RedisMemoryStore Constructors
Definition
Important
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Overloads
| RedisMemoryStore(IDatabase, Int32, Schema+VectorField+VectorAlgo, VectorDistanceMetric, Int32) |
Create a new instance of semantic memory using Redis. |
| RedisMemoryStore(String, Int32, Schema+VectorField+VectorAlgo, VectorDistanceMetric, Int32) |
Create a new instance of semantic memory using Redis. |
RedisMemoryStore(IDatabase, Int32, Schema+VectorField+VectorAlgo, VectorDistanceMetric, Int32)
Create a new instance of semantic memory using Redis.
public RedisMemoryStore (StackExchange.Redis.IDatabase database, int vectorSize = 1536, NRedisStack.Search.Schema.VectorField.VectorAlgo vectorIndexAlgorithm = NRedisStack.Search.Schema+VectorField+VectorAlgo.HNSW, Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric vectorDistanceMetric = Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric.COSINE, int queryDialect = 2);new Microsoft.SemanticKernel.Connectors.Redis.RedisMemoryStore : StackExchange.Redis.IDatabase * int * NRedisStack.Search.Schema.VectorField.VectorAlgo * Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric * int -> Microsoft.SemanticKernel.Connectors.Redis.RedisMemoryStorePublic Sub New (database As IDatabase, Optional vectorSize As Integer = 1536, Optional vectorIndexAlgorithm As Schema.VectorField.VectorAlgo = NRedisStack.Search.Schema+VectorField+VectorAlgo.HNSW, Optional vectorDistanceMetric As VectorDistanceMetric = Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric.COSINE, Optional queryDialect As Integer = 2)Parameters
- database
- StackExchange.Redis.IDatabase
The database of the Redis server.
- vectorSize
- Int32
Embedding vector size, defaults to 1536
- vectorIndexAlgorithm
- NRedisStack.Search.Schema.VectorField.VectorAlgo
Indexing algorithm for vectors, defaults to "HNSW"
- vectorDistanceMetric
- VectorDistanceMetric
Metric for measuring vector distances, defaults to "COSINE"
- queryDialect
- Int32
Query dialect, must be 2 or greater for vector similarity searching, defaults to 2
Applies to
RedisMemoryStore(String, Int32, Schema+VectorField+VectorAlgo, VectorDistanceMetric, Int32)
Create a new instance of semantic memory using Redis.
public RedisMemoryStore (string connectionString, int vectorSize = 1536, NRedisStack.Search.Schema.VectorField.VectorAlgo vectorIndexAlgorithm = NRedisStack.Search.Schema+VectorField+VectorAlgo.HNSW, Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric vectorDistanceMetric = Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric.COSINE, int queryDialect = 2);new Microsoft.SemanticKernel.Connectors.Redis.RedisMemoryStore : string * int * NRedisStack.Search.Schema.VectorField.VectorAlgo * Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric * int -> Microsoft.SemanticKernel.Connectors.Redis.RedisMemoryStorePublic Sub New (connectionString As String, Optional vectorSize As Integer = 1536, Optional vectorIndexAlgorithm As Schema.VectorField.VectorAlgo = NRedisStack.Search.Schema+VectorField+VectorAlgo.HNSW, Optional vectorDistanceMetric As VectorDistanceMetric = Microsoft.SemanticKernel.Connectors.Redis.VectorDistanceMetric.COSINE, Optional queryDialect As Integer = 2)Parameters
- connectionString
- String
Provide connection URL to a Redis instance
- vectorSize
- Int32
Embedding vector size, defaults to 1536
- vectorIndexAlgorithm
- NRedisStack.Search.Schema.VectorField.VectorAlgo
Indexing algorithm for vectors, defaults to "HNSW"
- vectorDistanceMetric
- VectorDistanceMetric
Metric for measuring vector distances, defaults to "COSINE"
- queryDialect
- Int32
Query dialect, must be 2 or greater for vector similarity searching, defaults to 2