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   "source": [
    "# How to use Neo4J with LinkML-Store\n",
    "\n",
    "This how-to guide shows you how to use linkml-store over a Neo4j database. linkml-store is a flexible framework that can be used with different backends, employing simple mappings via LinkML schemas.\n",
    "\n",
    "One of the main use cases here is being able to provide a schema for validating a property graph database, as well as making it easier to map between a property graph database and other representations (tabular/dataframe, JSON/YAML, RDF, RDFstar, OWL, ...)\n",
    "\n",
    "We will try and use some of the same examples as in [neo4j graph concepts guide](https://neo4j.com/docs/getting-started/appendix/graphdb-concepts/), making a simple Movie-oriented KG:\n",
    "\n",
    "![neo4j graph concepts](../images/graph_simple-arr.svg)\n",
    "\n",
    "## Running this how-to guide interactively.\n",
    "\n",
    "This guide is both documentation, and a Jupyter notebook.\n",
    "\n",
    "You can run the notebook locally. You will need a local neo4j database running, with authentication disabled.\n",
    "\n",
    "One way to do this is via Docker:    \n",
    "\n",
    "```bash\n",
    "docker run\n",
    "  --name myneo4j\n",
    "  --publish 7474:7474\n",
    "  --publish 7687:7687\n",
    "  --volume $HOME/neo4j/data/:/data \n",
    "  --volume $HOME/neo4j/logs/:/logs\n",
    "  -e NEO4J_AUTH=none\n",
    "  neo4j\n",
    "```    \n",
    "\n",
    "__IMPORTANT NOTE__ if you run this Notebook locally, it will overwrite the default `neo4j`. If you have the enterprise edition\n",
    "you can modify this notebook to use a different database."
   ]
  },
  {
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    },
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   "source": [
    "from linkml_store import Client\n",
    "\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b508417-2d56-43d9-b28a-7992173944ef",
   "metadata": {},
   "source": [
    "Ensure the database is empty, then connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee3e648ceb6cf9df",
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   "outputs": [],
   "source": [
    "db = client.attach_database(\"neo4j\", \"test\")\n",
    "db.drop()\n",
    "db = client.attach_database(\"neo4j\", \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302323cf-d1df-4424-b1e7-82d96f33dc27",
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   "source": [
    "## Using a schema\n",
    "\n",
    "It is not necessary to defined a LinkML schema in advance, but this has a lot of advantages:\n",
    "\n",
    "- validation\n",
    "- clarity for producers and consumers\n",
    "- declarative mappings and graph projections\n",
    "\n",
    "We'll use a predefined one here. You can see the source schemasheet [here](https://docs.google.com/spreadsheets/d/1oMrzA41tg_nisdWInnqKJrcvv30dOXuwAhznJYYPSB8/edit?gid=1499822522#gid=1499822522)"
   ]
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   "outputs": [],
   "source": [
    "db.load_schema_view(\"input/movies_kg/schema.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b49cc04-03b9-423d-bd5d-f93a39b6da5c",
   "metadata": {},
   "source": [
    "Next we'll visualization this using PlamtUML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "611b504d-a98b-4c61-8fa4-0506f471e820",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:File \"schema.yaml\", line 36, col 15: Unrecognized prefix: rdf\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "gen-plantuml --directory input/movies_kg/diagrams input/movies_kg/schema.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed869e7a-2315-4cd6-8e3b-2fdb37da8076",
   "metadata": {},
   "source": [
    "![UML Schema](input/movies_kg/diagrams/MoviesKG.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a685e92-f048-4ab9-889a-8a0721c626cc",
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   "source": [
    "Note that this is in many respects a \"normal\" UML type schema, with classes and class relationships.\n",
    "\n",
    "Here we define to base classes `Node` and `Edge`. We are treating edges as first-class entities in the model.\n",
    "We are following conventions and using `subject` and `object` for the head and tail nodes.\n",
    "\n",
    "You don't *need* to define your schema this way. In fact it's possible to map **any** LinkML schema to a property graph. You don't need\n",
    "classes like \"Node\" and \"Edge\", and you don't need a reification type model.\n",
    "    \n",
    "But having this kind of \"explicit\" model is easiest to make the relationship to Neo4J easier to grok."
