"""
文本切分器，用于将文档切分成块。
"""

from enum import Enum
from typing import List, Optional

from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from langchain_experimental.text_splitter import SemanticChunker


class SplitterType(str, Enum):
    RECURSIVE = "recursive"
    SEMANTIC = "semantic"
    PARENT_CHILD = "parent_child"


def get_splitter(splitter_type: SplitterType, **kwargs):
    """工厂函数，创建文本切分器。"""
    if splitter_type == SplitterType.RECURSIVE:
        chunk_size = kwargs.get("chunk_size", 500)
        chunk_overlap = kwargs.get("chunk_overlap", 50)
        return RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap,
            separators=["\n\n", "\n", "。", "！", "？", " ", ""],
        )
    elif splitter_type == SplitterType.SEMANTIC:
        embeddings = kwargs.pop("embeddings", None)
        if embeddings is None:
            raise ValueError("语义切分器需要提供 'embeddings' 参数")
        return SemanticChunkerAdapter(embeddings=embeddings, **kwargs)
    else:
        raise ValueError(f"不支持的切分器类型: {splitter_type}")


class SemanticChunkerAdapter(TextSplitter):
    """将 SemanticChunker 适配为 TextSplitter 接口。"""

    def __init__(self, embeddings, **kwargs):
        super().__init__(**kwargs)
        chunk_size = kwargs.pop("chunk_size", None)
        chunk_overlap = kwargs.pop("chunk_overlap", None)
        self._chunker = SemanticChunker(embeddings=embeddings, **kwargs)

    def split_text(self, text: str) -> List[str]:
        return self._chunker.split_text(text)


class ParentChildSplitter:
    """
    将文档切分为父块（大块）和子块（小块）。
    子块用于索引检索，父块用于存储上下文。
    """

    def __init__(
        self,
        parent_chunk_size: int = 1000,
        child_chunk_size: int = 200,
        parent_chunk_overlap: int = 100,
        child_chunk_overlap: int = 20,
    ):
        self.parent_splitter = RecursiveCharacterTextSplitter(
            chunk_size=parent_chunk_size,
            chunk_overlap=parent_chunk_overlap,
        )
        self.child_splitter = RecursiveCharacterTextSplitter(
            chunk_size=child_chunk_size,
            chunk_overlap=child_chunk_overlap,
        )

    def split_documents(self, documents: List[Document]) -> tuple[List[Document], List[Document]]:
        """
        返回:
            (父块列表, 子块列表)
        """
        parent_chunks = self.parent_splitter.split_documents(documents)
        child_chunks = self.child_splitter.split_documents(documents)

        # 将子块与父块 ID 关联（可选元数据）
        # 在实际实现中，需要将每个子块映射到对应的父块 ID。
        return parent_chunks, child_chunks