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Overview

Moderation API detects harmful or inappropriate content in text, helping you:
  • Content Filtering: Automatically filter inappropriate user submissions
  • Safety Review: Detect potential violations before publishing
  • Compliance Check: Ensure content meets platform guidelines
  • Risk Warning: Identify potentially harmful content types
Moderation API uses OpenAI’s moderation model and is free to use without consuming token quota.

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Quick Start

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Basic Example

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-laozhang-api-key",
    base_url="https://api.yelinai.com/v1"
)

response = client.moderations.create(
    input="Text content to be reviewed"
)

result = response.results[0]
print(f"Contains harmful content: {result.flagged}")
print(f"Category scores: {result.category_scores}")

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Batch Moderation

texts = ["Text 1", "Text 2", "Text 3"]

response = client.moderations.create(input=texts)

for i, result in enumerate(response.results):
    status = '⚠️ Flagged' if result.flagged else '✅ Safe'
    print(f"Text {i+1}: {status}")

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Detection Categories

CategoryDescription
hateHate speech targeting specific groups
hate/threateningThreatening hate speech
harassmentHarassing content
harassment/threateningThreatening harassment
self-harmSelf-harm related content
self-harm/intentIntent to self-harm
self-harm/instructionsSelf-harm instructions
sexualSexual content
sexual/minorsSexual content involving minors
violenceViolent content
violence/graphicGraphic violence

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Practical Examples

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1. User Input Filter

def check_content(text):
    """Check if content is safe"""
    response = client.moderations.create(input=text)
    result = response.results[0]
    
    if result.flagged:
        violations = [
            cat for cat, flagged in result.categories.model_dump().items()
            if flagged
        ]
        return False, violations
    
    return True, []

# Usage
is_safe, violations = check_content(user_input)
if not is_safe:
    print(f"⚠️ Violation categories: {violations}")

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2. Chatbot Safety Layer

def safe_chat(user_message):
    """Chat with safety checks"""
    # Check user input
    if client.moderations.create(input=user_message).results[0].flagged:
        return "Sorry, your message contains inappropriate content."
    
    # Process after safety check
    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[{"role": "user", "content": user_message}]
    )
    
    ai_reply = response.choices[0].message.content
    
    # Check AI response
    if client.moderations.create(input=ai_reply).results[0].flagged:
        return "Sorry, unable to generate appropriate response."
    
    return ai_reply

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3. Custom Threshold

def check_with_threshold(text, threshold=0.5):
    """Check with custom threshold"""
    response = client.moderations.create(input=text)
    result = response.results[0]
    
    scores = result.category_scores.model_dump()
    high_risk = {
        cat: score for cat, score in scores.items()
        if score > threshold
    }
    
    return len(high_risk) == 0, high_risk

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Best Practices

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Multi-layer Protection

def process_with_safety(user_input):
    # 1. Check user input
    if not is_safe(user_input):
        return "Input content violates guidelines"
    
    # 2. Process
    result = process(user_input)
    
    # 3. Check output
    if not is_safe(result):
        return "Unable to generate appropriate content"
    
    return result

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Pricing

Moderation API is currently free to use and does not count towards token consumption.

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FAQ

How accurate is moderation? Based on OpenAI’s model with high accuracy, but not 100% reliable. Recommend:
  • Combine with human review for critical scenarios
  • Set appropriate thresholds
  • Provide appeal channels
Does it support multiple languages? Supports multiple languages including Chinese. English works best. Are there rate limits? There are rate limits. Control request frequency for batch processing.

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Text Generation

Chat API documentation

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Content Safety

Content safety policy

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API Reference

API reference

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Data Security

Data privacy protection