fix(docs): Use @tool decorator for retriever in agentic RAG tutorial (#1774)

## Overview
Fixed the agentic RAG tutorial to use the `@tool` decorator instead of
`create_retriever_tool`. The previous approach caused a `TypeError` when
used with LangGraph's `ToolNode` because `create_retriever_tool` returns
a `functools.partial` object, which is incompatible with
`typing.get_type_hints()`.
## Type of change
**Type:** Fix typo/bug/link/formatting
## Related issues/PRs
- GitHub issue: N/A
- Feature PR: N/A
## Checklist
- [x] I have read the [contributing guidelines](README.md)
- [ ] I have tested my changes locally using `docs dev`
- [x] All code examples have been tested and work correctly
- [x] I have used **root relative** paths for internal links
- [ ] I have updated navigation in `src/docs.json` if needed
## Additional notes
The issue manifests as: `TypeError: functools.partial(...) is not a
module, class, method, or function` during graph compilation/execution.
The `@tool` decorator creates a proper `StructuredTool` that `ToolNode`
can correctly inspect.

Author: Shrey327

src/oss/langgraph/agentic-rag.mdx

@@ -150,15 +150,17 @@ Now that we have our split documents, we can index them into a vector store that
   )
   retriever = vectorstore.as_retriever()
   ```
-2. Create a retriever tool using LangChain's prebuilt `create_retriever_tool`:
+2. Create a retriever tool using the `@tool` decorator:
   ```python
-  from langchain_classic.tools.retriever import create_retriever_tool
+  from langchain.tools import tool
 
-  retriever_tool = create_retriever_tool(
-      retriever,
-      "retrieve_blog_posts",
-      "Search and return information about Lilian Weng blog posts.",
-  )
+  @tool
+  def retrieve_blog_posts(query: str) -> str:
+      """Search and return information about Lilian Weng blog posts."""
+      docs = retriever.invoke(query)
+      return "\n\n".join([doc.page_content for doc in docs])
+
+  retriever_tool = retrieve_blog_posts
   ```
 3. Test the tool:
   ```python