VolatileMemoryStore Class

A volatile memory store that stores data in memory.

Note: This class is marked as 'experimental' and may change in the future.

Initializes a new instance of the VolatileMemoryStore class.

Constructor

VolatileMemoryStore()

Methods

compute_similarity_scores	Computes the cosine similarity scores between a query embedding and a group of embeddings.
create_collection	Creates a new collection if it does not exist.
delete_collection	Deletes a collection.
does_collection_exist	Checks if a collection exists.
get	Gets a record.
get_batch	Gets a batch of records.
get_collections	Gets the list of collections.
get_nearest_match	Gets the nearest match to an embedding using cosine similarity.
get_nearest_matches	Gets the nearest matches to an embedding using cosine similarity.
remove	Removes a record.
remove_batch	Removes a batch of records.
upsert	Upserts a record.
upsert_batch	Upserts a batch of records.

compute_similarity_scores

Computes the cosine similarity scores between a query embedding and a group of embeddings.

compute_similarity_scores(embedding: ndarray, embedding_array: ndarray) -> ndarray

Parameters

Name	Description
embedding Required	<xref:semantic_kernel.memory.ndarray> The query embedding.
embedding_array Required	<xref:semantic_kernel.memory.ndarray> The group of embeddings.

Returns

Type	Description
<xref:ndarray>	The cosine similarity scores.

create_collection

Creates a new collection if it does not exist.

async create_collection(collection_name: str) -> None

Parameters

Name	Description
collection_name Required	str The name of the collection to create.

Returns

Type	Description
	None

delete_collection

Deletes a collection.

async delete_collection(collection_name: str) -> None

Parameters

Name	Description
collection_name Required	str The name of the collection to delete.

Returns

Type	Description
	None

does_collection_exist

Checks if a collection exists.

async does_collection_exist(collection_name: str) -> bool

Parameters

Name	Description
collection_name Required	str The name of the collection to check.

Returns

Type	Description
bool	True if the collection exists; otherwise, False.

get

Gets a record.

async get(collection_name: str, key: str, with_embedding: bool = False) -> MemoryRecord

Parameters

Name	Description
collection_name Required	str The name of the collection to get the record from.
key Required	str The unique database key of the record.
with_embedding	bool Whether to include the embedding in the result. (default: {False}) Default value: False

Returns

Type	Description
MemoryRecord	The record.

get_batch

Gets a batch of records.

async get_batch(collection_name: str, keys: list[str], with_embeddings: bool = False) -> list[MemoryRecord]

Parameters

Name	Description
collection_name Required	str The name of the collection to get the records from.
keys Required	<xref:List>[str] The unique database keys of the records.
with_embeddings	bool Whether to include the embeddings in the results. (default: {False}) Default value: False

Returns

Type	Description
List[MemoryRecord]	The records.

get_collections

Gets the list of collections.

async get_collections() -> list[str]

Returns

Type	Description
List[str]	The list of collections.

get_nearest_match

Gets the nearest match to an embedding using cosine similarity.

async get_nearest_match(collection_name: str, embedding: ndarray, min_relevance_score: float = 0.0, with_embedding: bool = False) -> tuple[MemoryRecord, float]

Parameters

Name	Description
collection_name Required	str The name of the collection to get the nearest match from.
embedding Required	<xref:semantic_kernel.memory.ndarray> The embedding to find the nearest match to.
min_relevance_score	float The minimum relevance score of the match. (default: {0.0}) Default value: 0.0
with_embedding	bool Whether to include the embedding in the result. (default: {False}) Default value: False

Returns

Type	Description
Tuple[MemoryRecord, float]	The record and the relevance score.

get_nearest_matches

Gets the nearest matches to an embedding using cosine similarity.

async get_nearest_matches(collection_name: str, embedding: ndarray, limit: int, min_relevance_score: float = 0.0, with_embeddings: bool = False) -> list[tuple[MemoryRecord, float]]

Parameters

Name	Description
collection_name Required	str The name of the collection to get the nearest matches from.
embedding Required	<xref:semantic_kernel.memory.ndarray> The embedding to find the nearest matches to.
limit Required	int The maximum number of matches to return.
min_relevance_score	float The minimum relevance score of the matches. (default: {0.0}) Default value: 0.0
with_embeddings	bool Whether to include the embeddings in the results. (default: {False}) Default value: False

Returns

Type	Description
List[Tuple[MemoryRecord, float]]	The records and their relevance scores.

remove

Removes a record.

async remove(collection_name: str, key: str) -> None

Parameters

Name	Description
collection_name Required	str The name of the collection to remove the record from.
key Required	str The unique database key of the record to remove.

Returns

Type	Description
	None

remove_batch

Removes a batch of records.

async remove_batch(collection_name: str, keys: list[str]) -> None

Parameters

Name	Description
collection_name Required	str The name of the collection to remove the records from.
keys Required	<xref:List>[str] The unique database keys of the records to remove.

Returns

Type	Description
	None

upsert

Upserts a record.

async upsert(collection_name: str, record: MemoryRecord) -> str

Parameters

Name	Description
collection_name Required	str The name of the collection to upsert the record into.
record Required	<xref:semantic_kernel.memory.MemoryRecord> The record to upsert.

Returns

Type	Description
str	The unique database key of the record.

upsert_batch

Upserts a batch of records.

async upsert_batch(collection_name: str, records: list[MemoryRecord]) -> list[str]

Parameters

Name	Description
collection_name Required	str The name of the collection to upsert the records into.
records Required	<xref:List>[<xref:MemoryRecord>] The records to upsert.

Returns

Type	Description
List[str]	The unique database keys of the records.

Attributes

is_experimental

is_experimental = True

stage_status

stage_status = 'experimental'