from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Union

from langchain_core.documents import Document as LCDocument
from langchain_core.runnables import run_in_executor
from langchain_core.stores import BaseStore
from langchain_core.vectorstores import VectorStore
from pytorch_ie.annotations import MultiSpan, Span

from .serializable_store import SerializableStore
from .span_embeddings import SpanEmbeddings


class SpanVectorStore(VectorStore, BaseStore, SerializableStore, ABC):
    """Abstract base class for vector stores specialized in storing
    and retrieving embeddings for text spans within documents."""

    METADATA_SPAN_KEY: str = "pie_labeled_span"
    """Key for the span data in the (langchain) document metadata."""
    SPAN_START_KEY: str = "start"
    """Key for the start of the span in the span data."""
    SPAN_END_KEY: str = "end"
    """Key for the end of the span in the span data."""
    METADATA_VECTOR_KEY: str = "vector"
    """Key for the vector in the (langchain) document metadata."""
    RELEVANCE_SCORE_KEY: str = "relevance_score"
    """Key for the relevance score in the (langchain) document metadata."""

    def __init__(
        self,
        label_mapping: Optional[Dict[str, List[str]]] = None,
        **kwargs: Any,
    ):
        """Initialize the SpanVectorStore.

        Args:
            label_mapping: Mapping from query span labels to target span labels. If provided,
                only spans with a label in the mapping for the query span's label are retrieved.
            **kwargs: Additional arguments.
        """
        self.label_mapping = label_mapping
        super().__init__(**kwargs)

    @property
    def embeddings(self) -> SpanEmbeddings:
        """Get the dense embeddings instance that is being used.

        Raises:
            ValueError: If embeddings are `None`.

        Returns:
            Embeddings: An instance of `Embeddings`.
        """
        result = super().embeddings
        if not isinstance(result, SpanEmbeddings):
            raise ValueError(f"Embeddings must be of type SpanEmbeddings, but got: {result}")
        return result

    @abstractmethod
    def get_by_ids_with_vectors(self, ids: Sequence[Union[str, int]], /) -> List[LCDocument]:
        """Get documents by their ids.

        Args:
            ids: List of document ids.

        Returns:
            List of documents including their vectors in the metadata at key `metadata_vector_key`.
        """
        ...

    @abstractmethod
    def construct_filter(
        self,
        query_span: Union[Span, MultiSpan],
        metadata_doc_id_key: str,
        doc_id_whitelist: Optional[Sequence[str]] = None,
        doc_id_blacklist: Optional[Sequence[str]] = None,
    ) -> Any:
        """Construct a filter for the retrieval. It should enforce that:
        - if the span is labeled, the retrieved span has the same label, or
        - if, in addition, a label mapping is provided, the retrieved span has a label that is in the mapping for the query span's label
        - if `doc_id_whitelist` is provided, the retrieved span is from a document in the whitelist
        - if `doc_id_blacklist` is provided, the retrieved span is not from a document in the blacklist

        Args:
            query_span: The query span.
            metadata_doc_id_key: The key in the metadata that holds the document id.
            doc_id_whitelist: A list of document ids to restrict the retrieval to.
            doc_id_blacklist: A list of document ids to exclude from the retrieval.

        Returns:
            A filter object.
        """
        ...

    @abstractmethod
    def similarity_search_with_score_by_vector(
        self, embedding: list[float], k: int = 4, **kwargs: Any
    ) -> list[LCDocument]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            **kwargs: Arguments to pass to the search method.

        Returns:
            List of Documents most similar to the query vector.
        """
        ...

    async def asimilarity_search_with_score_by_vector(
        self, embedding: list[float], k: int = 4, **kwargs: Any
    ) -> list[LCDocument]:
        """Async return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            **kwargs: Arguments to pass to the search method.

        Returns:
            List of Documents most similar to the query vector.
        """

        # This is a temporary workaround to make the similarity search
        # asynchronous. The proper solution is to make the similarity search
        # asynchronous in the vector store implementations.
        return await run_in_executor(
            None, self.similarity_search_with_score_by_vector, embedding, k=k, **kwargs
        )