Cactus Kotlin

Official Kotlin Multiplatform library for Cactus, a framework for deploying LLM and STT models locally in your app.

Installation

dependencyResolutionManagement {
    repositories {
        maven {
            name = "GitHubPackagesCactus"
            url = uri("https://maven.pkg.github.com/cactus-compute/cactus-kotlin")
            credentials {
                username = properties.getProperty("github.username") ?: System.getenv("GITHUB_ACTOR")
                password = properties.getProperty("github.token") ?: System.getenv("GITHUB_TOKEN")
            }
        }
    }
}

github.username=your-username
github.token=your-personal-access-token

kotlin {
    sourceSets {
        commonMain {
            dependencies {
                implementation("com.cactus:library:1.0.0-beta")
            }
        }
    }
}

<uses-permission android:name="android.permission.INTERNET" /> // for model downloads
<uses-permission android:name="android.permission.RECORD_AUDIO" /> // for transcription

Context Initialization (Required)

Before using any Cactus SDK functionality, you must initialize the context in your Activity's onCreate() method:

import com.cactus.CactusContextInitializer

class MainActivity : ComponentActivity() {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)

        // Initialize Cactus context (required)
        CactusContextInitializer.initialize(this)

        // ... rest of your code
    }
}

Language Model (LLM)

The CactusLM class provides text completion capabilities with support for function calling (WIP).

Basic Usage

import com.cactus.CactusLM
import com.cactus.CactusInitParams
import com.cactus.CactusCompletionParams
import com.cactus.ChatMessage
import kotlinx.coroutines.runBlocking

runBlocking {
    val lm = CactusLM()

    try {
        // Download a model by slug (e.g., "qwen3-0.6", "gemma3-270m")
        // If no model is specified, it defaults to "qwen3-0.6"
        // Throws exception on failure
        lm.downloadModel("qwen3-0.6")
        
        // Initialize the model
        // Throws exception on failure
        lm.initializeModel(
            CactusInitParams(
                model = "qwen3-0.6",
                contextSize = 2048
            )
        )

        // Generate completion with default parameters
        val result = lm.generateCompletion(
            messages = listOf(
                ChatMessage(content = "Hello, how are you?", role = "user")
            )
        )

        result?.let { response ->
            if (response.success) {
                println("Response: ${response.response}")
                println("Tokens per second: ${response.tokensPerSecond}")
                println("Time to first token: ${response.timeToFirstTokenMs}ms")
            }
        }
    } finally {
        // Clean up
        lm.unload()
    }
}

Inference Modes

CactusLM supports hybrid inference modes that allow fallback between local and cloud-based processing:

val result = lm.generateCompletion(
    messages = listOf(ChatMessage(content = "Hello!", role = "user")),
    params = CactusCompletionParams(
        mode = InferenceMode.LOCAL_FIRST, // Try local first, fallback to cloud
        cactusToken = "your_cactus_token"
    )
)

Available modes:

• InferenceMode.LOCAL - Local inference only (default)
• InferenceMode.REMOTE - Cloud-based inference only
• InferenceMode.LOCAL_FIRST - Try local first, fallback to cloud
• InferenceMode.REMOTE_FIRST - Try cloud first, fallback to local

To get a cactusToken, join our Discord community and contact us.

Streaming Completions

    val result = lm.generateCompletion(
    messages = listOf(ChatMessage("Tell me a story", "user")),
    params = CactusCompletionParams(maxTokens = 200),
        onToken = { token, tokenId ->
        print(token) // Print each token as it's generated
    }
)

Function Calling (Experimental)

import com.cactus.models.ToolParameter
import com.cactus.models.createTool

    val tools = listOf(
        createTool(
            name = "get_weather",
            description = "Get current weather for a location",
            parameters = mapOf(
                "location" to ToolParameter(
                    type = "string",
                    description = "City name",
                    required = true
                )
            )
        )
    )

    val result = lm.generateCompletion(
    messages = listOf(ChatMessage("What's the weather in New York?", "user")),
        params = CactusCompletionParams(
        maxTokens = 100,
            tools = tools
        )
    )

