feat(ai): prompt 에 notebooks 목록 + schema 의 notebook_match 필드

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/main/ai/InferenceProvider.ts

@@ -8,6 +8,7 @@ export interface GenerateInput {
   vocab?: string[]; // v0.2.3 #3 — top-N kebab-case 태그. 미전달 시 빈 배열로 처리.
   // v0.3.1 Cut F — 첨부 이미지. 미전달 시 텍스트 전용 처리.
   images?: Array<{ base64: string; mime: string }>;
+  notebooks?: string[]; // v0.4 Task 8 — 사용 가능한 노트북 목록. 미전달 시 빈 배열로 처리.
 }
 
 export interface GenerateOptions {

src/main/ai/LocalOllamaProvider.ts

@@ -67,7 +67,7 @@ export class LocalOllamaProvider implements InferenceProvider {
     const model = useVision ? opts!.visionModel! : this.model;
     const prompt = useVision
       ? buildVisionPrompt(input.text, input.todayKst, input.dueDateCandidates.map((c) => c.iso ?? c.matchedToken ?? ''), input.vocab ?? [])
-      : buildPrompt(input.text, input.todayKst, input.dueDateCandidates, input.vocab ?? []);
+      : buildPrompt(input.text, input.todayKst, input.dueDateCandidates, input.vocab ?? [], input.notebooks ?? []);
 
     // v0.3.13 — vision model 은 cold-start (모델 load + 이미지 encoding) 가 매우 느려
     // 120s 기본 timeout 으로 첫 호출 fail 빈번. gemma4:26b (MoE 25B) 같은 대형 vision

src/main/ai/prompt.ts

@@ -6,7 +6,8 @@ export function buildPrompt(
   rawText: string,
   todayKst: string,
   candidates: ParseResult[] = [],
-  vocab: string[] = []
+  vocab: string[] = [],
+  notebooks: string[] = []
 ): string {
   const candidateBlock = candidates.length > 0
     ? `\nDate candidates extracted by a Korean rule parser (these are HINTS — you decide which is correct, or pick null):
@@ -17,11 +18,19 @@ ${candidates.map((c, i) => `  ${i + 1}. ${c.iso ?? '(ambiguous)'} — matched to
     ? `\nExisting vocabulary tags (most-used first): ${vocab.join(', ')}\nPrefer reusing a vocabulary tag when the meaning matches; create new tags only when the meaning is genuinely new.\n`
     : '';
 
-  // candidateBlock & vocabBlock are self-delimited with leading/trailing \n
+  const notebookBlock = notebooks.length > 0
+    ? `\n사용 가능한 노트북: ${notebooks.join(', ')}\n이 노트가 위 노트북 중 하나에 명확히 속하면 "notebook_match" 에 그 이름을, 그렇지 않으면 null 을 반환. 기존 목록 안에서만 선택 — 새 이름 만들지 말 것.\n`
+    : '';
+
+  const notebookKeySpec = notebooks.length > 0
+    ? `\n- "notebook_match": 위 사용 가능한 노트북 목록 중 하나의 이름 또는 null`
+    : '';
+
+  // candidateBlock & vocabBlock & notebookBlock are self-delimited with leading/trailing \n
   return `You organize raw personal notes into structured metadata.
 
 Today's date in Korea Standard Time (KST): ${todayKst}
-${candidateBlock}${vocabBlock}
+${candidateBlock}${vocabBlock}${notebookBlock}
 Input note (raw text, may be fragmented, any language):
 ---
 ${rawText}
@@ -31,7 +40,7 @@ Return a JSON object with EXACTLY these keys:
 - "title": concise title in KOREAN (max 60 chars)
 - "summary": 3-line summary in KOREAN. Each line max 120 chars. Lines separated by "\\n".
 - "tags": array of 0 to 3 tags in lowercase kebab-case (English letters and digits only, e.g., "api-timeout", "weekly-retro"). Empty array if no clear tags.
-- "due_date": ISO YYYY-MM-DD if you are CONFIDENT about a deadline, else null. Consider rule candidates above as hints but use your own judgment — if multiple ambiguous candidates ("내일 모레", "이번 주 다음 주"), prefer null. If the user wrote "오늘 PR 리뷰" with no deadline implication, return null.
+- "due_date": ISO YYYY-MM-DD if you are CONFIDENT about a deadline, else null. Consider rule candidates above as hints but use your own judgment — if multiple ambiguous candidates ("내일 모레", "이번 주 다음 주"), prefer null. If the user wrote "오늘 PR 리뷰" with no deadline implication, return null.${notebookKeySpec}
 
 Rules:
 - title and summary MUST be written in Korean regardless of input language.

