diff --git a/crates/mem-cli/src/hybrid_query_worker.rs b/crates/mem-cli/src/hybrid_query_worker.rs
index a267d48..5165d8c 100644
--- a/crates/mem-cli/src/hybrid_query_worker.rs
+++ b/crates/mem-cli/src/hybrid_query_worker.rs
@@ -86,7 +86,7 @@ impl HybridQueryWorker {
         let mut query_ctx = self.optimizer.optimize_query(question).await?;
         
         // Stage 2: Generate embedding
-        query_ctx.embedding = Some(self.embeddings.embed(question).await?);
+        query_ctx.embedding = Some(self.embeddings.embed_one(question).await?);
         
         // Stage 3: Execute retrieval based on strategy
         let (semantic_results, lexical_results, metrics) = match &query_ctx.search_strategy {
diff --git a/crates/mem-cli/src/ingest_worker.rs b/crates/mem-cli/src/ingest_worker.rs
index f8014e9..732640e 100644
--- a/crates/mem-cli/src/ingest_worker.rs
+++ b/crates/mem-cli/src/ingest_worker.rs
@@ -64,7 +64,7 @@ impl IngestWorker {
             total_stored += 1;
 
             // Try to embed and create a basic L1 memory
-            if let Ok(embedding) = self.embeddings.embed(content).await {
+            if let Ok(embedding) = self.embeddings.embed_one(content).await {
                 let l1 = MemoryL1 {
                     id: Uuid::new_v4(),
                     project: project.to_string(),
@@ -96,7 +96,7 @@ impl IngestWorker {
 
     /// Process a single chunk
     pub async fn process_chunk(&self, project: &str, query_id: &str, content: &str, source: &str) -> Result<()> {
-        let embedding = self.embeddings.embed(content).await?;
+        let embedding = self.embeddings.embed_one(content).await?;
         let chunk = ChunkL0 {
             id: Uuid::new_v4(),
             project: project.to_string(),
diff --git a/crates/mem-cli/src/query_worker.rs b/crates/mem-cli/src/query_worker.rs
index 98f5da7..9b1a9cf 100644
--- a/crates/mem-cli/src/query_worker.rs
+++ b/crates/mem-cli/src/query_worker.rs
@@ -45,7 +45,7 @@ impl QueryWorker {
         let limit = limit.unwrap_or(5);
 
         // Embed the question
-        let question_embedding = self.embeddings.embed(question).await?;
+        let question_embedding = self.embeddings.embed_one(question).await?;
 
         // Search across all levels
         let mut candidates = Vec::new();