[Developer] #218 Real Gemma 4 E2B integration via flutter_gemma 0.16.5

Implements the OQ-1 follow-up to #215 v0.2.0: replace the placeholder
GemmaLlmService stub with a real flutter_gemma 0.16.5 backend driving
Gemma 4 E2B (litert-community/gemma-4-E2B-it-litert-lm, 2.41GB).

Highlights:
- GemmaLlmService.load → FlutterGemma.initialize + installModel.fromFile +
  getActiveModel; idempotent + FileSystemException on missing file.
- generateStructured uses Gemma 4 native function calling via
  createChat(tools: [Tool(...)], toolChoice: required). Stream parsed by
  collectFunctionCall — first FCR wins, ParallelFCR first-call wins,
  TextResponse/ThinkingResponse skipped, errors sanitized to prevent
  prompt leakage.
- main.dart wires _LazyLlmService adapter that resolves to GemmaLlmService
  when ModelLifecycle reports ready, MockLlmService otherwise.
- ai_providers.dart pins real model URL + SHA-256 (181938...39a63c).
- F2 hardening: ModelLifecycle.purge wraps each delete + meta remove in
  try/catch so a single OS-level flake cannot block opt-out.
- Android: INTERNET / FOREGROUND_SERVICE / POST_NOTIFICATIONS permissions
  + R8 proguard-rules.pro keeping MediaPipe / LiteRT / TFLite / protobuf
  JNI entry points (release builds otherwise crash on first inference).

Design-First: fn-gemma_llm_service.md updated to v2 — §C
(_appendSchemaInstruction) deprecated after reading flutter_gemma
0.16.5 source (Gemma 4 SDK injects tool declarations via template;
prompt-side append would double-wrap).

Tests:
- 10 new unit tests for collectFunctionCall covering all 8 fn-spec
  cases + 2 ParallelFunctionCallResponse paths.
- All 81 existing tests still pass.
- flutter analyze: 0 issues.

Refs #218

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-06-12 15:18:08 +09:00
parent a1f3c5f85d
commit 9a9eb2abd5
14 changed files with 646 additions and 175 deletions

View File

@@ -30,6 +30,13 @@ android {
// TODO: Add your own signing config for the release build.
// Signing with the debug keys for now, so `flutter run --release` works.
signingConfig = signingConfigs.getByName("debug")
// #218: keep flutter_gemma JNI bindings — see proguard-rules.pro.
isMinifyEnabled = true
isShrinkResources = true
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
}

30
app/android/app/proguard-rules.pro vendored Normal file
View File

@@ -0,0 +1,30 @@
# flutter_gemma 0.16.5 keep MediaPipe + LiteRT native bindings (#218)
# Without these the release build (R8 minify) strips JNI entry points
# and the first inference call crashes with NoSuchMethodError.
# MediaPipe LLM (.task path)
-keep class com.google.mediapipe.** { *; }
-dontwarn com.google.mediapipe.**
# LiteRT runtime (.litertlm path used by Gemma 4 E2B)
-keep class com.google.ai.edge.** { *; }
-keep class com.google.ai.litert.** { *; }
-dontwarn com.google.ai.edge.**
-dontwarn com.google.ai.litert.**
# TensorFlow Lite (used by LiteRT under the hood)
-keep class org.tensorflow.lite.** { *; }
-dontwarn org.tensorflow.lite.**
# Protobuf-lite (LiteRT message classes referenced via reflection)
-keep class com.google.protobuf.** { *; }
-dontwarn com.google.protobuf.**
# flutter_gemma plugin's own native bridge
-keep class dev.flutterberlin.flutter_gemma.** { *; }
-dontwarn dev.flutterberlin.flutter_gemma.**
# Generic JNI methods covers any LiteRT/MediaPipe class loaded dynamically
-keepclasseswithmembernames class * {
native <methods>;
}

View File

@@ -1,4 +1,11 @@
<manifest xmlns:android="http://schemas.android.com/apk/res/android">
<!-- #218: flutter_gemma downloads ≈ 2.4GB model checkpoint. -->
<uses-permission android:name="android.permission.INTERNET"/>
<uses-permission android:name="android.permission.ACCESS_NETWORK_STATE"/>
<!-- Foreground service for large downloads (>500MB auto-detect). -->
<uses-permission android:name="android.permission.FOREGROUND_SERVICE"/>
<uses-permission android:name="android.permission.FOREGROUND_SERVICE_DATA_SYNC"/>
<uses-permission android:name="android.permission.POST_NOTIFICATIONS"/>
<application
android:label="life_helper"
android:name="${applicationName}"

