from fair_forge.metrics.toxicity import Toxicity

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    # Group detection
    group_prototypes: dict[str, list[str]] | None = None,
    group_thresholds: dict[str, float] | None = None,
    group_default_threshold: float = 0.50,
    group_toxicity_threshold: float = 0.0,
    group_extractor: BaseGroupExtractor | None = None,

    # Embedding
    embedder: Embedder,  # e.g., SentenceTransformerEmbedder("all-MiniLM-L6-v2")

    # Clustering (HDBSCAN)
    toxicity_min_cluster_size: int = 5,
    toxicity_cluster_selection_epsilon: float = 0.0,
    toxicity_cluster_selection_method: str = "eom",
    toxicity_cluster_use_latent_space: bool = True,

    # UMAP
    umap_n_components: int = 2,
    umap_n_neighbors: int = 15,
    umap_min_dist: float = 0.1,
    umap_random_state: int = 42,
    umap_metric: str = "cosine",

    # DIDT weights
    w_DR: float = 1/3,
    w_ASB: float = 1/3,
    w_DTO: float = 1/3,

    # Statistical mode
    statistical_mode: StatisticalMode | None = None,

    # Other
    toxicity_loader: Type[ToxicityLoader] = HurtlexLoader,
    sentiment_analyzer: SentimentAnalyzer | None = None,
    verbose: bool = False,
    **kwargs,
) -> list[ToxicityMetric]

from fair_forge.metrics.bias import Bias

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    guardian: Type[Guardian],
    config: GuardianLLMConfig,
    confidence_level: float = 0.95,
    verbose: bool = False,
    **kwargs,
) -> list[BiasMetric]

from fair_forge.metrics.context import Context

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    model: BaseChatModel,
    use_structured_output: bool = False,
    bos_json_clause: str = "```json",
    eos_json_clause: str = "```",
    verbose: bool = False,
    **kwargs,
) -> list[ContextMetric]

from fair_forge.metrics.conversational import Conversational

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    model: BaseChatModel,
    use_structured_output: bool = False,
    bos_json_clause: str = "```json",
    eos_json_clause: str = "```",
    verbose: bool = False,
    **kwargs,
) -> list[ConversationalMetric]

from fair_forge.metrics.humanity import Humanity

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    verbose: bool = False,
    **kwargs,
) -> list[HumanityMetric]

from fair_forge.metrics.best_of import BestOf

@classmethod
def run(
    cls,
    retriever: Type[Retriever],
    *,
    model: BaseChatModel,
    use_structured_output: bool = False,
    bos_json_clause: str = "```json",
    eos_json_clause: str = "```",
    criteria: str = "BestOf",
    verbose: bool = False,
    **kwargs,
) -> list[BestOfMetric]

from fair_forge.core.base import FairForge

class FairForge(ABC):
    def __init__(
        self,
        retriever: Type[Retriever],
        verbose: bool = False,
        **kwargs,
    ):
        pass

    @abstractmethod
    def batch(
        self,
        session_id: str,
        context: str,
        assistant_id: str,
        batch: list[Batch],
        language: str | None,
    ) -> None:
        """Process a batch of conversations."""
        pass

    @classmethod
    def run(
        cls,
        retriever: Type[Retriever],
        **kwargs,
    ) -> list:
        """One-shot execution."""
        pass