{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c94240f5",
   "metadata": {
    "id": "c94240f5"
   },
   "source": [
    "# ArangoDB QA chain\n",
    "\n",
    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Langchain.ipynb)\n",
    "\n",
    "This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc0ee68",
   "metadata": {
    "id": "dbc0ee68"
   },
   "source": [
    "You can get a local ArangoDB instance running via the [ArangoDB Docker image](https://hub.docker.com/_/arangodb):  \n",
    "\n",
    "```\n",
    "docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb\n",
    "```\n",
    "\n",
    "An alternative is to use the [ArangoDB Cloud Connector package](https://github.com/arangodb/adb-cloud-connector#readme) to get a temporary cloud instance running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "izi6YoFC8KRH",
   "metadata": {
    "id": "izi6YoFC8KRH"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install python-arango # The ArangoDB Python Driver\n",
    "!pip install adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n",
    "!pip install openai\n",
    "!pip install langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62812aad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "62812aad",
    "outputId": "f7ed8346-d88b-40d1-eaff-68e97e0e157e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log: requesting new credentials...\n",
      "Succcess: new credentials acquired\n",
      "{\n",
      "  \"dbName\": \"TUT3sp29s3pjf1io0h4cfdsq\",\n",
      "  \"username\": \"TUTo6nkwgzkizej3kysgdyeo8\",\n",
      "  \"password\": \"TUT9vx0qjqt42i9bq8uik4v9\",\n",
      "  \"hostname\": \"tutorials.arangodb.cloud\",\n",
      "  \"port\": 8529,\n",
      "  \"url\": \"https://tutorials.arangodb.cloud:8529\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Instantiate ArangoDB Database\n",
    "import json\n",
    "from arango import ArangoClient\n",
    "from adb_cloud_connector import get_temp_credentials\n",
    "\n",
    "con = get_temp_credentials()\n",
    "\n",
    "db = ArangoClient(hosts=con[\"url\"]).db(\n",
    "    con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True\n",
    ")\n",
    "\n",
    "print(json.dumps(con, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0928915d",
   "metadata": {
    "id": "0928915d"
   },
   "outputs": [],
   "source": [
    "# Instantiate the ArangoDB-LangChain Graph\n",
    "from langchain.graphs import ArangoGraph\n",
    "\n",
    "graph = ArangoGraph(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995ea9b9",
   "metadata": {
    "id": "995ea9b9"
   },
   "source": [
    "## Populating the Database\n",
    "\n",
    "We will rely on the Python Driver to import our [GameOfThrones](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones) data into our database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fedd26b9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fedd26b9",
    "outputId": "fc7d9067-e4f5-495e-cd0c-79135bc16fc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': False,\n",
       " 'created': 4,\n",
       " 'errors': 0,\n",
       " 'empty': 0,\n",
       " 'updated': 0,\n",
       " 'ignored': 0,\n",
       " 'details': []}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if db.has_graph(\"GameOfThrones\"):\n",
    "    db.delete_graph(\"GameOfThrones\", drop_collections=True)\n",
    "\n",
    "db.create_graph(\n",
    "    \"GameOfThrones\",\n",
    "    edge_definitions=[\n",
    "        {\n",
    "            \"edge_collection\": \"ChildOf\",\n",
    "            \"from_vertex_collections\": [\"Characters\"],\n",
    "            \"to_vertex_collections\": [\"Characters\"],\n",
    "        },\n",
    "    ],\n",
    ")\n",
    "\n",
    "documents = [\n",
    "    {\n",
    "        \"_key\": \"NedStark\",\n",
    "        \"name\": \"Ned\",\n",
    "        \"surname\": \"Stark\",\n",
    "        \"alive\": True,\n",
    "        \"age\": 41,\n",
    "        \"gender\": \"male\",\n",
    "    },\n",
    "    {\n",
    "        \"_key\": \"CatelynStark\",\n",
    "        \"name\": \"Catelyn\",\n",
    "        \"surname\": \"Stark\",\n",
    "        \"alive\": False,\n",
    "        \"age\": 40,\n",
    "        \"gender\": \"female\",\n",
    "    },\n",
    "    {\n",
    "        \"_key\": \"AryaStark\",\n",
    "        \"name\": \"Arya\",\n",
    "        \"surname\": \"Stark\",\n",
    "        \"alive\": True,\n",
    "        \"age\": 11,\n",
    "        \"gender\": \"female\",\n",
    "    },\n",
    "    {\n",
    "        \"_key\": \"BranStark\",\n",
    "        \"name\": \"Bran\",\n",
    "        \"surname\": \"Stark\",\n",
    "        \"alive\": True,\n",
    "        \"age\": 10,\n",
    "        \"gender\": \"male\",\n",
    "    },\n",
    "]\n",
    "\n",
    "edges = [\n",
    "    {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/AryaStark\"},\n",
    "    {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/BranStark\"},\n",
    "    {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/AryaStark\"},\n",
    "    {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/BranStark\"},\n",
