"""
Qdrant 向量数据库包装器。
"""

import logging
import os
from typing import List, Optional, Dict, Any

from langchain_core.documents import Document
from langchain_qdrant import QdrantVectorStore as LangchainQdrantVS
from qdrant_client import QdrantClient
from qdrant_client.http import models
from qdrant_client.http.models import Distance, VectorParams

from .embedders import LlamaCppEmbedder

logger = logging.getLogger(__name__)

QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")


class QdrantVectorStore:
    """Qdrant 向量数据库操作包装器。"""

    def __init__(
        self,
        collection_name: str,
        embeddings: Optional[Any] = None,
    ):
        self.collection_name = collection_name
        self._client: Optional[QdrantClient] = None

        # 嵌入模型
        if embeddings is None:
            embedder = LlamaCppEmbedder()
            self.embeddings = embedder.as_langchain_embeddings()
        else:
            self.embeddings = embeddings

        # 先创建集合
        self.create_collection()

        # LangChain 向量存储
        self.vector_store = LangchainQdrantVS(
            client=self.get_client(),
            collection_name=self.collection_name,
            embedding=self.embeddings,
        )

    def get_client(self) -> QdrantClient:
        """懒加载客户端，每次获取时确保连接可用。"""
        if self._client is None:
            self._client = QdrantClient(
                url=QDRANT_URL,
                api_key=QDRANT_API_KEY,
                timeout=120,
                http2=False,
            )
        return self._client

    def refresh_client(self):
        """关闭旧连接，创建新连接。"""
        if self._client is not None:
            self._client.close()
            self._client = None

    def create_collection(self, vector_size: Optional[int] = None, force_recreate: bool = False):
        """创建集合，设置合适的向量维度。"""
        if vector_size is None:
            embedder = LlamaCppEmbedder()
            vector_size = embedder.get_embedding_dimension()

        client = self.get_client()
        collections = client.get_collections().collections
        exists = any(c.name == self.collection_name for c in collections)

        if exists and force_recreate:
            client.delete_collection(self.collection_name)
            exists = False

        if not exists:
            client.create_collection(
                collection_name=self.collection_name,
                vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
            )
            logger.info("集合 '%s' 已创建（维度=%d）", self.collection_name, vector_size)
        else:
            logger.info("集合 '%s' 已存在", self.collection_name)

    def add_documents(self, documents: List[Document], batch_size: int = 100):
        """将文档添加到向量数据库。"""
        if not documents:
            return []
        self.create_collection()
        ids = self.vector_store.add_documents(documents, batch_size=batch_size)
        logger.info("已向 '%s' 添加 %d 个文档", self.collection_name, len(ids))
        return ids

    def similarity_search(self, query: str, k: int = 5) -> List[Document]:
        return self.vector_store.similarity_search(query, k=k)

    def similarity_search_with_score(self, query: str, k: int = 5) -> List[tuple[Document, float]]:
        return self.vector_store.similarity_search_with_score(query, k=k)

    def delete_collection(self):
        self.get_client().delete_collection(self.collection_name)
        logger.info("集合 '%s' 已删除", self.collection_name)

    def get_collection_info(self) -> Dict[str, Any]:
        info = self.get_client().get_collection(self.collection_name)
        vectors_config = info.config.params.vectors
        if isinstance(vectors_config, dict):
            vector_size = next(iter(vectors_config.values())).size
        else:
            vector_size = vectors_config.size
        return {
            "name": self.collection_name,
            "vectors_count": info.points_count or 0,
            "status": info.status,
            "vector_size": vector_size,
        }

    def as_langchain_vectorstore(self):
        return self.vector_store

    def get_langchain_vectorstore(self):
        """返回 LangChain Qdrant 向量存储对象（别名）"""
        return self.vector_store

    def get_qdrant_client(self):
        """返回原生 Qdrant 客户端（如需手动管理 collection）"""
        return self.get_client()