   ]
  },
  {
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   "source": [
    "## Create Node and Edge collections\n",
    "\n",
    "We create two collections, one for nodes, and one for edges.\n",
    "\n",
    "Note: you can organize collections how you like - you could have multiple node collections,\n",
    "(e.g. different collections for persons, movies), and multiple edge collections. However, you can't mix\n",
    "nodes and edges in one collection. "
   ]
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   "source": [
    "node_collection = db.create_collection(\"Node\", alias=\"nodes\", recreate_if_exists=True)\n",
    "edge_collection = db.create_collection(\"Edge\", alias=\"nodes\", recreate_if_exists=True)"
   ]
  },
  {
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   "id": "a2350cc25dde8bf4",
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   "source": [
    "It's necessary to provide some kind of mechanism to indicate which collections are edge collections.\n",
    "This can be inferred from the schema, but here will will make it explicit.\n",
    "\n",
    "We'll use a default edge projections, which assigns special meaning to `subject`, `predicate`, and `object`."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from linkml_store.graphs.graph_map import EdgeProjection\n",
    "\n",
    "edge_collection.metadata.graph_projection = EdgeProjection()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fea7946-9da8-4988-bd24-84b3c4f6860a",
   "metadata": {},
   "source": [
    "## Adding Data"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# For convenience, we assign constants for each of the identifiers in the database\n",
    "TH = \"ACTOR:TH\"\n",
    "RZ = \"ACTOR:RZ\"\n",
    "FG = \"MOVIE:FG\"\n",
    "BTTF = \"MOVIE:BTTF\"\n",
    "FGC = \"CHARACTER:FGC\""
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "2e7ffb73d504d162",
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   "source": [
    "persons = [\n",
    "    { \"id\": TH, \"category\": \"Actor\", \"name\": \"Tom Hanks\"},\n",
    "    { \"id\": RZ, \"category\": \"Director\", \"name\": \"Robert Zemekis\"},\n",
    "]\n",
    "movies = [\n",
    "    { \"id\": FG, \"category\": \"Movie\", \"name\": \"Forest Gump\"},\n",
    "]\n",
    "characters = [\n",
    "    { \"id\": FGC,\"category\": \"Character\", \"name\": \"Forest Gump (Character)\"},\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e1026e66cac7bbe",
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   "source": [
    "edges = [\n",
    "    {\"subject\": RZ, \"predicate\": \"Directed\", \"object\": FG},\n",
    "    {\"subject\": TH, \"predicate\": \"ActedIn\", \"object\": FG, \"plays\": FGC},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddf29869-f9a7-4549-854e-6345777cd807",
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   "source": [
    "# Modify this to experiment with different ways of adding\n",
    "ADD_INDIVIDUALLY = True"
   ]
  },
  {
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   "source": [
    "if ADD_INDIVIDUALLY:\n",
    "    node_collection.insert(persons)\n",
    "    node_collection.insert(movies)\n",
    "    node_collection.insert(characters)\n",
    "    edge_collection.insert(edges)\n",
    "else:\n",
    "    g = {\n",
    "        \"nodes\": persons + movies + characters,\n",
    "        \"edges\": edges\n",
    "    }\n",
    "    db.insert(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8067aaaf-8285-45fa-a8e7-5ec3e9a6a0e4",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "We will visualize the whole database using py2neo, networkx, and matplotlib.\n",
    "This is not as visually attractive as the native neo4j UI but it works better in a notebook:"
   ]
  },
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   "cell_type": "code",
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   "id": "2200cf996924ed23",
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   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from linkml_store.utils.neo4j_utils import draw_neo4j_graph\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "draw_neo4j_graph()\n",
    "plt.title(\"Visualization of simple subset\", fontsize=16)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a778ad2-b827-47c7-a27a-f7363c23e445",
   "metadata": {},
   "source": [
    "Note this doesn't display edge properties (e.g. the \"plays\" edge property between Tom Hanks and Forrest Gump"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffd2ae8-451f-40ac-a16d-3b692d33f757",
   "metadata": {},
   "source": [
    "### Exploring in Neo4j\n",
    "\n",
    "If we go on over to http://localhost:7474/browser/ (if you are running this locally) you can see this in the normal UI:\n",
