from abc import ABC, abstractmethod

import numpy as np

from deepsearcher.loader.splitter import Chunk


class RetrievalResult:
    """
    Represents a result retrieved from the vector database.

    This class encapsulates the information about a retrieved document,
    including its embedding, text content, reference, metadata, and similarity score.

    Attributes:
        embedding: The vector embedding of the document.
        text: The text content of the document.
        reference: A reference to the source of the document.
        metadata: Additional metadata associated with the document.
        score: The similarity score of the document to the query.
    """

    def __init__(
        self,
        embedding: np.ndarray,
        text: str,
        reference: str,
        metadata: dict,
        score: float = 0.0,
    ):
        """
        Initialize a RetrievalResult object.

        Args:
            embedding: The vector embedding of the document.
            text: The text content of the document.
            reference: A reference to the source of the document.
            metadata: Additional metadata associated with the document.
            score: The similarity score of the document to the query. Defaults to 0.0.
        """
        self.embedding = embedding
        self.text = text
        self.reference = reference
        self.metadata = metadata
        self.score: float = score

    def __repr__(self):
        """
        Return a string representation of the RetrievalResult.

        Returns:
            A string representation of the RetrievalResult object.
        """
        return f"RetrievalResult(score={self.score}, embedding={self.embedding}, text={self.text}, reference={self.reference}), metadata={self.metadata}"


def deduplicate(results: list[RetrievalResult]) -> list[RetrievalResult]:
    """
    Remove duplicate results based on text content.

    This function removes duplicate results from a list of RetrievalResult objects
    by keeping only the first occurrence of each unique text content.

    Args:
        results: A list of RetrievalResult objects to deduplicate.

    Returns:
        A list of deduplicated RetrievalResult objects.
    """
    text = set()
    deduplicated = []
    for result in results:
        if result.text not in text:
            text.add(result.text)
            deduplicated.append(result)
    return deduplicated


class CollectionInfo:
    """
    Represents information about a collection in the vector database.

    This class encapsulates the name and description of a collection.

    Attributes:
        collection_name: The name of the collection.
        description: The description of the collection.
    """

    def __init__(self, collection_name: str, description: str):
        """
        Initialize a CollectionInfo object.

        Args:
            collection_name: The name of the collection.
            description: The description of the collection.
        """
        self.collection_name = collection_name
        self.description = description


class BaseVectorDB(ABC):
    """
    Abstract base class for vector database implementations.

    This class defines the interface for vector database implementations,
    including methods for initializing collections, inserting data, searching,
    listing collections, and clearing the database.

    Attributes:
        default_collection: The name of the default collection.
    """

    def __init__(
        self,
        *args,
        **kwargs,
    ):
        """
        Initialize a BaseVectorDB object.

        Args:
            default_collection: The name of the default collection. Defaults to "deepsearcher".
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """

    @abstractmethod
    def init_collection(
        self,
        dim: int,
        collection: str,
        description: str,
        force_rebuild=False,
        *args,
        **kwargs,
    ):
        """
        Initialize a collection in the vector database.

        Args:
            dim: The dimensionality of the vectors in the collection.
            collection: The name of the collection.
            description: The description of the collection.
            force_rebuild: If True, drop the existing collection and create a new one.
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """
        pass

    @abstractmethod
    def insert_data(self, collection: str, chunks: list[Chunk], *args, **kwargs):
        """
        Insert data into a collection in the vector database.

        Args:
            collection: The name of the collection.
            chunks: A list of Chunk objects to insert.
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """
        pass

    @abstractmethod
    def search_data(
        self, collection: str, vector: np.ndarray | list[float], *args, **kwargs
    ) -> list[RetrievalResult]:
        """
        Search for similar vectors in a collection.

        Args:
            collection: The name of the collection.
            vector: The query vector to search for.
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.

        Returns:
            A list of RetrievalResult objects representing the search results.
        """
        pass

    def list_collections(self, *args, **kwargs) -> list[CollectionInfo]:
        """
        List all collections in the vector database.

        Args:
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.

        Returns:
            A list of CollectionInfo objects representing the collections.
        """
        pass

    @abstractmethod
    def clear_collection(self, *args, **kwargs):
        """
        Clear the vector database.

        Args:
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """
        pass

    @abstractmethod
    def clear_collections(self, *args, **kwargs):
        """
        Clear all collections in the vector database.

        Args:
            *args: Variable length argument list.
            **kwargs: Arbitrary keyword arguments.
        """
        pass