.NET AI SDK reference

Overview

This topic documents how to get started with the .NET AI SDK, and links to reference information on all of the supported features.

Get started

LaunchDarkly AI SDKs interact with AI configs. AI configs are the LaunchDarkly resources that manage model configurations and messages for your generative AI applications.

You can use the .NET AI SDK to customize your AI config based on the context that you provide. This means both the messages and the model evaluation in your generative AI application are specific to each end user, at runtime. You can also use the AI SDKs to record metrics from your AI model generation, including duration and tokens.

Follow these instructions to start using the .NET AI SDK in your application.

Install the SDK

First, install the AI SDK as a dependency in your application using your application’s dependency manager. If you want to depend on a specific version, refer to the SDK releases page to identify the latest version.

The .NET AI SDK is built on the .NET (server-side) SDK, so you’ll need to install that as well. The .NET AI SDK requires version 8 or higher of the .NET (server-side) SDK.

Here’s how:

$
Install-Package LaunchDarkly.ServerSdk
>
Install-Package LaunchDarkly.ServerSdk.Ai

Next, import the namespaces in your application code. The namespace is not the same as the package name:

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using LaunchDarkly.Sdk.Server.Ai;
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using LaunchDarkly.Sdk.Server.Ai.Adapters;
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using LaunchDarkly.Sdk.Server.Ai.Config;

Initialize the client

After you install and import the SDK, create a single, shared instance of LdClient. When the LdClient is initialized, use it to initialize the LdAiClient. The LdAiClient is how you interact with AI configs. Specify the SDK key to authorize your application to connect to a particular environment within LaunchDarkly.

Here’s how:

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var baseClient = new LdClient(Configuration.Builder("sdk-key-123").StartWaitTime(TimeSpan.FromSeconds(5)).Build()));
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var aiClient = new LdAiClient(new LdClientAdapter(baseClient));

Configure the context

Next, configure the context that will use the AI config, that is, the context that will encounter generated AI content in your application. The context attributes determine which variation of the AI config LaunchDarkly serves to the end user, based on the targeting rules in your AI config. If you are using template variables in the messages in your AI config’s variations, the context attributes also fill in values for the template variables.

Here’s how:

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var context = Context.Builder("context-key-123abc")
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  .Name("Sandy")
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  .Build();

Customize an AI config

Then, use Config to customize the AI config. You need to call Config each time you generate content from your AI model.

This function returns the customized messages and model. Customization means that any variables you include in the messages when you define the AI config variation have their values set to the context attributes and variables you pass to Config. Then, you can pass the customized messages directly to your AI.

The customization process within the AI SDK is similar to evaluating flags in one of LaunchDarkly’s client-side, server-side, or edge SDKs, in that the SDK completes the customization without a separate network call.

Here’s how:

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var fallbackConfig = LdAiConfig.New()
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  .SetModelName("my-default-model")
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  .SetModelParam("temperature", LdValue.Of(0.8))
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  .AddMessage("", Role.system)
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  .SetModelProviderName('my-default-provider')
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  .SetEnabled(true)
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  .Build()
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var tracker = aiClient.Config(
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  "ai-config-key-123abc",
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  context,
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  fallbackConfig,
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  new Dictionary<string, object> {
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    { "exampleCustomVariable", "exampleCustomValue" }
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  }
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);

To learn more, read Customizing AI configs.

Record metrics from AI model generation

Finally, use the TrackRequest function to record metrics from your AI model generation.

Here’s how:

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var response = tracker.TrackRequest(Task.Run(() =>
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  {
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    // Make request to a provider, which automatically tracks metrics in LaunchDarkly.
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    // When sending the request to a provider, use details from tracker.Config.
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    // For instance, you can pass tracker.Config.Model and tracker.Config.Messages.
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    // Optionally, return response metadata, for example to do your own additional logging.
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    //
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    // CAUTION: If the call inside of Task.Run() throws an exception,
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    // the SDK will re-throw that exception
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    return new Response
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    {
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      Usage = new Usage { Total = 1, Input = 1, Output = 1 }, /* Token usage data */
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      Metrics = new Metrics { LatencyMs = 100 } /* Metrics data */
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    };
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  }
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));

If you would like to do any additional tracking, besides what LaunchDarkly provides, it is your responsibility to fill in the Response object with the data you want to track.

Make sure to call Config each time you generate content from your AI model:

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var tracker = aiClient.Config(
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  "ai-config-key-123abc",
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  context,
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  fallbackConfig,
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  new Dictionary<string, object> {
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    { "exampleCustomVariable", "exampleCustomValue" }
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  }
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);
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var response = tracker.TrackRequest(...)

To learn more, read Tracking AI metrics.

Supported features

This SDK supports the following features:

• Anonymous contexts
• Context configuration
• Customizing AI configs
• Private attributes
• Tracking AI metrics