paulandClaude Opus 4.8
2b4babf955
Source: task/album-detection (objective/linker-improvements) — project brief asks for artist OR album linking; MVP linked artists only.
- service/lib.js: pickAlbum (the exact-name gate, named for the album path) + linkResult now carries kind:"artist"|"album" (additive; defaults to artist so older clients keep working).
- service/resolver.js: one Spotify type=artist,album search per candidate, exact-match against either; returns {url,kind}; cache key includes the kind hint. resolveOne/resolveAll thread the LLM kind hint through.
- service/llm.js: parseClassification now returns Map(name -> "artist"|"album") so album-kind candidates route to the album path (.has() still works like the old Set).
- extension/extract.js: albumMatchesIn over two precise signals (quoted strings + album cue phrases "the album X", "X LP/EP"), folded into allMatchesIn.
- extension/content.js + styles.css: album links render italicised (.rsl-album) with an album tooltip; artist behavior unchanged.
- Tests: new pure-logic coverage in lib.test.js / llm.test.js / extract.test.cjs (album shaping, Map-kind routing, album candidate extraction). scripts/check.sh green.
- README.md + AGENTS.md: document album linking.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
54 lines
2.7 KiB
JavaScript
54 lines
2.7 KiB
JavaScript
// Pure helpers for the OPTIONAL local-LLM (ollama) classifier. The HTTP call lives in
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// resolver.js; the prompt builder + reply parser live here so they unit-test with
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// `node --test` (no network). The LLM answers one question: "which of these candidate
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// substrings are music artists/albums?" — the precision gate when it is configured.
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import { norm } from "./lib.js";
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// Small, fast, runs comfortably on a 16GB M2 (3.1B params, Q4 ~2GB). Override with OLLAMA_MODEL.
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export const DEFAULT_MODEL = "qwen2.5:3b";
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// Chat messages for an ollama /api/chat call (used with stream:false, format:"json").
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export function buildClassifyMessages(candidates) {
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const system =
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"You are a music-name classifier for a tool that links artist/album mentions in Reddit " +
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"music discussions. You are given a JSON array of candidate text snippets pulled from reddit " +
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"comments. For EACH candidate decide whether it names a music ARTIST/BAND, a music " +
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"ALBUM/release, or NEITHER. The snippets come from music threads, so prefer the music reading " +
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"when a candidate is a plausible band/album even if it is also an ordinary word. Mark ordinary " +
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"words, sentence fragments, generic phrases, place names, and non-music proper nouns as \"none\". " +
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'Reply with ONLY a JSON object of the form {"results":[{"name":<candidate verbatim>,' +
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'"kind":"artist"|"album"|"none"}]}. Use each candidate string VERBATIM as its name.';
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const user = JSON.stringify({ candidates });
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return [
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{ role: "system", content: system },
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{ role: "user", content: user },
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];
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}
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// Parse the model's JSON reply into a Map of norm(name) -> "artist" | "album" for the
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// candidates the model marked as music (the kind routes each to the artist or album Spotify
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// lookup). Defensive: only names that were actually in the candidate list count (guards
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// against the model inventing names); an unparseable reply yields an empty Map, and the
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// caller then falls back to the Spotify-exact-match gate. A Map still answers `.has()` like
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// the old Set, so a name absent from it is dropped exactly as before.
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export function parseClassification(text, candidates) {
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const allowed = new Set(candidates.map(norm));
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const confirmed = new Map();
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let obj;
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try {
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obj = JSON.parse(text);
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} catch {
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return confirmed;
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}
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const rows = Array.isArray(obj?.results) ? obj.results : Array.isArray(obj) ? obj : [];
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for (const row of rows) {
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if (!row || typeof row.name !== "string") continue;
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const k = norm(row.name);
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if (!allowed.has(k)) continue;
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const kind = String(row.kind ?? "").toLowerCase();
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if (kind === "artist" || kind === "album") confirmed.set(k, kind);
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}
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return confirmed;
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}
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