from typing import Optional, List, Dict, Any

from pymilvus import MilvusClient, DataType

from app.client.milvus_client_manager import milvus_client
from app.conf.app_config import app_config


class SummaryRepository:
    collection_name: str = 'message_summary'

    def __init__(self):
        self.dim = app_config.milvus.embedding_size
        self._ensure_connection()

    def _ensure_connection(self):
        if milvus_client.client is None:
            milvus_client.init()

    def create_collection(self, dim: int = None) -> bool:
        """
        创建消息摘要集合
        
        Args:
            dim: 向量维度，默认从配置读取
        
        Returns:
            是否成功创建（已存在返回False）
        """
        if dim is not None:
            self.dim = dim

        if milvus_client.has_collection(self.collection_name):
            return False

        schema = milvus_client.client.create_schema(
            auto_id=True,
            enable_dynamic_field=True,
            description="消息摘要向量集合"
        )

        schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True, auto_id=True)
        schema.add_field(field_name="msg_time", datatype=DataType.VARCHAR, max_length=100)
        schema.add_field(field_name="msg_type", datatype=DataType.INT8)
        schema.add_field(field_name="from_user", datatype=DataType.VARCHAR, max_length=150)
        schema.add_field(field_name="to_user", datatype=DataType.VARCHAR, max_length=150)
        schema.add_field(field_name="room_id", datatype=DataType.VARCHAR, max_length=150)
        schema.add_field(field_name="message_context", datatype=DataType.VARCHAR, max_length=65535)
        schema.add_field(field_name="summary", datatype=DataType.VARCHAR, max_length=65535)
        schema.add_field(field_name="message_dense_vector", datatype=DataType.FLOAT_VECTOR, dim=self.dim)
        schema.add_field(field_name="summary_dense_vector", datatype=DataType.FLOAT_VECTOR, dim=self.dim)
        schema.add_field(field_name="created_at", datatype=DataType.INT64)

        index_params = milvus_client.client.prepare_index_params()
        index_params.add_index(
            field_name="message_dense_vector",
            index_type="HNSW",
            metric_type="COSINE",
            params={"M": 32, "efConstruction": 300}
        )
        index_params.add_index(
            field_name="summary_dense_vector",
            index_type="HNSW",
            metric_type="COSINE",
            params={"M": 32, "efConstruction": 300}
        )
        milvus_client.client.create_collection(
            collection_name=self.collection_name,
            schema=schema,
            index_params=index_params
        )

        return True

    def insert(self, msg_time: List[str], msg_type: List[int], from_user: List[str],
               to_user: List[str], room_id: List[str],message_context: List[str], summary: List[str], message_dense_vector: List[List[float]],
               summary_dense_vector: List[List[float]]) -> List[int]:
        """
        插入消息摘要数据
        
        Args:
            msg_time: 消息时间列表
            msg_type: 消息类型列表
            from_user: 发送者列表
            to_user: 接收者列表
            room_id: 群聊ID列表
            summary: 消息摘要列表
            dense_vector: 向量列表
        
        Returns:
            插入的数据ID列表
        """
        self._ensure_connection()
        self.create_collection()
        import time
        created_at = [int(time.time() * 1000)] * len(msg_time)

        data = []
        for i in range(len(msg_time)):
            data.append({
                "msg_time": msg_time[i],
                "msg_type": msg_type[i],
                "from_user": from_user[i],
                "to_user": to_user[i],
                "room_id": room_id[i],
                "message_context": message_context[i],
                "summary": summary[i],
                "message_dense_vector": message_dense_vector[i],
                "summary_dense_vector": summary_dense_vector[i],
                "created_at": created_at[i]
            })

        result = milvus_client.client.insert(
            collection_name=self.collection_name,
            data=data
        )

        milvus_client.client.flush(self.collection_name)
        return result.get("ids", [])

    def delete(self, expr: str):
        """
        删除数据
        
        Args:
            expr: 删除条件表达式
        """
        self._ensure_connection()

        milvus_client.client.delete(
            collection_name=self.collection_name,
            filter=expr
        )
        milvus_client.client.flush(self.collection_name)

    def drop_collection(self):
        """删除集合"""
        milvus_client.drop_collection(self.collection_name)

    def get_collection_stats(self) -> Dict[str, Any]:
        """获取集合统计信息"""
        self._ensure_connection()
        
        try:
            result = milvus_client.client.get_collection_stats(collection_name=self.collection_name)
            return {
                "num_entities": result.get("row_count", 0),
                "collection_name": self.collection_name
            }
        except Exception as e:
            print(f"获取统计信息失败: {e}")
            return {"num_entities": 0, "collection_name": self.collection_name}

    def close(self):
        """关闭连接"""
        milvus_client.close()