from typing import List, Optional
from pydantic import BaseModel


class EmbedRequest(BaseModel):
    """
    Request model for single text embedding.

    Attributes:
        text: The input text to embed
        model_id: Identifier of the model to use
        prompt: Optional prompt for instruction-based models
    """

    text: str
    model_id: str
    prompt: Optional[str] = None


class BatchEmbedRequest(BaseModel):
    """
    Request model for batch text embedding.

    Attributes:
        texts: List of input texts to embed
        model_id: Identifier of the model to use
        prompt: Optional prompt for instruction-based models
    """

    texts: List[str]
    model_id: str
    prompt: Optional[str] = None


class EmbedResponse(BaseModel):
    """
    Response model for single text embedding.

    Attributes:
        embedding: The generated embedding vector
        dimension: Dimensionality of the embedding
        model_id: Identifier of the model used
        processing_time: Time taken to process the request
    """

    embedding: List[float]
    dimension: int
    model_id: str
    processing_time: float


class BatchEmbedResponse(BaseModel):
    """
    Response model for batch text embedding.

    Attributes:
        embeddings: List of generated embedding vectors
        dimension: Dimensionality of the embeddings
        model_id: Identifier of the model used
        processing_time: Time taken to process the request
    """

    embeddings: List[List[float]]
    dimension: int
    model_id: str
    processing_time: float


class SparseEmbedding(BaseModel):
    """
    Sparse embedding model.

    Attributes:
        text: The input text that was embedded
        indices: Indices of non-zero elements in the sparse vector
        values: Values corresponding to the indices
    """

    text: Optional[str] = None
    indices: List[int]
    values: List[float]


class SparseEmbedResponse(BaseModel):
    """
    Sparse embedding response model.

    Attributes:
        sparse_embedding: The generated sparse embedding
        model_id: Identifier of the model used
        processing_time: Time taken to process the request
    """

    sparse_embedding: SparseEmbedding
    model_id: str
    processing_time: float


class BatchSparseEmbedResponse(BaseModel):
    """
    Batch sparse embedding response model.

    Attributes:
        embeddings: List of generated sparse embeddings
        model_id: Identifier of the model used
    """

    embeddings: List[SparseEmbedding]
    model_id: str
    processing_time: float