Go AI SDK reference

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

Overview

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

The Go AI SDK is designed for use with LaunchDarkly’s AI Configs. It is in a pre-1.0 release and the API may change based on feedback. Active feature development is ongoing in the Python and Node.js AI SDKs, so this SDK receives new features at a slower pace. You can follow development or contribute on GitHub.

LaunchDarkly’s SDKs are open source. In addition to this reference guide, we provide source, API reference documentation, and sample applications:

Resource	Location
SDK API documentation	SDK API docs
GitHub repository	ldai
Sample application
Published module	pkg.go.dev

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.

This reference guide describes working specifically with the Go AI SDK. For a complete introduction to LaunchDarkly AI SDKs and how they interact with AI Configs, read Quickstart for AI Configs.

You can use the Go 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 Go AI SDK in your application.

Install the SDK

First, install the AI SDK as a dependency in your application. How you do this depends on what dependency management system you are using:

• If you are using the standard Go modules system, import the SDK packages in your code and go build will automatically download them. The SDK and its dependencies are modules.
• Otherwise, use the go get command and specify the SDK version, such as go get github.com/launchdarkly/go-server-sdk/ldai.

The Go AI SDK is built on the Go SDK, so you’ll need to install that as well.

Here’s how:

Go AI SDK

    import (
      ld "github.com/launchdarkly/go-server-sdk/v7"
      "github.com/launchdarkly/go-server-sdk/ldai"
    )

Initialize the client

After you install and import the SDK, create a single, shared instance of LDClient. Then, 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.

The Go SDK uses an SDK key. Keys are specific to each project and environment. They are available from Project settings, on the Environments list. To learn more about key types, read Keys.

Here’s how:

Go AI SDK

    client, _ = ld.MakeClient("YOUR_SDK_KEY", 5*time.Second)

    aiClient, err := ldai.NewClient(client)

    if err != nil {
      // Client couldn't be created
    }

This example assumes you’ve imported the LaunchDarkly SDK package as ld, as shown above.

The second return type in these code samples ( _ ) represents an error in case the LaunchDarkly client does not initialize. Consider naming the return value and using it with proper error handling.

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:

Go AI SDK

    context := ldcontext.NewBuilder("example-context-key").
        Kind("user").
        Name("Sandy Smith").
        SetString("email", "[e-mail]").
        SetValue("groups", ldvalue.ArrayOf(
          ldvalue.String("Acme"), ldvalue.String("Global Health Services"))).
        Build()

Customize an AI Config

Then, use Config() to customize the AI Config. 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 the Config() method.

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. The Config() function takes an AI Config key, a context, and a fallback value. It performs the evaluation, then returns a Config object with the customized messages and model, and a Tracker object to capture performance metrics. If it cannot perform the evaluation or LaunchDarkly is unreachable, it returns the fallback value. For example, you might use an empty, disabled Config as a fallback value, or a fully configured default. Either way, you should make sure to check for this case and handle it appropriately in your application.

After you call Config(), you can pass the customized messages directly to your AI.

Here’s how:

Go AI SDK

    fallbackValue := NewConfig().Build() // by default, the Config is disabled

    cfg, tracker := aiClient.Config("example-ai-config-key", context, fallbackValue, map[string]interface{}{"exampleCustomVariable": "exampleCustomValue"})

To learn more, read Customizing AI Configs.

Call provider, record metrics from AI model generation

Finally, use the TrackRequest function to make a request to your generative AI provider and record metrics from your AI model generation. Make sure to check whether the returned cfg is enabled, and handle the disabled case appropriately in your application.

Here’s how:

Go AI SDK, any model

    if cfg.Enabled() {

      response, err := tracker.TrackRequest(func(config *Config) (ProviderResponse, error) {

        // Make request to a provider, which automatically tracks metrics in LaunchDarkly.
        // When sending the request to a provider, use details from config.
        // For instance, you can pass a model parameter (config.ModelParam) or messages (config.Messages).
        // Optionally, return response metadata, for example to do your own additional logging.

        return ProviderResponse{
          Usage: TokenUsage{
            Total: 1, // Token usage data
          },
          Metrics: Metrics{
            Latency: 10 * time.Millisecond // Metrics data
          },
        }, nil
      })

    } else {

      // Application path to take when the cfg.config is disabled

    }

Alternatively, you can use the SDK’s other Track* functions to record these metrics manually. The TrackMetric function is expecting a response, so you may need to do this if your application requires streaming.

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