[docs]: change rag reference in vector store pages (#25125)

libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb

@@ -288,45 +288,13 @@
    "id": "901c75dc",
    "metadata": {},
    "source": [
-    "## Chain usage\n",
+    "## Usage for retrieval-augmented generation\n",
     "\n",
-    "The code below shows how to use the vector store as a retriever in a simple RAG chain:\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "```{=mdx}\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "619b5ef6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "from langchain import hub\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
-    "\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "rag_chain = (\n",
-    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "rag_chain.invoke(\"thud\")"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
    ]
   },
   {