diff --git a/docs/docs/tutorials/rag.ipynb b/docs/docs/tutorials/rag.ipynb
index 511378e3ff9..a4601ce3a21 100644
--- a/docs/docs/tutorials/rag.ipynb
+++ b/docs/docs/tutorials/rag.ipynb
@@ -1050,6 +1050,112 @@
     "graph = graph_builder.compile()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "28a62d34",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "<summary>Full Code:</summary>\n",
+    "\n",
+    "```python\n",
+    "from typing import Literal\n",
+    "\n",
+    "import bs4\n",
+    "from langchain import hub\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "from langgraph.graph import START, StateGraph\n",
+    "from typing_extensions import Annotated, List, TypedDict\n",
+    "\n",
+    "# Load and chunk contents of the blog\n",
+    "loader = WebBaseLoader(\n",
+    "    web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
+    "    bs_kwargs=dict(\n",
+    "        parse_only=bs4.SoupStrainer(\n",
+    "            class_=(\"post-content\", \"post-title\", \"post-header\")\n",
+    "        )\n",
+    "    ),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "all_splits = text_splitter.split_documents(docs)\n",
+    "\n",
+    "\n",
+    "# Update metadata (illustration purposes)\n",
+    "total_documents = len(all_splits)\n",
+    "third = total_documents // 3\n",
+    "\n",
+    "for i, document in enumerate(all_splits):\n",
+    "    if i < third:\n",
+    "        document.metadata[\"section\"] = \"beginning\"\n",
+    "    elif i < 2 * third:\n",
+    "        document.metadata[\"section\"] = \"middle\"\n",
+    "    else:\n",
+    "        document.metadata[\"section\"] = \"end\"\n",
+    "\n",
+    "\n",
+    "# Index chunks\n",
+    "vector_store = InMemoryVectorStore(embeddings)\n",
+    "_ = vector_store.add_documents(all_splits)\n",
+    "\n",
+    "\n",
+    "# Define schema for search\n",
+    "class Search(TypedDict):\n",
+    "    \"\"\"Search query.\"\"\"\n",
+    "\n",
+    "    query: Annotated[str, ..., \"Search query to run.\"]\n",
+    "    section: Annotated[\n",
+    "        Literal[\"beginning\", \"middle\", \"end\"],\n",
+    "        ...,\n",
+    "        \"Section to query.\",\n",
+    "    ]\n",
+    "\n",
+    "# Define prompt for question-answering\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "\n",
+    "# Define state for application\n",
+    "class State(TypedDict):\n",
+    "    question: str\n",
+    "    query: Search\n",
+    "    context: List[Document]\n",
+    "    answer: str\n",
+    "\n",
+    "\n",
+    "def analyze_query(state: State):\n",
+    "    structured_llm = llm.with_structured_output(Search)\n",
+    "    query = structured_llm.invoke(state[\"question\"])\n",
+    "    return {\"query\": query}\n",
+    "\n",
+    "\n",
+    "def retrieve(state: State):\n",
+    "    query = state[\"query\"]\n",
+    "    retrieved_docs = vector_store.similarity_search(\n",
+    "        query[\"query\"],\n",
+    "        filter=lambda doc: doc.metadata.get(\"section\") == query[\"section\"],\n",
+    "    )\n",
+    "    return {\"context\": retrieved_docs}\n",
+    "\n",
+    "\n",
+    "def generate(state: State):\n",
+    "    docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n",
+    "    messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n",
+    "    response = llm.invoke(messages)\n",
+    "    return {\"answer\": response.content}\n",
+    "\n",
+    "\n",
+    "graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])\n",
+    "graph_builder.add_edge(START, \"analyze_query\")\n",
+    "graph = graph_builder.compile()\n",
+    "```\n",
+    "\n",
+    "</details>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,