    "\n",
    "![neo4j screenshot of single edge](../images/neo4j-screenshot-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772cab28-0d76-4e93-9799-47bd99c8d296",
   "metadata": {},
   "source": [
    "## Validation\n",
    "\n",
    "One of the main use cases for LinkML and LinkML-Store is validation using a schema. Most of the rich features of LinkML validation\n",
    "are available for property graphs, we will illustrate some simple ones here.\n",
    "\n",
    "### Validating an existing database\n",
    "\n",
    "First we'll validate the database. Note that by default, validation is retrospective; it's possible to insert invalid data into the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49cafa21-4659-46e7-960e-722106230c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errs = list(db.iter_validate_database())\n",
    "errs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33b0fd0-81ed-449d-913e-f3d0cb85ff1f",
   "metadata": {},
   "source": [
    "Good, no errors!\n",
    "\n",
    "### Prospective Validation\n",
    "\n",
    "We can switch on prospective validation, to prevent any future modifications making the data invalid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ec48ca0-537f-4052-b56c-5d64c1dccb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_collection.metadata.validate_modifications = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c06f325-38dc-4cd7-a017-1b6e6965fff6",
   "metadata": {},
   "source": [
    "Let's test this out by inserting deliberately invalid data -- making up an edge property not defined\n",
    "in the schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0cd26973-3fee-446a-856a-86160fca3191",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got an exception (we expect this!!!)\n",
      "Validation errors: [ValidationResult(type='jsonschema validation', severity=<Severity.ERROR: 'ERROR'>, message=\"Additional properties are not allowed ('date' was unexpected) in /\", instance={'subject': 'ACTOR:RZ', 'predicate': 'Directed', 'object': 'MOVIE:BTTF', 'date': 1985}, instance_index=0, instantiates='Directed', context=[])]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    edge_collection.insert({ \"subject\": RZ, \"predicate\": \"Directed\", \"object\": BTTF, \"date\": 1985})\n",
    "except Exception as e:\n",
    "    print(\"Got an exception (we expect this!!!)\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7941adae-dddc-4fec-a009-b472f43e37af",
   "metadata": {},
   "source": [
    "We can also test referential integrity. By default, inserting an edge that points to a non-existent node will\n",
    "create a dangling node in Neo4j (Neo4j enforces that there must be some kind of node).\n",
    "\n",
    "We will change the policy and try and insert a dangling edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4f305bb-77d6-4b85-b52f-c8dbe2470b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got an exception (we expect this!!!)\n",
      "Node with identifier MOVIE:MADE_UP_NODE not found in the database.\n"
     ]
    }
   ],
   "source": [
    "from linkml_store.api.stores.neo4j.neo4j_collection import DeletePolicy\n",
    "\n",
    "edge_collection.delete_policy = DeletePolicy.ERROR\n",
    "\n",
    "try:\n",
    "    edge_collection.insert({ \"subject\": RZ, \"predicate\": \"Directed\", \"object\": \"MOVIE:MADE_UP_NODE\"})\n",
    "except Exception as e:\n",
    "    print(\"Got an exception (we expect this!!!)\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a42fc3-5be5-4f0b-876e-4fd2d862bef7",
   "metadata": {},
   "source": [
    "## Loading Data from a file\n",
    "\n",
    "Loading data into a graph is very much like loading any other data into a LinkML schema. So long as you conform\n",
    "to the schema we defined above (which has defined graph projections), then it can be loaded into Neo4J.\n",
    "\n",
    "Data could be combined in a single JSON file, but it's also convenient to use tabular or dataframe-oriented formats.\n",
    "\n",
    "Here we will load from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20f5252b-fd29-4a98-96bd-4bf276830471",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00ea1136-bf9f-4230-8ae4-8b2f79ee26cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>category</th>\n",
       "      <th>name</th>\n",
       "      <th>born</th>\n",
       "      <th>released</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>Actor</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>1956</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Director</td>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>1951</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>Actor</td>\n",
       "      <td>Hugo Weaving</td>\n",
