"""
离线 RAG 索引构建核心流水线。

使用 LangChain 的 ParentDocumentRetriever 实现父子块策略。
"""

import asyncio
import logging
import sys
from pathlib import Path
from dataclasses import dataclass, field
from typing import List, Union, Optional, Any, Dict

# 添加 backend 目录到路径以导入 rag_core
sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))

from httpx import RemoteProtocolError
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.stores import BaseStore
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from qdrant_client.http.exceptions import ResponseHandlingException

from .loaders import DocumentLoader
from .splitters import SplitterType, get_splitter

# 从 rag_core 导入
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))

from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore, create_parent_retriever

# 尝试导入新的 model_services（如果可用）
try:
    from app.model_services import get_embedding_service
    HAS_MODEL_SERVICES = True
except ImportError:
    HAS_MODEL_SERVICES = False

logger = logging.getLogger(__name__)

# ---------- 配置数据类 ----------
@dataclass
class DocstoreConfig:
    """文档存储配置（用于父块存储）。"""
    pool_config: Dict[str, Any] | None = None
    max_concurrency: int | None = None
    # 若要从外部注入已创建好的 docstore，可直接设置此字段
    instance: BaseStore | None = None

@dataclass
class IndexBuilderConfig:
    """索引构建器配置。"""
    collection_name: str = "rag_documents"
    splitter_type: SplitterType = SplitterType.PARENT_CHILD

    # 父块切分参数（仅当 splitter_type 为 PARENT_CHILD 时生效）
    parent_chunk_size: int = 1000
    parent_chunk_overlap: int = 100
    # 子块切分参数
    child_chunk_size: int = 200
    child_chunk_overlap: int = 20
    child_splitter_type: SplitterType = SplitterType.SEMANTIC   # 子块默认语义切分

    # 检索参数
    search_k: int = 5

    # 文档存储配置（仅父子块模式需要）
    docstore: DocstoreConfig = field(default_factory=DocstoreConfig)

    # 其他切分器参数（当 splitter_type 非父子块时使用）
    extra_splitter_kwargs: Dict[str, Any] = field(default_factory=dict)

# ---------- 索引构建器 ----------
class IndexBuilder:
    """RAG 索引构建主流水线，支持单块切分与父子块切分。"""

    def __init__(self, config: Optional[IndexBuilderConfig] = None, embeddings: Optional[Embeddings] = None, **kwargs):
        """
        Args:
            config: 索引构建器配置对象，优先级高于 kwargs
            embeddings: 可选的外部嵌入模型实例，如果提供则使用它
            **kwargs: 可直接传入配置参数，会合并到 config 中（为方便使用保留）
        """
        if config is None:
            config = IndexBuilderConfig(**kwargs)
        elif kwargs:
            # 合并 kwargs 到 config 的字段（仅更新已有字段）
            for key, value in kwargs.items():
                if hasattr(config, key):
                    setattr(config, key, value)

        self.config = config
        self._docstore_conn: Optional[str] = None  # 用于记录由 create_docstore 创建的连接信息

        # 初始化基础组件
        self.loader = DocumentLoader()
        
        # 设置嵌入模型 - 优先使用外部提供的，然后尝试使用新服务，最后回退到原来的方式
        if embeddings is not None:
            self.embeddings = embeddings
            self.embedder = None
            logger.info("使用外部提供的嵌入模型")
        elif HAS_MODEL_SERVICES:
            try:
                self.embeddings = get_embedding_service()
                self.embedder = None
                logger.info("使用 model_services 提供的嵌入服务")
            except Exception as e:
                logger.warning(f"获取嵌入服务失败，回退到 LlamaCppEmbedder: {e}")
                self.embedder = LlamaCppEmbedder()
                self.embeddings = self.embedder.as_langchain_embeddings()
        else:
            self.embedder = LlamaCppEmbedder()
            self.embeddings = self.embedder.as_langchain_embeddings()

        # 初始化向量存储
        self.vector_store = QdrantVectorStore(
            collection_name=config.collection_name,
            embeddings=self.embeddings if self.embedder is None else None,
        )

