`langchain_community.cache`.AzureCosmosDBSemanticCache¶

class langchain_community.cache.AzureCosmosDBSemanticCache(cosmosdb_connection_string: str, database_name: str, collection_name: str, embedding: Embeddings, *, cosmosdb_client: Optional[Any] = None, num_lists: int = 100, similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, dimensions: int = 1536, m: int = 16, ef_construction: int = 64, ef_search: int = 40, score_threshold: Optional[float] = None)[source]¶

Cache that uses Cosmos DB Mongo vCore vector-store backend

Parameters

cosmosdb_connection_string (str) – Cosmos DB Mongo vCore connection string
cosmosdb_client (Optional[Any]) – Cosmos DB Mongo vCore client
embedding (Embedding) – Embedding provider for semantic encoding and search.
database_name (str) – Database name for the CosmosDBMongoVCoreSemanticCache
collection_name (str) – Collection name for the CosmosDBMongoVCoreSemanticCache
num_lists (int) – This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has limited performance
dimensions (int) – Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000
similarity (CosmosDBSimilarityType) –
Similarity metric to use with the IVF index.
Possible options are:
- CosmosDBSimilarityType.COS (cosine distance),
- CosmosDBSimilarityType.L2 (Euclidean distance), and
- CosmosDBSimilarityType.IP (inner product).
kind (CosmosDBVectorSearchType) –
Type of vector index to create. Possible options are:
- vector-ivf
- vector-hnsw: available as a preview feature only,
  to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
m (int) – The max number of connections per layer (16 by default, minimum value is 2, maximum value is 100). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements.
ef_construction (int) – the size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4, maximum value is 1000). Higher ef_construction will result in better index quality and higher accuracy, but it will also increase the time required to build the index. ef_construction has to be at least 2 * m
ef_search (int) – The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at the cost of speed.
score_threshold (Optional[float]) – Maximum score used to filter the vector search documents.

Attributes

`DEFAULT_COLLECTION_NAME`
`DEFAULT_DATABASE_NAME`

Methods

`__init__`(cosmosdb_connection_string, ...[, ...])	param cosmosdb_connection_string Cosmos DB Mongo vCore connection string
`aclear`(**kwargs)	Clear cache that can take additional keyword arguments.
`alookup`(prompt, llm_string)	Look up based on prompt and llm_string.
`aupdate`(prompt, llm_string, return_val)	Update cache based on prompt and llm_string.
`clear`(**kwargs)	Clear semantic cache for a given llm_string.
`lookup`(prompt, llm_string)	Look up based on prompt and llm_string.
`update`(prompt, llm_string, return_val)	Update cache based on prompt and llm_string.

__init__(cosmosdb_connection_string: str, database_name: str, collection_name: str, embedding: Embeddings, *, cosmosdb_client: Optional[Any] = None, num_lists: int = 100, similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS, kind: CosmosDBVectorSearchType = CosmosDBVectorSearchType.VECTOR_IVF, dimensions: int = 1536, m: int = 16, ef_construction: int = 64, ef_search: int = 40, score_threshold: Optional[float] = None)[source]¶

Parameters

cosmosdb_connection_string (str) – Cosmos DB Mongo vCore connection string
cosmosdb_client (Optional[Any]) – Cosmos DB Mongo vCore client
embedding (Embedding) – Embedding provider for semantic encoding and search.
database_name (str) – Database name for the CosmosDBMongoVCoreSemanticCache
collection_name (str) – Collection name for the CosmosDBMongoVCoreSemanticCache
num_lists (int) – This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has limited performance
dimensions (int) – Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000
similarity (CosmosDBSimilarityType) –
Similarity metric to use with the IVF index.
Possible options are:
- CosmosDBSimilarityType.COS (cosine distance),
- CosmosDBSimilarityType.L2 (Euclidean distance), and
- CosmosDBSimilarityType.IP (inner product).
kind (CosmosDBVectorSearchType) –
Type of vector index to create. Possible options are:
- vector-ivf
- vector-hnsw: available as a preview feature only,
  to enable visit https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/preview-features
m (int) – The max number of connections per layer (16 by default, minimum value is 2, maximum value is 100). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements.
ef_construction (int) – the size of the dynamic candidate list for constructing the graph (64 by default, minimum value is 4, maximum value is 1000). Higher ef_construction will result in better index quality and higher accuracy, but it will also increase the time required to build the index. ef_construction has to be at least 2 * m
ef_search (int) – The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at the cost of speed.
score_threshold (Optional[float]) – Maximum score used to filter the vector search documents.

async aclear(**kwargs: Any) → None¶

Clear cache that can take additional keyword arguments.

Parameters: kwargs (Any) –
Return type: None

async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶

Look up based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –

Return type

Optional[Sequence[Generation]]

async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶

Update cache based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –
return_val (Sequence[Generation]) –

Return type

None

clear(**kwargs: Any) → None[source]¶

Clear semantic cache for a given llm_string.

Parameters: kwargs (Any) –
Return type: None

lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶

Look up based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –

Return type

Optional[Sequence[Generation]]

update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶

Update cache based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –
return_val (Sequence[Generation]) –

Return type

None

langchain_community.cache.AzureCosmosDBSemanticCache¶

`langchain_community.cache`.AzureCosmosDBSemanticCache¶