       "      <td>1960</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>Actor</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>1964</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>Actor</td>\n",
       "      <td>Michael J. Fox</td>\n",
       "      <td>1961</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>Movie</td>\n",
       "      <td>The Matrix</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MOVIE:TM2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>The Matrix Reloaded</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MOVIE:TM3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>The Matrix Revolutions</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  category                    name  born  released\n",
       "0   PERSON:TH     Actor               Tom Hanks  1956      <NA>\n",
       "1   PERSON:RZ  Director         Robert Zemeckis  1951      <NA>\n",
       "2   PERSON:HW     Actor            Hugo Weaving  1960      <NA>\n",
       "3   PERSON:KR     Actor            Keanu Reeves  1964      <NA>\n",
       "4  PERSON:MJF     Actor          Michael J. Fox  1961      <NA>\n",
       "5    MOVIE:FG     Movie            Forrest Gump  <NA>      1994\n",
       "6    MOVIE:TM     Movie              The Matrix  <NA>      1999\n",
       "7   MOVIE:TM2     Movie     The Matrix Reloaded  <NA>      2003\n",
       "8   MOVIE:TM3     Movie  The Matrix Revolutions  <NA>      2003\n",
       "9  MOVIE:BTTF     Movie      Back to the Future  <NA>      1985"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_df = pd.read_csv(\"input/movies_kg/nodes.csv\").convert_dtypes()\n",
    "nodes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f10f3fa6-4592-43be-8b77-ab2e88c65e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>predicate</th>\n",
       "      <th>object</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:Forrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:Neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>CHARACTER:MartyMcFly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:AgentSmith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject predicate      object                 plays\n",
       "0   PERSON:TH   ActedIn    MOVIE:FG     CHARACTER:Forrest\n",
       "1   PERSON:RZ  Directed    MOVIE:FG                  <NA>\n",
       "2   PERSON:RZ  Directed  MOVIE:BTTF                  <NA>\n",
       "3   PERSON:KR   ActedIn    MOVIE:TM         CHARACTER:Neo\n",
       "4  PERSON:MJF   ActedIn  MOVIE:BTTF  CHARACTER:MartyMcFly\n",
       "5   PERSON:HW   ActedIn    MOVIE:TM  CHARACTER:AgentSmith"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_df = pd.read_csv(\"input/movies_kg/edges.csv\").convert_dtypes()\n",
    "edges_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7dc5d74d-7418-4463-b6c1-87cddaa95bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "node_collection.insert(nodes_df.to_dict(orient=\"records\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fc18281-f930-4c93-aed6-b34cefec9d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: use type designator in validation \n",
    "edge_collection.metadata.validate_modifications = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "53bb3854-c581-4d2f-94b3-d4629e5bb4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_collection.insert(edges_df.to_dict(orient=\"records\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf4eca42-07bf-4f6a-bd59-0945f278a0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "draw_neo4j_graph()\n",
    "plt.title(\"Visualization of whole database\", fontsize=16)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973e1da4-242a-4de0-9752-f5f3fb36522a",
   "metadata": {},
   "source": [
    "The above display is a little cluttered.\n",
    "\n",
    "If you are running this notebook locally you can head over to your neo4j and explore via interactive views like this:\n",
    "\n",
    "![neo4j screenshot of more edges](../images/neo4j-screenshot-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be5ce56-2d9b-470f-a708-df929bbba9f2",
   "metadata": {},
   "source": [
    "## Queries\n",
    "\n",
    "The graph can be queried the same as any other database in linkml-store. Note that at this time we don't expose a graph-oriented API,\n",
    "we instead expose everything as node or edge collections. This has some advantages and disadvantages. The goal is not to replace something\n",
    "like Cypher (you can and should of course just do Cypher queries over the underlying neoj4 database if and when you need to).\n",
    "\n",
    "Let's start by querying for the entire contents of both collections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c1eb5f66-2b3a-4670-8a58-8a6a6b097364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>category</th>\n",
