| Name | Type | Description |
|---|---|---|
cosmosdb_connection_string* | str | Cosmos DB Mongo vCore connection string |
cosmosdb_client | Optional[Any] | Default: NoneCosmos 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 |
score_threshold | Optional[float] | Default: None |
application_name | str | Default: 'LangChain-CDBMongoVCore-SemanticCache-Python' |
Cache that uses Cosmos DB Mongo vCore vector-store backend
Database name for the CosmosDBMongoVCoreSemanticCache
Collection name for the CosmosDBMongoVCoreSemanticCache
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
Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000
Similarity metric to use with the IVF index.
Possible options are: - CosmosDBSimilarityType.COS (cosine distance), - CosmosDBSimilarityType.L2 (Euclidean distance), and - CosmosDBSimilarityType.IP (inner product).
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
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.
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
The size of the dynamic candidate list for search (40 by default). A higher value provides better recall at the cost of speed.
Maximum score used to filter the vector search documents.
Application name for the client for tracking and logging
