docs: fix Anthropic, OpenAI docstrings (#33566)

minor

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -1186,7 +1186,8 @@ class ChatAnthropic(BaseChatModel):
             See [Claude documentation](https://docs.claude.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta)
             for detail.
 
-    Extended context windows (beta):
+    !!! note title="Extended context windows (beta)"
+
         Claude Sonnet 4 supports a 1-million token context window, available in beta for
         organizations in usage tier 4 and organizations with custom rate limits.
 
@@ -1221,7 +1222,8 @@ class ChatAnthropic(BaseChatModel):
         for detail.
 
 
-    Token-efficient tool use (beta):
+    !!! note title="Token-efficient tool use (beta)"
+
         See LangChain [docs](https://python.langchain.com/docs/integrations/chat/anthropic/)
         for more detail.
 
@@ -1257,7 +1259,8 @@ class ChatAnthropic(BaseChatModel):
         Total tokens: 408
         ```
 
-    Context management:
+    !!! note title="Context management"
+
         Anthropic supports a context editing feature that will automatically manage the
         model's context window (e.g., by clearing tool results).
 
@@ -1276,7 +1279,8 @@ class ChatAnthropic(BaseChatModel):
         response = model_with_tools.invoke("Search for recent developments in AI")
         ```
 
-    Built-in tools:
+    !!! note title="Built-in tools"
+
         See LangChain [docs](https://python.langchain.com/docs/integrations/chat/anthropic/#built-in-tools)
         for more detail.
 
@@ -1402,7 +1406,7 @@ class ChatAnthropic(BaseChatModel):
             response = model_with_tools.invoke("What are my interests?")
             ```
 
-    Response metadata
+    !!! note title="Response metadata"
 
         ```python
         ai_msg = model.invoke(messages)

libs/partners/openai/langchain_openai/embeddings/azure.py

@@ -72,12 +72,11 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):  # type: ignore[override]
     Embed multiple texts:
         ```python
         input_texts = ["Document 1...", "Document 2..."]
-        ```
         vectors = embed.embed_documents(input_texts)
         print(len(vectors))
         # The first 3 coordinates for the first vector
         print(vectors[0][:3])
-
+        ```
         ```python
         2
         [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]