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# Copyright (c) Microsoft. All rights reserved.
import sys
from typing import Any
from pydantic import PrivateAttr
from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
from semantic_kernel.memory.memory_query_result import MemoryQueryResult
from semantic_kernel.memory.memory_record import MemoryRecord
from semantic_kernel.memory.memory_store_base import MemoryStoreBase
from semantic_kernel.memory.semantic_text_memory_base import SemanticTextMemoryBase
if sys.version_info >= (3, 13):
from warnings import deprecated
else:
from typing_extensions import deprecated
@deprecated("This class will be removed in a future version.")
class SemanticTextMemory(SemanticTextMemoryBase):
"""Class for semantic text memory."""
_storage: MemoryStoreBase = PrivateAttr()
_embeddings_generator: EmbeddingGeneratorBase = PrivateAttr()
def __init__(self, storage: MemoryStoreBase, embeddings_generator: EmbeddingGeneratorBase) -> None:
"""Initialize a new instance of SemanticTextMemory.
Args:
storage (MemoryStoreBase): The MemoryStoreBase to use for storage.
embeddings_generator (EmbeddingGeneratorBase): The EmbeddingGeneratorBase
to use for generating embeddings.
"""
super().__init__()
self._storage = storage
self._embeddings_generator = embeddings_generator
async def save_information(
self,
collection: str,
text: str,
id: str,
description: str | None = None,
additional_metadata: str | None = None,
embeddings_kwargs: dict[str, Any] | None = None,
) -> None:
"""Save information to the memory (calls the memory store's upsert method).
Args:
collection (str): The collection to save the information to.
text (str): The text to save.
id (str): The id of the information.
description (Optional[str]): The description of the information.
additional_metadata (Optional[str]): Additional metadata of the information.
embeddings_kwargs (Optional[Dict[str, Any]]): The embeddings kwargs of the information.
"""
if not await self._storage.does_collection_exist(collection_name=collection):
await self._storage.create_collection(collection_name=collection)
embedding = (await self._embeddings_generator.generate_embeddings([text], **(embeddings_kwargs or {})))[0]
data = MemoryRecord.local_record(
id=id,
text=text,
description=description,
additional_metadata=additional_metadata,
embedding=embedding,
)
await self._storage.upsert(collection_name=collection, record=data)
async def save_reference(
self,
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,
) -> None:
"""Save a reference to the memory (calls the memory store's upsert method).
Args:
collection (str): The collection to save the reference to.
text (str): The text to save.
external_id (str): The external id of the reference.
external_source_name (str): The external source name of the reference.
description (Optional[str]): The description of the reference.
additional_metadata (Optional[str]): Additional metadata of the reference.
embeddings_kwargs (Optional[Dict[str, Any]]): The embeddings kwargs of the reference.
"""
if not await self._storage.does_collection_exist(collection_name=collection):
await self._storage.create_collection(collection_name=collection)
embedding = (await self._embeddings_generator.generate_embeddings([text], **(embeddings_kwargs or {})))[0]
data = MemoryRecord.reference_record(
external_id=external_id,
source_name=external_source_name,
description=description,
additional_metadata=additional_metadata,
embedding=embedding,
)
await self._storage.upsert(collection_name=collection, record=data)
async def get(
self,
collection: str,
key: str,
) -> MemoryQueryResult | None:
"""Get information from the memory (calls the memory store's get method).
Args:
collection (str): The collection to get the information from.
key (str): The key of the information.
Returns:
Optional[MemoryQueryResult]: The MemoryQueryResult if found, None otherwise.
"""
record = await self._storage.get(collection_name=collection, key=key)
return MemoryQueryResult.from_memory_record(record, 1.0) if record else None
async def search(
self,
collection: str,
query: str,
limit: int = 1,
min_relevance_score: float = 0.0,
with_embeddings: bool = False,
embeddings_kwargs: dict[str, Any] | None = None,
) -> list[MemoryQueryResult]:
"""Search the memory (calls the memory store's get_nearest_matches method).
Args:
collection (str): The collection to search in.
query (str): The query to search for.
limit (int): The maximum number of results to return. (default: {1})
min_relevance_score (float): The minimum relevance score to return. (default: {0.0})
with_embeddings (bool): Whether to return the embeddings of the results. (default: {False})
embeddings_kwargs (Optional[Dict[str, Any]]): The embeddings kwargs of the information.
Returns:
List[MemoryQueryResult]: The list of MemoryQueryResult found.
"""
query_embedding = (await self._embeddings_generator.generate_embeddings([query], **(embeddings_kwargs or {})))[
0
]
results = await self._storage.get_nearest_matches(
collection_name=collection,
embedding=query_embedding,
limit=limit,
min_relevance_score=min_relevance_score,
with_embeddings=with_embeddings,
)
return [MemoryQueryResult.from_memory_record(r[0], r[1]) for r in results]
async def get_collections(self) -> list[str]:
"""Get the list of collections in the memory (calls the memory store's get_collections method).
Returns:
List[str]: The list of all the memory collection names.
"""
return await self._storage.get_collections()