Response

Generate a response using a Large Language Model (LLM)

GENResponse

returns GeneratedResponse

Response generation is one of the core uses of generative AI. In the GENTask system, the GENResponseTask class is used to generate text (for example, completing a prompt or answering a question). This task sends a prompt to a Large Language Model (LLM) and returns a response.

Input Modalities	Output
Text Image Audio Video	Text Text (Streaming) Tool Calls Speech Image (Rarely) File (Rarely)

Basic Usage

string prompt = "Explain quantum computing in simple terms.";

string result = await prompt
    .GENText()
    .SetModel(OpenAIIModel.GPT4o)
    .ExecuteAsync();

Debug.Log(result);

Streaming

// Streaming
string prompt = "Explain quantum computing in simple terms.";

await prompt
    .GENText()
    .SetModel(OpenAIModel.GPT4o)
    .StreamAsync(
        onTextReceived: chunk => Debug.Log(chunk),
        onStreamStart: () => Debug.Log("Started..."),
        onStreamEnd: () => Debug.Log("Finished.")
    );

Vision Request (Image Recognition)

// Vision Request
Texture2D myTexture = myImage.sprite.texture;
string prompt = "Tell me about this image.";

string result = await prompt
    .GENText()
    .Attach(myTexture)
    .SetModel(OpenAIModel.GPT4o)
    .ExecuteAsync();

Debug.Log(result);

---

GENObject<T>

returns StructuredOutput<T>

GENObject<T> lets you turn natural language prompts into strongly-typed Unity C# objects by guiding the AI to return JSON that matches your class definition.

It's perfect for generating game data like:

• 📦 Items

• 🎮 Quests

• 🗨️ NPC Dialogue Trees

• 📋 Skill Definitions

• 🧠 AI Configuration Profiles

Input Modalities	Output
Text Image Audio Video	Structured Outputs (JSON Mode)

---

Required Annotations:

These attributes are mandatory. Without them, the task will fail to register the schema.

Provider	Required Attributes
OpenAI, Ollama, OpenRouter	[StrictJsonSchema(...)] (on the class) [JsonSchema(...)] (on each property)
Gemini	[JsonSchema(...)] (on the class) [JsonSchema(...)] (on each property)

Generating an RPG Item

using Glitch9.AIDevKit.OpenAI;
using Glitch9.IO.Json.Schema;
using System.Collections.Generic;

[StrictJsonSchema("item_response", Description = "RPG-style inventory item", Strict = true)]
public class GameItem
{
    [JsonSchema("id", Description = "Unique item ID", Required = true)]
    public string Id { get; set; }

    [JsonSchema("name", Description = "Name of the item", Required = true)]
    public string Name { get; set; }

    [JsonSchema("rarity", Description = "Item rarity", Enum = new[] { "Common", "Uncommon", "Rare", "Epic", "Legendary" })]
    public string Rarity { get; set; }

    [JsonSchema("effects", Description = "List of effects applied when used")]
    public List<string> Effects { get; set; }
}


GameItem item = await "Generate a legendary sword with magical effects"
    .GENObject<GameItem>()
    .SetModel(OpenAIModel.GPT4o)
    .SetTemperature(0.4f)
    .ExecuteAsync();

Debug.Log(item.Name + " - " + item.Rarity); // "Blade of Eternity - Legendary"

Generating a Simple Quest

[StrictJsonSchema("quest_response", Description = "Basic RPG quest", Strict = true)]
public class Quest
{
    [JsonSchema("title", Description = "Quest name", Required = true)]
    public string Title { get; set; }

    [JsonSchema("description", Description = "What the player must do", Required = true)]
    public string Description { get; set; }

    [JsonSchema("objective", Description = "Main goal of the quest")]
    public string Objective { get; set; }

    [JsonSchema("rewardXP", Description = "XP rewarded upon completion")]
    public int RewardXP { get; set; }
}

Quest quest = await "Create a side quest for a fantasy game where a farmer asks you to find his lost chicken"
    .GENObject<Quest>()
    .SetModel(OpenAIModel.GPT4o)
    .ExecuteAsync();