Singlestoredb

SingleStoreVectorStore

Bases: VectorStore

SingleStore vector store.

This vector store stores embeddings within a SingleStore database table.

During query time, the index uses SingleStore to query for the top k most similar nodes.

Parameters:

Name	Type	Description	Default
table_name	str	Specifies the name of the table in use. Defaults to "embeddings".	'embeddings'
content_field	str	Specifies the field to store the content. Defaults to "content".	'content'
metadata_field	str	Specifies the field to store metadata. Defaults to "metadata".	'metadata'
vector_field	str	Specifies the field to store the vector. Defaults to "vector".	'vector'
Following	arguments pertain to the connection pool		required
pool_size	int	Determines the number of active connections in the pool. Defaults to 5.	5
max_overflow	int	Determines the maximum number of connections allowed beyond the pool_size. Defaults to 10.	10
timeout	float	Specifies the maximum wait time in seconds for establishing a connection. Defaults to 30.	30
Following	arguments pertain to the connection		required
host	str	Specifies the hostname, IP address, or URL for the database connection. The default scheme is "mysql".	required
user	str	Database username.	required
password	str	Database password.	required
port	int	Database port. Defaults to 3306 for non-HTTP connections, 80 for HTTP connections, and 443 for HTTPS connections.	required
database	str	Database name.	required

Examples:

pip install llama-index-vector-stores-singlestoredb

from llama_index.vector_stores.singlestoredb import SingleStoreVectorStore
import os

# can set the singlestore db url in env
# or pass it in as an argument to the SingleStoreVectorStore constructor
os.environ["SINGLESTOREDB_URL"] = "PLACEHOLDER URL"
vector_store = SingleStoreVectorStore(
    table_name="embeddings",
    content_field="content",
    metadata_field="metadata",
    vector_field="vector",
    timeout=30,
)

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/llama_index/vector_stores/singlestoredb/base.py

class SingleStoreVectorStore(VectorStore):
    """SingleStore vector store.

    This vector store stores embeddings within a SingleStore database table.

    During query time, the index uses SingleStore to query for the top
    k most similar nodes.

    Args:
        table_name (str, optional): Specifies the name of the table in use.
                Defaults to "embeddings".
        content_field (str, optional): Specifies the field to store the content.
            Defaults to "content".
        metadata_field (str, optional): Specifies the field to store metadata.
            Defaults to "metadata".
        vector_field (str, optional): Specifies the field to store the vector.
            Defaults to "vector".

        Following arguments pertain to the connection pool:

        pool_size (int, optional): Determines the number of active connections in
            the pool. Defaults to 5.
        max_overflow (int, optional): Determines the maximum number of connections
            allowed beyond the pool_size. Defaults to 10.
        timeout (float, optional): Specifies the maximum wait time in seconds for
            establishing a connection. Defaults to 30.

        Following arguments pertain to the connection:

        host (str, optional): Specifies the hostname, IP address, or URL for the
                database connection. The default scheme is "mysql".
        user (str, optional): Database username.
        password (str, optional): Database password.
        port (int, optional): Database port. Defaults to 3306 for non-HTTP
            connections, 80 for HTTP connections, and 443 for HTTPS connections.
        database (str, optional): Database name.

    Examples:
        `pip install llama-index-vector-stores-singlestoredb`

        ```python
        from llama_index.vector_stores.singlestoredb import SingleStoreVectorStore
        import os

        # can set the singlestore db url in env
        # or pass it in as an argument to the SingleStoreVectorStore constructor
        os.environ["SINGLESTOREDB_URL"] = "PLACEHOLDER URL"
        vector_store = SingleStoreVectorStore(
            table_name="embeddings",
            content_field="content",
            metadata_field="metadata",
            vector_field="vector",
            timeout=30,
        )
        ```

    """

    stores_text: bool = True
    flat_metadata: bool = True

    def __init__(
        self,
        table_name: str = "embeddings",
        content_field: str = "content",
        metadata_field: str = "metadata",
        vector_field: str = "vector",
        pool_size: int = 5,
        max_overflow: int = 10,
        timeout: float = 30,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        self.table_name = table_name
        self.content_field = content_field
        self.metadata_field = metadata_field
        self.vector_field = vector_field
        self.pool_size = pool_size
        self.max_overflow = max_overflow
        self.timeout = timeout

        self.connection_kwargs = kwargs
        self.connection_pool = QueuePool(
            self._get_connection,
            pool_size=self.pool_size,
            max_overflow=self.max_overflow,
            timeout=self.timeout,
        )

        self._create_table()

    @property
    def client(self) -> Any:
        """Return SingleStoreDB client."""
        return self._get_connection()

    @classmethod
    def class_name(cls) -> str:
        return "SingleStoreVectorStore"

    def _get_connection(self) -> Any:
        return s2.connect(**self.connection_kwargs)

    def _create_table(self) -> None:
        conn = self.connection_pool.connect()
        try:
            cur = conn.cursor()
            try:
                cur.execute(
                    f"""CREATE TABLE IF NOT EXISTS {self.table_name}
                    ({self.content_field} TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,
                    {self.vector_field} BLOB, {self.metadata_field} JSON);"""
                )
            finally:
                cur.close()
        finally:
            conn.close()

    def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
        """Add nodes to index.

