# rag_core/retriever_factory.py
from langchain_core.embeddings import Embeddings
from langchain_classic.retrievers import ParentDocumentRetriever
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from langchain_core.stores import BaseStore

from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore


def create_parent_retriever(
    collection_name: str = "rag_documents",
    parent_splitter: TextSplitter | None = None,
    child_splitter: TextSplitter | None = None,
    docstore: BaseStore | None = None,
    search_k: int = 5,
    parent_chunk_size: int = 1000,
    parent_chunk_overlap: int = 100,
    child_chunk_size: int = 200,
    child_chunk_overlap: int = 20,
    embeddings: Embeddings | None = None,
) -> ParentDocumentRetriever:
    """
    创建 ParentDocumentRetriever 实例。
    
    Args:
        collection_name: Qdrant 集合名称，默认 "rag_documents"
        parent_splitter: 父文档切分器，默认 None（使用默认参数创建）
        child_splitter: 子文档切分器，默认 None（使用默认参数创建）
        docstore: 文档存储实例，默认 None（使用默认参数创建）
        search_k: 检索时返回的结果数，默认 5
        parent_chunk_size: 父文档块大小，默认 1000
        parent_chunk_overlap: 父文档块重叠大小，默认 100
        child_chunk_size: 子文档块大小，默认 200
        child_chunk_overlap: 子文档块重叠大小，默认 20
        embeddings: 嵌入模型实例，默认 None（使用内部默认的 LocalLlamaCppEmbedder）
    
    Returns:
        ParentDocumentRetriever 实例
    """
    # 嵌入模型
    if embeddings is None:
        embedder = LlamaCppEmbedder()
        embeddings = embedder.as_langchain_embeddings()
    
    # 向量存储（只读）
    vector_store = QdrantVectorStore(collection_name=collection_name, embeddings=embeddings)

    # 切分器（若未提供则创建默认）
    if parent_splitter is None:
        parent_splitter = RecursiveCharacterTextSplitter(
            chunk_size=parent_chunk_size,
            chunk_overlap=parent_chunk_overlap,
        )
    if child_splitter is None:
        child_splitter = RecursiveCharacterTextSplitter(
            chunk_size=child_chunk_size,
            chunk_overlap=child_chunk_overlap,
        )

    # 文档存储
    if docstore is None:
        docstore, _ = create_docstore()

    return ParentDocumentRetriever(
        vectorstore=vector_store.get_langchain_vectorstore(),
        docstore=docstore,
        child_splitter=child_splitter,
        parent_splitter=parent_splitter,
        search_kwargs={"k": search_k},
    )