mirror of
https://github.com/FJurmanovic/sugarr.git
synced 2026-11-11 07:41:08 +00:00
Fran Jurmanović
ae8bfaa2eb
- 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.
81 lines
3.8 KiB
TypeScript
81 lines
3.8 KiB
TypeScript
import { describe, expect, it, vi } from 'vitest';
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import Fastify from 'fastify';
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import { RecommendationsController } from '../../../src/app/controllers/RecommendationsController';
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import type { AppConfig } from '../../../src/app/config/AppConfig';
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import type { RecommendationPipeline } from '../../../src/app/pipelines/RecommendationPipeline';
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function makeApp(config: Partial<AppConfig>, pipeline: Partial<RecommendationPipeline>) {
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const app = Fastify();
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new RecommendationsController(config as AppConfig, pipeline as RecommendationPipeline).register(app);
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return app;
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}
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describe('RecommendationsController', () => {
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it('GET /recommendations runs the pipeline with the configured count and LLM flag', async () => {
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const runAsync = vi.fn().mockResolvedValue([{ title: 'Dune', year: 2021, tmdbId: 1, reason: 'r' }]);
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const app = makeApp({ RECOMMEND_COUNT: 10, LLM_ENABLED: true }, { runAsync } as unknown as RecommendationPipeline);
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const res = await app.inject({ method: 'GET', url: '/recommendations' });
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expect(res.statusCode).toBe(200);
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expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
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});
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it('GET /recommendations?count= and force_llm/force_tmdb override config', async () => {
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const runAsync = vi.fn().mockResolvedValue([]);
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const app = makeApp({ RECOMMEND_COUNT: 10, LLM_ENABLED: false }, { runAsync } as unknown as RecommendationPipeline);
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await app.inject({ method: 'GET', url: '/recommendations?count=3&force_llm=true' });
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expect(runAsync).toHaveBeenCalledWith({ count: 3, useLlm: true });
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});
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it('falls back to the configured count for a malformed or non-positive count query param', async () => {
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const runAsync = vi.fn().mockResolvedValue([]);
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const app = makeApp({ RECOMMEND_COUNT: 10, LLM_ENABLED: true }, { runAsync } as unknown as RecommendationPipeline);
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await app.inject({ method: 'GET', url: '/recommendations?count=abc' });
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expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
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await app.inject({ method: 'GET', url: '/recommendations?count=-5' });
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expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
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await app.inject({ method: 'GET', url: '/recommendations?count=0' });
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expect(runAsync).toHaveBeenCalledWith({ count: 10, useLlm: true });
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});
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it('POST /recommendations/trigger auto-requests via Radarr when enabled', async () => {
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const recs = [{ title: 'Dune', year: 2021, tmdbId: 1, reason: 'r' }];
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const runAsync = vi.fn().mockResolvedValue(recs);
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const autoRequest = vi.fn().mockResolvedValue(undefined);
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const app = makeApp(
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{ RECOMMEND_COUNT: 10, LLM_ENABLED: true, RADARR_ENABLED: true },
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{ runAsync, autoRequest } as unknown as RecommendationPipeline
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);
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const res = await app.inject({ method: 'POST', url: '/recommendations/trigger' });
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expect(res.statusCode).toBe(200);
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expect(autoRequest).toHaveBeenCalledWith(recs);
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});
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it('POST /recommendations/search requires LLM to be enabled and a query body', async () => {
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const app = makeApp({ LLM_ENABLED: false }, {} as RecommendationPipeline);
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const res = await app.inject({ method: 'POST', url: '/recommendations/search', payload: { query: 'x' } });
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expect(res.statusCode).toBe(400);
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});
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it('POST /recommendations/search runs pipeline.searchAsync and returns results', async () => {
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const searchAsync = vi.fn().mockResolvedValue([{ title: 'Dune', year: 2021, tmdbId: 1, reason: 'r' }]);
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const app = makeApp(
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{ LLM_ENABLED: true, RECOMMEND_COUNT: 10 },
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{ searchAsync } as unknown as RecommendationPipeline
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);
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const res = await app.inject({
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method: 'POST',
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url: '/recommendations/search',
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payload: { query: 'sci-fi like Blade Runner' },
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});
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expect(res.statusCode).toBe(200);
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expect(searchAsync).toHaveBeenCalledWith('sci-fi like Blade Runner', 10);
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});
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});
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