diff --git a/medical/molecular_search/2_deep_dive_molecular_search.ipynb b/medical/molecular_search/2_deep_dive_molecular_search.ipynb
index 4078c4a..81b9a65 100644
--- a/medical/molecular_search/2_deep_dive_molecular_search.ipynb
+++ b/medical/molecular_search/2_deep_dive_molecular_search.ipynb
@@ -158,9 +158,9 @@
     "        utility.drop_collection(collection_name)\n",
     "    \n",
     "    fields = [\n",
-    "    FieldSchema(name='id', dtype=DataType.INT64, descrition='ids', is_primary=True, auto_id=False),\n",
-    "    FieldSchema(name='smiles', dtype=DataType.VARCHAR, descrition='SMILES', max_length=500),\n",
-    "    FieldSchema(name='embedding', dtype=DataType.BINARY_VECTOR, descrition='embedding vectors', dim=dim)\n",
+    "    FieldSchema(name='id', dtype=DataType.INT64, description='ids', is_primary=True, auto_id=False),\n",
+    "    FieldSchema(name='smiles', dtype=DataType.VARCHAR, description='SMILES', max_length=500),\n",
+    "    FieldSchema(name='embedding', dtype=DataType.BINARY_VECTOR, description='embedding vectors', dim=dim)\n",
     "    ]\n",
     "    schema = CollectionSchema(fields=fields, description='molecular similarity search')\n",
     "    collection = Collection(name=collection_name, schema=schema)\n",
diff --git a/nlp/question_answering/1_build_question_answering_engine.ipynb b/nlp/question_answering/1_build_question_answering_engine.ipynb
index c00d4c6..9ec7292 100644
--- a/nlp/question_answering/1_build_question_answering_engine.ipynb
+++ b/nlp/question_answering/1_build_question_answering_engine.ipynb
@@ -274,8 +274,8 @@
     "        utility.drop_collection(collection_name)\n",
     "    \n",
     "    fields = [\n",
-    "    FieldSchema(name='id', dtype=DataType.VARCHAR, descrition='ids', max_length=500, is_primary=True, auto_id=False),\n",
-    "    FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, descrition='embedding vectors', dim=dim)\n",
+    "    FieldSchema(name='id', dtype=DataType.VARCHAR, description='ids', max_length=500, is_primary=True, auto_id=False),\n",
+    "    FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, description='embedding vectors', dim=dim)\n",
     "    ]\n",
     "    schema = CollectionSchema(fields=fields, description='reverse image search')\n",
     "    collection = Collection(name=collection_name, schema=schema)\n",