fix(docs): improve classify_intent reliability and draft logging (#1721)

- Switch model from gpt-4 to gpt-5-nano because gpt-4 could not reliably
return valid JSON, causing classify_intent to fail.
- Update logging to avoid printing draft_response before it exists,
which previously caused an error in:
  print(f"Draft ready for review: {result['draft_response'][:100]}...")

## Overview
This PR updates the example to use `gpt-5-nano` instead of `gpt-4` for
the `classify_intent` step and fixes a logging statement that could
raise an exception when `draft_response` is not yet available.

## Type of change

**Type:** Update existing documentation

## Related issues/PRs
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Link to related issues, feature PRs, or discussions (if applicable)
-->
- GitHub issue:
- Feature PR:

<!-- For LangChain employees, if applicable: -->
- Linear issue:
- Slack thread:

## Checklist
<!-- Put an 'x' in all boxes that apply -->
- [x] I have read the [[contributing
guidelines](https://sider.ai/zh-CN/README.md)](README.md)
- [ ] I have tested my changes locally using `docs dev`
- [x] All code examples have been tested and work correctly
- [ ] I have used **root relative** paths for internal links
- [ ] I have updated navigation in `src/docs.json` if needed

(Internal team members only / optional): Create a preview deployment as
necessary using the [[Create Preview Branch
workflow](https://github.com/langchain-ai/docs/actions/workflows/create-preview-branch.yml)](https://github.com/langchain-ai/docs/actions/workflows/create-preview-branch.yml)

## Additional notes
- The switch to `gpt-5-nano` ensures structured JSON output for the
`classify_intent` node.
- The updated print statement prevents runtime errors when
`draft_response` is missing.

Author: yanglikun

src/oss/langgraph/thinking-in-langgraph.mdx

@@ -482,7 +482,7 @@ We'll implement each node as a simple function. Remember: nodes take state, do w
     from langchain_openai import ChatOpenAI
     from langchain.messages import HumanMessage
 
-    llm = ChatOpenAI(model="gpt-4")
+    llm = ChatOpenAI(model="gpt-5-nano")
 
     def read_email(state: EmailAgentState) -> dict:
         """Extract and parse email content"""
@@ -974,7 +974,7 @@ initial_state = {
 config = {"configurable": {"thread_id": "customer_123"}}
 result = app.invoke(initial_state, config)
 # The graph will pause at human_review
-print(f"Draft ready for review: {result['draft_response'][:100]}...")
+print(f"human review interrupt:{result['__interrupt__']}")
 
 # When ready, provide human input to resume
 from langgraph.types import Command