from abc import ABC, abstractmethod
from typing import Literal, Optional

from mem0.configs.embeddings.base import BaseEmbedderConfig


class EmbeddingBase(ABC):
    """Initialized a base embedding class

    :param config: Embedding configuration option class, defaults to None
    :type config: Optional[BaseEmbedderConfig], optional
    """

    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
        if config is None:
            self.config = BaseEmbedderConfig()
        else:
            self.config = config

    @abstractmethod
    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]]):
        """
        Get the embedding for the given text.

        Args:
            text (str): The text to embed.
            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
        Returns:
            list: The embedding vector.
        """
        pass

    def embed_batch(self, texts, memory_action="add"):
        """Embed multiple texts. Override in subclasses for native batch support.

        Default implementation calls embed() sequentially for each text.
        Subclasses with native batch APIs (e.g., OpenAI) should override
        this for better performance.

        Args:
            texts: List of text strings to embed.
            memory_action: The action context ("add", "search", "update").

        Returns:
            List of embedding vectors (list of floats), one per input text.
        """
        return [self.embed(text, memory_action) for text in texts]