maintaining the HTML structure, you can use the following: ```html 此外，在 mistral 官方网站上也提供了一段详细说明如何使用我们在 watsonx.ai 上使用的 mistral-large-2 模型进行函数调用的培训视频。我们所做的是通过 langchain 建立服务调用或 RAG 结构，使用 watsonx.ai 中的 mistral 模型。 ``` This HTML structure maintains the formatting and includes the translated text in simplified Chinese.

Certainly! Here's the HTML structure with the translated text in simplified Chinese: ```html

我还要感谢所有的队友，Seray Boynuyoğun、Ceyda Hamurcu、Bengü Sanem Pazvant、Merve Özmen、Yunus Emre Emik 和 Ahmet Sait Çelik。他们作为IBM土耳其客户工程团队的一员，对我们进行的这项工作做出了贡献。没有团队就不可能完成这项工作。团结一心！

我还要感谢所有的队友，Seray Boynuyoğun、Ceyda Hamurcu、Bengü Sanem Pazvant、Merve Özmen、Yunus Emre Emik 和 Ahmet Sait Çelik。他们作为IBM土耳其客户工程团队的一员，对我们进行的这项工作做出了贡献。没有团队就不可能完成这项工作。团结一心！