langchain/docs/extras/use_cases/graph/graph_hugegraph_qa.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "d2777010",
   "metadata": {},
   "source": [
    "# HugeGraph QA Chain\n",
    "\n",
    "This notebook shows how to use LLMs to provide a natural language interface to [HugeGraph](https://hugegraph.apache.org/cn/) database."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26dcbe4",
   "metadata": {},
   "source": [
    "You will need to have a running HugeGraph instance.\n",
    "You can run a local docker container by running the executing the following script:\n",
    "\n",
    "```\n",
    "docker run \\\n",
    "    --name=graph \\\n",
    "    -itd \\\n",
    "    -p 8080:8080 \\\n",
    "    hugegraph/hugegraph\n",
    "```\n",
    "\n",
    "If we want to connect HugeGraph in the application, we need to install python sdk:\n",
    "\n",
    "```\n",
    "pip3 install hugegraph-python\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64a29f1",
   "metadata": {},
   "source": [
    "If you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e53ab93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hugegraph.connection import PyHugeGraph\n",
    "\n",
    "client = PyHugeGraph(\"localhost\", \"8080\", user=\"admin\", pwd=\"admin\", graph=\"hugegraph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c3a50e",
   "metadata": {},
   "source": [
    "First, we create the schema for a simple movie database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef5372a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'create EdgeLabel success, Detail: \"b\\'{\"id\":1,\"name\":\"ActedIn\",\"source_label\":\"Person\",\"target_label\":\"Movie\",\"frequency\":\"SINGLE\",\"sort_keys\":[],\"nullable_keys\":[],\"index_labels\":[],\"properties\":[],\"status\":\"CREATED\",\"ttl\":0,\"enable_label_index\":true,\"user_data\":{\"~create_time\":\"2023-07-04 10:48:47.908\"}}\\'\"'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"schema\"\"\"\n",
    "schema = client.schema()\n",
    "schema.propertyKey(\"name\").asText().ifNotExist().create()\n",
    "schema.propertyKey(\"birthDate\").asText().ifNotExist().create()\n",
    "schema.vertexLabel(\"Person\").properties(\n",
    "    \"name\", \"birthDate\"\n",
    ").usePrimaryKeyId().primaryKeys(\"name\").ifNotExist().create()\n",
    "schema.vertexLabel(\"Movie\").properties(\"name\").usePrimaryKeyId().primaryKeys(\n",
    "    \"name\"\n",
    ").ifNotExist().create()\n",
    "schema.edgeLabel(\"ActedIn\").sourceLabel(\"Person\").targetLabel(\n",
    "    \"Movie\"\n",
    ").ifNotExist().create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016f7989",
   "metadata": {},
   "source": [
    "Then we can insert some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b7f4c370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1:Robert De Niro--ActedIn-->2:The Godfather Part II"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"graph\"\"\"\n",
    "g = client.graph()\n",
    "g.addVertex(\"Person\", {\"name\": \"Al Pacino\", \"birthDate\": \"1940-04-25\"})\n",
    "g.addVertex(\"Person\", {\"name\": \"Robert De Niro\", \"birthDate\": \"1943-08-17\"})\n",
    "g.addVertex(\"Movie\", {\"name\": \"The Godfather\"})\n",
    "g.addVertex(\"Movie\", {\"name\": \"The Godfather Part II\"})\n",
    "g.addVertex(\"Movie\", {\"name\": \"The Godfather Coda The Death of Michael Corleone\"})\n",
    "\n",
    "g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather\", {})\n",
    "g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Part II\", {})\n",
    "g.addEdge(\n",
    "    \"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Coda The Death of Michael Corleone\", {}\n",
    ")\n",
    "g.addEdge(\"ActedIn\", \"1:Robert De Niro\", \"2:The Godfather Part II\", {})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8f7788",
   "metadata": {},
   "source": [
    "## Creating `HugeGraphQAChain`\n",
    "\n",
    "We can now create the `HugeGraph` and `HugeGraphQAChain`. To create the `HugeGraph` we simply need to pass the database object to the `HugeGraph` constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f1f68fcf",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.chains import HugeGraphQAChain\n",
    "from langchain.graphs import HugeGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b86ebfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = HugeGraph(\n",
    "    username=\"admin\",\n",
    "    password=\"admin\",\n",
    "    address=\"localhost\",\n",
    "    port=8080,\n",
    "    graph=\"hugegraph\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e262540b",
   "metadata": {},
   "source": [
    "## Refresh graph schema information\n",
    "\n",
    "If the schema of database changes, you can refresh the schema information needed to generate Gremlin statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "134dd8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph.refresh_schema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e78b8e72",
   "metadata": {
    "ExecuteTime": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']]\n",
      "Edge properties: [name: ActedIn, properties: []]\n",
      "Relationships: ['Person--ActedIn-->Movie']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(graph.get_schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c27e813",
   "metadata": {},
   "source": [
    "## Querying the graph\n",
    "\n",
    "We can now use the graph Gremlin QA chain to ask question of the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3b23dead",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "76aecc93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new  chain...\u001b[0m\n",
      "Generated gremlin:\n",
      "\u001b[32;1m\u001b[1;3mg.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)\u001b[0m\n",
      "Full Context:\n",
      "\u001b[32;1m\u001b[1;3m[{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Al Pacino played in The Godfather.'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Who played in The Godfather?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "869f0258",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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