feat: spotify-only matching + optional local-LLM gate + google→bandcamp fallback (closes task/drop-musicbrainz-google-fallback, closes task/ollama-classifier)

Source: human-feedback note 01KVDYV3A35B57N15CRBQDXPCH (project/reddit-spotify-linker,
2026-06-18) — "remove the musicbrainz integration and just use spotify"; a google search
for "<name> bandcamp" fallback; and "would a local llm be better able to categorise these
... include instructions for running something suitable via ollama (16GB M2)".

- Remove MusicBrainz entirely (viaMusicBrainz + url-rels + rate-gate; lib relUrls/mbResult).
  Spotify is now the only music API; it both gates by exact-name match and supplies the
  direct artist link.
- Optional ollama LLM gate (OLLAMA_URL enables it, OLLAMA_MODEL default qwen2.5:3b) called
  over plain HTTP -> stays zero npm-dep. It classifies which candidates are real
  artists/albums (the matching-quality lever). Pure prompt/parse logic in new llm.js
  (unit-tested in llm.test.js); the fetch + resolveAll gate wiring live in resolver.js.
- A confirmed name links to its direct Spotify page when available, else a Google search for
  "<name> bandcamp". New "google" primary platform rendered by the extension (styles.css
  blue accent + platform-aware link title in content.js).
- README: drop MusicBrainz; document the two gates + a runnable local-LLM setup for a 16GB
  MacBook M2. AGENTS.md: flip the old "no ML/NER" standing decision per the human override.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
paul
2026-06-19 12:50:07 +00:00
co-authored by Claude Opus 4.8
parent b97c82d92e
commit ef57761859
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// Pure helpers for the OPTIONAL local-LLM (ollama) classifier. The HTTP call lives in
// resolver.js; the prompt builder + reply parser live here so they unit-test with
// `node --test` (no network). The LLM answers one question: "which of these candidate
// substrings are music artists/albums?" — the precision gate when it is configured.
import { norm } from "./lib.js";
// Small, fast, runs comfortably on a 16GB M2 (3.1B params, Q4 ~2GB). Override with OLLAMA_MODEL.
export const DEFAULT_MODEL = "qwen2.5:3b";
// Chat messages for an ollama /api/chat call (used with stream:false, format:"json").
export function buildClassifyMessages(candidates) {
const system =
"You are a music-name classifier for a tool that links artist/album mentions in Reddit " +
"music discussions. You are given a JSON array of candidate text snippets pulled from reddit " +
"comments. For EACH candidate decide whether it names a music ARTIST/BAND, a music " +
"ALBUM/release, or NEITHER. The snippets come from music threads, so prefer the music reading " +
"when a candidate is a plausible band/album even if it is also an ordinary word. Mark ordinary " +
"words, sentence fragments, generic phrases, place names, and non-music proper nouns as \"none\". " +
'Reply with ONLY a JSON object of the form {"results":[{"name":<candidate verbatim>,' +
'"kind":"artist"|"album"|"none"}]}. Use each candidate string VERBATIM as its name.';
const user = JSON.stringify({ candidates });
return [
{ role: "system", content: system },
{ role: "user", content: user },
];
}
// Parse the model's JSON reply into a Set of norm(name) the model marked artist/album.
// Defensive: only names that were actually in the candidate list count (guards against the
// model inventing names); an unparseable reply yields an empty Set, and the caller then
// falls back to the Spotify-exact-match gate.
export function parseClassification(text, candidates) {
const allowed = new Set(candidates.map(norm));
const confirmed = new Set();
let obj;
try {
obj = JSON.parse(text);
} catch {
return confirmed;
}
const rows = Array.isArray(obj?.results) ? obj.results : Array.isArray(obj) ? obj : [];
for (const row of rows) {
if (!row || typeof row.name !== "string") continue;
const k = norm(row.name);
if (!allowed.has(k)) continue;
const kind = String(row.kind ?? "").toLowerCase();
if (kind === "artist" || kind === "album") confirmed.add(k);
}
return confirmed;
}