    "]\n",
    "\n",
    "db.collection(\"Characters\").import_bulk(documents)\n",
    "db.collection(\"ChildOf\").import_bulk(edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c1a8ea",
   "metadata": {
    "id": "58c1a8ea"
   },
   "source": [
    "## Getting & Setting the ArangoDB Schema\n",
    "\n",
    "An initial ArangoDB Schema is generated upon instantiating the `ArangoDBGraph` object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e3de44f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4e3de44f",
    "outputId": "6102f0c6-4a94-4e00-b93e-eb8de2f7d9d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"Graph Schema\": [],\n",
      "    \"Collection Schema\": []\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# The schema should be empty here,\n",
    "# since `graph` was initialized prior to ArangoDB Data ingestion (see above).\n",
    "\n",
    "import json\n",
    "\n",
    "print(json.dumps(graph.schema, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fe76ccd",
   "metadata": {
    "id": "1fe76ccd"
   },
   "outputs": [],
   "source": [
    "graph.set_schema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "mZ679anj_-Er",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mZ679anj_-Er",
    "outputId": "e05229c7-bc61-4803-d720-47e3e9b2b350"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"Graph Schema\": [\n",
      "        {\n",
      "            \"graph_name\": \"GameOfThrones\",\n",
      "            \"edge_definitions\": [\n",
      "                {\n",
      "                    \"edge_collection\": \"ChildOf\",\n",
      "                    \"from_vertex_collections\": [\n",
      "                        \"Characters\"\n",
      "                    ],\n",
      "                    \"to_vertex_collections\": [\n",
      "                        \"Characters\"\n",
      "                    ]\n",
      "                }\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"Collection Schema\": [\n",
      "        {\n",
      "            \"collection_name\": \"ChildOf\",\n",
      "            \"collection_type\": \"edge\",\n",
      "            \"edge_properties\": [\n",
      "                {\n",
      "                    \"name\": \"_key\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_id\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_from\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_to\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_rev\",\n",
      "                    \"type\": \"str\"\n",
      "                }\n",
      "            ],\n",
      "            \"example_edge\": {\n",
      "                \"_key\": \"266218884025\",\n",
      "                \"_id\": \"ChildOf/266218884025\",\n",
      "                \"_from\": \"Characters/AryaStark\",\n",
      "                \"_to\": \"Characters/NedStark\",\n",
      "                \"_rev\": \"_gVPKGSq---\"\n",
      "            }\n",
      "        },\n",
      "        {\n",
      "            \"collection_name\": \"Characters\",\n",
      "            \"collection_type\": \"document\",\n",
      "            \"document_properties\": [\n",
      "                {\n",
      "                    \"name\": \"_key\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_id\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"_rev\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"name\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"surname\",\n",
      "                    \"type\": \"str\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"alive\",\n",
      "                    \"type\": \"bool\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"age\",\n",
      "                    \"type\": \"int\"\n",
      "                },\n",
      "                {\n",
      "                    \"name\": \"gender\",\n",
      "                    \"type\": \"str\"\n",
      "                }\n",
      "            ],\n",
      "            \"example_document\": {\n",
      "                \"_key\": \"NedStark\",\n",
      "                \"_id\": \"Characters/NedStark\",\n",
      "                \"_rev\": \"_gVPKGPi---\",\n",
      "                \"name\": \"Ned\",\n",
      "                \"surname\": \"Stark\",\n",
      "                \"alive\": true,\n",
      "                \"age\": 41,\n",
      "                \"gender\": \"male\"\n",
      "            }\n",
      "        }\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# We can now view the generated schema\n",
    "\n",
    "import json\n",
    "\n",
    "print(json.dumps(graph.schema, indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a3c677",
   "metadata": {
    "id": "68a3c677"
   },
   "source": [
    "## Querying the ArangoDB Database\n",
    "\n",
    "We can now use the ArangoDB Graph QA Chain to inquire about our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "635c4018",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7476ce98",
   "metadata": {
    "id": "7476ce98"
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.chains import ArangoGraphQAChain\n",