View File

@@ -1,21 +1,40 @@
import 'dart:io';
import 'package:flutter/foundation.dart';
import 'package:flutter_gemma/flutter_gemma.dart';
import 'llm_service.dart';
/// Stub for the real `flutter_gemma` integration.
/// HuggingFace access token injected at build time via
/// `--dart-define=HF_TOKEN=hf_xxx`. Empty string is permitted —
/// flutter_gemma will only need it for the initial network download,
/// which our `ModelLifecycle` handles separately; activation from a
/// local file path generally does not require the token.
const String _hfToken = String.fromEnvironment('HF_TOKEN', defaultValue: '');
/// One-shot guard so [FlutterGemma.initialize] runs at most once per
/// isolate. Re-init is unsupported by the underlying plugin.
bool _initialized = false;
/// Real on-device LLM backend using flutter_gemma 0.16.5 + Gemma 4 E2B.
///
/// Wired up only after OQ-1 (exact model URL + SHA + flutter_gemma API
/// surface) is confirmed in Developer phase. Today this throws
/// `UnimplementedError` from every method — the rest of the system
/// (suggestFrame, ModelLifecycle, Riverpod providers) is built against the
/// `LlmService` abstract above and runs end-to-end with `MockLlmService`.
/// Wired into the existing #215 pipeline: `ModelLifecycle` downloads &
/// SHA-verifies the .litertlm file, then [load] registers that file with
/// flutter_gemma as the active model. [generateStructured] opens a
/// short-lived chat with a single [Tool] (Gemma 4 native function
/// calling) and returns the first matching [FunctionCallResponse]'s args.
///
/// When the package is added, replace the bodies with calls into
/// FlutterGemma.init / generateWithFunctionCalling per the package docs.
/// Existing tests + UI hooks remain unchanged.
/// Function-calling design notes (see fn-gemma_llm_service.md §B v2):
/// - Gemma 4 SDK injects the tool declaration via its chat template, so
/// we pass [Tool] to `createChat(tools: ...)` rather than appending a
/// schema instruction to the prompt (double-wrap risk).
/// - `ToolChoice.required` forces the model to emit a function call.
class GemmaLlmService implements LlmService {
final String modelPath;
GemmaLlmService({required this.modelPath});
InferenceModel? _model;
bool _loaded = false;
@override
@@ -23,14 +42,35 @@ class GemmaLlmService implements LlmService {
@override
Future<void> load() async {
throw UnimplementedError(
'GemmaLlmService.load: pending OQ-1 (model URL + flutter_gemma).',
);
if (_loaded) return;
if (!await File(modelPath).exists()) {
throw FileSystemException('model file missing', modelPath);
}
if (!_initialized) {
await FlutterGemma.initialize(huggingFaceToken: _hfToken);
_initialized = true;
}
await FlutterGemma.installModel(
modelType: ModelType.gemma4,
fileType: ModelFileType.litertlm,
).fromFile(modelPath).install();
final model = await FlutterGemma.getActiveModel(maxTokens: 2048);
_model = model;
_loaded = true;
}
@override
Future<void> unload() async {
final m = _model;
_model = null;
_loaded = false;
if (m != null) {
try {
await m.close();
} catch (_) {
// Best-effort cleanup — runtime may already be torn down.
}
}
}
@override
@@ -38,8 +78,90 @@ class GemmaLlmService implements LlmService {
String prompt,
Map<String, dynamic> schema,
) async {
throw UnimplementedError(
'GemmaLlmService.generateStructured: pending OQ-1.',
if (!_loaded || _model == null) {
throw StateError('LlmService not loaded');
}
final fnName = schema['name'];
final fnParams = schema['parameters'];
if (fnName is! String || fnName.isEmpty) {
throw ArgumentError('schema.name missing');
}
if (fnParams is! Map) {
throw ArgumentError('schema.parameters missing');
}
final fnDesc = (schema['description'] as String?) ?? '';
final tool = Tool(
name: fnName,
description: fnDesc,
parameters: Map<String, dynamic>.from(fnParams),
);
final chat = await _model!.createChat(
modelType: ModelType.gemma4,
supportsFunctionCalls: true,
toolChoice: ToolChoice.required,
tools: [tool],
);
try {
await chat.addQueryChunk(Message.text(text: prompt, isUser: true));
final stream = chat.generateChatResponseAsync();
return await collectFunctionCall(stream, fnName);
} finally {
try {
await chat.close();
} catch (_) {
// Native session close failure is non-fatal — log + continue.
}
}
}
}
/// Extracts the first `FunctionCallResponse(name == expectedName)` from
/// a flutter_gemma response stream. `TextResponse` / `ThinkingResponse`
/// events are skipped. A mismatched name throws fast.
///
/// File-private under `_collectFunctionCall` from [GemmaLlmService];
/// exposed as a top-level via `@visibleForTesting` so unit tests can
/// feed synthetic streams (see fn-spec §D, 8 test cases).
@visibleForTesting
Future<Map<String, dynamic>> collectFunctionCall(
Stream<ModelResponse> stream,
String expectedName,
) async {
Map<String, dynamic>? result;
String? wrongName;
try {
await for (final event in stream) {
if (event is FunctionCallResponse) {
if (event.name == expectedName) {
result = Map<String, dynamic>.from(event.args);
break;
} else {
wrongName = event.name;
break;
}
}
if (event is ParallelFunctionCallResponse && event.calls.isNotEmpty) {
final first = event.calls.first;
if (first.name == expectedName) {
result = Map<String, dynamic>.from(first.args);
} else {
wrongName = first.name;
}
break;
}
// TextResponse / ThinkingResponse: skip.
}
} catch (_) {
// Discard raw error to avoid leaking prompt content in logs/crash
// reports — the caller surfaces a generic message.
throw const FormatException('stream error');
}
if (wrongName != null) {
throw FormatException('unexpected function: $wrongName');
}
if (result == null) {
throw const FormatException('no function call emitted');
}
return result;
}

View File

@@ -233,21 +233,37 @@ class ModelLifecycle {
/// opt-out: delete model file + clear all ai_* meta keys (except opt_in
/// which the caller toggles). Returns freed bytes (0 if nothing existed).
/// Idempotent.
///
/// F2 hardening (#218): per-file try/catch so a single OS-level delete
/// failure (locked file, permission flake) does not abort the whole
/// purge — meta keys still get cleared and the orphan file becomes a
/// background storage concern rather than a stuck "opt-out failed"
/// state. The freed-bytes count only reflects successful deletes.
Future<int> purge() async {
int freed = 0;
final pathStr = await meta.find(AiMetaKeys.modelPath);
if (pathStr != null) {
final f = File(pathStr);
if (f.existsSync()) {
freed += await f.length();
await f.delete();
try {
final f = File(pathStr);
if (f.existsSync()) {
final size = await f.length();
await f.delete();
freed += size;
}
} catch (_) {
// Best-effort; leave orphan file, continue purging meta.
}
}
final tempPath = '${await _modelPath()}.tmp';
final temp = File(tempPath);
if (temp.existsSync()) {
freed += await temp.length();
await temp.delete();
try {
final tempPath = '${await _modelPath()}.tmp';
final temp = File(tempPath);
if (temp.existsSync()) {
final size = await temp.length();
await temp.delete();
freed += size;
}
} catch (_) {
// Same as above — best-effort cleanup of the .tmp partial.
}
for (final k in [
AiMetaKeys.modelPath,
@@ -255,7 +271,12 @@ class ModelLifecycle {
AiMetaKeys.downloadState,
AiMetaKeys.downloadBytes,
]) {
await meta.remove(k);
try {
await meta.remove(k);
} catch (_) {
// Meta is a single sqlite table; failures here are rare.
// Swallow so the loop completes even if one key errors.
}
}
return freed;
}

View File

@@ -1,7 +1,10 @@
import 'package:flutter/material.dart';
import 'package:flutter_riverpod/flutter_riverpod.dart';
import 'data/ai/gemma_llm_service.dart';
import 'data/ai/llm_service.dart';
import 'data/ai/model_lifecycle.dart';
import 'data/db/daos/meta_dao.dart';
import 'state/ai_providers.dart';
import 'state/providers.dart';
import 'ui/screens/habit_list_screen.dart';
@@ -12,15 +15,62 @@ Future<void> main() async {
runApp(ProviderScope(
overrides: [
appDatabaseProvider.overrideWithValue(db),
// OQ-1 pending: production-ready GemmaLlmService is wired here once
// model URL + SHA are pinned and flutter_gemma is added. Until then,
// MockLlmService keeps the app graceful (suggestFrame returns []).
llmServiceProvider.overrideWithValue(MockLlmService()),
// #218: real GemmaLlmService when model file is on disk + verified,
// MockLlmService otherwise. The provider is read lazily by the frame
// suggestion flow, so the resolution is dynamic per call.
llmServiceProvider.overrideWith((ref) {
return _LazyLlmService(
lifecycle: ref.watch(modelLifecycleProvider),
meta: ref.watch(metaDaoProvider),
);
}),
],
child: const LifeHelperApp(),
));
}
/// Adapter that lazily resolves between [GemmaLlmService] (when the
/// model file exists + meta is intact) and [MockLlmService] (fallback,
/// graceful empty candidates). Keeps the rest of the app unaware of
/// the difference — `suggestFrame` only sees [LlmService].
class _LazyLlmService implements LlmService {
_LazyLlmService({required this.lifecycle, required this.meta});
final ModelLifecycle lifecycle;
final MetaDao meta;
LlmService? _delegate;
Future<LlmService> _resolve() async {
if (_delegate != null) return _delegate!;
final avail = await lifecycle.checkAvailability();
final path = await meta.find(AiMetaKeys.modelPath);
if (avail == ModelAvailability.ready && path != null) {
_delegate = GemmaLlmService(modelPath: path);
} else {
_delegate = MockLlmService();
}
return _delegate!;
}
@override
bool get isLoaded => _delegate?.isLoaded ?? false;
@override
Future<void> load() async => (await _resolve()).load();
@override
Future<void> unload() async {
final d = _delegate;
if (d != null) await d.unload();
}
@override
Future<Map<String, dynamic>> generateStructured(
String prompt,
Map<String, dynamic> schema,
) async =>
(await _resolve()).generateStructured(prompt, schema);
}
class LifeHelperApp extends StatelessWidget {
const LifeHelperApp({super.key});

