from abc import ABC, abstractmethod
from typing import Any, Dict


class BaseEmbeddingFactory(ABC):
    @abstractmethod
    def create(self, model: str, **kwargs: Any):
        raise NotImplementedError


class OpenAIEmbeddingFactory(BaseEmbeddingFactory):
    def create(self, model: str, **kwargs: Any):
        from langchain_openai import OpenAIEmbeddings

        return OpenAIEmbeddings(model=model, **kwargs)


class OllamaEmbeddingFactory(BaseEmbeddingFactory):
    def create(self, model: str, **kwargs: Any):
        from langchain_ollama import OllamaEmbeddings

        return OllamaEmbeddings(model=model, **kwargs)


class BedrockEmbeddingFactory(BaseEmbeddingFactory):
    def create(self, model: str, **kwargs: Any):
        from langchain_aws import BedrockEmbeddings

        return BedrockEmbeddings(model_id=model, **kwargs)


class HuggingFaceEmbeddingFactory(BaseEmbeddingFactory):
    def create(self, model: str, **kwargs: Any):
        from langchain_huggingface import HuggingFaceEmbeddings

        return HuggingFaceEmbeddings(model_name=model, **kwargs)


EMBEDDING_FACTORIES: Dict[str, BaseEmbeddingFactory] = {
    "openai": OpenAIEmbeddingFactory(),
    "ollama": OllamaEmbeddingFactory(),
    "bedrock": BedrockEmbeddingFactory(),
    "huggingface": HuggingFaceEmbeddingFactory(),
}


def create_embedding_model(provider: str, model: str, **kwargs: Any):
    """
    Create an embedding model instance for the given provider.

    Args:
        provider: The provider name (openai, ollama, bedrock, huggingface).
        model: The model identifier.
        **kwargs: Additional keyword arguments passed to the model constructor.

    Returns:
        An embedding model instance.
    """
    key = provider.strip().lower()

    if key not in EMBEDDING_FACTORIES:
        raise ValueError(
            f"Unsupported embedding provider: {provider}. "
            f"Available providers: {list(EMBEDDING_FACTORIES.keys())}"
        )

    return EMBEDDING_FACTORIES[key].create(model=model, **kwargs)