Namespace Glitch9.AIDevKit

Classes

Represents a single chat session, including its metadata, settings, messages, and serialization logic. Provides support for saving, loading, summarizing, and auto-saving the session.

CompletionRequestBase.CompletionRequestBuilder<TBuilder, TRequest>

CompletionResult<T>

Base class for generated contents via CompletionRequest or ChatCompletionRequest. This class contains ToolCalls that the AI model wants to call.

An array of content parts with a defined type, each can be of type Text or Image_Url when passing in images. You can pass multiple images by adding multiple Image_Url content parts. Image input is only supported when using the gpt-4-visual-preview model.

DeprecatedModelException

EmptyResponseException

ErrorResponse

ErrorResponseWrapper

FileBase64ContentPart

FileContentPart

FileData

FileIdContentPart

FileLibrary

ScriptableObject database for storing file data. This database is used to keep track of the files available in the AI library.

FileLibrary.Repo

Database for storing file data.

FileRef

FunctionCall

Represents a function call to be used with the AI chat.

FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.03 specification. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

FunctionDeclarationConverter

FunctionDelegate<TArgument, TResult>

Provides an abstract base class for implementing function delegates with specific argument and result types.

FunctionResponse

A predicted FunctionResponse returned from the model that contains a string representing the Name with the arguments and their values.

GENAudioInputTask<TSelf, TOuput>

GENAudioIsolationTask

GENAudioOutputTask<TSelf, TPrompt>

GENChatTask

Task for generating text using an LLM model. Supports instructions and role-based prompts.

GENCompletionTask<TSelf, TPrompt, TOutput>

Base class for GENTasks which utilize a CompletionRequest or a ChatCompletionRequest.

GENContentTask

Task for generating text using an LLM model. Supports instructions and role-based prompts.

GENImageTask

Task for generating image(s) from text using supported models (e.g., OpenAI DALL·E, Google Imagen).

GENImageVariationTask

Legacy API. Only works with DALL·E 2.

GENInpaintTask

Task for editing an existing image based on a text prompt and optional mask (OpenAI or Google Gemini).

GENModerationTask

Audio not supported yet.

GENObjectTask<T>

Task for generating structured output (e.g., JSON) using an LLM model.

GENSequence

GENSoundEffectTask

Task for generating sound effects based on a text prompt.

GENSpeechTask

Task for generating synthetic speech (text-to-speech) using the specified model.

GENTaskExtensions

Provides fluent, chainable extension methods that create and configure AI generation tasks. These helpers let you start a task directly from the host object (string, AudioClip, Texture2D, etc.) and then continue the configuration via the task's fluent API.

Typical usage:

// Create a chat-like text generation
"Describe a cat playing piano."
    .GENText()
    .SetModel(OpenAIModel.GPT4o)
    .ExecuteAsync();

// Transcribe recorded speech
audioClip.GENTranscript().ExecuteAsync();

GENTask<TSelf, TPrompt, TOutput>

Abstract base class for all generative AI tasks (text, image, audio). Supports text, image, and audio prompts with fluent configuration methods.

GENTextTask

GENTranscriptTask

Task for converting speech audio into text (speech-to-text).

GENTranslationTask

Task for translating speech into English text using the speech translation model.

GENVideoTask

GENVoiceChangeTask

GeneratedAsset<TAsset, TFile>

Base class for generated assets: images, audios, and videos. These assets are 'files' that can be downloaded and used in the application.

GeneratedAudio

Represents a generated audio or a collection of generated audios. This class provides implicit conversions to AudioClip and Sprite types for easy usage.

GeneratedContent

GeneratedImage

Represents a generated image or a collection of generated images. This class provides implicit conversions to Texture2D and Sprite types for easy usage.

GeneratedImageExtensions

GeneratedResult<T>

GeneratedText

GeneratedVideo

Unity does not support creation of a VideoClip on runtime. Therefore, GeneratedVideo will only save the urls of the video files.

ImageBase64ContentPart

ImageContentPart

ImageContentPartConverter

ImageFileIdContentPart

ImageRef

ImageUrlContentPart

InpaintPrompt

A specialized prompt for inpainting tasks.
This class is used to pass the instruction and the image to the inpainting model for GENInpaintTask.

JsonSchemaFormat

LocalModelOptionsConverter

Logprobs

Log probability information for the choice.

LogprobsContent

A list of message content tokens with log probability information.

Model

ScriptableObject representation of a generative AI model with metadata, configuration, and pricing information. Supports token limits, ownership, creation time, and dynamic pricing for various content types (text, image, audio).

