add everyting for docs

docs/extras/use_cases/graph/graph_nebula_qa.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c94240f5",
+   "metadata": {},
+   "source": [
+    "# NebulaGraphQAChain\n",
+    "\n",
+    "This notebook shows how to use LLMs to provide a natural language interface to NebulaGraph database."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "dbc0ee68",
+   "metadata": {},
+   "source": [
+    "You will need to have a running NebulaGraph cluster, for which you can run a containerized cluster by running the following script:\n",
+    "\n",
+    "```bash\n",
+    "curl -fsSL nebula-up.siwei.io/install.sh | bash\n",
+    "```\n",
+    "\n",
+    "Other options are:\n",
+    "- Install as a [Docker Desktop Extension](https://www.docker.com/blog/distributed-cloud-native-graph-database-nebulagraph-docker-extension/). See [here](https://docs.nebula-graph.io/3.5.0/2.quick-start/1.quick-start-workflow/)\n",
+    "- NebulaGraph Cloud Service. See [here](https://www.nebula-graph.io/cloud)\n",
+    "- Deploy from package, source code, or via Kubernetes. See [here](https://docs.nebula-graph.io/)\n",
+    "\n",
+    "Once the cluster is running, we could create the SPACE and SCHEMA for the database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c82f4141",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install ipython-ngql\n",
+    "%load_ext ngql\n",
+    "\n",
+    "# connect ngql jupyter extension to nebulagraph\n",
+    "%ngql --address 127.0.0.1 --port 9669 --user root --password nebula\n",
+    "# create a new space\n",
+    "%ngql CREATE SPACE IF NOT EXISTS langchain(partition_num=1, replica_factor=1, vid_type=fixed_string(128));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eda0809a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait for a few seconds for the space to be created.\n",
+    "%ngql USE langchain;"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "119fe35c",
+   "metadata": {},
+   "source": [
+    "Create the schema, for full dataset, refer [here](https://www.siwei.io/en/nebulagraph-etl-dbt/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5aa796ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%ngql\n",
+    "CREATE TAG IF NOT EXISTS movie(name string);\n",
+    "CREATE TAG IF NOT EXISTS person(name string, birthdate string);\n",
+    "CREATE EDGE IF NOT EXISTS acted_in();\n",
+    "CREATE TAG INDEX IF NOT EXISTS person_index ON person(name(128));\n",
+    "CREATE TAG INDEX IF NOT EXISTS movie_index ON movie(name(128));"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "66e4799a",
+   "metadata": {},
+   "source": [
+    "Wait for schema creation to complete, then we can insert some data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d8eea530",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "UsageError: Cell magic `%%ngql` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%ngql\n",
+    "INSERT VERTEX person(name, birthdate) VALUES \"Al Pacino\":(\"Al Pacino\", \"1940-04-25\");\n",
+    "INSERT VERTEX movie(name) VALUES \"The Godfather II\":(\"The Godfather II\");\n",
+    "INSERT VERTEX movie(name) VALUES \"The Godfather Coda: The Death of Michael Corleone\":(\"The Godfather Coda: The Death of Michael Corleone\");\n",
+    "INSERT EDGE acted_in() VALUES \"Al Pacino\"->\"The Godfather II\":();\n",
+    "INSERT EDGE acted_in() VALUES \"Al Pacino\"->\"The Godfather Coda: The Death of Michael Corleone\":();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62812aad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import NebulaGraphQAChain\n",
+    "from langchain.graphs import NebulaGraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0928915d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = NebulaGraph(\n",
+    "    space=\"langchain\",\n",
+    "    username=\"root\",\n",
+    "    password=\"nebula\",\n",
+    "    address=\"127.0.0.1\",\n",
+    "    port=9669,\n",
+    "    session_pool_size=30,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "58c1a8ea",
+   "metadata": {},
+   "source": [
+    "## Refresh graph schema information\n",
+    "\n",
+    "If the schema of database changes, you can refresh the schema information needed to generate nGQL statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e3de44f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# graph.refresh_schema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1fe76ccd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Node properties: [{'tag': 'movie', 'properties': [('name', 'string')]}, {'tag': 'person', 'properties': [('name', 'string'), ('birthdate', 'string')]}]\n",
+      "Edge properties: [{'edge': 'acted_in', 'properties': []}]\n",
+      "Relationships: ['(:person)-[:acted_in]->(:movie)']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(graph.get_schema)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "68a3c677",
+   "metadata": {},
+   "source": [
+    "## Querying the graph\n",
+    "\n",
+    "We can now use the graph cypher QA chain to ask question of the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7476ce98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = NebulaGraphQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ef8ee27b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new NebulaGraphQAChain chain...\u001b[0m\n",
+      "Generated nGQL:\n",
+      "\u001b[32;1m\u001b[1;3mMATCH (p:`person`)-[:acted_in]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II'\n",
+      "RETURN p.`person`.`name`\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m{'p.person.name': ['Al Pacino']}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Al Pacino played in The Godfather II.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Who played in The Godfather II?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}