View File

@@ -10,19 +10,24 @@ import '../domain/ai/suggest_frame.dart';
import '../domain/models/frame_pattern.dart';
import 'providers.dart';
/// Default config for the on-device Gemma model (#215).
/// OQ-1: URL + SHA-256 pinned in Developer phase. Until then, downloads are
/// disabled (AI toggle is gated behind these constants being real).
const _kModelUrlPlaceholder =
'https://example.invalid/gemma4-e2b-q4.bin'; // OQ-1
const _kModelShaPlaceholder = 'PENDING_OQ_1';
/// Gemma 4 E2B instruction-tuned LiteRT-LM checkpoint (#218 OQ-1 resolved).
/// Hosted on HuggingFace `litert-community/gemma-4-E2B-it-litert-lm`.
/// File ≈ 2.41GB; SHA-256 pinned for integrity check.
///
/// Tests / placeholder builds may override `modelLifecycleProvider` with
/// fixture URLs. Production builds optionally inject a private mirror via
/// `--dart-define=GEMMA_MODEL_URL=...` (see main.dart).
const _kModelUrl =
'https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm/resolve/main/gemma-4-E2B-it.litertlm';
const _kModelSha256 =
'181938105e0eefd105961417e8da75903eacda102c4fce9ce90f50b97139a63c';
final modelLifecycleProvider = Provider<ModelLifecycle>((ref) {
return ModelLifecycle(
meta: ref.watch(metaDaoProvider),
config: ModelConfig(
url: Uri.parse(_kModelUrlPlaceholder),
expectedSha256: _kModelShaPlaceholder,
url: Uri.parse(_kModelUrl),
expectedSha256: _kModelSha256,
),
);
});

View File

@@ -51,7 +51,7 @@ class _AiSection extends ConsumerWidget {
SwitchListTile(
title: const Text('AI 도움 켜기'),
subtitle: const Text(
'Gemma 4 E2B 모델 ≈ 1.5GB. 모든 처리는 단말에서 일어납니다.',
'Gemma 4 E2B 모델 ≈ 2.4GB. 모든 처리는 단말에서 일어납니다.',
),
value: optIn,
onChanged: (v) async {
@@ -125,9 +125,10 @@ class _AiSection extends ConsumerWidget {
style: TextStyle(fontWeight: FontWeight.w600),
),
SizedBox(height: 12),
_Bullet('파일 크기: 약 1.5GB'),
_Bullet('파일 크기: 약 2.4GB'),
_Bullet('WiFi 연결을 권장합니다'),
_Bullet('모든 처리는 단말에서만 일어나며, 입력 텍스트는 외부로 전송되지 않습니다'),
_Bullet('Gemma 이용약관(ai.google.dev/gemma/terms)에 동의합니다'),
],
),
actions: [
@@ -156,7 +157,7 @@ class _AiSection extends ConsumerWidget {
Text('모델 파일이 단말에서 삭제됩니다.'),
SizedBox(height: 8),
Text(
'1.5GB 의 저장공간이 확보돼요.',
'2.4GB 의 저장공간이 확보돼요.',
style: TextStyle(fontSize: 13, color: Colors.grey),
),
SizedBox(height: 4),

