from typing import List, Type

from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.faiss import FAISS

from .debug import FakeEmbeddings, FakeVectorStore
from .parsing import File


class FolderIndex:
    """Index for a collection of files (a folder)"""

    def __init__(self, files: List[File], index: VectorStore):
        self.name: str = "default"
        self.files = files
        self.index: VectorStore = index

    @staticmethod
    def _combine_files(files: List[File]) -> List[Document]:
        """Combines all the documents in a list of files into a single list."""

        all_texts = []
        for file in files:
            for doc in file.docs:
                doc.metadata["file_name"] = file.name
                doc.metadata["file_id"] = file.id
                all_texts.append(doc)

        return all_texts

    @classmethod
    def from_files(
        cls, files: List[File], embeddings: Embeddings, vector_store: Type[VectorStore]
    ) -> "FolderIndex":
        """Creates an index from files."""

        all_docs = cls._combine_files(files)

        index = vector_store.from_documents(
            documents=all_docs,
            embedding=embeddings,
        )

        return cls(files=files, index=index)


def embed_files(
    files: List[File], embedding: str, vector_store: str, **kwargs
) -> FolderIndex:
    model_name = "BAAI/bge-small-en"
    model_kwargs = {'device': 'cpu'}
    encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
    model_norm = HuggingFaceBgeEmbeddings(
        model_name=model_name,
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs
    )
    # embeddings = OpenAIEmbeddings
    embeddings = model_norm
    return FolderIndex.from_files(
        files=files, embeddings=embeddings, vector_store=FAISS
    )