AzureCosmosDBMongoVCoreSemanticCache

AzureCosmosDBMongoVCoreSemanticCache(
  self,
  cosmosdb_connection_string: str,
  database_name: str,
  collection_name:

Bases

BaseCache

Name	Type	Description
`cosmosdb_connection_string`*	`str`	Cosmos DB Mongo vCore connection string
`cosmosdb_client`	`Optional[Any]`	Default:`None` Cosmos DB Mongo vCore client
`embedding`*	`Embedding`	Embedding provider for semantic encoding and search.
`database_name`*	`str`
`collection_name`*	`str`
`num_lists`	`int`	Default:`100`
`dimensions`	`int`	Default:`1536`
`similarity`	`CosmosDBSimilarityType`	Default:`CosmosDBSimilarityType.COS`
`kind`	`CosmosDBVectorSearchType`	Default:`CosmosDBVectorSearchType.VECTOR_IVF`
`m`	`int`	Default:`16`
`ef_construction`	`int`	Default:`64`
`ef_search`	`int`	Default:`40`
`max_degree`	`int`	Default:`32`
`l_build`	`int`	Default:`50`
`l_search`	`int`	Default:`40`
`score_threshold`	`Optional[float]`	Default:`None`
`application_name`	`str`	Default:`'langchainpy'`
`compression`	`Optional[CosmosDBVectorSearchCompression]`	Default:`None`
`pq_compressed_dims`	`Optional[int]`	Default:`None`
`pq_sample_size`	`Optional[int]`	Default:`None`
`oversampling`	`Optional[float]`	Default:`None`

constructor

__init__

Name	Type
cosmosdb_connection_string	str
database_name	str
collection_name	str
embedding	Embeddings
cosmosdb_client	Optional[Any]
num_lists	int
similarity	CosmosDBSimilarityType
kind	CosmosDBVectorSearchType
dimensions	int
m	int
ef_construction	int
max_degree	int
l_build	int
l_search	int
ef_search	int
application_name	str
score_threshold	Optional[float]
compression	Optional[CosmosDBVectorSearchCompression]
pq_compressed_dims	Optional[int]
pq_sample_size	Optional[int]
oversampling	Optional[float]