    "\n",
    "chain = ArangoGraphQAChain.from_llm(\n",
    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef8ee27b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "ef8ee27b",
    "outputId": "6008ee92-a3ec-4968-d48d-6a5b66403959"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "WITH Characters\n",
      "FOR character IN Characters\n",
      "FILTER character.name == \"Ned\" AND character.surname == \"Stark\"\n",
      "RETURN character.alive\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Yes, Ned Stark is alive.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Is Ned Stark alive?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9CSig1BgA76q",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "9CSig1BgA76q",
    "outputId": "3060cf15-68e0-4f8a-cdfd-68af3f0e5fbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "WITH Characters\n",
      "FOR character IN Characters\n",
      "FILTER character.name == \"Arya\" && character.surname == \"Stark\"\n",
      "RETURN character.age\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[11]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Arya Stark is 11 years old.'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"How old is Arya Stark?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9Fzdic_pA_4y",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "9Fzdic_pA_4y",
    "outputId": "9bd93580-964e-4c53-e273-6723dad5f375"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "WITH Characters, ChildOf\n",
      "FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
      "    FILTER p.vertices[-1]._key == 'NedStark'\n",
      "    RETURN p\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Are Arya Stark and Ned Stark related?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "zq_oeDpAOXpF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "zq_oeDpAOXpF",
    "outputId": "a47f37b5-4d7b-41c6-fbc7-7b1abc25fa20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "WITH Characters, ChildOf\n",
      "FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
      "FILTER v.alive == false\n",
      "RETURN e\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Does Arya Stark have a dead parent?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ob_3aGauGd7d",
   "metadata": {
    "id": "Ob_3aGauGd7d"
   },
   "source": [
    "## Chain Modifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3P490E2dGiBp",
   "metadata": {
    "id": "3P490E2dGiBp"
   },
   "source": [
    "You can alter the values of the following `ArangoDBGraphQAChain` class variables to modify the behaviour of your chain results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1B9h3PvzJ41T",
   "metadata": {
    "id": "1B9h3PvzJ41T"
   },
   "outputs": [],
   "source": [
    "# Specify the maximum number of AQL Query Results to return\n",
    "chain.top_k = 10\n",
    "\n",
    "# Specify whether or not to return the AQL Query in the output dictionary\n",
    "chain.return_aql_query = True\n",
    "\n",
    "# Specify whether or not to return the AQL JSON Result in the output dictionary\n",
    "chain.return_aql_result = True\n",
    "\n",
    "# Specify the maximum amount of AQL Generation attempts that should be made\n",
    "chain.max_aql_generation_attempts = 5\n",
    "\n",
    "# Specify a set of AQL Query Examples, which are passed to\n",
    "# the AQL Generation Prompt Template to promote few-shot-learning.\n",
    "# Defaults to an empty string.\n",
    "chain.aql_examples = \"\"\"\n",
    "# Is Ned Stark alive?\n",
    "RETURN DOCUMENT('Characters/NedStark').alive\n",
    "\n",
    "# Is Arya Stark the child of Ned Stark?\n",
    "FOR e IN ChildOf\n",
    "    FILTER e._from == \"Characters/AryaStark\" AND e._to == \"Characters/NedStark\"\n",
    "    RETURN e\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49cnjYV-PUv3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209
    },
    "id": "49cnjYV-PUv3",
    "outputId": "f05f0a86-f922-47d1-91b9-5380ef1f996d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "RETURN DOCUMENT('Characters/NedStark').alive\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Yes, according to the information in the database, Ned Stark is alive.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Is Ned Stark alive?\")\n",
    "\n",
    "# chain(\"Is Ned Stark alive?\") # Returns a dictionary with the AQL Query & AQL Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "nWfALJ8dPczE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "id": "nWfALJ8dPczE",
    "outputId": "1235baae-f3f7-438e-ef24-658cd17f727d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
      "AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
      "FOR e IN ChildOf\n",
      "    FILTER e._from == \"Characters/BranStark\" AND e._to == \"Characters/NedStark\"\n",
      "    RETURN e\n",
      "\u001b[0m\n",
      "AQL Result:\n",
      "\u001b[32;1m\u001b[1;3m[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Is Bran Stark the child of Ned Stark?\")"
   ]
  }
 ],
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