diff --git a/docs/api_reference/scripts/custom_formatter.py b/docs/api_reference/scripts/custom_formatter.py
index d8efb5e558d..b74231eec98 100644
--- a/docs/api_reference/scripts/custom_formatter.py
+++ b/docs/api_reference/scripts/custom_formatter.py
@@ -17,7 +17,10 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        element = element.a.code.span
+        try:
+            element = element.a.code.span
+        except Exception:
+            continue
         # Get the text content of the element
         content = element.get_text()
 
diff --git a/libs/core/langchain_core/language_models/__init__.py b/libs/core/langchain_core/language_models/__init__.py
index 116f478d568..f6a074fb202 100644
--- a/libs/core/langchain_core/language_models/__init__.py
+++ b/libs/core/langchain_core/language_models/__init__.py
@@ -4,7 +4,7 @@ text prompts.
 LangChain has two main classes to work with language models: **Chat Models**
 and "old-fashioned" **LLMs**.
 
-## Chat Models
+**Chat Models**
 
 Language models that use a sequence of messages as inputs and return chat messages
 as outputs (as opposed to using plain text). These are traditionally newer models (
@@ -21,7 +21,7 @@ the following guide for more information on how to implement a custom Chat Model
 
 https://python.langchain.com/v0.2/docs/how_to/custom_chat_model/
 
-## LLMs
+**LLMs**
 
 Language models that takes a string as input and returns a string.
 These are traditionally older models (newer models generally are Chat Models, see below).
@@ -35,6 +35,8 @@ To implement a custom LLM, inherit from `BaseLLM` or `LLM`.
 Please see the following guide for more information on how to implement a custom LLM:
 
 https://python.langchain.com/v0.2/docs/how_to/custom_llm/
+
+
 """  # noqa: E501
 
 from langchain_core.language_models.base import (