チケットルーティング

def classify_support_request(ticket_contents):
    # 分類タスク用のプロンプトを定義します
    classification_prompt = f"""You will be acting as a customer support ticket classification system. Your task is to analyze customer support requests and output the appropriate classification intent for each request, along with your reasoning.

        Here is the customer support request you need to classify:

        <request>{ticket_contents}</request>

        Please carefully analyze the above request to determine the customer's core intent and needs. Consider what the customer is asking for has concerns about.

        First, write out your reasoning and analysis of how to classify this request inside <reasoning> tags.

        Then, output the appropriate classification label for the request inside a <intent> tag. The valid intents are:
        <intents>
        <intent>Support, Feedback, Complaint</intent>
        <intent>Order Tracking</intent>
        <intent>Refund/Exchange</intent>
        </intents>

        A request may have ONLY ONE applicable intent. Only include the intent that is most applicable to the request.

        As an example, consider the following request:
        <request>Hello! I had high-speed fiber internet installed on Saturday and my installer, Kevin, was absolutely fantastic! Where can I send my positive review? Thanks for your help!</request>

        Here is an example of how your output should be formatted (for the above example request):
        <reasoning>The user seeks information in order to leave positive feedback.</reasoning>
        <intent>Support, Feedback, Complaint</intent>

        Here are a few more examples:
        <examples>
        <example 2>
        Example 2 Input:
        <request>I wanted to write and personally thank you for the compassion you showed towards my family during my father's funeral this past weekend. Your staff was so considerate and helpful throughout this whole process; it really took a load off our shoulders. The visitation brochures were beautiful. We'll never forget the kindness you showed us and we are so appreciative of how smoothly the proceedings went. Thank you, again, Amarantha Hill on behalf of the Hill Family.</request>

        Example 2 Output:
        <reasoning>User leaves a positive review of their experience.</reasoning>
        <intent>Support, Feedback, Complaint</intent>
        </example 2>
        <example 3>

        […]

        </example 8>
        <example 9>
        Example 9 Input:
        <request>Your website keeps sending ad-popups that block the entire screen. It took me twenty minutes just to finally find the phone number to call and complain. How can I possibly access my account information with all of these popups? Can you access my account for me, since your website is broken? I need to know what the address is on file.</request>

        Example 9 Output:
        <reasoning>The user requests help accessing their web account information.</reasoning>
        <intent>Support, Feedback, Complaint</intent>
        </example 9>

        Remember to always include your classification reasoning before your actual intent output. The reasoning should be enclosed in <reasoning> tags and the intent in <intent> tags. Return only the reasoning and the intent.
        """

import re

# Claude APIクライアントのインスタンスを作成します
client = anthropic.Anthropic()

# デフォルトのモデルを設定します
DEFAULT_MODEL = "claude-haiku-5-5"


def classify_support_request(ticket_contents):
    # 分類タスク用のプロンプトを定義します
    classification_prompt = f"""You will be acting as a customer support ticket classification system.
        […]
        ... The reasoning should be enclosed in <reasoning> tags and the intent in <intent> tags. Return only the reasoning and the intent.
        """
    # サポートリクエストを分類するため、プロンプトをAPIに送信します。
    message = client.messages.create(
        model=DEFAULT_MODEL,
        max_tokens=2048,
        output_config={"effort": "low"},
        messages=[{"role": "user", "content": classification_prompt}],
        stream=False,
    )
    reasoning_and_intent = next(
        (block.text for block in message.content if block.type == "text"), ""
    )

    # Pythonの正規表現ライブラリを使用して`reasoning`を抽出します。
    reasoning_match = re.search(
        r"<reasoning>(.*?)</reasoning>", reasoning_and_intent, re.DOTALL
    )
    reasoning = reasoning_match.group(1).strip() if reasoning_match else ""

    # 同様に、`intent`も抽出します。
    intent_match = re.search(r"<intent>(.*?)</intent>", reasoning_and_intent, re.DOTALL)
    intent = intent_match.group(1).strip() if intent_match else ""

    return reasoning, intent

import re

# Claude APIクライアントのインスタンスを作成します
client = anthropic.Anthropic()

# デフォルトのモデルを設定します
DEFAULT_MODEL = "claude-haiku-5-5"


def classify_support_request(request, actual_intent):
    # 分類タスク用のプロンプトを定義します
    classification_prompt = f"""You will be acting as a customer support ticket classification system.
        […]
        ...The reasoning should be enclosed in <reasoning> tags and the intent in <intent> tags. Return only the reasoning and the intent.
        """

    message = client.messages.create(
        model=DEFAULT_MODEL,
        max_tokens=2048,
        output_config={"effort": "low"},
        messages=[{"role": "user", "content": classification_prompt}],
    )
    usage = message.usage  # Get the usage statistics for the API call for how many input and output tokens were used.
    reasoning_and_intent = next(
        (block.text for block in message.content if block.type == "text"), ""
    )

    # Pythonの正規表現ライブラリを使用して`reasoning`を抽出します。
    reasoning_match = re.search(
        r"<reasoning>(.*?)</reasoning>", reasoning_and_intent, re.DOTALL
    )
    reasoning = reasoning_match.group(1).strip() if reasoning_match else ""

    # 同様に、`intent`も抽出します。
    intent_match = re.search(r"<intent>(.*?)</intent>", reasoning_and_intent, re.DOTALL)
    intent = intent_match.group(1).strip() if intent_match else ""

    # モデルの予測が正しいかどうかを確認します。
    correct = actual_intent.strip() == intent.strip()

    # reasoning、intent、correct、usageを返します。
    return reasoning, intent, correct, usage