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vectord: delete _run_embedding_job_legacy — 44 lines of explicit dead code
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lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts

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Function was labeled "Legacy single-pipeline embedding (replaced by
supervisor)" with a #[allow(dead_code)] attribute. Zero callers across
the workspace. This is exactly what `#[allow(dead_code)]` is supposed
to silently flag as "I know this is dead but I'm not committing to
removing it" — so let's commit to removing it.

Iter memory grep for this pattern showed 5 remaining #[allow(dead_code)]
attributes in the workspace (1 here, 4 in gateway/access.rs). The four
in access.rs are waiting on P13-001 (queryd → AccessControl wiring)
before removing — that's cross-crate work. This one was self-contained.

Net: -44 lines of dead code + comment. Workspace warnings unchanged at 11.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
root 2026-04-24 06:22:27 -05:00
parent 3963b28b50
commit d122703e9a
1 changed files with 0 additions and 45 deletions
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45
crates/vectord/src/service.rs
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@ -470,51 +470,6 @@ async fn copy_key(
storaged::ops::put(dst, key, data).await
}
// --- unused legacy function below, kept for reference ---
#[allow(dead_code)]
/// Legacy single-pipeline embedding (replaced by supervisor).
async fn _run_embedding_job_legacy(
job_id: &str,
index_name: &str,
chunks: &[chunker::TextChunk],
ai_client: &AiClient,
store: &Arc<dyn ObjectStore>,
tracker: &jobs::JobTracker,
) -> Result<String, String> {
let batch_size = 32;
let mut all_vectors: Vec<Vec<f64>> = Vec::new();
let start = std::time::Instant::now();
for (i, batch) in chunks.chunks(batch_size).enumerate() {
let texts: Vec<String> = batch.iter().map(|c| c.text.clone()).collect();
let embed_resp = ai_client.embed(EmbedRequest {
texts,
model: None,
}).await.map_err(|e| format!("embed batch {} error: {e}", i))?;
all_vectors.extend(embed_resp.embeddings);
// Update progress
let elapsed = start.elapsed().as_secs_f32();
let rate = if elapsed > 0.0 { all_vectors.len() as f32 / elapsed } else { 0.0 };
tracker.update_embed_progress(job_id, all_vectors.len(), rate).await;
// Log every 100 batches
if (i + 1) % 100 == 0 {
let pct = (all_vectors.len() as f32 / chunks.len() as f32) * 100.0;
let eta = if rate > 0.0 { (chunks.len() - all_vectors.len()) as f32 / rate } else { 0.0 };
tracing::info!("job {job_id}: {}/{} chunks ({pct:.0}%), {rate:.0}/sec, ETA {eta:.0}s",
all_vectors.len(), chunks.len());
}
}
// Store
let key = store::store_embeddings(store, index_name, chunks, &all_vectors).await?;
Ok(key)
}
// --- Job Status ---
async fn list_jobs(State(state): State<VectorState>) -> impl IntoResponse {