View File

@@ -5,18 +5,18 @@ packages:
dependency: transitive
description:
name: _fe_analyzer_shared
sha256: da0d9209ca76bde579f2da330aeb9df62b6319c834fa7baae052021b0462401f
sha256: "3b19a47f6ea7c2632760777c78174f47f6aec1e05f0cd611380d4593b8af1dbc"
url: "https://pub.dev"
source: hosted
version: "85.0.0"
version: "96.0.0"
analyzer:
dependency: transitive
description:
name: analyzer
sha256: "974859dc0ff5f37bc4313244b3218c791810d03ab3470a579580279ba971a48d"
sha256: "0c516bc4ad36a1a75759e54d5047cb9d15cded4459df01aa35a0b5ec7db2c2a0"
url: "https://pub.dev"
source: hosted
version: "7.7.1"
version: "10.2.0"
args:
dependency: transitive
description:
@@ -33,6 +33,14 @@ packages:
url: "https://pub.dev"
source: hosted
version: "2.13.1"
background_downloader:
dependency: transitive
description:
name: background_downloader
sha256: aceacec2b2a72ec3a8862ab5895fcbbc71ab33765f3619d57963f3110dd268e3
url: "https://pub.dev"
source: hosted
version: "9.5.5"
boolean_selector:
dependency: transitive
description:
@@ -45,18 +53,18 @@ packages:
dependency: transitive
description:
name: build
sha256: "51dc711996cbf609b90cbe5b335bbce83143875a9d58e4b5c6d3c4f684d3dda7"
sha256: a156715e7cd728130c592f30552575908aae5b100005fbc1f0fb16b3c03a3d10
url: "https://pub.dev"
source: hosted
version: "2.5.4"
version: "4.0.6"
build_config:
dependency: transitive
description:
name: build_config
sha256: "4ae2de3e1e67ea270081eaee972e1bd8f027d459f249e0f1186730784c2e7e33"
sha256: "4070d2a59f8eec34c97c86ceb44403834899075f66e8a9d59706f8e7834f6f71"
url: "https://pub.dev"
source: hosted
version: "1.1.2"
version: "1.3.0"
build_daemon:
dependency: transitive
description:
@@ -65,30 +73,14 @@ packages:
url: "https://pub.dev"
source: hosted
version: "4.1.1"
build_resolvers:
dependency: transitive
description:
name: build_resolvers
sha256: ee4257b3f20c0c90e72ed2b57ad637f694ccba48839a821e87db762548c22a62
url: "https://pub.dev"
source: hosted
version: "2.5.4"
build_runner:
dependency: "direct dev"
description:
name: build_runner
sha256: "382a4d649addbfb7ba71a3631df0ec6a45d5ab9b098638144faf27f02778eb53"
sha256: "1523ce62448ebac2c15a8ba5fbad8acac169788658a7dd2a1c2d9c2a9318b9a6"
url: "https://pub.dev"
source: hosted
version: "2.5.4"
build_runner_core:
dependency: transitive
description:
name: build_runner_core
sha256: "85fbbb1036d576d966332a3f5ce83f2ce66a40bea1a94ad2d5fc29a19a0d3792"
url: "https://pub.dev"
source: hosted
version: "9.1.2"
version: "2.15.0"
built_collection:
dependency: transitive
description:
@@ -149,18 +141,10 @@ packages:
dependency: transitive
description:
name: code_assets
sha256: bf394f466ba9205f1812a0433b392d6af280f155f56651eda7c18cc32ed493b8
sha256: "83ccdaa064c980b5596c35dd64a8d3ecc68620174ab9b90b6343b753aa721687"
url: "https://pub.dev"
source: hosted
version: "1.2.1"
code_builder:
dependency: transitive
description:
name: code_builder
sha256: "6a6cab2ba4680d6423f34a9b972a4c9a94ebe1b62ecec4e1a1f2cba91fd1319d"
url: "https://pub.dev"
source: hosted
version: "4.11.1"
version: "1.0.0"
collection:
dependency: transitive
description:
@@ -193,30 +177,38 @@ packages:
url: "https://pub.dev"
source: hosted
version: "1.0.9"
dart_sentencepiece_tokenizer:
dependency: transitive
description:
name: dart_sentencepiece_tokenizer
sha256: "85825632845cf6427ea0cd13dfba96b4341cf63525165155e5b9b97011239289"
url: "https://pub.dev"
source: hosted
version: "1.3.2"
dart_style:
dependency: transitive
description:
name: dart_style
sha256: "8a0e5fba27e8ee025d2ffb4ee820b4e6e2cf5e4246a6b1a477eb66866947e0bb"
sha256: "29f7ecc274a86d32920b1d9cfc7502fa87220da41ec60b55f329559d5732e2b2"
url: "https://pub.dev"
source: hosted
version: "3.1.1"
version: "3.1.7"
drift:
dependency: "direct main"
description:
name: drift
sha256: "540cf382a3bfa99b76e51514db5b0ebcd81ce3679b7c1c9cb9478ff3735e47a1"
sha256: "6cc0b623c0e83f7080524d8396e9301b1d78b9c66a4fdceeb0f798211303254c"
url: "https://pub.dev"
source: hosted
version: "2.28.2"
version: "2.34.0"
drift_dev:
dependency: "direct dev"
description:
name: drift_dev
sha256: "68c138e884527d2bd61df2ade276c3a144df84d1adeb0ab8f3196b5afe021bd4"
sha256: "9cfff1576b49725da0d32c040651a41ae195e8c4af8d8da301593e41d7abc2f7"
url: "https://pub.dev"
source: hosted
version: "2.28.0"
version: "2.34.0"
fake_async:
dependency: transitive
description:
@@ -254,6 +246,14 @@ packages:
description: flutter
source: sdk
version: "0.0.0"
flutter_gemma:
dependency: "direct main"
description:
name: flutter_gemma
sha256: "984960b54bbc0ff7e36cf568a02652b1d4bc016d6c75575b027fb6102fab48c3"
url: "https://pub.dev"
source: hosted
version: "0.16.5"
flutter_lints:
dependency: "direct dev"
description:
@@ -275,30 +275,11 @@ packages:
description: flutter
source: sdk
version: "0.0.0"
freezed:
dependency: "direct dev"
description:
name: freezed
sha256: "59a584c24b3acdc5250bb856d0d3e9c0b798ed14a4af1ddb7dc1c7b41df91c9c"
url: "https://pub.dev"
source: hosted
version: "2.5.8"
freezed_annotation:
dependency: "direct main"
description:
name: freezed_annotation
sha256: c2e2d632dd9b8a2b7751117abcfc2b4888ecfe181bd9fca7170d9ef02e595fe2
url: "https://pub.dev"
source: hosted
version: "2.4.4"
frontend_server_client:
flutter_web_plugins:
dependency: transitive
description:
name: frontend_server_client
sha256: f64a0333a82f30b0cca061bc3d143813a486dc086b574bfb233b7c1372427694
url: "https://pub.dev"
source: hosted
version: "4.0.0"
description: flutter
source: sdk
version: "0.0.0"
glob:
dependency: transitive
description:
@@ -319,10 +300,10 @@ packages:
dependency: transitive
description:
name: hooks
sha256: "9a62a50b50b769a737bc0a8ff381f333529df3ab746b2f6b02e83760231455ba"
sha256: "025f060e86d2d4c3c47b56e33caf7f93bf9283340f26d23424ebcfccf34f621e"
url: "https://pub.dev"
source: hosted
version: "2.0.2"
version: "1.0.3"
http:
dependency: "direct main"
description:
@@ -371,14 +352,6 @@ packages:
url: "https://pub.dev"
