diff --git a/docs/docs/integrations/chat/kinetica.ipynb b/docs/docs/integrations/chat/kinetica.ipynb
index 685e4c5b826ee..a8c84d6a541d2 100644
--- a/docs/docs/integrations/chat/kinetica.ipynb
+++ b/docs/docs/integrations/chat/kinetica.ipynb
@@ -98,13 +98,14 @@
    "outputs": [],
    "source": [
     "from langchain_community.chat_models.kinetica import KineticaChatLLM\n",
+    "\n",
     "kinetica_llm = KineticaChatLLM()\n",
     "\n",
     "# Test table we will create\n",
     "table_name = \"demo.user_profiles\"\n",
     "\n",
     "# LLM Context we will create\n",
-    "kinetica_ctx = 'demo.test_llm_ctx'"
+    "kinetica_ctx = \"demo.test_llm_ctx\""
    ]
   },
   {
@@ -250,15 +251,17 @@
     "from typing import Generator\n",
     "\n",
     "Faker.seed(5467)\n",
-    "faker = Faker(locale='en-US')\n",
+    "faker = Faker(locale=\"en-US\")\n",
+    "\n",
     "\n",
     "def profile_gen(count: int) -> Generator:\n",
-    "  for id in range(0, count):\n",
-    "      rec = dict(id=id, **faker.simple_profile())\n",
-    "      rec['birthdate'] = pd.Timestamp(rec['birthdate'])\n",
-    "      yield rec\n",
+    "    for id in range(0, count):\n",
+    "        rec = dict(id=id, **faker.simple_profile())\n",
+    "        rec[\"birthdate\"] = pd.Timestamp(rec[\"birthdate\"])\n",
+    "        yield rec\n",
+    "\n",
     "\n",
-    "load_df = pd.DataFrame.from_records(data=profile_gen(100), index='id')\n",
+    "load_df = pd.DataFrame.from_records(data=profile_gen(100), index=\"id\")\n",
     "load_df.head()"
    ]
   },
@@ -359,10 +362,13 @@
    "source": [
     "from gpudb import GPUdbTable\n",
     "\n",
-    "gpudb_table = GPUdbTable.from_df(load_df, db=kinetica_llm.kdbc, \n",
-    "                                table_name=table_name, \n",
-    "                                clear_table=True,\n",
-    "                                load_data=True)\n",
+    "gpudb_table = GPUdbTable.from_df(\n",
+    "    load_df,\n",
+    "    db=kinetica_llm.kdbc,\n",
+    "    table_name=table_name,\n",
+    "    clear_table=True,\n",
+    "    load_data=True,\n",
+    ")\n",
     "\n",
     "# See the Kinetica column types\n",
     "gpudb_table.type_as_df()"
@@ -403,7 +409,7 @@
     "\n",
     "from gpudb import GPUdbException\n",
     "\n",
-    "sql=f\"\"\"\n",
+    "sql = f\"\"\"\n",
     "CREATE OR REPLACE CONTEXT {kinetica_ctx}\n",
     "(\n",
     "    TABLE = demo.test_profiles\n",
@@ -418,15 +424,17 @@
     ")\n",
     "\"\"\"\n",
     "\n",
+    "\n",
     "def _check_error(response: dict) -> None:\n",
-    "    status = response['status_info']['status']\n",
-    "    if (status != 'OK'):\n",
-    "        message = response['status_info']['message']\n",
-    "        raise GPUdbException('[%s]: %s' % (status, message))\n",
+    "    status = response[\"status_info\"][\"status\"]\n",
+    "    if status != \"OK\":\n",
+    "        message = response[\"status_info\"][\"message\"]\n",
+    "        raise GPUdbException(\"[%s]: %s\" % (status, message))\n",
+    "\n",
     "\n",
     "response = kinetica_llm.kdbc.execute_sql(sql)\n",
     "_check_error(response)\n",
-    "response['status_info']"
+    "response[\"status_info\"]"
    ]
   },
   {
@@ -509,7 +517,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models.kinetica import KineticaSqlOutputParser, KineticaSqlResponse\n",
+    "from langchain_community.chat_models.kinetica import (\n",
+    "    KineticaSqlOutputParser,\n",
+    "    KineticaSqlResponse,\n",
+    ")\n",
+    "\n",
     "chain = prompt_template | kinetica_llm | KineticaSqlOutputParser(kdbc=kinetica_llm.kdbc)"
    ]
   },
@@ -602,7 +614,9 @@
    ],
    "source": [
     "# Here you must ask a question relevant to the LLM context provided in the prompt template.\n",
-    "response: KineticaSqlResponse = chain.invoke({\"input\": \"What are the female users ordered by username?\"})\n",
+    "response: KineticaSqlResponse = chain.invoke(\n",
+    "    {\"input\": \"What are the female users ordered by username?\"}\n",
+    ")\n",
     "\n",
     "print(f\"SQL: {response.sql}\")\n",
     "response.dataframe.head()"