feat(moderation): add support for more models (#228)

src/genai/schemas/descriptions.py

@@ -48,10 +48,14 @@ class Descriptions:
 
     # Params.Moderations
     MODERATIONS = "Leverages various models to detect hate speech in the provided inputs and generated outputs."
-    HAP = "Mechanism for detecting hate/abuse/profanity on a sentence level."
-    HAP_INPUT = "Enable/Disable HAP detection on the provided input."
-    HAP_OUTPUT = "Enable/Disable HAP detection on the generated output."
-    HAP_THRESHOLD = "The number from interval <0, 1> that causes the sentence to be flagged (default is 0.75)."
+    MODERATION_HAP = "Mechanism for detecting hate/abuse/profanity on a sentence level."
+    MODERATION_STIGMA = "Mechanism for detecting stigma-based discrimination (i.e., discrimination against an individual or group based on an attribute or characteristic that is devalued in a particular social context)."  # noqa
+    MODERATION_IMPLICIT_HATE = "Mechanism for detecting implicit hate (i.e., subtle hatred against a specific individual or group of individuals)."  # noqa
+    MODERATION_TYPE_INPUT = "Enable/Disable detection on the provided input."
+    MODERATION_TYPE_OUTPUT = "Enable/Disable detection on the generated output."
+    MODERATION_TYPE_THRESHOLD = (
+        "The number from interval <0, 1> that causes the sentence to be flagged (default is 0.75)."
+    )
 
     # Params.Chat
     CONVERSATION_ID = "ID of the conversation. Always empty for the first request. Pass only in case you want to join existing conversation."

src/genai/schemas/generate_params.py

@@ -41,16 +41,30 @@ def __init__(self, *args, **kwargs):
 # Link to doc : https://workbench.res.ibm.com/docs/api-reference#generate
 
 
-class HAPOptions(BaseModel):
-    input: bool = Field(description=tx.HAP_INPUT, default=True)
-    output: bool = Field(description=tx.HAP_OUTPUT, default=True)
-    threshold: float = Field(description=tx.HAP_THRESHOLD, ge=0, le=1, multiple_of=0.01, default=0.75)
+class ModerationTypeOptions(BaseModel):
+    input: bool = Field(description=tx.MODERATION_TYPE_INPUT, default=True)
+    output: bool = Field(description=tx.MODERATION_TYPE_OUTPUT, default=True)
+    threshold: float = Field(description=tx.MODERATION_TYPE_THRESHOLD, ge=0, le=1, multiple_of=0.01, default=0.75)
+
+
+class HAPOptions(ModerationTypeOptions):
+    pass
+
+
+class StigmaOptions(ModerationTypeOptions):
+    pass
+
+
+class ImplicitHateOptions(ModerationTypeOptions):
+    pass
 
 
 class ModerationsOptions(BaseModel):
     model_config = ConfigDict(extra="allow", populate_by_name=True)
 
-    hap: Union[bool, HAPOptions] = Field(description=tx.HAP, default=False)
+    hap: Union[bool, HAPOptions] = Field(description=tx.MODERATION_HAP, default=False)
+    stigma: Union[bool, StigmaOptions] = Field(description=tx.MODERATION_STIGMA, default=False)
+    implicit_hate: Union[bool, ImplicitHateOptions] = Field(description=tx.MODERATION_IMPLICIT_HATE, default=False)
 
 
 class GenerateParams(BaseModel):

src/genai/schemas/responses.py

@@ -66,15 +66,29 @@ class TextPosition(GenAiResponseModel):
     end: int
 
 
-class HAPResult(GenAiResponseModel):
+class ModerationTypeResult(GenAiResponseModel):
     flagged: bool
     score: float
     success: bool
     position: TextPosition
 
 
+class HAPResult(ModerationTypeResult):
+    pass
+
+
+class StigmaResult(ModerationTypeResult):
+    pass
+
+
+class ImplicitHateResult(ModerationTypeResult):
+    pass
+
+
 class ModerationResult(GenAiResponseModel):
-    hap: List[HAPResult]
+    hap: Optional[List[HAPResult]] = None
+    stigma: Optional[List[StigmaResult]] = None
+    implicit_hate: Optional[List[ImplicitHateResult]] = None
 
 
 class GeneratedToken(GenAiResponseModel):