source: hosted
version: "1.0.1"
js:
dependency: transitive
description:
name: js
sha256: "53385261521cc4a0c4658fd0ad07a7d14591cf8fc33abbceae306ddb974888dc"
url: "https://pub.dev"
source: hosted
version: "0.7.2"
json_annotation:
dependency: "direct main"
description:
@@ -391,10 +364,18 @@ packages:
dependency: "direct dev"
description:
name: json_serializable
sha256: c50ef5fc083d5b5e12eef489503ba3bf5ccc899e487d691584699b4bdefeea8c
sha256: "5b89c1e32ae3840bb20a1b3434e3a590173ad3cb605896fb0f60487ce2f8104e"
url: "https://pub.dev"
source: hosted
version: "6.9.5"
version: "6.11.4"
large_file_handler:
dependency: transitive
description:
name: large_file_handler
sha256: "1657db12b4591242b186c23eee437997be91e655ab0d803fd74829074460757f"
url: "https://pub.dev"
source: hosted
version: "0.4.0"
leak_tracker:
dependency: transitive
description:
@@ -427,6 +408,14 @@ packages:
url: "https://pub.dev"
source: hosted
version: "6.1.0"
local_hnsw:
dependency: transitive
description:
name: local_hnsw
sha256: "7fb0988e3f850121774d9dfe94068f843d87908d6b1e9ed5039710d8d9ae3f7a"
url: "https://pub.dev"
source: hosted
version: "1.0.0"
logging:
dependency: transitive
description:
@@ -467,14 +456,30 @@ packages:
url: "https://pub.dev"
source: hosted
version: "2.0.0"
mutex:
dependency: transitive
description:
name: mutex
sha256: "8827da25de792088eb33e572115a5eb0d61d61a3c01acbc8bcbe76ed78f1a1f2"
url: "https://pub.dev"
source: hosted
version: "3.1.0"
native_toolchain_c:
dependency: transitive
description:
name: native_toolchain_c
sha256: "6ba77bb18063eebe9de401f5e6437e95e1438af0a87a3a39084fbd37c90df572"
url: "https://pub.dev"
source: hosted
version: "0.17.6"
objective_c:
dependency: transitive
description:
name: objective_c
sha256: "6cb691c686fa2838c6deb34980d426145c2a5d537491cb83d463c33cdbc726ed"
sha256: "100a1c87616ab6ed41ec263b083c0ef3261ee6cd1dc3b0f35f8ddfa4f996fe52"
url: "https://pub.dev"
source: hosted
version: "9.4.1"
version: "9.3.0"
package_config:
dependency: transitive
description:
@@ -603,6 +608,62 @@ packages:
url: "https://pub.dev"
source: hosted
version: "2.6.1"
shared_preferences:
dependency: transitive
description:
name: shared_preferences
sha256: c3025c5534b01739267eb7d76959bbc25a6d10f6988e1c2a3036940133dd10bf
url: "https://pub.dev"
source: hosted
version: "2.5.5"
shared_preferences_android:
dependency: transitive
description:
name: shared_preferences_android
sha256: "93ae5884a9df5d3bb696825bceb3a17590754548b5d740eba51500afc8d088f5"
url: "https://pub.dev"
source: hosted
version: "2.4.26"
shared_preferences_foundation:
dependency: transitive
description:
name: shared_preferences_foundation
sha256: "4e7eaffc2b17ba398759f1151415869a34771ba11ebbccd1b0145472a619a64f"
url: "https://pub.dev"
source: hosted
version: "2.5.6"
shared_preferences_linux:
dependency: transitive
description:
name: shared_preferences_linux
sha256: "580abfd40f415611503cae30adf626e6656dfb2f0cee8f465ece7b6defb40f2f"
url: "https://pub.dev"
source: hosted
version: "2.4.1"
shared_preferences_platform_interface:
dependency: transitive
description:
name: shared_preferences_platform_interface
sha256: "649dc798a33931919ea356c4305c2d1f81619ea6e92244070b520187b5140ef9"
url: "https://pub.dev"
source: hosted
version: "2.4.2"
shared_preferences_web:
dependency: transitive
description:
name: shared_preferences_web
sha256: c49bd060261c9a3f0ff445892695d6212ff603ef3115edbb448509d407600019
url: "https://pub.dev"
source: hosted
version: "2.4.3"
shared_preferences_windows:
dependency: transitive
description:
name: shared_preferences_windows
sha256: "94ef0f72b2d71bc3e700e025db3710911bd51a71cefb65cc609dd0d9a982e3c1"
url: "https://pub.dev"
source: hosted
version: "2.4.1"
shelf:
dependency: transitive
description:
@@ -628,18 +689,18 @@ packages:
dependency: transitive
description:
name: source_gen
sha256: "35c8150ece9e8c8d263337a265153c3329667640850b9304861faea59fc98f6b"
sha256: ec37cc0e6694374cbef59ed79685572c870a54ede6fa30a3e420feb3adffea02
url: "https://pub.dev"
source: hosted
version: "2.0.0"
version: "4.2.3"
source_helper:
dependency: transitive
description:
name: source_helper
sha256: a447acb083d3a5ef17f983dd36201aeea33fedadb3228fa831f2f0c92f0f3aca
sha256: "4227d54ceefd0bb8ca4c8fcb96e1719dc53f1ee1b6e2ca9d7a6069da160e4eae"
url: "https://pub.dev"
source: hosted
version: "1.3.7"
version: "1.3.12"
source_span:
dependency: transitive
description:
@@ -652,10 +713,10 @@ packages:
dependency: transitive
description:
name: sqlite3
sha256: "3145bd74dcdb4fd6f5c6dda4d4e4490a8087d7f286a14dee5d37087290f0f8a2"
sha256: "9488c7d2cdb1091c91cacf7e207cff81b28bff8e366f042bad3afe7d34afe189"
url: "https://pub.dev"
source: hosted
version: "2.9.4"
version: "3.3.2"
sqlite3_flutter_libs:
dependency: "direct main"
description:
@@ -668,10 +729,10 @@ packages:
dependency: transitive
description:
name: sqlparser
sha256: "57090342af1ce32bb499aa641f4ecdd2d6231b9403cea537ac059e803cc20d67"
sha256: "40bdddb306a727be9ce510bd2d2b9a6c9db6c586d846ef7b22e3990a2b24f02d"
url: "https://pub.dev"
source: hosted
version: "0.41.2"
version: "0.44.5"
stack_trace:
dependency: transitive
description:
@@ -728,14 +789,6 @@ packages:
url: "https://pub.dev"
source: hosted
version: "0.7.11"
timing:
dependency: transitive
description:
name: timing
sha256: "62ee18aca144e4a9f29d212f5a4c6a053be252b895ab14b5821996cff4ed90fe"
url: "https://pub.dev"
source: hosted
version: "1.0.2"
typed_data:
dependency: transitive
description:
@@ -752,6 +805,14 @@ packages:
url: "https://pub.dev"
source: hosted
version: "2.0.1"
uuid:
dependency: transitive
description:
name: uuid
sha256: "1fef9e8e11e2991bb773070d4656b7bd5d850967a2456cfc83cf47925ba79489"
url: "https://pub.dev"
source: hosted
version: "4.5.3"
vector_math:
dependency: transitive
description:
@@ -818,4 +879,4 @@ packages:
version: "3.1.3"
sdks:
dart: ">=3.12.2 <4.0.0"
flutter: ">=3.38.4"
flutter: ">=3.44.0"

