crates/vectord/src/service.rs

@@ -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 {