SemanticTextMemory Class

Class for semantic text memory.

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

Initialize a new instance of SemanticTextMemory.

Constructor

SemanticTextMemory(storage: MemoryStoreBase, embeddings_generator: EmbeddingGeneratorBase)

Parameters

Name	Description
storage Required	<xref:semantic_kernel.memory.MemoryStoreBase> The MemoryStoreBase to use for storage.
embeddings_generator Required	<xref:semantic_kernel.memory.EmbeddingGeneratorBase> The EmbeddingGeneratorBase to use for generating embeddings.

Methods

get	Get information from the memory (calls the memory store's get method).
get_collections	Get the list of collections in the memory (calls the memory store's get_collections method).
model_post_init	This function is meant to behave like a BaseModel method to initialise private attributes. It takes context as an argument since that's what pydantic-core passes when calling it.
save_information	Save information to the memory (calls the memory store's upsert method).
save_reference	Save a reference to the memory (calls the memory store's upsert method).
search	Search the memory (calls the memory store's get_nearest_matches method).

get

Get information from the memory (calls the memory store's get method).

async get(collection: str, key: str) -> MemoryQueryResult | None

Parameters

Name	Description
collection Required	str The collection to get the information from.
key Required	str The key of the information.

Returns

Type	Description
Optional[MemoryQueryResult]	The MemoryQueryResult if found, None otherwise.

get_collections

Get the list of collections in the memory (calls the memory store's get_collections method).

async get_collections() -> list[str]

Returns

Type	Description
List[str]	The list of all the memory collection names.

model_post_init

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

model_post_init(context: Any, /) -> None

Positional-Only Parameters

Name	Description
context Required

Parameters

Name	Description
self Required	The BaseModel instance.
context Required	The context.

save_information

Save information to the memory (calls the memory store's upsert method).

async save_information(collection: str, text: str, id: str, description: str | None = None, additional_metadata: str | None = None, embeddings_kwargs: dict[str, Any] | None = {}) -> None

Parameters

Name	Description
collection Required	str The collection to save the information to.
text Required	str The text to save.
id Required	str The id of the information.
description	<xref:Optional>[str] The description of the information. Default value: None
additional_metadata	<xref:Optional>[str] Additional metadata of the information. Default value: None
embeddings_kwargs	<xref:Optional>[<xref:Dict>[str,<xref: Any>]] The embeddings kwargs of the information. Default value: {}

save_reference

Save a reference to the memory (calls the memory store's upsert method).

async save_reference(collection: str, text: str, external_id: str, external_source_name: str, description: str | None = None, additional_metadata: str | None = None, embeddings_kwargs: dict[str, Any] | None = {}) -> None

Parameters

Name	Description
collection Required	str The collection to save the reference to.
text Required	str The text to save.
external_id Required	str The external id of the reference.
external_source_name Required	str The external source name of the reference.
description	<xref:Optional>[str] The description of the reference. Default value: None
additional_metadata	<xref:Optional>[str] Additional metadata of the reference. Default value: None
embeddings_kwargs	<xref:Optional>[<xref:Dict>[str,<xref: Any>]] The embeddings kwargs of the reference. Default value: {}

search

Search the memory (calls the memory store's get_nearest_matches method).

async search(collection: str, query: str, limit: int = 1, min_relevance_score: float = 0.0, with_embeddings: bool = False, embeddings_kwargs: dict[str, Any] | None = {}) -> list[MemoryQueryResult]

Parameters

Name	Description
collection Required	str The collection to search in.
query Required	str The query to search for.
limit	int The maximum number of results to return. (default: {1}) Default value: 1
min_relevance_score	float The minimum relevance score to return. (default: {0.0}) Default value: 0.0
with_embeddings	bool Whether to return the embeddings of the results. (default: {False}) Default value: False
embeddings_kwargs	<xref:Optional>[<xref:Dict>[str,<xref: Any>]] The embeddings kwargs of the information. Default value: {}

Returns

Type	Description
List[MemoryQueryResult]	The list of MemoryQueryResult found.

Attributes

is_experimental

is_experimental = True

stage_status

stage_status = 'experimental'