Fix final review findings: key leak, model fallback, error resilience, docs

- ApiClient: strip query string from ApiError messages so API keys
  (e.g. TMDb's api_key query param) never leak into 500 response
  bodies or logs.
- LlmTool: accept an optional defaultModel, wired from AppConfig's
  LLM_MODEL, so Langfuse prompts that omit a model config fall back
  to the configured LLM rather than silently requesting gpt-4o-mini.
- RecommendationPipeline: restore the Go original's graceful
  degradation — library/Radarr fetch failures, per-suggestion TMDb
  resolution failures, and per-signal TMDb similarity/recommendation
  failures are now logged and skipped instead of aborting the whole
  run (applies to both runInternal and searchAsync).
- RecommendationsController: validate the ?count= query param
  (must be a positive integer) instead of silently producing NaN.
- Commit the Langfuse prompt definitions (langfuse/*.prompt.json) and
  add a README covering setup, prompt seeding, cache invalidation,
  and the HTTP API surface — the branch was not otherwise runnable by
  anyone but the author.

test/app/controllers/RecommendationsController.test.ts

@@ -28,6 +28,20 @@ describe('RecommendationsController', () => {
     expect(runAsync).toHaveBeenCalledWith({ count: 3, useLlm: true });
   });
 
+  it('falls back to the configured count for a malformed or non-positive count query param', async () => {
+    const runAsync = vi.fn().mockResolvedValue([]);
+    const app = makeApp({ RECOMMEND_COUNT: 10, LLM_ENABLED: true }, { runAsync } as unknown as RecommendationPipeline);
+
+    await app.inject({ method: 'GET', url: '/recommendations?count=abc' });
+    expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
+
+    await app.inject({ method: 'GET', url: '/recommendations?count=-5' });
+    expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
+
+    await app.inject({ method: 'GET', url: '/recommendations?count=0' });
+    expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
+  });
+
   it('POST /recommendations/trigger auto-requests via Radarr when enabled', async () => {
     const recs = [{ title: 'Dune', year: 2021, tmdbId: 1, reason: 'r' }];
     const runAsync = vi.fn().mockResolvedValue(recs);