       "      <th>born</th>\n",
       "      <th>released</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Forest Gump</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest Gump (Character)</td>\n",
       "      <td>CHARACTER:FGC</td>\n",
       "      <td>Character</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Director</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hugo Weaving</td>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Michael J. Fox</td>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>Actor</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>ACTOR:TH</td>\n",
       "      <td>Actor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Robert Zemekis</td>\n",
       "      <td>ACTOR:RZ</td>\n",
       "      <td>Director</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>The Matrix Reloaded</td>\n",
       "      <td>MOVIE:TM2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The Matrix Revolutions</td>\n",
       "      <td>MOVIE:TM3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name             id   category    born  released\n",
       "0               Forest Gump       MOVIE:FG      Movie     NaN       NaN\n",
       "1   Forest Gump (Character)  CHARACTER:FGC  Character     NaN       NaN\n",
       "2                 Tom Hanks      PERSON:TH      Actor  1956.0       NaN\n",
       "3           Robert Zemeckis      PERSON:RZ   Director  1951.0       NaN\n",
       "4              Hugo Weaving      PERSON:HW      Actor  1960.0       NaN\n",
       "5              Keanu Reeves      PERSON:KR      Actor  1964.0       NaN\n",
       "6            Michael J. Fox     PERSON:MJF      Actor  1961.0       NaN\n",
       "7              Forrest Gump       MOVIE:FG      Movie     NaN    1994.0\n",
       "8        Back to the Future     MOVIE:BTTF      Movie     NaN    1985.0\n",
       "9                 Tom Hanks       ACTOR:TH      Actor     NaN       NaN\n",
       "10               The Matrix       MOVIE:TM      Movie     NaN    1999.0\n",
       "11           Robert Zemekis       ACTOR:RZ   Director     NaN       NaN\n",
       "12      The Matrix Reloaded      MOVIE:TM2      Movie     NaN    2003.0\n",
       "13   The Matrix Revolutions      MOVIE:TM3      Movie     NaN    2003.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_collection.find().rows_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eece59f6-b415-4c04-8234-251685e7f737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>predicate</th>\n",
       "      <th>object</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACTOR:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACTOR:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:FGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:Forrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:Forrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PERSON:RZ</td>\n",
       "      <td>Directed</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:Neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>CHARACTER:MartyMcFly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:AgentSmith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject predicate      object                 plays\n",
       "0    ACTOR:RZ  Directed    MOVIE:FG                   NaN\n",
       "1    ACTOR:TH   ActedIn    MOVIE:FG         CHARACTER:FGC\n",
       "2   PERSON:TH   ActedIn    MOVIE:FG     CHARACTER:Forrest\n",
       "3   PERSON:TH   ActedIn    MOVIE:FG     CHARACTER:Forrest\n",
       "4   PERSON:RZ  Directed    MOVIE:FG                   NaN\n",
       "5   PERSON:RZ  Directed    MOVIE:FG                   NaN\n",
       "6   PERSON:RZ  Directed  MOVIE:BTTF                   NaN\n",
       "7   PERSON:KR   ActedIn    MOVIE:TM         CHARACTER:Neo\n",
       "8  PERSON:MJF   ActedIn  MOVIE:BTTF  CHARACTER:MartyMcFly\n",
       "9   PERSON:HW   ActedIn    MOVIE:TM  CHARACTER:AgentSmith"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_collection.find().rows_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08de72d0-149a-4252-992e-7ae4a3bc35fc",
   "metadata": {},
   "source": [
    "We can also use standard key-value mongodb-like queries; over nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9784409a-5629-495e-bb94-b799b01abf62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>born</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1956.0</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>Actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>Hugo Weaving</td>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>Actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964.0</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>Actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961.0</td>\n",
