Overview The moderation endpoint classifies text and image content to detect potentially harmful material across multiple categories. Learn more in the moderation guide .
Method client.moderations.create( input = "Sample text to moderate" , model = "omni-moderation-latest" )
Parameters Input to classify. Can be:
A single string
An array of strings
An array of multi-modal input objects (text and images)
The content moderation model to use. Available models:
omni-moderation-latest - Latest omni-modal modelomni-moderation-2024-09-26 - Specific omni-modal versiontext-moderation-latest - Latest text-only modeltext-moderation-stable - Stable text-only model Learn more in the moderation guide and about available models . Response Returns a ModerationCreateResponse object containing:
Unique identifier for the moderation request
The model used to generate the moderation results
List of moderation objects, one for each input Whether any category was flagged
Boolean flags for each category:
harassment - Harassing languageharassment/threatening - Harassment with violence/harmhate - Hate speech based on protected attributeshate/threatening - Hateful content with violence/harmillicit - Instructions for wrongdoingillicit/violent - Violent wrongdoing instructionsself-harm - Self-harm promotionself-harm/instructions - Self-harm instructionsself-harm/intent - Self-harm intent expressionsexual - Sexual contentsexual/minors - Sexual content involving minorsviolence - Violence depictionviolence/graphic - Graphic violence Confidence scores (0-1) for each category with the same keys as categories
category_applied_input_types
Input types (text/image) that each score applies to
Examples Text Moderation from openai import OpenAI client = OpenAI() response = client.moderations.create( input = "I want to hurt someone" ) result = response.results[ 0 ] if result.flagged: print ( "Content flagged!" ) print ( f "Violence score: { result.category_scores.violence } " ) print ( f "Categories flagged: { [k for k, v in result.categories.model_dump().items() if v] } " )
Multi-Modal Moderation response = client.moderations.create( model = "omni-moderation-latest" , input = [ { "type" : "text" , "text" : "Check this image" }, { "type" : "image_url" , "image_url" : { "url" : "https://example.com/image.jpg" } } ] )
Batch Moderation response = client.moderations.create( input = [ "First text to check" , "Second text to check" , "Third text to check" ] ) for idx, result in enumerate (response.results): print ( f "Input { idx } : Flagged= { result.flagged } " )
Async Usage from openai import AsyncOpenAI client = AsyncOpenAI() response = await client.moderations.create( input = "Text to moderate" )