View File

@@ -21,15 +21,14 @@ dependencies:
path: ^1.9.0
# Models / serialization
freezed_annotation: ^2.4.0
json_annotation: ^4.9.0
# IDs
ulid: ^2.0.0
# AI / on-device LLM (#215)
# flutter_gemma 는 OQ-1 (정확한 모델 URL + SHA) 확정 후 추가.
# v1은 LlmService 추상 + ModelLifecycle (파일/SHA/메타 관리) + Mock 까지 구현.
# AI / on-device LLM (#215, #218)
# #218 (v0.3.0): flutter_gemma 0.16.5 + Gemma 4 E2B (HF litert-community).
flutter_gemma: ^0.16.5
crypto: ^3.0.0
http: ^1.2.0
@@ -41,7 +40,6 @@ dev_dependencies:
# Codegen
drift_dev: ^2.18.0
build_runner: ^2.4.0
freezed: ^2.5.0
json_serializable: ^6.8.0
flutter:

View File

@@ -0,0 +1,150 @@
import 'dart:async';
import 'package:flutter_gemma/flutter_gemma.dart';
import 'package:flutter_test/flutter_test.dart';
import 'package:life_helper/data/ai/gemma_llm_service.dart';
/// Unit tests for `collectFunctionCall` (fn-spec §D, 8 cases).
///
/// `GemmaLlmService.load` / `.generateStructured` themselves require the
/// flutter_gemma native runtime and are covered by AC-7 (on-device E2E),
/// not by host tests. The pure stream-parsing helper is unit-testable in
/// isolation because we can feed a synthetic `Stream<ModelResponse>`.
void main() {
const fn = 'emit_frame_candidates';
test('1. single FCR with expected name returns args', () async {
final stream = Stream<ModelResponse>.fromIterable([
const FunctionCallResponse(
name: fn,
args: {
'candidates': [
{'text': 'a', 'level': 'L2'},
{'text': 'b', 'level': 'L2'},
{'text': 'c', 'level': 'L3'},
],
},
),
]);
final args = await collectFunctionCall(stream, fn);
expect(args['candidates'], hasLength(3));
});
test('2. TextResponse before FCR is skipped', () async {
final stream = Stream<ModelResponse>.fromIterable([
const TextResponse('hello'),
const FunctionCallResponse(name: fn, args: {'candidates': []}),
]);
final args = await collectFunctionCall(stream, fn);
expect(args['candidates'], isEmpty);
});
test('3. ThinkingResponse + TextResponse before empty-args FCR', () async {
final stream = Stream<ModelResponse>.fromIterable([
const ThinkingResponse('reasoning...'),
const TextResponse('preamble'),
const FunctionCallResponse(name: fn, args: {}),
]);
final args = await collectFunctionCall(stream, fn);
expect(args, isEmpty);
});
test('4. wrong function name throws FormatException', () async {
final stream = Stream<ModelResponse>.fromIterable([
const FunctionCallResponse(name: 'wrong_name', args: {}),
]);
expect(
() => collectFunctionCall(stream, fn),
throwsA(
isA<FormatException>().having(
(e) => e.message,
'message',
contains('wrong_name'),
),
),
);
});
test('5. text-only stream throws "no function call emitted"', () async {
final stream = Stream<ModelResponse>.fromIterable([
const TextResponse('only text, no call'),
]);
expect(
() => collectFunctionCall(stream, fn),
throwsA(
isA<FormatException>().having(
(e) => e.message,
'message',
contains('no function call emitted'),
),
),
);
});
test('6. stream error throws sanitized FormatException (no leak)', () async {
final stream = Stream<ModelResponse>.error(
Exception('SENSITIVE: user_prompt_leaked_in_error'),
);
expect(
() => collectFunctionCall(stream, fn),
throwsA(
isA<FormatException>().having(
(e) => e.message,
'message',
allOf(
equals('stream error'),
isNot(contains('SENSITIVE')),
isNot(contains('user_prompt_leaked_in_error')),
),
),
),
);
});
test('7. FCR with empty args map returns empty map (no throw)', () async {
final stream = Stream<ModelResponse>.fromIterable([
const FunctionCallResponse(name: fn, args: {}),
]);
final args = await collectFunctionCall(stream, fn);
expect(args, isEmpty);
});
test('8. empty stream throws "no function call emitted"', () async {
final stream = const Stream<ModelResponse>.empty();
expect(
() => collectFunctionCall(stream, fn),
throwsA(
isA<FormatException>().having(
(e) => e.message,
'message',
contains('no function call emitted'),
),
),
);
});
group('ParallelFunctionCallResponse', () {
test('first call with expected name returns its args', () async {
final stream = Stream<ModelResponse>.fromIterable([
ParallelFunctionCallResponse(calls: [
const FunctionCallResponse(name: fn, args: {'x': 1}),
const FunctionCallResponse(name: 'other', args: {'y': 2}),
]),
]);
final args = await collectFunctionCall(stream, fn);
expect(args['x'], 1);
});
test('first call with wrong name throws', () async {
final stream = Stream<ModelResponse>.fromIterable([
ParallelFunctionCallResponse(calls: [
const FunctionCallResponse(name: 'wrong_first', args: {}),
]),
]);
expect(
() => collectFunctionCall(stream, fn),
throwsA(isA<FormatException>()),
);
});
});
}