       "      <td>Michael J. Fox</td>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>Actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>ACTOR:TH</td>\n",
       "      <td>Actor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     born            name          id category\n",
       "0  1956.0       Tom Hanks   PERSON:TH    Actor\n",
       "1  1960.0    Hugo Weaving   PERSON:HW    Actor\n",
       "2  1964.0    Keanu Reeves   PERSON:KR    Actor\n",
       "3  1961.0  Michael J. Fox  PERSON:MJF    Actor\n",
       "4     NaN       Tom Hanks    ACTOR:TH    Actor"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_collection.find({\"category\": \"Actor\"}).rows_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9c0dc0-92a4-49d1-9d94-d969fe48e7ec",
   "metadata": {},
   "source": [
    "over edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c6bfb063-fbb7-478a-8741-1d2fc000b781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>predicate</th>\n",
       "      <th>object</th>\n",
       "      <th>plays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACTOR:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:FGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:Forrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PERSON:TH</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:FG</td>\n",
       "      <td>CHARACTER:Forrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PERSON:KR</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:Neo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PERSON:MJF</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:BTTF</td>\n",
       "      <td>CHARACTER:MartyMcFly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PERSON:HW</td>\n",
       "      <td>ActedIn</td>\n",
       "      <td>MOVIE:TM</td>\n",
       "      <td>CHARACTER:AgentSmith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject predicate      object                 plays\n",
       "0    ACTOR:TH   ActedIn    MOVIE:FG         CHARACTER:FGC\n",
       "1   PERSON:TH   ActedIn    MOVIE:FG     CHARACTER:Forrest\n",
       "2   PERSON:TH   ActedIn    MOVIE:FG     CHARACTER:Forrest\n",
       "3   PERSON:KR   ActedIn    MOVIE:TM         CHARACTER:Neo\n",
       "4  PERSON:MJF   ActedIn  MOVIE:BTTF  CHARACTER:MartyMcFly\n",
       "5   PERSON:HW   ActedIn    MOVIE:TM  CHARACTER:AgentSmith"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_collection.find({\"predicate\": \"ActedIn\"}).rows_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3479c326-4fd2-4c61-a5ee-0ecc179e11f7",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f6a1caa0-fc53-4f07-8ffe-43e2f380a04a",
   "metadata": {},
   "source": [
    "## Facet Counts\n",
    "\n",
    "We can also do basic analytic queries such as Solr-style facet grouping and counting.\n",
    "\n",
    "We'll make use of a lightweight function that converts linkml-store facet counts to dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2cc9203c-7049-4e40-8bbc-affa3363f95c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from linkml_store.utils.pandas_utils import facet_summary_to_dataframe_unmelted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97fdc974-a403-4f0e-bcf3-9657d773048b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Movie</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Actor</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Director</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Character</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category  Value\n",
       "0      Movie      6\n",
       "1      Actor      5\n",
       "2   Director      2\n",
       "3  Character      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = node_collection.query_facets(facet_columns=[\"category\"])\n",
    "facet_summary_to_dataframe_unmelted(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e44dc44b-16a2-483c-94b4-8385d18b5b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: implement facet counts for edges\n",
    "#r = edge_collection.query_facets(facet_columns=[\"subject\", \"predicate\", \"object\"])\n",
    "#facet_summary_to_dataframe_unmelted(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d81abb-0610-44b4-b895-24b21afa2d3e",
   "metadata": {},
   "source": [
    "## Indexing via LLM embeddings\n",
    "\n",
    "Neo4j can be indexed and search via LLM embeddings, just like any other linkml-store wrapped database.\n",
    "\n",
    "__TODO__ show examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "499ac4ea-989a-4ba7-b417-a84721fb0a6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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