Available Models

You can get a list of available models:

lm.getModels()

LLM API Reference

CactusLM Class

• suspend fun downloadModel(model: String = "qwen3-0.6"): Boolean - Download a model
• suspend fun initializeModel(params: CactusInitParams): Boolean - Initialize model for inference
• suspend fun generateCompletion(messages: List<ChatMessage>, params: CactusCompletionParams = CactusCompletionParams(), onToken: CactusStreamingCallback? = null): CactusCompletionResult? - Generate text completion with support for streaming and inference modes
• suspend fun generateEmbedding(text: String, modelName: String? = null): CactusEmbeddingResult? - Generate text embeddings
• fun unload() - Free model from memory
• suspend fun getModels(): List<CactusModel> - Get available LLM models
• fun isLoaded(): Boolean - Check if model is loaded

Data Classes

• CactusInitParams(model: String?, contextSize: Int?) - Model initialization parameters
• CactusCompletionParams(temperature: Double, topK: Int, topP: Double, maxTokens: Int, stopSequences: List<String>, tools: List<Tool>?, mode: InferenceMode, cactusToken: String?, model: String?) - Completion parameters
• ChatMessage(content: String, role: String, timestamp: Long?) - Chat message format
• CactusCompletionResult - Contains response, timing metrics, and success status
• CactusEmbeddingResult(success: Boolean, embeddings: List<Double>, dimension: Int, errorMessage: String?) - Embedding generation result
• InferenceMode - Enum for inference modes (LOCAL, REMOTE, LOCAL_FIRST, REMOTE_FIRST)

Embeddings

The CactusLM class also provides text embedding generation capabilities for semantic similarity, search, and other NLP tasks.

Basic Usage

import com.cactus.CactusLM
import com.cactus.CactusInitParams
import kotlinx.coroutines.runBlocking

runBlocking {
    val lm = CactusLM()
    
    // Download and initialize a model (same as for completions)
    lm.downloadModel("qwen3-0.6")
    lm.initializeModel(CactusInitParams(model = "qwen3-0.6", contextSize = 2048))

    // Generate embeddings for a text
    val result = lm.generateEmbedding(
        text = "This is a sample text for embedding generation"
    )

    result?.let { embedding ->
        if (embedding.success) {
            println("Embedding dimension: ${embedding.dimension}")
            println("Embedding vector length: ${embedding.embeddings.size}")
        } else {
            println("Embedding generation failed: ${embedding.errorMessage}")
        }
    }

    lm.unload()
}

Embedding API Reference

CactusLM Class (Embedding Methods)

• suspend fun generateEmbedding(text: String, modelName: String? = null): CactusEmbeddingResult? - Generate embeddings for the given text.
• suspend fun getModels(): List<CactusModel> - Get a list of available models. Results are cached locally to reduce network requests.
• fun unload() - Unload the current model and free resources.
• fun isLoaded(): Boolean - Check if a model is currently loaded.

Data Classes

• CactusInitParams(model: String? = null, contextSize: Int? = null) - Parameters for model initialization.
• CactusCompletionParams(model: String? = null, temperature: Double? = null, topK: Int? = null, topP: Double? = null, maxTokens: Int = 200, stopSequences: List<String> = listOf("<|im_end|>", "<end_of_turn>"), tools: List<CactusTool> = emptyList(), mode: InferenceMode = InferenceMode.LOCAL, cactusToken: String? = null) - Parameters for text completion.
• CactusCompletionResult(success: Boolean, response: String? = null, timeToFirstTokenMs: Double? = null, totalTimeMs: Double? = null, tokensPerSecond: Double? = null, prefillTokens: Int? = null, decodeTokens: Int? = null, totalTokens: Int? = null, toolCalls: List<ToolCall>? = emptyList()) - The result of a text completion.
• CactusEmbeddingResult(success: Boolean, embeddings: List<Double> = listOf(), dimension: Int? = null, errorMessage: String? = null) - The result of embedding generation.
• ChatMessage(content: String, role: String, timestamp: Long? = null) - A chat message with role (e.g., "user", "assistant").
• CactusModel(created_at: String, slug: String, download_url: String, size_mb: Int, supports_tool_calling: Boolean, supports_vision: Boolean, name: String, isDownloaded: Boolean = false, quantization: Int = 8) - Information about an available model.
• InferenceMode - Enum for selecting inference mode (LOCAL, REMOTE, LOCAL_FIRST, REMOTE_FIRST).
• ToolCall(name: String, arguments: Map<String, String>) - Represents a tool call returned by the model.
• CactusTool(type: String = "function", function: CactusFunction) - Defines a tool that can be called by the model.
• CactusFunction(name: String, description: String, parameters: ToolParametersSchema) - Function definition for a tool.
• ToolParametersSchema(type: String = "object", properties: Map<String, ToolParameter>, required: List<String>) - Schema for tool parameters.
• ToolParameter(type: String, description: String, required: Boolean = false) - A parameter definition for a tool.

