Categorygithub.com/launchdarkly/go-server-sdk/ldai
modulepackage
0.6.0
Repository: https://github.com/launchdarkly/go-server-sdk.git
Documentation: pkg.go.dev

# README

LaunchDarkly Server-side AI SDK for Go

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[!CAUTION] This library is a alpha version and should not be considered ready for production use while this message is visible.

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LaunchDarkly overview

LaunchDarkly is a feature management platform that serves trillions of feature flags daily to help teams build better software, faster. Get started using LaunchDarkly today!

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Getting started

Import the module:

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

Configure the base LaunchDarkly Server SDK:

sdkClient, _ = ld.MakeClient("your-sdk-key", 5*time.Second)

Instantiate the AI client, passing in the base Server SDK:

aiClient, err := ldai.NewClient(sdkClient)

Fetch a model configuration for a specific LaunchDarkly context:

// The default value 'ldai.Disabled()' be returned if LaunchDarkly is unavailable or the config 
// cannot be fetched. To customize the default value, use ldai.NewConfig().
config, tracker := aiClient.Config("your-model-key", ldcontext.New("user-key"), ldai.Disabled(), nil)

// Access the methods on config, and optionally use the returned tracker to generate analytic events
// related to usage of the model config. 

Learn more

Read our documentation for in-depth instructions on configuring and using LaunchDarkly. You can also head straight to the complete reference guide for this SDK.

Contributing

We encourage pull requests and other contributions from the community. Check out our contributing guidelines for instructions on how to contribute to this library.

About LaunchDarkly

  • LaunchDarkly is a continuous delivery platform that provides feature flags as a service and allows developers to iterate quickly and safely. We allow you to easily flag your features and manage them from the LaunchDarkly dashboard. With LaunchDarkly, you can:
    • Roll out a new feature to a subset of your users (like a group of users who opt-in to a beta tester group), gathering feedback and bug reports from real-world use cases.
    • Gradually roll out a feature to an increasing percentage of users, and track the effect that the feature has on key metrics (for instance, how likely is a user to complete a purchase if they have feature A versus feature B?).
    • Turn off a feature that you realize is causing performance problems in production, without needing to re-deploy, or even restart the application with a changed configuration file.
    • Grant access to certain features based on user attributes, like payment plan (eg: users on the ‘gold’ plan get access to more features than users in the ‘silver’ plan). Disable parts of your application to facilitate maintenance, without taking everything offline.
  • LaunchDarkly provides feature flag SDKs for a wide variety of languages and technologies. Read our documentation for a complete list.
  • Explore LaunchDarkly

# Packages

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# Functions

Disabled is a helper that returns a built Config that is disabled and contains no messages.
NewClient creates a new AI Client.
NewConfig returns a new ConfigBuilder.

# Constants

FeedbackNegative is negative feedback.
FeedbackPositive is positive feedback.
{{ x-release-please-version }}.

# Structs

Client is the main entrypoint for the AI SDK.
Config represents an AI config.
ConfigBuilder is used to define a default AI config, returned when LaunchDarkly is unreachable or there is an error evaluating the config.
Metrics represents the metrics returned by a model provider for a specific request.
ProviderResponse represents the response from a model provider for a specific request.
TokenUsage represents the token usage returned by a model provider for a specific request.
Tracker is used to track metrics for AI config evaluation.

# Interfaces

EventSink represents the Tracker's requirements for delivering analytic events.
ServerSDK defines the required methods for the AI SDK to interact with LaunchDarkly.
Stopwatch is used to measure the duration of a task.

# Type aliases

Feedback represents the feedback provided by a user for a model evaluation.