"""SingleStore reader."""

from typing import List

from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.database import DatabaseReader


class SingleStoreReader(BaseReader):
    """
    SingleStore reader.

    Args:
        scheme (str): Database Scheme.
        host (str): Database Host.
        port (str): Database Port.
        user (str): Database User.
        password (str): Database Password.
        dbname (str): Database Name.
        table_name (str): Table Name.
        content_field (str): Content Field.
        vector_field (str): Vector Field.

    """

    def __init__(
        self,
        scheme: str,
        host: str,
        port: str,
        user: str,
        password: str,
        dbname: str,
        table_name: str,
        content_field: str = "text",
        vector_field: str = "embedding",
    ):
        """Initialize with parameters."""
        self.scheme = scheme
        self.host = host
        self.port = port
        self.user = user
        self.password = password
        self.dbname = dbname
        self.table_name = table_name
        self.content_field = content_field
        self.vector_field = vector_field

        try:
            import pymysql

            pymysql.install_as_MySQLdb()
        except ImportError:
            pass

        self.DatabaseReader = DatabaseReader
        self.reader = self.DatabaseReader(
            scheme=self.scheme,
            host=self.host,
            port=self.port,
            user=self.user,
            password=self.password,
            dbname=self.dbname,
        )

    def load_data(self, search_embedding: str, top_k: int = 5) -> List[Document]:
        """
        Load data from SingleStore.

        Args:
            search_embedding (str): The embedding to search.
            top_k (int): Number of results to return.

        Returns:
            List[Document]: A list of documents.

        """
        query = f"""
        SELECT {self.content_field}, DOT_PRODUCT_F64({self.vector_field}, JSON_ARRAY_PACK_F64(\'{search_embedding}\')) AS score
        FROM {self.table_name}
        ORDER BY score
        DESC LIMIT {top_k}
        """

        return self.reader.load_data(query=query)

"""SingleStore reader."""

from typing import List

from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.database import DatabaseReader


class SingleStoreReader(BaseReader):
    """SingleStore reader.

    Args:
        scheme (str): Database Scheme.
        host (str): Database Host.
        port (str): Database Port.
        user (str): Database User.
        password (str): Database Password.
        dbname (str): Database Name.
        table_name (str): Table Name.
        content_field (str): Content Field.
        vector_field (str): Vector Field.
    """

    def __init__(
        self,
        scheme: str,
        host: str,
        port: str,
        user: str,
        password: str,
        dbname: str,
        table_name: str,
        content_field: str = "text",
        vector_field: str = "embedding",
    ):
        """Initialize with parameters."""
        self.scheme = scheme
        self.host = host
        self.port = port
        self.user = user
        self.password = password
        self.dbname = dbname
        self.table_name = table_name
        self.content_field = content_field
        self.vector_field = vector_field

        try:
            import pymysql

            pymysql.install_as_MySQLdb()
        except ImportError:
            pass

        self.DatabaseReader = DatabaseReader
        self.reader = self.DatabaseReader(
            scheme=self.scheme,
            host=self.host,
            port=self.port,
            user=self.user,
            password=self.password,
            dbname=self.dbname,
        )

    def load_data(self, search_embedding: str, top_k: int = 5) -> List[Document]:
        """Load data from SingleStore.

        Args:
            search_embedding (str): The embedding to search.
            top_k (int): Number of results to return.

        Returns:
            List[Document]: A list of documents.
        """
        query = f"""
        SELECT {self.content_field}, DOT_PRODUCT_F64({self.vector_field}, JSON_ARRAY_PACK_F64(\'{search_embedding}\')) AS score
        FROM {self.table_name}
        ORDER BY score
        DESC LIMIT {top_k}
        """

        return self.reader.load_data(query=query)
