diff --git a/reproduce/Step_0_index.py b/reproduce/Step_0_index.py
index 2e98e27..97dae26 100644
--- a/reproduce/Step_0_index.py
+++ b/reproduce/Step_0_index.py
@@ -11,7 +11,7 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from minirag import MiniRAG
 from minirag.llm import (
     gpt_4o_mini_complete,
-    hf_embedding,
+    hf_embed,
 )
 from minirag.utils import EmbeddingFunc
 from transformers import AutoModel, AutoTokenizer
@@ -69,7 +69,7 @@ rag = MiniRAG(
     embedding_func=EmbeddingFunc(
         embedding_dim=384,
         max_token_size=1000,
-        func=lambda texts: hf_embedding(
+        func=lambda texts: hf_embed(
             texts,
             tokenizer=AutoTokenizer.from_pretrained(EMBEDDING_MODEL),
             embed_model=AutoModel.from_pretrained(EMBEDDING_MODEL),
diff --git a/reproduce/Step_1_QA.py b/reproduce/Step_1_QA.py
index efe4622..3383bc4 100644
--- a/reproduce/Step_1_QA.py
+++ b/reproduce/Step_1_QA.py
@@ -13,7 +13,7 @@ from tqdm import trange
 from minirag import MiniRAG, QueryParam
 from minirag.llm import (
     hf_model_complete,
-    hf_embedding,
+    hf_embed,
 )
 from minirag.utils import EmbeddingFunc
 from transformers import AutoModel, AutoTokenizer
@@ -71,7 +71,7 @@ rag = MiniRAG(
     embedding_func=EmbeddingFunc(
         embedding_dim=384,
         max_token_size=1000,
-        func=lambda texts: hf_embedding(
+        func=lambda texts: hf_embed(
             texts,
             tokenizer=AutoTokenizer.from_pretrained(EMBEDDING_MODEL),
             embed_model=AutoModel.from_pretrained(EMBEDDING_MODEL),