knowledge graphs/knowledge_graph_rebel_vs_llamaindex.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Building Knowledge Graphs: REBEL, LlamaIndex, and REBEL + LlamaIndex\n",
        "\n"
      ],
      "metadata": {
        "id": "P6NoSS9W-n2L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we'll explore how knowledge graphs are constructed using three approaches: REBEL, LlamaIndex, and a combination of REBEL + LlamaIndex. Our primary focus will be on evaluating the resulting triplets and their count from each method. While I've also demonstrated querying the knowledge graphs using LlamaIndex's Knowledge Graph Query Engine, our main emphasis remains on the building process."
      ],
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        "id": "ydGP1c_F-oq4"
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      "execution_count": null,
      "metadata": {
        "id": "mSMKLM3qW75N"
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      "outputs": [],
      "source": [
        "!pip install python-dotenv\n",
        "!pip install datasets\n",
        "!pip install langchain\n",
        "!pip install transformers\n",
        "!pip install neo4j\n",
        "!pip install llama-index\n",
        "!pip install ipython-ngql nebula3-python networkx pyvis\n",
        "!pip install torch\n",
        "!pip install -U huggingface_hub"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oh0lujL2XOzp"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we're setting up essential tools and libraries. We'll use these for handling datasets, tokenizing input, managing text chunks, and working with sequence-to-sequence language models."
      ],
      "metadata": {
        "id": "ojh8MaB1_mDn"
      }
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      "outputs": [],
      "source": [
        "import os\n",
        "import random\n",
        "import json\n",
        "import hashlib\n",
        "from datasets import load_dataset\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
      ]
    },
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            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
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              "Generating test split: 0 examples [00:00, ? examples/s]"
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      "source": [
        "validation_data, test_data = load_dataset(\"suolyer/pile_wikipedia\", split=['validation', 'test'])"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0jNAaY9ig_pR"
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      "source": [
        "data = []\n",
        "random_rows = random.sample(range(len(test_data)), 10)\n",
        "build_data = [test_data[val]['text'] for val in random_rows]"
      ]
    },
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            "text/plain": [
              "'Sébastien Pan\\n\\nSébastien Pan (born 9 July 1984) is a French composer and musician, best known for his work on motion picture and animated TV series.\\n\\nBiography \\nBorn in Montbéliard, France, Sébastien Pan gained experience writing music for motion pictures, animated TV series and TV commercials at Imaginex Studios, an international award winning audio post-production house.\\n\\nBesides writing for live action movies and TV series, Sebastien began his collaboration with the director Wang YunFei in scoring the animation movie \"Yugo & Lala\" (aka Ava & Lala) in 2012, followed by \"Yugo & Lala 2\" in 2014 and \"Kwai Boo, Crazy space adventure\" in 2015. \"Kwai Boo\" marks the first time a Chinese animation project has received investment from a Hollywood giant, in this case 20th Century Fox.\\n\\nFilmography\\n\\nFilm\\n\\nTelevision\\n\\nTV Commercials\\nPan also scored more than 30 international TV commercials and worked with renowned advertising agencies such as Saatchi and Saatchi, Leo Burnett Worldwide, DDB, the Agency…\\n\\nBook Soundtrack\\nIn 2013, he wrote a 52-minute symphonic piece based on the fantasy novel \"Autre Monde – L\\'Alliance des Trois\" written by the best selling French author Maxime Chattam.\\n\\nReferences\\n\\nExternal links\\n \\n Official website\\n\\nCategory:1984 births\\nCategory:Living people\\nCategory:People from Montbéliard\\nCategory:French composers'"
            ]
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      "source": [
        "build_data[0]"
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      "source": [
        "max1 = -1\n",
        "ans = \"\"\n",
        "for val in build_data:\n",
        "  if len(val.split()) > max1:\n",
        "    max1 = len(val.split())\n",
        "    ans = val\n",
        "max1"
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            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
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          "metadata": {},
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      ],
      "source": [
        "m = hashlib.md5()\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0314CXCMjD6c"
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      "source": [
        "def bert_len(text):\n",
        "    tokens = tokenizer.encode(text)\n",
        "    return len(tokens)\n",
