Dotnet SDK for Spice.ai.
- Minimum supported version: .NET 8.0
- Supports .NET Standard 2.0+, .NET 8.0, .NET 9.0, and .NET 10.0.
- Asynchronous I/O.
dotnet add package spiceaiFollow the quickstart guide to install and run Spice locally.
using Spice;
using var client = new SpiceClientBuilder().Build();using Spice;
using var client = new SpiceClientBuilder().Build();
var data = await client.Query("SELECT * FROM my_table LIMIT 10;");Use parameterized queries to prevent SQL injection and improve performance:
using Spice;
using var client = new SpiceClientBuilder().Build();
var parameters = new Dictionary<string, object>
{
{ "product_id", 42 },
{ "min_price", 10.0 }
};
var data = await client.Query(
"SELECT * FROM products WHERE id = :product_id AND price >= :min_price",
parameters);For more control over parameter types, use QueryWithParams with positional placeholders ($1, $2, etc.). This uses the ADBC protocol and supports explicit type specification:
Note: The
QueryWithParamsmethod requires the ADBC FlightSQL driver to support prepared statements. The pure C# driver (Apache.Arrow.Adbc.Drivers.FlightSql) currently does not implementPrepare(). For full parameterized query support, the Go-based interop driver (Apache.Arrow.Adbc.Drivers.Interop.FlightSql) is required. See the Apache ADBC documentation for more details.
using Spice;
using Spice.Params;
using var client = new SpiceClientBuilder().Build();
// Basic usage with type inference
var data = await client.QueryWithParams(
"SELECT * FROM products WHERE id = $1 AND price >= $2",
42, // Inferred as Int32
10.50 // Inferred as Double
);
// Read the results as Arrow data
while (await data!.ReadNextRecordBatchAsync() is { } batch)
{
// Process batch...
}Use the Param class for explicit control over Arrow data types:
using Spice;
using Spice.Params;
using var client = new SpiceClientBuilder().Build();
// Explicitly typed parameters
var data = await client.QueryWithParams(
"SELECT * FROM orders WHERE customer_id = $1 AND order_date >= $2 AND total > $3",
Param.Int64(12345), // Explicit Int64
Param.Date32(new DateTime(2024, 1, 1)), // Date without time
Param.Decimal128(100.00m, 10, 2) // Decimal with precision/scale
);
// Supported Param types:
// - Integers: Param.Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64
// - Floating point: Param.Float, Double
// - Text/Binary: Param.String, Binary
// - Boolean: Param.Boolean
// - Date/Time: Param.Date32, Date64, Time32, Time64, Timestamp
// - Duration: Param.DurationSeconds, DurationMilliseconds, DurationMicroseconds, DurationNanoseconds
// - Decimal: Param.Decimal128, Decimal256
// - Null: Param.NullYou can mix inferred and explicit types in the same query:
var data = await client.QueryWithParams(
"SELECT * FROM users WHERE name = $1 AND age > $2 AND verified = $3",
"John", // Inferred as String
Param.Int16(18), // Explicit Int16
true // Inferred as Boolean
);Trigger a refresh of an accelerated dataset:
using Spice;
using var client = new SpiceClientBuilder().Build();
await client.RefreshDatasetAsync("my_dataset");Override the dataset's configured refresh settings for a single refresh by passing
RefreshOptions. Any option left unset falls back to the dataset's Spicepod configuration.
using Spice;
using Spice.Datasets;
using var client = new SpiceClientBuilder().Build();
await client.RefreshDatasetAsync("taxi_trips", new RefreshOptions()
.WithRefreshSql("SELECT * FROM taxi_trips WHERE tip_amount > 10.0")
.WithRefreshMode(RefreshMode.Append)
.WithMaxJitter(TimeSpan.FromSeconds(10)));Object initializer syntax works too:
await client.RefreshDatasetAsync("taxi_trips", new RefreshOptions
{
RefreshSql = "SELECT * FROM taxi_trips WHERE tip_amount > 10.0",
RefreshMode = RefreshMode.Append,
MaxJitter = TimeSpan.FromSeconds(10),
});| Option | Type | Description |
|---|---|---|
RefreshSql |
string? |
The SQL statement used for this refresh. Defaults to the dataset's refresh_sql. |
RefreshMode |
RefreshMode? |
Full replaces the accelerated data; Append adds newly returned rows. Defaults to the dataset's refresh_mode. |
MaxJitter |
TimeSpan? |
Maximum jitter added before the refresh starts. Defaults to the dataset's refresh_jitter_max. |
All options are optional — leave any of them unset (null) to fall back to the dataset's configured value.
Note: On-demand refreshes apply to the
fullandappendrefresh modes. Datasets accelerated withchangesmode are kept up to date by change data capture and are not refreshed through this API.
