diff --git a/libs/community/langchain_community/chat_models/kinetica.py b/libs/community/langchain_community/chat_models/kinetica.py
index ee98c6f1372a5..78dbf1955a557 100644
--- a/libs/community/langchain_community/chat_models/kinetica.py
+++ b/libs/community/langchain_community/chat_models/kinetica.py
@@ -293,6 +293,7 @@ class KineticaChatLLM(BaseChatModel):
     """Kinetica LLM Chat Model API.
 
     Prerequisites for using this API:
+
     * The ``gpudb`` and ``typeguard`` packages installed.
     * A Kinetica DB instance.
     * Kinetica host specified in ``KINETICA_URL``
diff --git a/libs/community/tests/integration_tests/chat_models/test_kinetica.py b/libs/community/tests/integration_tests/chat_models/test_kinetica.py
index d86aa929f8c99..64d875621b34f 100644
--- a/libs/community/tests/integration_tests/chat_models/test_kinetica.py
+++ b/libs/community/tests/integration_tests/chat_models/test_kinetica.py
@@ -42,11 +42,17 @@ class TestKineticaChatLLM:
 
     For more information see https://docs.kinetica.com/7.1/sql-gpt/concepts/.
 
-    These integration tests follow a workflow: 1. The `test_setup()` will create a table
-    with fake user profiles and and a related LLM context for the table. 2. The LLM
-    context is retrieved from the DB and used to create a chat prompt template. 3. A
-    chain is constructed from the chat prompt template. 4. The chain is executed to
-    generate the SQL and execute the query.
+    These integration tests follow a workflow:
+
+    1. The `test_setup()` will create a table with fake user profiles and and a related
+       LLM context for the table.
+
+    2. The LLM context is retrieved from the DB and used to create a chat prompt
+       template.
+
+    3. A chain is constructed from the chat prompt template.
+
+    4. The chain is executed to generate the SQL and execute the query.
     """
 
     table_name = "demo.test_profiles"