Helper Functions

• createTool(name: String, description: String, parameters: Map<String, ToolParameter>): CactusTool - Helper function to create a tool with the correct schema.

Speech-to-Text (STT)

The CactusSTT class provides speech recognition capabilities using on-device models from providers like Vosk and Whisper.

Choosing a Transcription Provider

You can select a transcription provider when initializing CactusSTT. The available providers are:

• TranscriptionProvider.VOSK (Default): Uses Vosk for transcription.
• TranscriptionProvider.WHISPER: Uses Whisper for transcription.

import com.cactus.CactusSTT
import com.cactus.TranscriptionProvider

// Initialize with the VOSK provider (default)
val sttVosk = CactusSTT() 

// Or explicitly initialize with the WHISPER provider
val sttWhisper = CactusSTT(TranscriptionProvider.WHISPER)

Basic Usage

With Vosk

import com.cactus.CactusSTT
import com.cactus.SpeechRecognitionParams
import kotlinx.coroutines.runBlocking

runBlocking {
    val stt = CactusSTT()

    // Download STT model (default: vosk-en-us)
    val downloadSuccess = stt.download("vosk-en-us")
    
    // Initialize the model
    val initSuccess = stt.init("vosk-en-us")

    // Transcribe from microphone
    val result = stt.transcribe(
        SpeechRecognitionParams(
            maxSilenceDuration = 1000L,
            maxDuration = 30000L,
            sampleRate = 16000
        )
    )

    result?.let { transcription ->
        if (transcription.success) {
            println("Transcribed: ${transcription.text}")
            println("Processing time: ${transcription.processingTime}ms")
        }
    }

    // Transcribe from audio file
    val fileResult = stt.transcribe(
        SpeechRecognitionParams(),
        filePath = "/path/to/audio.wav"
    )

    // Stop transcription
    stt.stop()
}

Tool Filtering

The ToolFilterService enables intelligent filtering of tools to optimize function calling by selecting only the most relevant tools for a given user query.

Configuration

Configure tool filtering when creating a CactusLM instance:

import com.cactus.CactusLM
import com.cactus.services.ToolFilterConfig
import com.cactus.services.ToolFilterStrategy

// Enable with default settings (SIMPLE strategy, max 3 tools)
val lm = CactusLM(
    enableToolFiltering = true,
    toolFilterConfig = ToolFilterConfig.simple(maxTools = 3)
)

// Custom configuration with SIMPLE strategy
val lm = CactusLM(
    enableToolFiltering = true,
    toolFilterConfig = ToolFilterConfig(
        strategy = ToolFilterStrategy.SIMPLE,
        maxTools = 5,
        similarityThreshold = 0.3
    )
)

// Use SEMANTIC strategy for more accurate filtering
val lm = CactusLM(
    enableToolFiltering = true,
    toolFilterConfig = ToolFilterConfig(
        strategy = ToolFilterStrategy.SEMANTIC,
        maxTools = 3,
        similarityThreshold = 0.5
    )
)

