from typing import List

import numpy as np
from bertopic.backend import BaseEmbedder
from langchain_core.embeddings import Embeddings


class LangChainBackend(BaseEmbedder):
    """LangChain Embedding Model.

    This class uses the LangChain Embedding class to embed the documents.
    Argument:
        embedding_model: A LangChain Embedding Instance.

    Examples:
    ```python
    from langchain_community.embeddings import HuggingFaceInstructEmbeddings
    from bertopic.backend import LangChainBackend

    hf_embedding = HuggingFaceInstructEmbeddings()
    langchain_embedder = LangChainBackend(hf_embedding)
    ```
    """

    def __init__(self, embedding_model: Embeddings):
        self.embedding_model = embedding_model

    def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
        """Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        # Prepare documents, replacing empty strings with a single space
        prepared_documents = [" " if doc == "" else doc for doc in documents]
        response = self.embedding_model.embed_documents(prepared_documents)
        return np.array(response)