        Args:
            nodes: List[BaseNode]: list of nodes with embeddings

        """
        conn = self.connection_pool.connect()
        cursor = conn.cursor()
        try:
            for node in nodes:
                embedding = node.get_embedding()
                metadata = node_to_metadata_dict(
                    node, remove_text=True, flat_metadata=self.flat_metadata
                )
                cursor.execute(
                    "INSERT INTO {} VALUES (%s, JSON_ARRAY_PACK(%s), %s)".format(
                        self.table_name
                    ),
                    (
                        node.get_content(metadata_mode=MetadataMode.NONE) or "",
                        "[{}]".format(",".join(map(str, embedding))),
                        json.dumps(metadata),
                    ),
                )
        finally:
            cursor.close()
            conn.close()
        return [node.node_id for node in nodes]

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        """
        Delete nodes using with ref_doc_id.

        Args:
            ref_doc_id (str): The doc_id of the document to delete.

        """
        conn = self.connection_pool.connect()
        cursor = conn.cursor()
        try:
            cursor.execute(
                f"DELETE FROM {self.table_name} WHERE JSON_EXTRACT_JSON(metadata, 'ref_doc_id') = %s",
                ('"' + ref_doc_id + '"',),
            )
        finally:
            cursor.close()
            conn.close()

    def query(
        self, query: VectorStoreQuery, filter: Optional[dict] = None, **kwargs: Any
    ) -> VectorStoreQueryResult:
        """
        Query index for top k most similar nodes.

        Args:
            query (VectorStoreQuery): Contains query_embedding and similarity_top_k attributes.
            filter (Optional[dict]): A dictionary of metadata fields and values to filter by. Defaults to None.

        Returns:
            VectorStoreQueryResult: Contains nodes, similarities, and ids attributes.
        """
        query_embedding = query.query_embedding
        similarity_top_k = query.similarity_top_k
        conn = self.connection_pool.connect()
        where_clause: str = ""
        where_clause_values: List[Any] = []

        if filter:
            where_clause = "WHERE "
            arguments = []

            def build_where_clause(
                where_clause_values: List[Any],
                sub_filter: dict,
                prefix_args: Optional[List[str]] = None,
            ) -> None:
                prefix_args = prefix_args or []
                for key in sub_filter:
                    if isinstance(sub_filter[key], dict):
                        build_where_clause(
                            where_clause_values, sub_filter[key], [*prefix_args, key]
                        )
                    else:
                        arguments.append(
                            "JSON_EXTRACT({}, {}) = %s".format(
                                {self.metadata_field},
                                ", ".join(["%s"] * (len(prefix_args) + 1)),
                            )
                        )
                        where_clause_values += [*prefix_args, key]
                        where_clause_values.append(json.dumps(sub_filter[key]))

            build_where_clause(where_clause_values, filter)
            where_clause += " AND ".join(arguments)

        results: Sequence[Any] = []
        if query_embedding:
            try:
                cur = conn.cursor()
                formatted_vector = "[{}]".format(",".join(map(str, query_embedding)))
                try:
                    logger.debug("vector field: %s", formatted_vector)
                    logger.debug("similarity_top_k: %s", similarity_top_k)
                    cur.execute(
                        f"SELECT {self.content_field}, {self.metadata_field}, "
                        f"DOT_PRODUCT({self.vector_field}, "
                        "JSON_ARRAY_PACK(%s)) as similarity_score "
                        f"FROM {self.table_name} {where_clause} "
                        f"ORDER BY similarity_score DESC LIMIT {similarity_top_k}",
                        (formatted_vector, *tuple(where_clause_values)),
                    )
                    results = cur.fetchall()
                finally:
                    cur.close()
            finally:
                conn.close()

        nodes = []
        similarities = []
        ids = []
        for result in results:
            text, metadata, similarity_score = result
            node = metadata_dict_to_node(metadata)
            node.set_content(text)
            nodes.append(node)
            similarities.append(similarity_score)
            ids.append(node.node_id)

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

client property

client: Any

Return SingleStoreDB client.

add

add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]

Add nodes to index.

Parameters:

Name	Type	Description	Default
nodes	List[BaseNode]	List[BaseNode]: list of nodes with embeddings	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/llama_index/vector_stores/singlestoredb/base.py

def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
    """Add nodes to index.