// Disable tool filtering
val lm = CactusLM(enableToolFiltering = false)

Configuration Parameters

• strategy - The filtering algorithm: SIMPLE (default, fast) or SEMANTIC (slower but more accurate)
• maxTools - Maximum number of tools to pass to the model (default: null, meaning no limit)
• similarityThreshold - Minimum score required for a tool to be included (default: 0.3)

Available Voice Models

// Get list of available voice models
stt.getVoiceModels()

// Check if model is downloaded
stt.isModelDownloaded("vosk-en-us")

STT API Reference

CactusSTT Class

• CactusSTT(provider: TranscriptionProvider = TranscriptionProvider.VOSK) - Constructor to specify the transcription provider.
• suspend fun download(model: String = "vosk-en-us"): Boolean - Download an STT model (e.g., "vosk-en-us" or "whisper-tiny-en"). Defaults to last downloaded model.
• suspend fun init(model: String = "vosk-en-us"): Boolean - Initialize an STT model for transcription. Defaults to last downloaded model.
• suspend fun transcribe(params: SpeechRecognitionParams = SpeechRecognitionParams(), filePath: String? = null, mode: TranscriptionMode = TranscriptionMode.LOCAL, apiKey: String? = null): SpeechRecognitionResult? - Transcribe speech from microphone or file. Supports different transcription modes.
• suspend fun warmUpWispr(apiKey: String) - Warms up the remote Wispr service for lower latency.
• fun stop() - Stop ongoing transcription.
• fun isReady(): Boolean - Check if the STT service is initialized and ready.
• suspend fun getVoiceModels(provider: TranscriptionProvider = this.provider): List<VoiceModel> - Get a list of available voice models for the specified provider. Defaults to the instance's provider.
• suspend fun isModelDownloaded(modelName: String = "vosk-en-us"): Boolean - Check if a specific model has been downloaded. Defaults to last downloaded model.

Data Classes

• TranscriptionProvider - Enum for selecting the provider (VOSK, WHISPER).
• SpeechRecognitionParams(maxSilenceDuration: Long = 1000L, maxDuration: Long = 30000L, sampleRate: Int = 16000) - Parameters for controlling speech recognition.
• SpeechRecognitionResult(success: Boolean, text: String? = null, eventSuccess: Boolean = true, processingTime: Double? = null) - The result of a transcription.
• VoiceModel(created_at: String, slug: String, language: String, url: String, size_mb: Int, file_name: String, provider: String = "vosk", isDownloaded: Boolean = false) - Contains information about an available voice model.
• TranscriptionMode - Enum for transcription mode (LOCAL, REMOTE, LOCAL_FIRST, REMOTE_FIRST).

Platform-Specific Setup

Android

• Works automatically - native libraries included
• Requires API 24+ (Android 7.0)
• ARM64 architecture supported

iOS

• Add the Cactus package dependency in Xcode
• Requires iOS 12.0+
• Supports ARM64 and Simulator ARM64

Building the Library

To build the library from source:

# Build the library and publish to localMaven
./build_library.sh

Telemetry Setup (Optional)

Cactus comes with powerful built-in telemetry that lets you monitor your projects. Create a token on the Cactus dashboard and get started with a one-line setup in your app:

import com.cactus.services.CactusTelemetry

// Initialize telemetry for usage analytics (optional)
CactusTelemetry.setTelemetryToken("your_token_here")

Example App

Navigate to the example app and run it:

cd kotlin/example

# For desktop
./gradlew :composeApp:run

# For Android/iOS - use Android Studio or Xcode

The example app demonstrates:

• Model downloading and initialization
• Text completion with streaming
• Function calling
• Speech-to-text transcription
• Error handling and status management

Performance Tips

1. Model Selection: Choose smaller models for faster inference on mobile devices
2. Context Size: Reduce context size for lower memory usage
3. Memory Management: Always call unload() when done with models
4. Batch Processing: Reuse initialized models for multiple completions
5. Model Caching: Use getModels() for efficient model discovery - results are cached locally to reduce network requests

Next Steps