        "\n",
        "def create_chunk_dataset(content):\n",
        "      m.update(content.encode('utf-8'))\n",
        "      uid = m.hexdigest()[:12]\n",
        "      text_splitter = RecursiveCharacterTextSplitter(\n",
        "          chunk_size = 400,\n",
        "          chunk_overlap  = 40,\n",
        "          length_function = bert_len,\n",
        "          separators=['\\n\\n', '\\n', ' ', ''],\n",
        "      )\n",
        "      chunks = text_splitter.split_text(content)\n",
        "      for i, chunk in enumerate(chunks):\n",
        "          data.append({\n",
        "              'id': f'{uid}-{i}',\n",
        "              'text': chunk\n",
        "          })\n",
        "\n",
        "for dt in build_data:\n",
        "    create_chunk_dataset(dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R348MMMYkymk"
      },
      "outputs": [],
      "source": [
        "filename = '../data/knowledge graphs/rebel_llamaindex/wiki_chunks.jsonl'\n",
        "# save\n",
        "with open(filename, 'w') as outfile:\n",
        "    for x in data:\n",
        "        outfile.write(json.dumps(x) + '\\n')\n",
        "\n",
        "# load\n",
        "# data = []\n",
        "# with open(filename, 'r') as f:\n",
        "#     for line in tqdm(f):\n",
        "#         val = json.loads(line)\n",
        "#         data.append(val)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_i3SHkRRXTRf"
      },
      "source": [
        "## REBEL: Relation Extraction By End-to-end Language generation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we extract relation triplets from given text using the REBEL model. A utility function extract_triplets  is defined to parse the model's output and extract relation triplets.  Also the tokenizer and model are initialized from Babelscape/rebel-large."
      ],
      "metadata": {
        "id": "hp7KNU0U_sp1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e848gZdhkykg"
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      "outputs": [],
      "source": [
        "def extract_triplets(text):\n",
        "    triplets = []\n",
        "    relation, subject, relation, object_ = '', '', '', ''\n",
        "    text = text.strip()\n",
        "    current = 'x'\n",
        "    for token in text.replace(\"<s>\", \"\").replace(\"<pad>\", \"\").replace(\"</s>\", \"\").split():\n",
        "        if token == \"<triplet>\":\n",
        "            current = 't'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "                relation = ''\n",
        "            subject = ''\n",
        "        elif token == \"<subj>\":\n",
        "            current = 's'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "            object_ = ''\n",
        "        elif token == \"<obj>\":\n",
        "            current = 'o'\n",
        "            relation = ''\n",
        "        else:\n",
        "            if current == 't':\n",
        "                subject += ' ' + token\n",
        "            elif current == 's':\n",
        "                object_ += ' ' + token\n",
        "            elif current == 'o':\n",
        "                relation += ' ' + token\n",
        "    if subject != '' and relation != '' and object_ != '':\n",
        "        triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "    return triplets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "005KFZ2jqShH",
        "outputId": "0203e5f6-9c2f-4644-8c8c-9b737f1353c5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3eded1c5a1248fd90f7a71ce90476fb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da0d710ed03d453684420b022cd0344e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "13d6c968a1ac4d8abde89e0537dd7826",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "593522da367140eabe50c85371b2cec9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6ec16dae9b04e41b3a3808460d520cf",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/123 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "90c18bb120a94daa95b202f56b8934e6",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/344 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "2258171b320d4554a3b7f140e7a8de24",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a8d32140ce24bcda6526227b06ef7df",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"Babelscape/rebel-large\")\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"Babelscape/rebel-large\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gZklR_U9wocI"
      },
      "outputs": [],
      "source": [
        "gen_kwargs = {\n",
        "    \"max_length\": 256,\n",
        "    \"length_penalty\": 0,\n",
        "    \"num_beams\": 3,\n",
        "    \"num_return_sequences\": 1,\n",
        "}\n",
        "\n",
        "triples = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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          ]
        },
        "id": "LKM3HJCOkyey",
        "outputId": "2365a7b6-0129-4644-ee7d-3dce060f208b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba6986cdfed14ac387d1359470fb380e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def generate_triples(texts):\n",
        "\n",