src/main/ai/schema.ts

@@ -8,7 +8,8 @@ const RawResponseSchema = z.object({
   title: z.string().trim().min(1).max(200),
   summary: z.string().min(1),
   tags: z.array(z.string()).default([]),
-  due_date: z.string().regex(ISO_DATE_REGEX).nullable().optional()
+  due_date: z.string().regex(ISO_DATE_REGEX).nullable().optional(),
+  notebook_match: z.string().nullable().optional()
 });
 
 export interface AiResponse {
@@ -16,6 +17,7 @@ export interface AiResponse {
   summary: string;
   tags: string[];
   dueDate: string | null;
+  notebookMatch: string | null;
 }
 
 function normalizeSummary(raw: string): string {
@@ -69,6 +71,7 @@ export function parseAiResponse(raw: unknown): AiResponse {
     title: finalTitle.slice(0, 60),
     summary: normalizeSummary(parsed.summary),
     tags: parsed.tags.filter((t) => KEBAB_REGEX.test(t)).slice(0, 3),
-    dueDate: validateDueDate(parsed.due_date)
+    dueDate: validateDueDate(parsed.due_date),
+    notebookMatch: parsed.notebook_match ?? null
   };
 }

tests/unit/AiWorker.test.ts

@@ -14,7 +14,7 @@ function makeProvider(overrides: Partial<InferenceProvider> = {}): InferenceProv
   return {
     name: 'mock',
     generate: vi.fn(async (): Promise<AiResponse> => ({
-      title: '제목', summary: 'a\nb\nc', tags: ['tag'], dueDate: null
+      title: '제목', summary: 'a\nb\nc', tags: ['tag'], dueDate: null, notebookMatch: null
     })),
     healthCheck: vi.fn(async () => ({ ok: true })),
     ...overrides
@@ -77,7 +77,7 @@ describe('AiWorker', () => {
         running++; max = Math.max(max, running);
         await new Promise((r) => setTimeout(r, 10));
         running--;
-        return { title: '제목', summary: 'a\nb\nc', tags: [], dueDate: null };
+        return { title: '제목', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
       })
     });
     const w = new AiWorker(repo, new ProviderHolder(provider), { backoffsMs: [0, 0, 0] });
@@ -137,7 +137,8 @@ describe('AiWorker', () => {
         title: '아무 메모',
         summary: 'a\nb\nc',
         tags: [],
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }),
       healthCheck: async () => ({ ok: true })
     } as any;
@@ -159,7 +160,7 @@ describe('AiWorker', () => {
       generate: async (input: any) => {
         seen.todayKst = input.todayKst;
         seen.dueDateCandidates = input.dueDateCandidates;
-        return { title: '메모', summary: 'a\nb\nc', tags: [], dueDate: null };
+        return { title: '메모', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
       },
       healthCheck: async () => ({ ok: true })
     } as any;
@@ -181,7 +182,7 @@ describe('AiWorker', () => {
       name: 'mock',
       generate: async (input: any) => {
         captured = input;
-        return { title: '내일', summary: 'a\nb\nc', tags: [], dueDate: null };
+        return { title: '내일', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
       },
       healthCheck: async () => ({ ok: true })
     } as any;
@@ -409,7 +410,7 @@ describe('AiWorker — unreachable/timeout infinite retry (v0.2.3 #2)', () => {
       generate: vi.fn(async (): Promise<AiResponse> => {
         callCount += 1;
         if (callCount <= 2) throw new Error('ECONNREFUSED');
-        return { title: 't', summary: 's', tags: [], dueDate: null };
+        return { title: 't', summary: 's', tags: [], dueDate: null, notebookMatch: null };
       })
     });
     const w = new AiWorker(repo, new ProviderHolder(provider), {
@@ -442,7 +443,7 @@ describe('AiWorker — vocab fetch + per-tag hit/miss (v0.2.3 #3 T7)', () => {
 
     const { id } = repo.create({ rawText: 'x' });
     const generateMock = vi.fn(async () => ({
-      title: '제목', summary: 'a\nb\nc', tags: ['design'], dueDate: null
+      title: '제목', summary: 'a\nb\nc', tags: ['design'], dueDate: null, notebookMatch: null
     }));
     const w = new AiWorker(repo, new ProviderHolder(makeProvider({ generate: generateMock })), {
       backoffsMs: [0, 0, 0]
@@ -465,7 +466,8 @@ describe('AiWorker — vocab fetch + per-tag hit/miss (v0.2.3 #3 T7)', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design', 'newtag'],  // 1 hit + 1 miss
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -495,7 +497,8 @@ describe('AiWorker — vocab fetch + per-tag hit/miss (v0.2.3 #3 T7)', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design', 'meeting', 'qa'],
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -520,7 +523,8 @@ describe('AiWorker — vocab fetch + per-tag hit/miss (v0.2.3 #3 T7)', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design', 'meeting', 'qa'],
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -544,7 +548,8 @@ describe('AiWorker — vocab fetch + per-tag hit/miss (v0.2.3 #3 T7)', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design', 'design', 'meeting'],  // 중복 'design' 의도적
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -581,7 +586,8 @@ describe('vocab COLLATE NOCASE', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['Design'],  // AI returns capitalized — DB COLLATE NOCASE matches 'design'
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -610,7 +616,8 @@ describe('vocab COLLATE NOCASE', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design'],  // AI returns lowercase — DB COLLATE NOCASE matches 'Design'
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];
@@ -634,7 +641,8 @@ describe('vocab COLLATE NOCASE', () => {
       generate: vi.fn(async () => ({
         title: 't', summary: 'a\nb\nc',
         tags: ['design'],  // same lowercase — should still hit
-        dueDate: null
+        dueDate: null,
+        notebookMatch: null
       }))
     });
     const emits: EmittedEvent[] = [];