ModelCapabilityExtensions

ModelFamily

Defines the family names of various AI models and services.

ModelFilter

ModelLibrary

ScriptableObject database for storing model data. This database is used to keep track of the models available in the AI library.

ModelLibrary.Repo

Database for storing model data.

ModelOptions

This class defines a flexible set of parameters that control how text is generated by a language model. These options are entirely optional, but tuning them allows for precise control over randomness, token filtering, sampling behavior, and performance tuning.

ModelOptions.Builder

ModelPrice

ModelPriceArrayExtensions

ModelRequest

ModelRequest.ModelRequestBuilder<TBuilder, TRequest>

ModelResponse

Base class for all responses from AI models.

ModerationOptions

Configuration options for chat moderation.

NotSupportedFeatureException

NotSupportedGENTaskException

Thrown when a GenAI provider does not support a specific feature.

OllamaModelOptionsConverter

ProjectContext

Prompt

Added on 2025-05-15.

New class added to simplify the prompting system.

Prompt types are:

string, string[]
Texture2D, Texture2D[]
AudioClip, AudioClip[]
VideoClip, VideoClip[]
FileInfo, FileInfo[]

Instead of this class, GENChat still uses UserMessage which already supports all of the above types.

PromptFeedback

A set of the feedback metadata the prompt specified in GenerateContentRequest.Contents.

PromptHistory

ScriptableObject database for storing prompt history data. This database is used to keep track of the prompts sent to the AI and their responses. It is useful for debugging and analyzing the performance of the AI.

PromptHistory.Repo

Database for storing prompt history data.

PromptRecord

ReasoningOptions

ResponseFormat

ResponseFormatConverter

Converts Format(Enum) or ResponseFormat (struct wrapper) in Json format to ResponseFormat(Enum).

ResponseFormatExtensions

SafetyFeedback

Safety feedback for an entire request.

This field is populated if content in the input and/or response is blocked due to safety settings. SafetyFeedback may not exist for every HarmCategory. Each SafetyFeedback will return the safety settings used by the request as well as the lowest HarmProbability that should be allowed in order to return a result.

SafetyRating

A safety rating associated with a {@link GenerateContentCandidate}

SafetySetting

Safety setting, affecting the safety-blocking behavior. Passing a safety setting for a category changes the allowed probability that content is blocked.

SerializedAudioContent

SerializedContent<T>

SerializedFileContent

SerializedImageContent

SerializedTextContent

SerializedVideoContent

SpeechOutputOptions

StopCompletionException

StreamOptions

StrictJsonSchema

StrictJsonSchemaAttribute

StrictJsonSchemaConverter

SystemMessage

TextContentPart

TextContentPartConverter

TextRef

ToolCall

ToolMessage

Transcript

Usage

Usage statistics for the completion request.

UsageConverter

UsageExtensions

UsageTypeExtensions

UserLocation

UserMessage

Voice

VoiceFilter

VoiceGenderConverter

VoiceLibrary

ScriptableObject database for storing voice data used for TTS (Text-to-Speech) and other voice-related tasks.

VoiceLibrary.Repo

Database for storing voice data.

VoiceTypeConverter

WebSearchOptions

WebSearchOptionsWrapper

Interfaces

IAIDevKitAssetFilter<T>

IChatbot

IFunctionDelegate

Represents a common interface for all function delegates.

IGENTask

IGenerated

ISerializedContent

Enums

AIProvider

AnnotationType

ChatRole

ContentPartType

HarmBlockThreshold

Block at and beyond a specified harm probability.

HarmCategory

Represents the category of harm that a piece of content may fall into. This is used in moderation tasks to classify content based on its potential harm.

HarmProbability

Probability that a prompt or candidate matches a harm category.

ImageDetail

ImageFormat

ImageQuality

The quality of the image that will be generated. HighDefinition creates images with finer details and greater consistency across the image. This param is only supported for OpenAIModel.DallE3.

ImageSize

ImageStyle

The style of the generated images. Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images. This param is only supported for OpenAIModel.DallE3.

Modality

"Modality" refers to the type or form of data that a model is designed to process, either as input or output. In AI and machine learning contexts, modality describes the nature of the information being handled — such as text, image, audio, or video.

For example:

A text-to-text model like GPT-4 processes text inputs and generates text outputs.
A text-to-image model like DALL·E takes text prompts and produces images.
A multimodal model like Gemini can process multiple types of data simultaneously, such as combining text and image inputs.

The concept of modality helps categorize models based on the kinds of sensory or informational data they handle, and is especially important for understanding the capabilities and limitations of a model.