joungmin
6ab4c0da7d
설계서대로 구현. flutter_gemma 실제 통합은 OQ-1 (모델 URL+SHA) 확정 후.
v1은 LlmService 추상 + ModelLifecycle (다운로드/SHA/purge) + Riverpod
providers + 다이얼로그 + Settings 화면까지. main.dart 가 MockLlmService 를
override 해 모든 경로가 graceful (suggest 결과는 빈 리스트).
추가:
- lib/data/ai/{llm_service,gemma_llm_service,model_lifecycle}.dart
- lib/domain/ai/{frame_candidate,few_shot_builder,parse_response,suggest_frame}.dart
- lib/state/ai_providers.dart (aiSettings + modelAvailability + frameSuggestions)
- lib/ui/screens/settings_screen.dart (opt-in 토글 + 모델 상태 표시)
- lib/ui/widgets/frame_suggestion_dialog.dart (후보 3개 카드 + 다시 시도)
- HabitCreateScreen: "AI 제안" 버튼 (opt-in + ready 일 때만 노출)
- MetaDao.remove(key) 추가 (purge 용)
테스트 31개 신규 추가 (총 62개 통과):
- test/domain/ai/{suggest_frame, few_shot_builder, parse_response}_test.dart
- test/data/ai/model_lifecycle_test.dart (download/SHA/purge/availability)
flutter analyze 0 issue, flutter build apk --debug 통과.
Refs #215
84 lines
2.4 KiB
Dart
84 lines
2.4 KiB
Dart
import '../../data/ai/llm_service.dart';
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import '../frame/validate_frame_level.dart';
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import '../models/frame_pattern.dart';
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import 'few_shot_builder.dart';
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import 'frame_candidate.dart';
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import 'parse_response.dart';
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/// JSON schema (function-calling parameters) for the model output.
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const Map<String, dynamic> kFrameCandidatesSchema = {
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'type': 'object',
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'properties': {
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'candidates': {
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'type': 'array',
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'minItems': 1,
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'maxItems': 3,
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'items': {
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'type': 'object',
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'properties': {
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'level': {'type': 'string', 'enum': ['L2', 'L3']},
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'framed_text': {'type': 'string', 'maxLength': 120},
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'confidence': {'type': 'number', 'minimum': 0, 'maximum': 1},
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'source_pattern_id': {'type': 'string'},
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},
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'required': ['level', 'framed_text'],
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},
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},
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},
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'required': ['candidates'],
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};
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const Duration _defaultTimeout = Duration(seconds: 10);
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const int _maxRawTextLength = 200;
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/// Main domain function (#215 §A). Pure-ish: depends only on injected
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/// [llm] and [framePatterns]. Never throws — failure returns an empty list
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/// so callers (UI) can decide messaging (graceful degradation, AC-9).
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Future<List<FrameCandidate>> suggestFrame(
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SuggestFrameInput input, {
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required LlmService llm,
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required List<FramePatternModel> framePatterns,
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Duration timeout = _defaultTimeout,
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}) async {
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final raw = input.rawText.trim();
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if (raw.isEmpty || raw.length > _maxRawTextLength) {
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return const [];
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}
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final prompt = buildFewShotPrompt(input, framePatterns);
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Map<String, dynamic> json;
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try {
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json = await llm
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.generateStructured(prompt, kFrameCandidatesSchema)
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.timeout(timeout);
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} catch (_) {
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// Timeout / StateError / FormatException / anything else: graceful.
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return const [];
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}
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List<FrameCandidate> candidates;
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try {
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candidates = parseFrameCandidates(json);
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} on FormatException {
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return const [];
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}
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// Drop L0/L1 + avoidance-violating via validateFrameLevel.
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final validated = <FrameCandidate>[];
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for (final c in candidates) {
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final result = validateFrameLevel(
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FrameInput(
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level: c.level,
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framedText: c.framedText,
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originalText: input.rawText,
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),
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knownPatterns: framePatterns,
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);
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if (result.status == FrameValidationStatus.reject) continue;
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validated.add(c);
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if (validated.length >= 3) break;
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}
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return validated;
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}
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