DeviceAI

// build.gradle.kts
implementation("dev.deviceai:core:0.0.4")
implementation("dev.deviceai:speech:0.0.4")   // STT + TTS
implementation("dev.deviceai:llm:0.0.4")      // LLM + RAG

class MyApp : Application() {
    override fun onCreate() {
        super.onCreate()
        PlatformStorage.initialize(this)
        DeviceAI.initialize(context = this)
    }
}

DeviceAI.initialize(context = this, apiKey = "<YOUR_API_KEY>") {
    telemetry = TelemetryLevel.Minimal
    appVersion = BuildConfig.VERSION_NAME
}

SpeechBridge.initStt(modelPath, SttConfig(language = "en", useGpu = true))

// From raw audio samples
val text = SpeechBridge.transcribeAudio(samples)  // FloatArray, 16kHz mono

// From a WAV file
val textFromFile = SpeechBridge.transcribe("/path/to/audio.wav")

SpeechBridge.shutdownStt()

ModelRegistry.initialize()
val voice = ModelRegistry.getTtsTarballVoices(languageCode = "en").first()   // ~67 MB, once
val local = ModelRegistry.download(voice) { progress -> /* … */ }.getOrThrow()

val ok = SpeechBridge.initTts(
    modelPath  = local.modelPath,
    tokensPath = local.configPath!!,
    config     = TtsConfig(
        speechRate = 1.0f,
        dataDir    = local.ttsDataDir(),      // espeak-ng-data, required for Piper
        voicesPath = local.ttsVoicesPath(),   // "" for Piper; voices.bin for Kokoro
    ),
)
check(ok) { "TTS engine failed to initialise" }

val pcm: ShortArray = SpeechBridge.synthesize("Hello from DeviceAI.")
val sampleRate = SpeechBridge.ttsSampleRate()   // 22050 for Piper — configure AudioTrack with this

SpeechBridge.shutdownTts()

val session = DeviceAI.llm.chat("/path/to/model.gguf") {
    systemPrompt = "You are a helpful assistant."
    maxTokens = 512
    temperature = 0.7f
    useGpu = true
}

// Streaming (recommended for UI)
session.send("What is Kotlin?").collect { token -> print(token) }

// Multi-turn — history managed automatically
session.send("Give me an example.").collect { print(it) }

// Lifecycle
session.cancel()        // abort generation
session.clearHistory()  // fresh conversation
session.close()         // unload model

val store = BM25RagStore(rawChunks = listOf(
    "DeviceAI supports Android and iOS.",
    "LLM inference uses llama.cpp with Vulkan GPU."
))
val session = DeviceAI.llm.chat("/path/to/model.gguf") { ragStore = store }
session.send("What GPU does DeviceAI use?").collect { print(it) }

DeviceAI.initialize(context = this, apiKey = "<YOUR_API_KEY>") {
    telemetry = TelemetryLevel.Off      // default — nothing sent
    telemetry = TelemetryLevel.Minimal  // model load/unload + inference metrics
    telemetry = TelemetryLevel.Full     // includes OTA downloads + manifest syncs
}

DeviceAI.initialize(context = this, apiKey = "<YOUR_API_KEY>") {
    telemetry = TelemetryLevel.Minimal
    telemetrySink = object : TelemetrySink {
        override suspend fun ingest(events: List<TelemetryEvent>) {
            myAnalytics.track(events)
        }
    }
}