IsSpiceHealthyAsync reports whether the runtime process is up. IsSpiceReadyAsync reports
whether it has finished loading every component and can serve queries — that is the one to gate
application startup on.
using Spice;
using var client = new SpiceClientBuilder().Build();
if (await client.IsSpiceHealthyAsync())
{
Console.WriteLine("Spice is up");
}
if (await client.IsSpiceReadyAsync())
{
Console.WriteLine("Spice is ready to serve queries");
}Both probes return false when the runtime is unreachable rather than throwing, so they can be
polled directly. Pass a CancellationToken to end the poll loop after a deadline — cancelling it
throws OperationCanceledException out of the in-flight probe call, so wrap the loop in a
try/catch (or let it propagate) rather than expecting a false result:
using var cts = new CancellationTokenSource(TimeSpan.FromSeconds(30));
try
{
while (!await client.IsSpiceReadyAsync(cts.Token))
{
await Task.Delay(TimeSpan.FromSeconds(1), cts.Token);
}
}
catch (OperationCanceledException)
{
// Did not become ready within 30 seconds.
}On Spice.ai Cloud the readiness endpoint is authenticated — configure an API key with
WithSpiceCloud or WithApiKey.
using Spice;
// Connect to Spice on a custom host/port
using var client = new SpiceClientBuilder()
.WithFlightAddress("grpc://my-server:50051")
.WithHttpAddress("http://my-server:8090")
.Build();
// Enable TLS for self-hosted Spice
using var tlsClient = new SpiceClientBuilder()
.WithFlightAddress("grpc+tls://my-server:50051")
.WithHttpAddress("https://my-server:8090")
.WithTls(true)
.Build();using Spice;
using var client = new SpiceClientBuilder()
.WithSpiceCloud("API_KEY") // Automatically configures endpoints and enables TLS
.Build();using Spice;
using var client = new SpiceClientBuilder()
.WithSpiceCloud("API_KEY")
.Build();
var data = await client.Query("SELECT * FROM eth.recent_blocks LIMIT 10;");using Spice;
using var client = new SpiceClientBuilder()
.WithSpiceCloud("API_KEY")
.Build();
var parameters = new Dictionary<string, object>
{
{ "nation_name", "CHINA" },
{ "min_key", 0 }
};
var data = await client.Query(
"SELECT * FROM tpch.nation WHERE n_name = :nation_name AND n_nationkey >= :min_key",
parameters);using Spice;
using Spice.Datasets;
using var client = new SpiceClientBuilder()
.WithSpiceCloud("API_KEY")
.Build();
await client.RefreshDatasetAsync("my_dataset");
// Or with refresh overrides for this refresh only
await client.RefreshDatasetAsync("my_dataset", new RefreshOptions()
.WithRefreshMode(RefreshMode.Append));SearchAsync runs vector similarity, keyword, and hybrid search against datasets that have
an embedding column and a loaded embedding model.
using Spice;
using Spice.Search;
using var client = new SpiceClientBuilder().Build();
var response = await client.SearchAsync(new SearchRequest("tickets to Tokyo")
{
Datasets = new[] { "app_messages" },
Limit = 3,
});
Console.WriteLine($"{response.Results.Count} matches in {response.DurationMs}ms");
foreach (var match in response.Results)
{
Console.WriteLine($"{match.Dataset} {match.Score}");
}Only Text is required. Datasets restricts the search — leave it unset to search every
dataset with an embedding column. Limit caps matches per dataset, Where applies an SQL
predicate before the search, and AdditionalColumns names extra columns to return. Setting
Keywords pre-filters the embedding column with a lexical search before the vector search
runs, making the search hybrid:
var response = await client.SearchAsync(new SearchRequest("tickets to Tokyo")
{
Where = "city = 'Tokyo'",
AdditionalColumns = new[] { "timestamp" },
Keywords = new[] { "plane", "tickets" },
});Each SearchMatch carries the Dataset it was found in, its similarity Score, the
matched column values in Matches, the row's PrimaryKey, the columns requested via
AdditionalColumns in Data, and any Metadata. The runtime omits the last three when
empty; they default to empty dictionaries, so they can be read without a null check.
NsqlAsync answers a natural-language question by having the runtime's configured LLM
generate SQL, then running it. NsqlGenerateSqlAsync translates the question into SQL
without running it. Both require an LLM model configured in the Spicepod.
using Spice;
using Spice.Nsql;
using var client = new SpiceClientBuilder().Build();
var result = await client.NsqlAsync(new NsqlRequest("top 5 customers by revenue"));
Console.WriteLine(result.SQL);
foreach (var row in result.Data)
{
Console.WriteLine(row["customer_id"]);
}
// Or generate the SQL without running it
var sql = await client.NsqlGenerateSqlAsync(new NsqlRequest("how many orders"));The SpiceClient implements IDisposable and should be properly disposed to release network resources (gRPC channels, HTTP clients). Use the using statement or using declaration for automatic disposal:
// Using statement (automatically disposes when scope exits)
using (var client = new SpiceClientBuilder().WithSpiceCloud("API_KEY").Build())
{
var data = await client.Query("SELECT * FROM tpch.customer LIMIT 10;");
// Process data...
} // Client is disposed here
// Or using declaration (C# 8.0+)
using var client = new SpiceClientBuilder().WithSpiceCloud("API_KEY").Build();
var data = await client.Query("SELECT * FROM tpch.customer LIMIT 10;");
// Client is disposed at end of scopeImportant: Always dispose of SpiceClient instances to prevent resource leaks, especially in long-running applications or when creating multiple client instances.
Check out our Documentation to learn more about how to use the Dotnet SDK.