        # 根据切分类型初始化相关组件
        self._init_splitters_and_retriever()

    # ---------- 私有初始化方法 ----------
    def _init_splitters_and_retriever(self) -> None:
        """根据配置初始化切分器和检索器。"""
        if self.config.splitter_type == SplitterType.PARENT_CHILD:
            self._init_parent_child_mode()
        else:
            self._init_single_splitter_mode()

    def _init_single_splitter_mode(self) -> None:
        """单一切分模式（递归或语义）。"""
        splitter_kwargs = self.config.extra_splitter_kwargs.copy()
        if self.config.splitter_type == SplitterType.SEMANTIC:
            splitter_kwargs["embeddings"] = self.embeddings
        self.splitter = get_splitter(self.config.splitter_type, **splitter_kwargs)
        self.retriever = None
        self.docstore = None
        logger.info("使用单一 %s 切分器", self.config.splitter_type.value)

    def _init_parent_child_mode(self) -> None:
        cfg = self.config

        # 父块切分器（索引构建需要，必须保留）
        self.parent_splitter = RecursiveCharacterTextSplitter(
            chunk_size=cfg.parent_chunk_size,
            chunk_overlap=cfg.parent_chunk_overlap,
        )

        # 子块切分器（索引构建需要）
        if cfg.child_splitter_type == SplitterType.SEMANTIC:
            self.child_splitter = get_splitter(
                SplitterType.SEMANTIC,
                embeddings=self.embeddings,
                **cfg.extra_splitter_kwargs
            )
        else:
            self.child_splitter = RecursiveCharacterTextSplitter(
                chunk_size=cfg.child_chunk_size,
                chunk_overlap=cfg.child_chunk_overlap,
            )

        # 文档存储
        self.docstore = self._create_or_use_docstore()

        # 使用工厂函数创建检索器，避免重复代码
        self.retriever = create_parent_retriever(
            collection_name=cfg.collection_name,
            parent_splitter=self.parent_splitter,
            child_splitter=self.child_splitter,
            docstore=self.docstore,
            search_k=cfg.search_k,
            embeddings=self.embeddings if self.embedder is None else None,
        )
        logger.info("ParentDocumentRetriever 初始化完成")

    def _create_or_use_docstore(self) -> BaseStore:
        """创建或获取文档存储实例。"""
        cfg = self.config.docstore
        if cfg.instance is not None:
            logger.debug("使用外部注入的文档存储")
            return cfg.instance

        # 使用 create_docstore 创建 PostgreSQL 存储
        docstore, conn_info = create_docstore(
            pool_config=cfg.pool_config,
            max_concurrency=cfg.max_concurrency,
        )
        self._docstore_conn = conn_info
        logger.info("文档存储已创建（PostgreSQL）")
        return docstore

    # ---------- 公共构建方法 ----------
    async def build_from_file(self, file_path: Union[str, Path]) -> int:
        """从单个文件构建索引。"""
        logger.info("加载文件: %s", file_path)
        documents = self.loader.load_file(file_path)
        logger.info("已加载 %d 个文档", len(documents))
        return await self._process_documents(documents)

    async def build_from_directory(
        self, directory_path: Union[str, Path], recursive: bool = True
    ) -> int:
        """从目录递归构建索引。"""
        logger.info("加载目录: %s (递归=%s)", directory_path, recursive)
        documents = self.loader.load_directory(directory_path, recursive=recursive)
        logger.info("已从目录加载 %d 个文档", len(documents))
        return await self._process_documents(documents)

    async def _process_documents(self, documents: List[Document]) -> int:
        """处理文档列表，分发给相应的索引逻辑。"""
        if not documents:
            logger.warning("没有文档需要处理")
            return 0

        if self.config.splitter_type == SplitterType.PARENT_CHILD:
            return await self._index_with_parent_child(documents)
        else:
            return await self._index_with_single_splitter(documents)

    async def _index_with_single_splitter(self, documents: List[Document]) -> int:
        """单一模式：切分后直接写入向量库。"""
        chunks = self.splitter.split_documents(documents)   # type: ignore[union-attr]
        logger.info("已切分为 %d 个块", len(chunks))

