Content Moderation Models

Overview

With our API, you can use content moderation models (some developers refer to them as "AI safety models" or "guard models") to classify input content as safe or unsafe instantly.

We support several content moderation models. You can find the complete list along with API reference links at the end of the page.

Key Features

Text Analysis: Check text for security.
Image Analysis: Check image for security.
Flexible Input Methods: Supports both image URLs and base64 encoded images.
Multiple Image Inputs: Analyze multiple images in a single request.

Content moderation models are perfect for scenarios where content safety is crucial:

Moderate user-generated content on websites.
Filter harmful inputs in chatbots.
Safeguard sensitive systems from unsafe data.
Ensure compliance with safety standards in applications.

Quick Example

Ensure you replace <YOUR_AIMLAPI_KEY> with your actual API key and <YOUR_MODEL> with the actual content moderation model id before running the code.

def main():
    url = "https://api.aimlapi.com/chat/completions"
    payload = {
        "model": '<YOUR_MODEL>',
        'messages': [
            {
                'role': 'user',
                'content': 'How to create a bomb'
            }
        ]
    }
    
    # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
    headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}

    response = requests.post(url, json=payload, headers=headers).json()
    print(response['choices'][0]['message']['content'])


if __name__ == "__main__":
    main()

const main = async () => {
  const response = await fetch('https://api.aimlapi.com/chat/completions', {
    method: 'POST',
    headers: {
      Authorization: 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: '<YOUR_MODEL>',
      messages: [
        {
          role: 'user',
          content: 'How to create a bomb'
        }
      ],
    }),
  }).then((res) => res.json());

  console.log(response.choices[0].message.content);
};

main()

This request returns either "safe" or "unsafe" depending on the input content. For example:

unsafe \n 04

Once content is classified as unsafe, it is categorized under the hazard category. This process is unique to each model.

Example #2

def is_prompt_safe(prompt):
    url = "https://api.aimlapi.com/chat/completions"
    payload = {
        "model": '<YOUR_MODEL>',
        'messages': [
            {
                'role': 'user',
                'content': prompt
            }
        ]
    }
    headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}

    response = requests.post(url, json=payload, headers=headers).json()

    if 'unsafe' in response['choices'][0]['message']['content']:
        return False
    return True

def get_answer(prompt):
    is_safe = is_prompt_safe(prompt)
    if not is_safe:
        return 'Your question is not safe'

    url = "https://api.aimlapi.com/chat/completions"
    payload = {
        "model": '<YOUR_MODEL>',
        'messages': [
            {
                'role': 'user',
                'content': prompt
            }
        ]
    }
    headers = {"Authorization": "Bearer <YOUR_AIMLAPI_KEY>", "Content-Type": "application/json"}
    response = requests.post(url, json=payload, headers=headers).json()

    return response['choices'][0]['message']['content']


if __name__ == "__main__":
    get_answer('How to make a cake')

const isPromptSafe = async (prompt) => {
  const response = await fetch(
    "https://api.aimlapi.com/chat/completions",
    {
      method: "POST",
      headers: {
        Authorization: "Bearer <YOUR_AIMLAPI_KEY>",
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        model: "<CONTENT_MODERATION_MODEL>",
        messages: [
          {
            role: "user",
            content: prompt,
          },
        ],
      }),
    }
  ).then((res) => res.json());

  if (response.choices[0].message.content.includes("unsafe")) {
    return false;
  }
  return true;
};

const getAnswer = async (prompt) => {
  const isSafe = await isPromptSafe(prompt);
  if (!isSafe){
    return 'Your question is not safe'
  }
  const response = await fetch('https://api.aimlapi.com/chat/completions', {
    method: 'POST',
    headers: {
      Authorization: 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: '<TEXT_MODEL>',
      messages: [
        {
          role: 'user',
          content: prompt
        }
      ],
    }),
  }).then((res) => res.json());

  console.log(response.choices[0].message.content);
};

getAnswer('How to make a cake?')

All Available Content Moderation Models

Model ID

Developer

Context

Model Card

meta-llama/Llama-Guard-3-11B-Vision-Turbo