test/app/pipelines/RecommendationPipeline.test.ts

@@ -171,6 +171,119 @@ describe('RecommendationPipeline.runAsync', () => {
     expect(tool.runAsync).not.toHaveBeenCalled();
     expect(result).toEqual([{ title: 'Similar A', year: 2010, tmdbId: 20, reason: 'Recommended by TMDb' }]);
   });
+
+  it('continues with an incomplete exclusion set when fetching the library fails', async () => {
+    const jellyfin = {
+      getFavoriteMovies: vi.fn().mockResolvedValue([movie('Dune', 2021, '1')]),
+      getRecentlyWatched: vi.fn(),
+      getAllMovies: vi.fn().mockRejectedValue(new Error('jellyfin down')),
+    } as unknown as JellyfinClient;
+    const tmdb = {
+      searchMovie: vi.fn().mockResolvedValue({ id: 3, title: 'Interstellar', year: 2014 } as TMDbMovie),
+    } as unknown as TmdbClient;
+    const tool = {
+      runAsync: vi.fn().mockResolvedValue([{ title: 'Interstellar', year: 2014, reason: 'r' }]),
+    } as unknown as MovieRecommenderTool;
+    const searchTool = { runAsync: vi.fn() } as unknown as MovieSearchTool;
+    const logger = { warn: vi.fn() } as unknown as Logger;
+
+    const pipeline = new RecommendationPipeline(makeTracer(), logger, {
+      jellyfin,
+      tmdb,
+      tool,
+      searchTool,
+      minRatedMovies: 1,
+      maxRetry: 0,
+    });
+
+    const result = await pipeline.runAsync({ count: 1, useLlm: true });
+
+    expect(result).toEqual([{ title: 'Interstellar', year: 2014, tmdbId: 3, reason: 'r' }]);
+    expect(logger.warn).toHaveBeenCalledWith(
+      expect.objectContaining({ err: expect.any(Error) }),
+      'Could not fetch library; exclusion may be incomplete'
+    );
+  });
+
+  it('skips a suggestion whose TMDb resolution fails instead of aborting the run', async () => {
+    const jellyfin = {
+      getFavoriteMovies: vi.fn().mockResolvedValue([movie('Dune', 2021, '1')]),
+      getRecentlyWatched: vi.fn(),
+      getAllMovies: vi.fn().mockResolvedValue([]),
+    } as unknown as JellyfinClient;
+    const tmdb = {
+      searchMovie: vi
+        .fn()
+        .mockRejectedValueOnce(new Error('tmdb down'))
+        .mockResolvedValueOnce({ id: 3, title: 'Interstellar', year: 2014 } as TMDbMovie),
+    } as unknown as TmdbClient;
+    const tool = {
+      runAsync: vi.fn().mockResolvedValue([
+        { title: 'Unresolvable', year: 1999, reason: 'r1' },
+        { title: 'Interstellar', year: 2014, reason: 'r2' },
+      ]),
+    } as unknown as MovieRecommenderTool;
+    const searchTool = { runAsync: vi.fn() } as unknown as MovieSearchTool;
+    const logger = { warn: vi.fn() } as unknown as Logger;
+
+    const pipeline = new RecommendationPipeline(makeTracer(), logger, {
+      jellyfin,
+      tmdb,
+      tool,
+      searchTool,
+      minRatedMovies: 1,
+      maxRetry: 0,
+    });
+
+    const result = await pipeline.runAsync({ count: 2, useLlm: true });
+
+    expect(result).toEqual([{ title: 'Interstellar', year: 2014, tmdbId: 3, reason: 'r2' }]);
+    expect(logger.warn).toHaveBeenCalledWith(
+      expect.objectContaining({ err: expect.any(Error), title: 'Unresolvable' }),
+      'TMDb search failed, skipping suggestion'
+    );
+  });
+
+  it('skips a signal movie whose TMDb similar/recommendations lookup fails in the fallback path', async () => {
+    const jellyfin = {
+      getFavoriteMovies: vi
+        .fn()
+        .mockResolvedValue([movie('Broken', 2000, '1'), movie('Dune', 2021, '2')]),
+      getRecentlyWatched: vi.fn(),
+      getAllMovies: vi.fn().mockResolvedValue([]),
+    } as unknown as JellyfinClient;
+    const tmdb = {
+      getSimilar: vi.fn().mockImplementation((id: number) => {
+        if (id === 1) return Promise.reject(new Error('tmdb down'));
+        return Promise.resolve([{ id: 20, title: 'Similar A', year: 2010 } as TMDbMovie]);
+      }),
+      getRecommendations: vi.fn().mockImplementation((id: number) => {
+        if (id === 1) return Promise.reject(new Error('tmdb down'));
+        return Promise.resolve([]);
+      }),
+      searchMovie: vi.fn().mockResolvedValue({ id: 20, title: 'Similar A', year: 2010 } as TMDbMovie),
+    } as unknown as TmdbClient;
+    const tool = { runAsync: vi.fn() } as unknown as MovieRecommenderTool;
+    const searchTool = { runAsync: vi.fn() } as unknown as MovieSearchTool;
+    const logger = { warn: vi.fn() } as unknown as Logger;
+
+    const pipeline = new RecommendationPipeline(makeTracer(), logger, {
+      jellyfin,
+      tmdb,
+      tool,
+      searchTool,
+      minRatedMovies: 2,
+      maxRetry: 0,
+    });
+
+    const result = await pipeline.runAsync({ count: 1, useLlm: false });
+
+    expect(result).toEqual([{ title: 'Similar A', year: 2010, tmdbId: 20, reason: 'Recommended by TMDb' }]);
+    expect(logger.warn).toHaveBeenCalledWith(
+      expect.objectContaining({ err: expect.any(Error), tmdbId: 1 }),
+      'TMDb similar/recommendations failed, skipping signal movie'
+    );
+  });
 });
 
 describe('RecommendationPipeline.autoRequest', () => {
@@ -283,4 +396,34 @@ describe('RecommendationPipeline.searchAsync', () => {
     expect(result).toEqual([{ title: 'New Find', year: 2020, tmdbId: 6, reason: 'matches query' }]);
     expect(searchTool.runAsync).toHaveBeenCalledWith({ query: 'sci-fi like Dune', count: 5 }, expect.anything());
   });
+
+  it('continues with an empty exclusion set when fetching the library fails', async () => {
+    const jellyfin = {
+      getAllMovies: vi.fn().mockRejectedValue(new Error('jellyfin down')),
+    } as unknown as JellyfinClient;
+    const tmdb = {
+      searchMovie: vi.fn().mockResolvedValue({ id: 6, title: 'New Find', year: 2020 } as TMDbMovie),
+    } as unknown as TmdbClient;
+    const searchTool = {
+      runAsync: vi.fn().mockResolvedValue([{ title: 'New Find', year: 2020, reason: 'matches query' }]),
+    } as unknown as MovieSearchTool;
+    const logger = { warn: vi.fn() } as unknown as Logger;
+
+    const pipeline = new RecommendationPipeline(makeTracer(), logger, {
+      jellyfin,
+      tmdb,
+      tool: {} as MovieRecommenderTool,
+      searchTool,
+      minRatedMovies: 1,
+      maxRetry: 0,
+    });
+
+    const result = await pipeline.searchAsync('sci-fi like Dune', 5);
+
+    expect(result).toEqual([{ title: 'New Find', year: 2020, tmdbId: 6, reason: 'matches query' }]);
+    expect(logger.warn).toHaveBeenCalledWith(
+      expect.objectContaining({ err: expect.any(Error) }),
+      'Could not fetch library; exclusion may be incomplete'
+    );
+  });
 });