        self.vector_store.create_collection()
        self.vector_store.add_documents(chunks)
        return len(chunks)

    async def _index_with_parent_child(self, documents: List[Document]) -> int:
        """父子模式：使用 ParentDocumentRetriever 批量添加。"""
        self.vector_store.create_collection()
        assert self.retriever is not None

        batch_size = 10
        total = len(documents)
        processed = 0

        for i in range(0, total, batch_size):
            batch = documents[i:i + batch_size]
            await self._add_batch_with_retry(batch, i // batch_size + 1)
            processed += len(batch)
            logger.info("批次 %d: 已处理 %d/%d", i // batch_size + 1, processed, total)

        logger.info("ParentDocumentRetriever 索引完成，共处理 %d 个文档", processed)
        return processed

    async def _add_batch_with_retry(self, batch: List[Document], batch_no: int) -> None:
        """添加批次，失败时自动重试（处理网络波动）。"""
        max_retries = 5
        base_delay = 2
        for attempt in range(max_retries):
            try:
                await self.retriever.aadd_documents(batch)   # type: ignore[union-attr]
                logger.info("批次 %d 成功添加 %d 个文档", batch_no, len(batch))
                return
            except (RemoteProtocolError, ConnectionError, OSError, ResponseHandlingException) as e:
                if attempt == max_retries - 1:
                    logger.error("批次 %d 重试 %d 次后仍然失败: %s", batch_no, max_retries, e)
                    raise
                wait_time = base_delay * (2 ** attempt)
                error_type = type(e).__name__
                logger.warning(
                    "批次 %d 遇到网络异常 [%s]，%d秒后重试 (%d/%d): %s",
                    batch_no, error_type, wait_time, attempt + 1, max_retries, e
                )
                self.vector_store.refresh_client()
                logger.debug("批次 %d 已刷新 Qdrant 客户端连接", batch_no)
                await asyncio.sleep(wait_time)

    # ---------- 信息获取方法 ----------
    def get_collection_info(self) -> Any:
        """获取向量库集合信息。"""
        return self.vector_store.get_collection_info()

    def get_child_splitter(self) -> TextSplitter:
        """获取当前使用的子块切分器。"""
        if self.config.splitter_type == SplitterType.PARENT_CHILD:
            return self.child_splitter   # type: ignore[return-value]
        return self.splitter   # type: ignore[return-value]

    def get_parent_splitter(self) -> RecursiveCharacterTextSplitter:
        """获取父块切分器（仅父子模式可用）。"""
        if self.config.splitter_type != SplitterType.PARENT_CHILD:
            raise RuntimeError("父块切分器仅在父子块模式下可用")
        return self.parent_splitter   # type: ignore[return-value]

    def get_docstore(self) -> BaseStore:
        """获取文档存储实例（仅父子模式可用）。"""
        if self.config.splitter_type != SplitterType.PARENT_CHILD:
            raise RuntimeError("文档存储仅在父子块模式下可用")
        assert self.docstore is not None
        return self.docstore

    # ---------- 资源管理 ----------
    def close(self) -> None:
        """关闭资源（同步版本，供上下文管理器使用）。"""
        if self.docstore is not None and hasattr(self.docstore, "aclose"):
            try:
                loop = asyncio.get_running_loop()
            except RuntimeError:
                # 无运行中的事件循环，创建临时循环
                loop = asyncio.new_event_loop()
                loop.run_until_complete(self.docstore.aclose())   # type: ignore[attr-defined]
                loop.close()
            else:
                # 已有运行中的循环，创建任务（用户自行等待）
                loop.create_task(self.docstore.aclose())   # type: ignore[attr-defined]
        logger.info("IndexBuilder 资源已关闭")

    async def aclose(self) -> None:
        """异步关闭资源。"""
        if self.docstore is not None and hasattr(self.docstore, "aclose"):
            await self.docstore.aclose()   # type: ignore[attr-defined]
        logger.info("IndexBuilder 资源已异步关闭")

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.close()
        return False

    async def __aenter__(self):
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.aclose()
        return False