diff --git a/docs/docs/integrations/retrievers/self_query/myscale_self_query.ipynb b/docs/docs/integrations/retrievers/self_query/myscale_self_query.ipynb
index c26e04d0fd2..ff44f6c6d95 100644
--- a/docs/docs/integrations/retrievers/self_query/myscale_self_query.ipynb
+++ b/docs/docs/integrations/retrievers/self_query/myscale_self_query.ipynb
@@ -162,12 +162,14 @@
    "source": [
     "from langchain.chains.query_constructor.base import AttributeInfo\n",
     "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain_openai import OpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "metadata_field_info = [\n",
     "    AttributeInfo(\n",
     "        name=\"genre\",\n",
-    "        description=\"The genres of the movie\",\n",
+    "        description=\"The genres of the movie. \"\n",
+    "        \"It only supports equal and contain comparisons. \"\n",
+    "        \"Here are some examples: genre = [' A '], genre = [' A ', 'B'], contain (genre, 'A')\",\n",
     "        type=\"list[string]\",\n",
     "    ),\n",
     "    # If you want to include length of a list, just define it as a new column\n",
@@ -193,7 +195,7 @@
     "    ),\n",
     "]\n",
     "document_content_description = \"Brief summary of a movie\"\n",
-    "llm = OpenAI(temperature=0)\n",
+    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4o\")\n",
     "retriever = SelfQueryRetriever.from_llm(\n",
     "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
     ")"
diff --git a/libs/experimental/langchain_experimental/sql/vector_sql.py b/libs/experimental/langchain_experimental/sql/vector_sql.py
index aa69c2d6966..a82f7b51822 100644
--- a/libs/experimental/langchain_experimental/sql/vector_sql.py
+++ b/libs/experimental/langchain_experimental/sql/vector_sql.py
@@ -40,7 +40,7 @@ class VectorSQLOutputParser(BaseOutputParser[str]):
     @classmethod
     def from_embeddings(
         cls, model: Embeddings, distance_func_name: str = "distance", **kwargs: Any
-    ) -> BaseOutputParser:
+    ) -> VectorSQLOutputParser:
         return cls(model=model, distance_func_name=distance_func_name, **kwargs)
 
     def parse(self, text: str) -> str: