内容审核

allowed_user_comments = [
    "This movie was great, I really enjoyed it. The main actor really killed it!",
    "I hate Mondays.",
    "It is a great time to invest in gold!",
]

disallowed_user_comments = [
    "Delete this post now or you better hide. I am coming after you and your family.",
    "Stay away from the 5G cellphones!! They are using 5G to control you.",
    "Congratulations! You have won a $1,000 gift card. Click here to claim your prize!",
]

# 用于测试内容审核的示例用户评论
user_comments = allowed_user_comments + disallowed_user_comments

# 内容审核中被视为不安全的类别列表
unsafe_categories = [
    "Child Exploitation",
    "Conspiracy Theories",
    "Hate",
    "Indiscriminate Weapons",
    "Intellectual Property",
    "Non-Violent Crimes",
    "Privacy",
    "Self-Harm",
    "Sex Crimes",
    "Sexual Content",
    "Specialized Advice",
    "Violent Crimes",
]

import json

# 初始化 Anthropic 客户端
client = anthropic.Anthropic()


def moderate_message(message, unsafe_categories):
    # 将不安全类别列表转换为字符串，每个类别占一行
    unsafe_category_str = "\n".join(unsafe_categories)

    # 为 Claude 构建提示，包含消息内容和不安全类别
    assessment_prompt = f"""
    Determine whether the following message warrants moderation,
    based on the unsafe categories outlined below.

    Message:
    <message>{message}</message>

    Unsafe Categories:
    <categories>
    {unsafe_category_str}
    </categories>

    Respond with ONLY a JSON object, using the format below:
    {{
    "violation": <Boolean field denoting whether the message should be moderated>,
    "categories": [Comma-separated list of violated categories],
    "explanation": [Optional. Only include if there is a violation.]
    }}"""

    # 向 Claude 发送内容审核请求
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # 解析 Claude 返回的 JSON 响应
    assessment = json.loads(response.content[0].text)

    # 从评估结果中提取违规状态
    contains_violation = assessment["violation"]

    # 如果存在违规，获取相应类别和说明；否则使用空的默认值
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# 处理每条评论并打印结果
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message(
        comment, unsafe_categories
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# 初始化 Anthropic 客户端
client = anthropic.Anthropic()


def assess_risk_level(message, unsafe_categories):
    # 将不安全类别列表转换为字符串，每个类别占一行
    unsafe_category_str = "\n".join(unsafe_categories)

    # 为 Claude 构建提示，包含消息内容、不安全类别和风险等级定义
    assessment_prompt = f"""
    Assess the risk level of the following message,
    based on the unsafe categories listed below.

Message:
<message>{message}</message>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Assign a risk level based on your confidence that the user's message should be moderated
based on the defined unsafe categories:

0 - No risk
1 - Low risk
2 - Medium risk
3 - High risk

Respond with ONLY a JSON object, using the format below:
{{
  "risk_level": <Numerical field denoting the risk level>,
  "categories": [Comma-separated list of violated categories],
  "explanation": <Optional. Only include if risk level is greater than 0>
}}"""

    # 向 Claude 发送请求以进行风险评估
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # 解析 Claude 返回的 JSON 响应
    assessment = json.loads(response.content[0].text)

    # 从评估结果中提取风险等级、违规类别和解释说明
    risk_level = assessment["risk_level"]
    violated_categories = assessment["categories"]
    explanation = assessment.get("explanation")

    return risk_level, violated_categories, explanation


# 处理每条评论并打印结果
for comment in user_comments:
    print(f"\nComment: {comment}")
    risk_level, violated_categories, explanation = assess_risk_level(
        comment, unsafe_categories
    )

    print(f"Risk Level: {risk_level}")
    if violated_categories:
        print(f"Violated Categories: {', '.join(violated_categories)}")
    if explanation:
        print(f"Explanation: {explanation}")