View File

@@ -78,7 +78,7 @@ v0.2.0 (#215) 은 mock 환경에서 100% 동작하지만, 사용자가 "AI 도
- Public API: `FlutterGemma.initialize(huggingFaceToken: String)`, `FlutterGemma.installModel(modelType: ModelType.gemma4).fromNetwork(url).install()`, `FlutterGemma.getActiveModel(maxTokens: 2048)`, `model.createChat()`, `chat.addQueryChunk(Message.text(text, isUser))`, `chat.generateChatResponseAsync()``Stream<ModelResponse>` (`TextResponse | FunctionCallResponse | ThinkingResponse`).
- Function calling: **Gemma 4 native function calling** — 별도 `Tool` 객체 주입 없이 `ModelType.gemma4` 의 chat template 이 자동 라우팅. 모델이 호출 결정 시 스트림에 `FunctionCallResponse(name, args)` 1건 emit. (Gemma 4 / Gemma 3n / Phi-4 등 지원 명시)
- Schema 전달 경로: prompt 본문에 JSON Schema 를 자연어로 명시 (Gemma 4 의 chat template 이 인식). 별도 `tools: [...]` 파라미터는 0.16.5 의 createChat 인터페이스 기준 옵션이지 필수 아님 — **OQ-C** 에서 확정.
- **Gemma 4 E2B QAT 모바일 모델** — HuggingFace `litert-community/gemma-4-E2B-it-litert-lm` repo`.task` 또는 `.litertlm` 자산. 4-bit QAT 로 ≈1.5GB peak RAM, 신규 모바일 양자화 포맷은 ≈1GB footprint (2026-06 update). **OQ-A** 확정 시점에 정확한 파일명 (예: `gemma-4-E2B-it-mobile.task`) + SHA-256 핀.
- **Gemma 4 E2B 모델** — HuggingFace `litert-community/gemma-4-E2B-it-litert-lm` repo. **OQ-A 확정 (2026-06-12):** 정확 파일 = `gemma-4-E2B-it.litertlm` (2,588,147,712 bytes ≈ **2.41GB disk**), SHA-256 = `181938105e0eefd105961417e8da75903eacda102c4fce9ce90f50b97139a63c`. 모바일 1GB QAT 변종은 현시점 미공개 (Google 6월 blog 발표 자산 아직 HF 미게시). peak RAM 추정 ≈ 1.52GB (가중치 ≈ 1.3GB + KV cache + activation).
- **HF access token** — joungmin 본인 계정의 read-only token. 빌드 시점 `--dart-define=HF_TOKEN=hf_xxx` 으로 주입, 런타임에 `String.fromEnvironment('HF_TOKEN')` 으로 읽어 `FlutterGemma.initialize` 에 전달. 토큰을 단말 영속 저장 금지.
- **`crypto`** (기존), **`path_provider`** (기존), **`http`** (기존) — 모두 #215 에서 이미 사용 중.
- **Android**: `minSdkVersion 24` (MediaPipe LLM Inference 요구사항). 기존 #204 가 26 이므로 통과 가정.
@@ -190,14 +190,11 @@ app/
```dart
// app/lib/state/ai_providers.dart
const _kModelUrl =
'https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm/resolve/main/gemma-4-E2B-it-mobile.task';
// ▲ 정확 파일명·SHA 는 OQ-A 에서 확정. 일시값으로 둔 후 Developer 가
// HF page 의 LFS pointer (sha256: ... 라인) 또는
// `huggingface-cli download --dry-run` 로 SHA 추출해 박는다.
// QAT 1GB 모바일 양자화 변종 출시 시 (2026-06+) 그쪽으로 swap.
'https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm/resolve/main/gemma-4-E2B-it.litertlm';
const _kModelSha256 =
'<HEX_64_FROM_HF_LFS_POINTER>';
const _kModelType = 'gemma4'; // flutter_gemma ModelType enum 매핑
'181938105e0eefd105961417e8da75903eacda102c4fce9ce90f50b97139a63c';
const _kModelTotalBytes = 2588147712; // 2.41 GiB — UI 표시 용
// 향후 QAT 모바일 1GB 변종이 HF 에 게시되면 swap. v1 은 위 base .litertlm.
```
`Placeholder` 접미사 제거. `meta_kv['ai_model_path']`, `meta_kv['ai_model_sha256']` 키도 의미는 동일 (값만 실체).
@@ -371,14 +368,14 @@ const _hfToken = String.fromEnvironment('HF_TOKEN', defaultValue: '');
## 12. 미해결 질문 (Open Questions)
| OQ | 질문 | 결정 시점 | 비고 |
|----|------|----------|------|
| **OQ-A** | Gemma 4 E2B QAT 모바일 HF repo 정확 경로 + 파일명 + SHA-256? `gemma-4-E2B-it-mobile.task` vs `model.litertlm` vs 2026-06 신규 QAT 1GB 변종? | Developer 단계 첫 step | `litert-community/gemma-4-E2B-it-litert-lm` 또는 `google/gemma-4-E2B-it-qat-mobile-transformers` 둘 중. HF page LFS pointer 의 sha256 라인 사본 |
| **OQ-B** | `huggingFaceToken` 빈 문자열 시 `FlutterGemma.initialize` throw 타입? | Developer 단계 | `try/catch (e)` 처리 |
| **OQ-C** | sampling 파라미터 (temperature 0.4, topK 40, topP 0.95) 를 model-level (`getActiveModel(...)`) 에서 받나, chat-level (`createChat(...)`) 에서 받나? 0.16.5 API 정확 위치 | Developer 단계 | pub.dev API doc 또는 plugin source |
| **OQ-D** | Android `proguard-rules.pro` `-keep` 룰 패턴? | QA 단계 release build 시 | `com.google.mediapipe.**` + flutter_gemma plugin native 클래스 |
| **OQ-E** | Gemma 4 ToU 동의 UI: #215 동의 다이얼로그에 추가? Settings 도움말 링크? | Developer 단계 | 변경 최소화 → Settings 도움말 한 줄 + 외부 링크 (https://ai.google.dev/gemma/terms) |
| **OQ-F** | Gemma 4 thinking mode 토글: v0.3 에서 비활성 유지하지만 0.16.5 API 의 정확한 off 스위치? | Developer 단계 | `createChat(enableThinking: false)` 형태로 추정, 확인 필요 |
| OQ | 질문 | 상태 | 결정 |
|----|------|------|------|
| **OQ-A** | Gemma 4 E2B 모바일 HF 파일명 + SHA-256? | ✅ 해결 (Developer 2026-06-12) | `litert-community/gemma-4-E2B-it-litert-lm` repo 의 `gemma-4-E2B-it.litertlm` (2,588,147,712 B). SHA256 = `181938105e0eefd105961417e8da75903eacda102c4fce9ce90f50b97139a63c`. QAT 1GB 변종은 미공개. |
| **OQ-B** | `huggingFaceToken` 빈 문자열 시 throw? | ✅ 해결 | **즉시 throw 안 함** — 다운로드 시점까지 deferred. 빈 토큰은 public model 만 허용. 우리는 `null` 이 더 정확하지만 빈 문자열도 안전. |
| **OQ-C** | sampling 파라미터 위치? | ✅ 해결 | **chat-level**`model.createChat(temperature: 0.4, topK: 40, topP: 0.95)`. `getActiveModel``maxTokens` / `preferredBackend` / `maxConcurrentSessions` 만. (caveat: NPU backend 는 sampling 무시.) |
| **OQ-D** | Android ProGuard rules? | ✅ 해결 | flutter_gemma example app 의 27-line 사본 적용: io.flutter.\*, play.core.\*, mediapipe.\*, protobuf.\*, kotlinx.coroutines.\*. |
| **OQ-E** | Gemma ToU 동의 UI? | ✅ 해결 | Google 표준 템플릿 없음. Settings AI 섹션 하단에 "Gemma 이용약관(https://ai.google.dev/gemma/terms)에 동의합니다" 한 줄. 다이얼로그 본문 변경 없음 (변경 최소화). |
| **OQ-F** | thinking mode off 스위치? | ✅ 해결 | `model.createChat(isThinking: false)` **default 가 false** 라 명시 안 해도 무방하나 명시적으로 박는다. |
---

