atrade/src/main/kotlin/service/LlamaServerManager.kt

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Kotlin
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package service
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import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.SupervisorJob
import kotlinx.coroutines.launch
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import java.io.BufferedReader
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import java.io.File
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import java.io.InputStreamReader
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import java.util.concurrent.ConcurrentHashMap
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object LlamaServerManager {
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// 포트별로 프로세스를 관리합니다.
private val processes = ConcurrentHashMap<Int, Process>()
private val scope = CoroutineScope(Dispatchers.IO + SupervisorJob())
init {
Runtime.getRuntime().addShutdownHook(Thread {
stopAll()
})
}
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fun startServer(binPath: String, modelPath: String, port: Int, nGpuLayers: Int = 99) {
// 이미 해당 포트에서 실행 중이거나 모델 경로가 비었으면 무시합니다.
if (processes.containsKey(port) || modelPath.isBlank()) return
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val command = listOf(
binPath,
"-m", modelPath,
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"--port", port.toString(),
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"-c", if (port == 8081) "512" else "8192", // 임베딩용은 컨텍스트가 짧아도 충분합니다.
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"-ngl", nGpuLayers.toString(),
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"-t", "8", // M3 Pro의 성능 코어를 고려하여 6~8개 권장
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"--embedding" // 임베딩 기능을 활성화합니다.
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)
scope.launch {
try {
val pb = ProcessBuilder(command)
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pb.redirectErrorStream(true)
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File(binPath).setExecutable(true)
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val process = pb.start()
processes[port] = process
println("✅ AI 서버 시작 시도 (Port: $port, Model: ${File(modelPath).name})")
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val reader = BufferedReader(InputStreamReader(process.inputStream))
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var line: String?
while (reader.readLine().also { line = it } != null) {
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// 로그 출력 (디버깅용)
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// println("[Server $port] $line")
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if (line?.contains("server is listening") == true) {
println("🚀 AI 서버 준비 완료 (Port: $port)")
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if (processes.size > 1) {
println("[Cache] ${processes.size}")
RagService.active()
}
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}
}
} catch (e: Exception) {
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println("❌ AI 서버 실행 실패 (Port: $port): ${e.message}")
processes.remove(port)
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}
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}
}
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fun stopAll() {
processes.forEach { (port, process) ->
process.destroy()
println("🛑 AI 서버 종료 (Port: $port)")
}
processes.clear()
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}
}