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Currently confirmed offline model download and communications are working. Next step is to build the JSON schema for local models to understand tool calling

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mmanningau
2026-02-28 14:20:22 +11:00
parent 93a2c48e4b
commit d4322740e2
5 changed files with 46 additions and 26 deletions
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4
.idea/deploymentTargetSelector.xml generated
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@@ -4,10 +4,10 @@
<selectionStates>
<SelectionState runConfigName="app">
<option name="selectionMode" value="DROPDOWN" />
<DropdownSelection timestamp="2026-02-25T23:24:39.552459762Z">
<DropdownSelection timestamp="2026-02-28T03:12:27.182833316Z">
<Target type="DEFAULT_BOOT">
<handle>
<DeviceId pluginId="LocalEmulator" identifier="path=/home/michael/.android/avd/Pixel_8_API_35.avd" />
<DeviceId pluginId="PhysicalDevice" identifier="serial=461ed66e" />
</handle>
</Target>
</DropdownSelection>

44
app/src/main/java/net/mmanningau/alice/LlamaCppAdapter.kt
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@@ -1,5 +1,6 @@
package net.mmanningau.alice
import com.llamatik.library.platform.LlamaBridge // The correct Llamatik import!
import dev.langchain4j.data.message.AiMessage
import dev.langchain4j.data.message.ChatMessage
import dev.langchain4j.data.message.SystemMessage
@@ -8,9 +9,10 @@ import dev.langchain4j.model.chat.ChatLanguageModel
import dev.langchain4j.model.output.Response
import java.io.File
import dev.langchain4j.agent.tool.ToolSpecification
class LlamaCppAdapter(private val modelPath: String) : ChatLanguageModel {
// We will initialize the native C++ engine here once the model is downloaded
private var isEngineLoaded = false
private fun getOrInitEngine() {
@@ -19,35 +21,43 @@ class LlamaCppAdapter(private val modelPath: String) : ChatLanguageModel {
if (!modelFile.exists()) {
throw IllegalStateException("Model file not found at: $modelPath. Please download a model first.")
}
// TODO: In the next step, we will call Llamatik's native init function here
// to load the .gguf file into the device's memory.
// Boot the native C++ backend via Llamatik's Kotlin bridge
LlamaBridge.initGenerateModel(modelPath)
isEngineLoaded = true
}
}
override fun generate(messages: List<ChatMessage>): Response<AiMessage> {
// Ensure the engine is booted
getOrInitEngine()
// 1. Translation IN: Convert LangChain4j messages into a raw text prompt format
// Local models usually need a specific prompt template (like ChatML).
// We will build a basic conversational wrapper here.
// 1. Translation IN: Format specifically for Qwen 2.5 (ChatML)
val promptBuilder = java.lang.StringBuilder()
for (message in messages) {
when (message) {
is SystemMessage -> promptBuilder.append("System: ${message.text()}\n")
is UserMessage -> promptBuilder.append("User: ${message.text()}\n")
is AiMessage -> promptBuilder.append("Alice: ${message.text()}\n")
is SystemMessage -> promptBuilder.append("<|im_start|>system\n${message.text()}<|im_end|>\n")
is UserMessage -> promptBuilder.append("<|im_start|>user\n${message.text()}<|im_end|>\n")
is AiMessage -> promptBuilder.append("<|im_start|>assistant\n${message.text()}<|im_end|>\n")
}
}
promptBuilder.append("Alice: ")
// Prompt the AI to start generating its response
promptBuilder.append("<|im_start|>assistant\n")
// 2. Execution: (Placeholder until we wire the specific Llamatik execution call)
// val responseText = LlamatikEngine.generate(promptBuilder.toString())
val responseText = "This is a placeholder response from the local Llama.cpp engine!"
// 2. Execution: Run it on the local hardware using Llamatik
val responseText = LlamaBridge.generate(promptBuilder.toString())
// 3. Translation OUT: Wrap the raw string back into LangChain4j's format
val finalAiMessage = AiMessage(responseText)
return Response.from(finalAiMessage)
// 3. Translation OUT: Clean up any trailing ChatML tags the engine might leave behind
val cleanResponse = responseText.replace("<|im_end|>", "").trim()
return Response.from(AiMessage(cleanResponse))
}
// This catches LangChain4j when it tries to send tools to our local engine
override fun generate(
messages: List<ChatMessage>,
toolSpecifications: List<ToolSpecification>
): Response<AiMessage> {
// For Phase 1, we simply ignore the tools and route it to our standard text generator
// so we can prove the local GPU engine is successfully generating tokens!
return generate(messages)
}
}