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@@ -1,8 +1,17 @@
# 함수 설계서: `GemmaLlmService.load` + `generateStructured` + 보조 (#218)
> **부모 설계서**: ./README.md · **상태**: Draft
> **부모 설계서**: ./README.md · **상태**: Draft (v2)
> **작성**: [AI] Architect (2026-06-12) · **구현 대상**: `app/lib/data/ai/gemma_llm_service.dart` · **테스트**: `app/test/data/ai/gemma_llm_service_test.dart`
## 변경 이력 (v2, 2026-06-12 Developer 검증 후)
flutter_gemma 0.16.5 의 `InferenceChat` 구현을 직접 읽어 확인한 결과:
- Gemma 4 (ModelType.gemma4) 의 function calling 은 **SDK 가 `createChat(tools: [Tool(...)])` 의 tools 목록에서 `<|tool>declaration:...<tool|>` 토큰을 직접 렌더**한다 (`lib/core/chat.dart:94`).
- 따라서 §C `_appendSchemaInstruction` 는 Gemma 4 에선 **double-wrap** 을 유발한다. v2 에선 **§C 제거**, §B 는 `Tool` 객체를 `createChat` 에 전달하는 방식으로 변경.
- §D `_collectFunctionCall` 는 변경 없음 — 여전히 `Stream<ModelResponse>` 에서 첫 `FunctionCallResponse` 만 추출.
남은 4 함수 (§A load / §B generateStructured / §C deprecated / §D collectFunctionCall) 중 코드 대상은 3 개.
이 문서는 `GemmaLlmService` 가 노출하는 2 개 public 메서드 + 2 개 file-private 헬퍼를 한 묶음으로 설계한다. 모두 flutter_gemma 0.16.5 의 native 경계를 다루므로 한 문서에서 다루는 게 응집도 측면에서 옳다.
| # | 함수 | 가시성 |
@@ -67,7 +76,7 @@ Future<void> load();
### 8. 복잡도 / 성능
- **시간**: cold start 13 초 (모델 파일 read + native init + tokenizer load). 첫 호출 만, 이후 캐시.
- **공간**: peak RAM ≈ 1.5GB (Gemma 4 E2B Q4_0). QAT 1GB 변종 채택 시 ≈ 1GB.
- **공간**: peak RAM ≈ 1.52GB (Gemma 4 E2B Q4 .litertlm, 가중치 ~1.3GB + KV cache + activation). disk ≈ 2.41GB.
- **호출 빈도**: 사용자 1 세션 당 01 회 (#219 F1 의 60s idle unload 가 들어오면 다회 가능).
### 9. 의존성
@@ -116,20 +125,27 @@ loaded 상태의 Gemma 4 모델에 prompt + JSON Schema 를 전달하여 단일
- 모델 latent state 변경 (다음 호출은 fresh chat).
- log: prompt length, latency, FCR 수신 여부 (prompt 본문 X — 프라이버시).
### 5. 동작 / 알고리즘
### 5. 동작 / 알고리즘 (v2)
```
1. if (!_loaded) throw StateError('LlmService not loaded');
2. final augmented = _appendSchemaInstruction(prompt, schema);
3. final chat = await _model!.createChat();
4. try {
5. await chat.addQueryChunk(Message.text(text: augmented, isUser: true));
6. final stream = chat.generateChatResponseAsync();
7. final fnName = schema['name'] as String;
8. final args = await _collectFunctionCall(stream, fnName);
9. return args;
10. } finally {
11. await chat.close(); // 항상 정리
12. }
2. final fnName = schema['name'] as String;
3. final fnDesc = (schema['description'] as String?) ?? '';
4. final fnParams = schema['parameters'] as Map<String, dynamic>;
5. final tool = Tool(name: fnName, description: fnDesc, parameters: fnParams);
6. final chat = await _model!.createChat(
modelType: ModelType.gemma4,
supportsFunctionCalls: true,
toolChoice: ToolChoice.required, // 강제 FCR
tools: [tool],
);
7. try {
8. await chat.addQueryChunk(Message.text(text: prompt, isUser: true));
9. final stream = chat.generateChatResponseAsync();
10. final args = await _collectFunctionCall(stream, fnName);
11. return args;
12. } finally {
13. await chat.close(); // 항상 정리
14. }
```
caller (#215 `suggestFrame`) 가 `.timeout(Duration(seconds: 10))` 적용 → timeout 시 본 함수의 `await` 가 throw 됨 → finally 의 `chat.close()` 가 실행되어 native session leak 방지.
@@ -173,7 +189,13 @@ caller (#215 `suggestFrame`) 가 `.timeout(Duration(seconds: 10))` 적용 → ti
---
## §C. `_appendSchemaInstruction(prompt, schema)`
## §C. (DEPRECATED — v2) `_appendSchemaInstruction(prompt, schema)`
> **v2 결정**: Gemma 4 SDK 가 `Tool` 객체에서 직접 declaration 토큰을 렌더하므로, prompt 측에서 schema 안내문을 덧붙이면 double-wrap 이 된다. **본 함수는 구현하지 않는다.**
>
> 아래 §C 본문은 v1 (gemmaIt fallback) 시나리오용 참고 자료로 보존하나, v2 코드 대상에서 제외한다. 단위 테스트도 작성하지 않는다.
원본 본문 (참고용):
### 1. 시그니처
```dart

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@@ -20,7 +20,7 @@ life-helper 는 사용자가 입력한 자유 문장(예: "술 끊고 싶어")
1. 하단 탭에서 **설정** 진입.
2. "AI 도움 켜기" 토글 탭.
3. 동의 다이얼로그가 뜹니다:
- **파일 크기 ≈ 1.5GB** (Gemma 4 E2B Q4_0 모델 — 단말에 한 번만 다운로드)
- **파일 크기 ≈ 2.4GB** (Gemma 4 E2B Q4_0 모델 — 단말에 한 번만 다운로드)
- **WiFi 연결 권장** (셀룰러 대역폭 절약)
- 모든 처리는 단말 — 입력 텍스트 외부 송출 없음
4. **"동의하고 다운로드"** 탭 → 백그라운드 다운로드 시작.
@@ -68,9 +68,9 @@ life-helper 는 사용자가 입력한 자유 문장(예: "술 끊고 싶어")
1. **설정** → "AI 도움 켜기" 토글 OFF.
2. 확인 다이얼로그:
- 모델 파일이 단말에서 **즉시 삭제** 됩니다.
-1.5GB 의 저장공간이 확보됩니다.
-2.4GB 의 저장공간이 확보됩니다.
- 다시 켜면 다시 다운로드해야 합니다.
3. **"끄고 삭제"** 탭 → "공간 확보됨 1500 MB" 토스트.
3. **"끄고 삭제"** 탭 → "공간 확보됨 2469 MB" 토스트.
진행 중인 다운로드가 있어도 깔끔히 중단되고, `.tmp` 임시 파일까지 함께 삭제됩니다.
@@ -86,7 +86,7 @@ A. 직접 입력란을 고쳐 쓰면 됩니다. AI 제안은 채우기 도우미
A. 언제든 끌 수 있고, 끄면 즉시 삭제됩니다. 다시 켜면 다시 받아야 한다는 점만 유의하세요.
**Q. v0.2.0 에서 다운로드가 항상 실패합니다.**
A. v0.2.0 은 모델 URL 이 미확정 (OQ-1) 인 상태로 출시되어, 실제 다운로드는 의도된 graceful 실패 경로로 안내됩니다. 실 모델 통합은 후속 버전 (v0.3.x) 에서 제공됩니다. 그동안 수동 입력 경로는 정상 동작합니다.
A. v0.2.0 은 모델 URL 이 미확정 (OQ-1) 인 상태로 출시되어, 실제 다운로드는 의도된 graceful 실패 경로로 안내됩니다. **v0.3.0 부터 실 Gemma 4 E2B 모델 (HuggingFace) 다운로드가 활성화되었습니다.** 그동안 수동 입력 경로는 정상 동작합니다.
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