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update augorWorker to use openRouter configuration and set llmModel in config

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ImBenji 2026-04-22 22:06:59 +01:00
parent 3ed86409d7
commit ca087eba5c
1 changed files with 0 additions and 93 deletions
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93
src/embeddings.js
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@ -108,99 +108,6 @@ function padEmbeddingForVec0(values) {
return Buffer.from(padded.buffer);
}
// backfill store from vec0 for any embeddings that predate multi-model support.
// only runs when store is completely empty, so we never stamp the wrong model on existing data.
try {
const storeEmpty = !db.prepare(`SELECT 1 FROM article_embedding_store LIMIT 1`).get();
if (storeEmpty) {
// use model from meta if available, fall back to config model
const metaRow = db.prepare(`SELECT model FROM article_embedding_meta LIMIT 1`).get();
const legacyModel = (metaRow && metaRow.model) || EMBEDDING_MODEL;
db.prepare(`
INSERT OR IGNORE INTO article_embedding_store (article_id, model, embedding)
SELECT e.article_id, ?, e.embedding FROM article_embeddings e
`).run(legacyModel);
}
} catch (err) {
console.error('embedding store backfill failed:', err);
}
// if the config model changed, rebuild the vec0 search index from store.
// only proceeds if the store actually has embeddings for the new model.
function rebuildVec0IfModelChanged() {
const stale = db.prepare(`
SELECT 1 FROM article_embedding_meta WHERE model != ? LIMIT 1
`).get(EMBEDDING_MODEL);
// also sync any store entries that didn't make it into vec0
const missing = db.prepare(`
SELECT article_id, embedding FROM article_embedding_store
WHERE model = ?
AND NOT EXISTS (SELECT 1 FROM article_embeddings WHERE article_id = article_embedding_store.article_id)
`).all(EMBEDDING_MODEL);
if (missing.length > 0) {
const insertVec = db.prepare(`INSERT OR IGNORE INTO article_embeddings (article_id, embedding) VALUES (?, ?)`);
const insertMeta = db.prepare(`INSERT OR IGNORE INTO article_embedding_meta (article_id, model) VALUES (?, ?)`);
const sync = db.transaction(() => {
for (const row of missing) {
const vals = new Float32Array(row.embedding.buffer, row.embedding.byteOffset, row.embedding.byteLength / 4);
insertVec.run(BigInt(row.article_id), padEmbeddingForVec0(vals));
insertMeta.run(row.article_id, EMBEDDING_MODEL);
}
});
sync();
console.log(`synced ${missing.length} store embeddings into vec0`);
}
if (!stale) return;
const storeCount = db.prepare(`
SELECT COUNT(*) AS n FROM article_embedding_store WHERE model = ?
`).get(EMBEDDING_MODEL);
if (!storeCount || storeCount.n === 0) {
console.log(`embedding model changed to ${EMBEDDING_MODEL} but store has no embeddings for it yet — skipping index rebuild`);
return;
}
console.log(`embedding model changed to ${EMBEDDING_MODEL}, rebuilding search index...`);
db.exec('BEGIN');
try {
db.exec('DELETE FROM article_embeddings');
db.exec('DELETE FROM article_embedding_meta');
const rows = db.prepare(`
SELECT article_id, embedding FROM article_embedding_store WHERE model = ?
`).all(EMBEDDING_MODEL);
const insertVec = db.prepare(`INSERT INTO article_embeddings (article_id, embedding) VALUES (?, ?)`);
const insertMeta = db.prepare(`INSERT INTO article_embedding_meta (article_id, model) VALUES (?, ?)`);
for (const row of rows) {
const vals = new Float32Array(row.embedding.buffer, row.embedding.byteOffset, row.embedding.byteLength / 4);
const padded = new Float32Array(VEC0_DIM);
padded.set(vals);
insertVec.run(BigInt(row.article_id), Buffer.from(padded.buffer));
insertMeta.run(row.article_id, EMBEDDING_MODEL);
}
db.exec('COMMIT');
console.log(`rebuilt search index with ${rows.length} embeddings`);
} catch (err) {
db.exec('ROLLBACK');
throw err;
}
}
rebuildVec0IfModelChanged();
let embeddingBackfillRunning = false;
const embeddingJobsRunning = new Set();