tests/unit/AiWorker.vision.test.ts

@@ -65,7 +65,7 @@ describe('AiWorker — vision path (v0.3.1 Cut F)', () => {
       opts?: Parameters<InferenceProvider['generate']>[1]
     ): Promise<AiResponse> => {
       calls.push([input, opts]);
-      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null };
+      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
     });
     const getVisionModel = vi.fn(async (): Promise<string | null> => 'gemma3:12b-vision');
     const worker = makeWorker(generate, getVisionModel);
@@ -87,7 +87,7 @@ describe('AiWorker — vision path (v0.3.1 Cut F)', () => {
       opts?: Parameters<InferenceProvider['generate']>[1]
     ): Promise<AiResponse> => {
       calls.push([input, opts]);
-      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null };
+      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
     });
     const getVisionModel = vi.fn(async (): Promise<string | null> => null);
     const worker = makeWorker(generate, getVisionModel);
@@ -110,7 +110,7 @@ describe('AiWorker — vision path (v0.3.1 Cut F)', () => {
       opts?: Parameters<InferenceProvider['generate']>[1]
     ): Promise<AiResponse> => {
       calls.push([input, opts]);
-      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null };
+      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
     });
     const getVisionModel = vi.fn(async (): Promise<string | null> => 'gemma4:26b');
     const worker = makeWorker(generate, getVisionModel);
@@ -136,7 +136,7 @@ describe('AiWorker — vision path (v0.3.1 Cut F)', () => {
       { noteId: id, kind: 'image', relPath: `media/${id}/1.png`, mime: 'image/png', bytes: 1 },
       { noteId: id, kind: 'image', relPath: `media/${id}/2.png`, mime: 'image/png', bytes: 1 }
     ]);
-    const generate = vi.fn(async (): Promise<AiResponse> => ({ title: 't', summary: 'a\nb\nc', tags: [], dueDate: null }));
+    const generate = vi.fn(async (): Promise<AiResponse> => ({ title: 't', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null }));
     const getVisionModel = vi.fn(async (): Promise<string | null> => 'gemma4:26b');
     const worker = makeWorker(generate, getVisionModel);
     await worker.enqueue(id);
@@ -157,7 +157,7 @@ describe('AiWorker — vision path (v0.3.1 Cut F)', () => {
       opts?: Parameters<InferenceProvider['generate']>[1]
     ): Promise<AiResponse> => {
       calls.push([input, opts]);
-      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null };
+      return { title: 't', summary: 'a\nb\nc', tags: [], dueDate: null, notebookMatch: null };
     });
     const getVisionModel = vi.fn(async (): Promise<string | null> => 'gemma3:12b-vision');
     const worker = makeWorker(generate, getVisionModel);

tests/unit/ai-schema.test.ts

@@ -112,4 +112,19 @@ describe('parseAiResponse', () => {
     });
     expect(r.dueDate).toBeNull();
   });
+
+  it('parseAiResponse — notebook_match valid string', () => {
+    const r = parseAiResponse({ title: '제목입니다', summary: '한\n두\n셋', tags: [], due_date: null, notebook_match: '회사' });
+    expect(r.notebookMatch).toBe('회사');
+  });
+
+  it('parseAiResponse — notebook_match null', () => {
+    const r = parseAiResponse({ title: '제목입니다', summary: '한\n두\n셋', tags: [], due_date: null, notebook_match: null });
+    expect(r.notebookMatch).toBeNull();
+  });
+
+  it('parseAiResponse — notebook_match 누락 시 null', () => {
+    const r = parseAiResponse({ title: '제목입니다', summary: '한\n두\n셋', tags: [], due_date: null });
+    expect(r.notebookMatch).toBeNull();
+  });
 });

tests/unit/prompt.test.ts

@@ -29,3 +29,18 @@ describe('prompt', () => {
     expect(jsonRulesIdx).toBeGreaterThan(vocabIdx);
   });
 });
+
+describe('buildPrompt with notebooks', () => {
+  it('notebooks 목록이 prompt 에 포함됨', () => {
+    const p = buildPrompt('hi', '2026-05-14', [], [], ['기본', '회사']);
+    expect(p).toContain('기본');
+    expect(p).toContain('회사');
+    expect(p).toContain('notebook_match');
+  });
+
+  it('notebooks 빈 배열 시 notebook 섹션 생략', () => {
+    const p = buildPrompt('hi', '2026-05-14', [], [], []);
+    expect(p).not.toContain('notebook_match');
+    expect(p).not.toContain('사용 가능한 노트북');
+  });
+});