11
app/src/main/java/net/mmanningau/alice/LlmManager.kt
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@@ -11,6 +11,7 @@ import java.time.Duration
import java.text.SimpleDateFormat
import java.util.Date
import java.util.Locale
import android.content.Context
object LlmManager {
@@ -28,7 +29,7 @@ object LlmManager {
// Initialization now makes the dao optional so the UI can safely call it!
fun initialize(
dao: ChatDao?, mode: String, url: String, modelName: String, apiKey: String, systemPrompt: String
context: Context,dao: ChatDao?, mode: String, url: String, modelName: String, apiKey: String, systemPrompt: String
) {
// Only update the DAO if one was passed in (like on app boot)
if (dao != null) {
@@ -50,8 +51,10 @@ object LlmManager {
.logRequests(true)
.logResponses(true)
.build()
} else {
chatModel = null // MLC Engine goes here later!
} else if (mode == "Local") {
// NEW: Grab the absolute path from the registry and boot the middleman!
val fullPath = ModelRegistry.getModelPath(context, modelName)
chatModel = LlamaCppAdapter(fullPath)
}
// Database Startup Logic
@@ -102,7 +105,7 @@ object LlmManager {
}
fun chat(userText: String): String {
if (currentMode == "MLC") return "System: MLC LLM On-Device engine is selected but not yet installed."
if (currentMode == "Local" && chatModel == null) return "System: Llamatik Ollama On-Device engine is selected but not yet installed."
val currentModel = chatModel ?: return "Error: LLM engine not initialized."
// If the history size is 1, it means only the System prompt exists. This is the first message!

11
app/src/main/java/net/mmanningau/alice/MainActivity.kt
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@@ -201,7 +201,7 @@ fun MainChatScreen() {
val response = LlmManager.chat(userText)
messages = messages + ChatMessage(response, false)
} catch (e: Exception) {
messages = messages + ChatMessage("Connection Error: Is the local LLM server running?", false)
messages = messages + ChatMessage("System Error: ${e.message}", false)
}
}
}
@@ -387,7 +387,7 @@ fun SettingsScreen(onBackClicked: () -> Unit) {
.putString("systemPrompt", systemPrompt)
.apply()
LlmManager.initialize(null, llmMode, llmUrl, modelName, apiKey, systemPrompt)
LlmManager.initialize(context, null, llmMode, llmUrl, modelName, apiKey, systemPrompt)
SkillManager.updateDirectory(skillsPath)
onBackClicked()
@@ -481,6 +481,13 @@ fun ModelManagerScreen(onBackClicked: () -> Unit) {
// Save the exact filename so LlmManager knows which one to boot up
prefs.edit().putString("modelName", model.fileName).apply()
activeModelName = model.fileName
// NEW: Hot-reload the LlmManager instantly!
val mode = prefs.getString("llmMode", "Local") ?: "Local"
val url = prefs.getString("llmUrl", "") ?: ""
val apiKey = prefs.getString("apiKey", "") ?: ""
val prompt = prefs.getString("systemPrompt", "You are a helpful AI assistant.") ?: "You are a helpful AI assistant."
LlmManager.initialize(context, null, mode, url, model.fileName, apiKey, prompt)
},
modifier = Modifier.fillMaxWidth(),
colors = ButtonDefaults.buttonColors(

2
app/src/main/java/net/mmanningau/alice/net/mmanningau/AliceApp.kt
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@@ -38,6 +38,6 @@ class AliceApp : Application() {
).allowMainThreadQueries().build() // We use allowMainThreadQueries for immediate boot loading
// Pass the DAO into the manager!
LlmManager.initialize(db.chatDao(), savedMode, savedUrl, savedModel, savedApiKey, savedSystemPrompt)
LlmManager.initialize(this,db.chatDao(), savedMode, savedUrl, savedModel, savedApiKey, savedSystemPrompt)
}
}