vd.backends.memory

In-memory backend — the reference adapter.

Stores documents in a plain dict and answers queries with brute-force similarity in pure Python. Always available (no third-party dependency), and the canonical example of how a backend implements the small set of raw primitives that AbstractCollection builds on. Use it for tests, notebooks, and corpora small enough that an ANN index is overkill.

Classes

MemoryClient(*[, embedder])	In-memory vector database client.
MemoryCollection(name, *[, embedder, ...])	A collection backed by an in-process dict[str, Document].

class vd.backends.memory.MemoryClient(*, embedder=None, **config)

Bases: AbstractClient

In-memory vector database client.

Examples

>>> import vd
>>> client = vd.connect('memory')
>>> col = client.create_collection('docs')
>>> col['a'] = vd.Document(id='a', text='cat', vector=[1.0, 0.0])
>>> col['b'] = vd.Document(id='b', text='dog', vector=[0.0, 1.0])
>>> [r['id'] for r in col.search([0.9, 0.1], limit=1)]
['a']

backend_name: str = 'memory'

The registry name of this backend (e.g. "chroma"). Adapters set it.

create_collection(name, *, dimension=None, metric='cosine', **index_config)

Create a new collection.

Parameters:

• name (str) – Collection name.

• dimension (Optional[int]) – Vector dimension. May be None for backends that can infer it from the first written vector; required up front by backends that cannot.

• metric (str) – Distance metric: "cosine", "dot", or "l2".

• **index_config – Backend-specific index tuning (HNSW M/ef, IVF nlist, …). Documented per adapter; never abstracted into a common enum.

Raises:

ValueError – If a collection of that name already exists.

Return type:

MemoryCollection

delete_collection(name)

Drop a collection; raise KeyError if absent.

Return type:

None

get_collection(name)

Return an existing collection; raise KeyError if absent.

Return type:

MemoryCollection

list_collections()

Iterate collection names.

Return type:

Iterator[str]

class vd.backends.memory.MemoryCollection(name, *, embedder=None, dimension=None, metric='cosine')

Bases: AbstractCollection

A collection backed by an in-process dict[str, Document].

property native: dict[str, Document]

The raw dict[str, Document] backing this collection (escape hatch).

supported_filter_operators: frozenset = frozenset({'$and', '$eq', '$exists', '$gt', '$gte', '$in', '$lt', '$lte', '$ne', '$nin', '$not', '$or'})

the full language. Adapters narrow this; search() validates against it.

Type:

Filter operators this backend can honor. Default