Bagatur/self query doc update (#12461)

cookbook/self_query_hotel_search.ipynb

@@ -7,9 +7,33 @@
    "source": [
     "# Building hotel room search with self-querying retrieval\n",
     "\n",
+    "In this example we'll walk through how to build and iterate on a hotel room search service that leverages an LLM to generate structured filter queries that can then be passed to a vector store.\n",
+    "\n",
+    "For an introduction to self-querying retrieval [check out the docs](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d621de99-d993-4f4b-b94a-d02b2c7ad4e0",
+   "metadata": {},
+   "source": [
+    "## Imports and data prep\n",
+    "\n",
+    "In this example we use `ChatOpenAI` for the model and `ElasticsearchStore` for the vector store, but these can be swapped out with an LLM/ChatModel and [any VectorStore that support self-querying](https://python.langchain.com/docs/integrations/retrievers/self_query/).\n",
+    "\n",
     "Download data from: https://www.kaggle.com/datasets/keshavramaiah/hotel-recommendation"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ecd1fbb-bdba-420b-bcc7-5ea8a232ab11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain lark openai elasticsearch pandas"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -1142,7 +1166,9 @@
     }
    ],
    "source": [
-    "results = retriever.get_relevant_documents(\"I want to stay somewhere highly rated along the coast. I want a room with a patio and a fireplace.\")\n",
+    "results = retriever.get_relevant_documents(\n",
+    "    \"I want to stay somewhere highly rated along the coast. I want a room with a patio and a fireplace.\"\n",
+    ")\n",
     "for res in results:\n",
     "    print(res.page_content)\n",
     "    print(\"\\n\" + \"-\" * 20 + \"\\n\")"