hyrax.vector_dbs

Submodules

• hyrax.vector_dbs.chromadb_impl
• hyrax.vector_dbs.qdrantdb_impl
• hyrax.vector_dbs.vector_db_factory
• hyrax.vector_dbs.vector_db_interface

Classes

ChromaDB

Implementation of the VectorDB interface using ChromaDB as the backend.

Package Contents

class ChromaDB(config, context)

Bases: hyrax.vector_dbs.vector_db_interface.VectorDB

Implementation of the VectorDB interface using ChromaDB as the backend.

__init__()

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

chromadb_client = None

collection = None

shard_index = 0

shard_size = 0

shard_size_limit

vector_size_limit

min_shards_for_parallelization = 50

connect()

Create a database connection

create()

Create a new database

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

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

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

Get the ids of the k nearest neighbors for a given id in the database.

Parameters:

• id (Union[str | int]) – The id of the vector in the database for which we want to find the k nearest neighbors. If type int is provided, it will be converted to a string.

• k (int, optional) – The number of nearest neighbors to return. By default 1, return only the closest neighbor - this is almost always the same as the input.

Returns:

Dictionary with input id as the key and the ids of the k nearest neighbors as the value. Because this function accepts only 1 id, the key will always be 0. i.e. {0: [id1, id2, …]}

Return type:

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

Raises:

ValueError – If more than one vector is found for the given id

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

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

Parameters:

• vectors (Union[np.ndarray, list[np.ndarray]]) – The vector 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]]]

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

Retrieve the vectors associated with a list of ids.

Parameters:

ids (list[Union[str, int]]) – The ids of the vectors to retrieve. For ChromaDB instances, these should always be strings.

Returns:

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

Return type:

dict[str, list[float]]

_get_ids(ids: list[str | int]) → set[str]

For the given list of ids, return the ids that are already in the database.

Parameters:

ids (list[Union[str, int]]) – The ids of the vectors to retrieve. For ChromaDB instances, these should always be strings.

Returns:

Set of ids that are already in the database.

Return type:

set(str)