def METHOD_NAME(
        self,
        texts: Iterable[str],
        metadatas: Optional[list[dict]] = None,
        ids: Optional[list[str]] = None,
        namespace: Optional[str] = None,
        batch_size: int = 32,
        **kwargs: Any,
) -> list[str]:
    """
    Add texts to the vector store.
    Args:
        texts : The texts to add.
        metadatas : The metadatas to add.
        ids : The ids to add.
        namespace : The namespace to add.
        batch_size : The batch size to add.
        **kwargs : The keyword arguments to add.
    Returns:
        The list of ids vectors stored in pinecone.
    """
    if namespace is None:
        namespace = self.namespace
    vectors = []
    ids = ids or [str(uuid.uuid4()) for _ in texts]
    if len(ids) < len(texts):
        raise ValueError("Number of ids must match number of texts.")
    for text, id in zip(texts, ids):
        metadata = metadatas.pop(0) if metadatas else {}
        metadata[self.text_field] = text
        vectors.append((id, self.embedding_model.get_embedding(text), metadata))
    self.add_embeddings_to_vector_db({"vectors": vectors})
    return ids