Singlestore

SingleStoreReader

Bases: BaseReader

SingleStore reader.

Parameters:

Name	Type	Description	Default
scheme	str	Database Scheme.	required
host	str	Database Host.	required
port	str	Database Port.	required
user	str	Database User.	required
password	str	Database Password.	required
dbname	str	Database Name.	required
table_name	str	Table Name.	required
content_field	str	Content Field.	'text'
vector_field	str	Vector Field.	'embedding'

Source code in llama-index-integrations/readers/llama-index-readers-singlestore/llama_index/readers/singlestore/base.py

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)

load_data

load_data(search_embedding: str, top_k: int = 5) -> List[Document]

Load data from SingleStore.

Parameters:

Name	Type	Description	Default
search_embedding	str	The embedding to search.	required
top_k	int	Number of results to return.	5

Returns:

Type	Description
List[Document]	List[Document]: A list of documents.

Source code in llama-index-integrations/readers/llama-index-readers-singlestore/llama_index/readers/singlestore/base.py

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)