Files
learnharness/scripts/embed-graph.ts
T
amosblomqvist f5edc7f0a5 Semantic topic clusters: local embeddings replace Louvain
Louvain only ever found the reasoning chains (the graph is disconnected
by nature — every connected component became its own cluster) and
edge-orphans could never join anything. Clusters are now RELATED TOPICS:
propositions grouped by meaning.

- Embedder (src/server/embeddings.ts): in-process transformers.js v4
  running EmbeddingGemma-300M q8 (MTEB 68.4, ~300MB one-time download),
  MRL-truncated to 512d, "title: none | text:" prefix on 100% of inputs,
  L2-normalized so cosine = dot. Model/dim mismatch voids the ledger
  (full re-embed). Injected at the composition root — the agent layer
  never imports the server layer (architecture law).
- Ledger (data/graph/embeddings.json): model + dim + vectors + the
  persisted cluster partition. Reclustering runs at the END of every
  embedding batch (post-merge hook, both front doors) — never per read
  (the agglomerative pass is ~1.5s at 338 nodes).
- Clustering (src/core/semantic-clusters.ts): hand-rolled average-linkage
  agglomerative over cosine (npm HDBSCAN is an abandoned alpha). Stragglers
  stay singletons by default; attachStragglers is the all-in toggle.
  Threshold 0.60 swept on the real graph: 26 topics, 74/90 edge-orphans
  land in real topics, 10 stragglers.
- Consumers (snapshot, settle, namer, replay) read the persisted
  partition — one cluster concept everywhere; core-survival label
  matching unchanged (20/26 topics kept labels).
- Louvain retired: detectCommunities + graphology* deps deleted
  (graphology-louvain had leaked into the client bundle since the
  clusters wave); scripts/bench-graph.ts (dead FA2 bench) removed.
- scripts/embed-graph.ts: backfill/cold-start (338 nodes in 150s
  including the one-time model download).
2026-09-13 14:29:16 +03:00

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TypeScript

/**
* Embed the whole graph (backfill / cold start): one-time pass that
* fills the embedding ledger for every node — the same embedNewNodes the
* post-merge hook runs, just with an empty (or stale-model) ledger so
* every node is "new". First run downloads the model weights (~300MB)
* to the local HF cache; subsequent runs embed only what's missing.
*
* npx tsx scripts/embed-graph.ts
*/
import { graphStore } from "../src/store/graph.ts";
import { embedNewNodes, EMBEDDING_MODEL, EMBEDDING_DIM } from "../src/server/embeddings.ts";
async function main() {
const t0 = Date.now();
console.log(`[embed] model: ${EMBEDDING_MODEL} (dim ${EMBEDDING_DIM}, q8)`);
const { embedded, total } = await embedNewNodes(graphStore);
console.log(`[embed] embedded ${embedded} nodes → ledger holds ${total} vectors (${((Date.now() - t0) / 1000).toFixed(1)}s)`);
}
main().catch((err) => {
console.error("[embed] failed:", err);
process.exit(1);
});