    Args:
        nodes: List[BaseNode]: list of nodes with embeddings

    """
    conn = self.connection_pool.connect()
    cursor = conn.cursor()
    try:
        for node in nodes:
            embedding = node.get_embedding()
            metadata = node_to_metadata_dict(
                node, remove_text=True, flat_metadata=self.flat_metadata
            )
            cursor.execute(
                "INSERT INTO {} VALUES (%s, JSON_ARRAY_PACK(%s), %s)".format(
                    self.table_name
                ),
                (
                    node.get_content(metadata_mode=MetadataMode.NONE) or "",
                    "[{}]".format(",".join(map(str, embedding))),
                    json.dumps(metadata),
                ),
            )
    finally:
        cursor.close()
        conn.close()
    return [node.node_id for node in nodes]

delete

delete(ref_doc_id: str, **delete_kwargs: Any) -> None

Delete nodes using with ref_doc_id.

Parameters:

Name	Type	Description	Default
ref_doc_id	str	The doc_id of the document to delete.	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/llama_index/vector_stores/singlestoredb/base.py

def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
    """
    Delete nodes using with ref_doc_id.

    Args:
        ref_doc_id (str): The doc_id of the document to delete.

    """
    conn = self.connection_pool.connect()
    cursor = conn.cursor()
    try:
        cursor.execute(
            f"DELETE FROM {self.table_name} WHERE JSON_EXTRACT_JSON(metadata, 'ref_doc_id') = %s",
            ('"' + ref_doc_id + '"',),
        )
    finally:
        cursor.close()
        conn.close()

query

query(query: VectorStoreQuery, filter: Optional[dict] = None, **kwargs: Any) -> VectorStoreQueryResult

Query index for top k most similar nodes.

Parameters:

Name	Type	Description	Default
query	VectorStoreQuery	Contains query_embedding and similarity_top_k attributes.	required
filter	Optional[dict]	A dictionary of metadata fields and values to filter by. Defaults to None.	None

Returns:

Name	Type	Description
VectorStoreQueryResult	VectorStoreQueryResult	Contains nodes, similarities, and ids attributes.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/llama_index/vector_stores/singlestoredb/base.py

def query(
    self, query: VectorStoreQuery, filter: Optional[dict] = None, **kwargs: Any
) -> VectorStoreQueryResult:
    """
    Query index for top k most similar nodes.

    Args:
        query (VectorStoreQuery): Contains query_embedding and similarity_top_k attributes.
        filter (Optional[dict]): A dictionary of metadata fields and values to filter by. Defaults to None.

    Returns:
        VectorStoreQueryResult: Contains nodes, similarities, and ids attributes.
    """
    query_embedding = query.query_embedding
    similarity_top_k = query.similarity_top_k
    conn = self.connection_pool.connect()
    where_clause: str = ""
    where_clause_values: List[Any] = []

    if filter:
        where_clause = "WHERE "
        arguments = []

        def build_where_clause(
            where_clause_values: List[Any],
            sub_filter: dict,
            prefix_args: Optional[List[str]] = None,
        ) -> None:
            prefix_args = prefix_args or []
            for key in sub_filter:
                if isinstance(sub_filter[key], dict):
                    build_where_clause(
                        where_clause_values, sub_filter[key], [*prefix_args, key]
                    )
                else:
                    arguments.append(
                        "JSON_EXTRACT({}, {}) = %s".format(
                            {self.metadata_field},
                            ", ".join(["%s"] * (len(prefix_args) + 1)),
                        )
                    )
                    where_clause_values += [*prefix_args, key]
                    where_clause_values.append(json.dumps(sub_filter[key]))

        build_where_clause(where_clause_values, filter)
        where_clause += " AND ".join(arguments)

    results: Sequence[Any] = []
    if query_embedding:
        try:
            cur = conn.cursor()
            formatted_vector = "[{}]".format(",".join(map(str, query_embedding)))
            try:
                logger.debug("vector field: %s", formatted_vector)
                logger.debug("similarity_top_k: %s", similarity_top_k)
                cur.execute(
                    f"SELECT {self.content_field}, {self.metadata_field}, "
                    f"DOT_PRODUCT({self.vector_field}, "
                    "JSON_ARRAY_PACK(%s)) as similarity_score "
                    f"FROM {self.table_name} {where_clause} "
                    f"ORDER BY similarity_score DESC LIMIT {similarity_top_k}",
                    (formatted_vector, *tuple(where_clause_values)),
                )
                results = cur.fetchall()
            finally:
                cur.close()
        finally:
            conn.close()

    nodes = []
    similarities = []
    ids = []
    for result in results:
        text, metadata, similarity_score = result
        node = metadata_dict_to_node(metadata)
        node.set_content(text)
        nodes.append(node)
        similarities.append(similarity_score)
        ids.append(node.node_id)

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)