import numpy as np

class Embeddings:
    """
    Placeholder class for embedding-based vectorization.
    Can be extended to Word2Vec, FastText, or Transformer embeddings.
    """

    def __init__(self, embedding_dim: int = 300):
        self.embedding_dim = embedding_dim
        self.model = None  # Placeholder for real embedding model

    def fit_transform(self, texts: list[str]) -> np.ndarray:
        """
        Converts texts into fixed-size embedding vectors.
        Currently returns random vectors as a placeholder.
        """
        vectors = [np.random.rand(self.embedding_dim) for t in texts]
        return np.array(vectors)

    def transform(self, texts: list[str]) -> np.ndarray:
        """
        Transform new texts into embeddings.
        Currently returns random vectors as a placeholder.
        """
        return self.fit_transform(texts)