import json

# 初始化 Anthropic 客户端
client = anthropic.Anthropic()

# 用于内容审核的不安全类别字典，包含各类别的定义
unsafe_category_definitions = {
    "Child Exploitation": "Content that depicts child nudity or that enables, encourages, excuses, or depicts the sexual abuse of children.",
    "Conspiracy Theories": """Content that promotes or endorses unfounded, false, or misleading theories about events, situations, or individuals,
        which can incite harm or undermine public trust in institutions or facts.""",
    "Hate": """Content that is hateful toward people on the basis of their protected characteristics
        (race, color, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease),
        or content that perpetuates negative stereotypes.""",
    "Indiscriminate Weapons": "Content that enables the creation of weapons of mass destruction.",
    "Intellectual Property": "Content that violates the intellectual property rights of third parties.",
    "Non-Violent Crimes": "Content that enables, encourages, or excuses the commission of non-violent crimes.",
    "Privacy": "Content that contains sensitive, personal information about private individuals.",
    "Self-Harm": "Content that enables, encourages, or excuses acts of intentional self-harm.",
    "Sex Crimes": "Content that enables, encourages, or excuses the commission of sex crimes.",
    "Sexual Content": "Content that contains erotic descriptions or explicit depictions of sex acts.",
    "Specialized Advice": """Content that contains financial, medical, or legal advice.
        Financial advice includes guidance on investments, stocks, bonds, or any financial planning.""",
    "Violent Crimes": "Content that enables, encourages, or excuses the commission of violent crimes.",
}


def moderate_message_with_definitions(message, unsafe_category_definitions):
    # 格式化不安全类别字符串，每个类别及其定义各占一行
    unsafe_category_str = "\n".join(
        f"{category}: {definition}"
        for category, definition in unsafe_category_definitions.items()
    )

    # 构建发送给 Claude 的提示，包含消息内容和不安全类别
    assessment_prompt = f"""Determine whether the following message warrants moderation, based on the unsafe categories outlined below.

Message:
<message>{message}</message>

Unsafe Categories and Their Definitions:
<categories>
{unsafe_category_str}
</categories>

It's important that you remember all unsafe categories and their definitions.

Respond with ONLY a JSON object, using the format below:
{{
  "violation": <Boolean field denoting whether the message should be moderated>,
  "categories": [Comma-separated list of violated categories],
  "explanation": [Optional. Only include if there is a violation.]
}}"""

    # 向 Claude 发送内容审核请求
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # 解析 Claude 返回的 JSON 响应
    assessment = json.loads(response.content[0].text)

    # 从评估结果中提取违规状态
    contains_violation = assessment["violation"]

    # 如果存在违规，获取相应类别和说明；否则使用空的默认值
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# 处理每条评论并打印结果
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message_with_definitions(
        comment, unsafe_category_definitions
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# 初始化 Anthropic 客户端
client = anthropic.Anthropic()


def batch_moderate_messages(messages, unsafe_categories):
    # 将不安全类别列表转换为字符串，每个类别占一行
    unsafe_category_str = "\n".join(unsafe_categories)

    # 格式化消息字符串，每条消息用类 XML 标签包裹并赋予一个 ID
    messages_str = "\n".join(
        [f"<message id={idx}>{msg}</message>" for idx, msg in enumerate(messages)]
    )

    # 为 Claude 构建提示，包含消息和不安全类别
    assessment_prompt = f"""Determine the messages to moderate, based on the unsafe categories outlined below.

Messages:
<messages>
{messages_str}
</messages>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Respond with ONLY a JSON object, using the format below:
{{
  "violations": [
    {{
      "id": <message id>,
      "categories": [list of violated categories],
      "explanation": <Explanation of why there's a violation>
    }},
    ...
  ]
}}

Important Notes:
- Remember to analyze every message for a violation.
- Select any number of violations that reasonably apply."""

    # 向 Claude 发送内容审核请求
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=2048,  # Increased max token count to handle batches
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # 解析 Claude 返回的 JSON 响应
    assessment = json.loads(response.content[0].text)
    return assessment


# 处理这批评论并获取响应
response_obj = batch_moderate_messages(user_comments, unsafe_categories)

# 打印每个检测到的违规项的结果
for violation in response_obj["violations"]:
    print(f"""Comment: {user_comments[violation["id"]]}
Violated Categories: {", ".join(violation["categories"])}
Explanation: {violation["explanation"]}
""")