hyrax.vector_dbs.vector_db_interface

hyrax.vector_dbs.vector_db_interface#

Classes#

VectorDB

Interface for a vector database

Module Contents#

class VectorDB(config: dict | None = None, context: dict | None = None)[source]#

Bases: abc.ABC

Interface for a vector database

__init__()[source]#

Create a new instance of a VectorDB object.

Parameters:

  • config (dict, optional) – An instance of the runtime configuration, by default None

  • context (dict, optional) – An instance of the context object, by default None

config[source]#
context[source]#
abstractmethod connect()[source]#

Connect to an existing database

abstractmethod create()[source]#

Create a new database

abstractmethod insert(ids: list[str | int], vectors: list[numpy.ndarray])[source]#

Insert a batch of vectors into the database.

Parameters:

  • ids (list[Union[str, int]]) – The ids to associate with the vectors

  • vectors (list[np.ndarray]) – The vectors to insert into the database

abstractmethod search_by_id(id: str | int, k: int = 1) → dict[int, list[str | int]][source]#

Get the ids of the k nearest neighbors for a given id in the database. Should use the provided id to look up the vector, then call search_by_vector.

Parameters:

  • id (Union[str, int]) – The id of the vector in the database for which we want to find the k nearest neighbors

  • k (int, optional) – The number of nearest neighbors to return, by default 1, return only the closest neighbor

Returns:

Dictionary with input vector index as the key and the ids of the k nearest neighbors as the value.

Return type:

dict[int, list[Union[str, int]]]

abstractmethod search_by_vector(vectors: numpy.ndarray | list[numpy.ndarray], k: int = 1) → dict[int, list[str | int]][source]#

Get the ids of the k nearest neighbors for a given vector.

Parameters:

  • vectors (Union[np.array, list[np.ndarray]]) – The one or more vectors to use when searching for nearest neighbors

  • k (int, optional) – The number of nearest neighbors to return, by default 1, return only the closest neighbor

Returns:

Dictionary with input vector index as the key and the ids of the k nearest neighbors as the value.

Return type:

dict[int, list[Union[str, int]]]

abstractmethod get_by_id(ids: list[str | int]) → dict[str | int, list[float]][source]#

Retrieve the vectors associated with a list of ids.

Parameters:

ids (list[Union[str, int]]) – The ids of the vectors to retrieve.

Returns:

Dictionary with the ids as the keys and the vectors as the values.

Return type:

dict[Union[str, int], list[float]]