diff --git a/example/sdk/dataset_example.py b/example/sdk/dataset_example.py
index 252ed02c0..3a0504d8d 100644
--- a/example/sdk/dataset_example.py
+++ b/example/sdk/dataset_example.py
@@ -36,10 +36,8 @@ try:
     updated_dataset = dataset_instance.update(updated_message)
 
     # get the dataset (list datasets)
-    dataset_list = ragflow_instance.list_datasets(id=dataset_instance.id)
-    dataset_instance_2 = dataset_list[0]
     print(dataset_instance)
-    print(dataset_instance_2)
+    print(updated_dataset)
 
     # delete the dataset (delete datasets)
     to_be_deleted_datasets = [dataset_instance.id]
diff --git a/graphrag/general/entity_embedding.py b/graphrag/general/entity_embedding.py
index 746e2442f..78c7fc31d 100644
--- a/graphrag/general/entity_embedding.py
+++ b/graphrag/general/entity_embedding.py
@@ -21,7 +21,7 @@ class NodeEmbeddings:
     embeddings: np.ndarray
 
 
-def embed_nod2vec(
+def embed_node2vec(
     graph: nx.Graph | nx.DiGraph,
     dimensions: int = 1536,
     num_walks: int = 10,
@@ -44,13 +44,13 @@ def embed_nod2vec(
     return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
 
 
-def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings:
+def run(graph: nx.Graph, args: dict[str, Any]) -> dict:
     """Run method definition."""
     if args.get("use_lcc", True):
         graph = stable_largest_connected_component(graph)
 
     # create graph embedding using node2vec
-    embeddings = embed_nod2vec(
+    embeddings = embed_node2vec(
         graph=graph,
         dimensions=args.get("dimensions", 1536),
         num_walks=args.get("num_walks", 10),
diff --git a/graphrag/search.py b/graphrag/search.py
index d39f482d0..ebc8f4a88 100644
--- a/graphrag/search.py
+++ b/graphrag/search.py
@@ -23,7 +23,7 @@ import trio
 
 from api.utils import get_uuid
 from graphrag.query_analyze_prompt import PROMPTS
-from graphrag.utils import get_entity_type2sampels, get_llm_cache, set_llm_cache, get_relation
+from graphrag.utils import get_entity_type2samples, get_llm_cache, set_llm_cache, get_relation
 from rag.utils import num_tokens_from_string, get_float
 from rag.utils.doc_store_conn import OrderByExpr
 
@@ -42,7 +42,7 @@ class KGSearch(Dealer):
         return response
 
     def query_rewrite(self, llm, question, idxnms, kb_ids):
-        ty2ents = trio.run(lambda: get_entity_type2sampels(idxnms, kb_ids))
+        ty2ents = trio.run(lambda: get_entity_type2samples(idxnms, kb_ids))
         hint_prompt = PROMPTS["minirag_query2kwd"].format(query=question,
                                                           TYPE_POOL=json.dumps(ty2ents, ensure_ascii=False, indent=2))
         result = self._chat(llm, hint_prompt, [{"role": "user", "content": "Output:"}], {})
diff --git a/graphrag/utils.py b/graphrag/utils.py
index 6b80d7fe8..6abe5f9a9 100644
--- a/graphrag/utils.py
+++ b/graphrag/utils.py
@@ -561,7 +561,7 @@ def merge_tuples(list1, list2):
     return result
 
 
-async def get_entity_type2sampels(idxnms, kb_ids: list):
+async def get_entity_type2samples(idxnms, kb_ids: list):
     es_res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids, "size": 10000, "fields": ["content_with_weight"]}, idxnms, kb_ids))
 
     res = defaultdict(list)