        "  model_inputs = tokenizer(texts, max_length=512, padding=True, truncation=True, return_tensors='pt')\n",
        "  generated_tokens = model.generate(\n",
        "      model_inputs[\"input_ids\"].to(model.device),\n",
        "      attention_mask=model_inputs[\"attention_mask\"].to(model.device),\n",
        "      **gen_kwargs\n",
        "  )\n",
        "  decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False)\n",
        "  for idx, sentence in enumerate(decoded_preds):\n",
        "      et = extract_triplets(sentence)\n",
        "      for t in et:\n",
        "        triples.append((t['head'], t['type'], t['tail']))\n",
        "\n",
        "for i in tqdm(range(0, len(data), 2)):\n",
        "  try:\n",
        "    texts = [data[i]['text'], data[i+1]['text']]\n",
        "  except:\n",
        "    texts = [data[i]['text']]\n",
        "  generate_triples(texts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qXidOeACzVdu"
      },
      "outputs": [],
      "source": [
        "distinct_triples = list(set(triples))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qDucsBG7zWNE"
      },
      "outputs": [],
      "source": [
        "# save\n",
        "with open('../data/knowledge graphs/rebel_llamaindex/rebel_triples.json', 'w') as file:\n",
        "    json.dump(distinct_triples, file)\n",
        "\n",
        "# load\n",
        "with open('../data/knowledge graphs/rebel_llamaindex/rebel_triples.json', 'r') as file:\n",
        "    loaded_triples = json.load(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wk13Om6EXMgz",
        "outputId": "98da227c-9198-40bf-ac67-00f3aaad5d2e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[['Edward III', 'child', 'John of Gaunt'],\n",
              " ['Playing God', 'cast member', 'David Duchovny'],\n",
              " [\"Union–Republican People's Commissariat of the Armed Forces of the Soviet Union\",\n",
              "  'replaces',\n",
              "  \"People's Commissariat of the Navy of the Soviet\"],\n",
              " ['Somerset County, Pennsylvania',\n",
              "  'located in the administrative territorial entity',\n",
              "  'Pennsylvania'],\n",
              " ['1860 United States presidential election', 'candidate', 'Abraham Lincoln']]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "loaded_triples[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJWZ3rgT7hgH",
        "outputId": "06617bfd-90d4-4db1-92fe-8e61e7b9b4fc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(loaded_triples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KECx3eYS7hgI"
      },
      "source": [
        "## Nebula Graph"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NebulaGraph is an open-source distributed, scalable, and high-performance graph database designed to manage vast amounts of interconnected data. NebulaGraph has been widely used for social media, recommendation systems, knowledge graphs, security, capital flows, fraud detection, AI, etc."
      ],
      "metadata": {
        "id": "oMkWTiYf_0KA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k_xF2Jfb7hgI",
        "outputId": "68da59fd-8126-4edc-9d8a-f1529e5c84cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:llama_index.indices.service_context:chunk_size_limit is deprecated, please specify chunk_size instead\n"
          ]
        }
      ],
      "source": [
        "from dotenv import load_dotenv\n",
        "load_dotenv()\n",
        "os.environ[\"OPENAI_API_KEY\"] = os.getenv('OPENAI_API_KEY')\n",
        "\n",
        "import logging\n",
        "import sys\n",
        "\n",
        "logging.basicConfig(\n",
        "    stream=sys.stdout, level=logging.INFO\n",
        ")  # logging.DEBUG for more verbose output\n",
        "\n",
        "from llama_index import (\n",
        "    KnowledgeGraphIndex,\n",
        "    LLMPredictor,\n",
        "    ServiceContext,\n",
        "    SimpleDirectoryReader,\n",
        ")\n",
        "from llama_index.storage.storage_context import StorageContext\n",
        "from llama_index.graph_stores import NebulaGraphStore\n",
        "from llama_index.llms import OpenAI\n",
        "\n",
        "from IPython.display import Markdown, display\n",
        "\n",
        "\n",
        "# define LLM\n",
        "# NOTE: at the time of demo, text-davinci-002 did not have rate-limit errors\n",
        "llm = OpenAI(temperature=0, model=\"text-davinci-002\")\n",
        "service_context = ServiceContext.from_defaults(llm=llm, chunk_size_limit=512)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_RdE016Z7hgN"
      },
      "outputs": [],
      "source": [
        "os.environ[\"NEBULA_USER\"] = \"root\"\n",
        "os.environ[\"NEBULA_PASSWORD\"] = \"nebula\"  # default is \"nebula\"\n",
        "os.environ[\n",
        "    \"NEBULA_ADDRESS\"\n",
        "] = \"127.0.0.1:9669\"\n",
        "\n",
        "space_name = \"llamaindex\"\n",
        "edge_types, rel_prop_names = [\"relationship\"], [\n",
        "    \"relationship\"\n",
        "]  # default, could be omit if create from an empty kg\n",
        "tags = [\"entity\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pwT-yUaR7hgN"
      },
      "outputs": [],
      "source": [
        "graph_store = NebulaGraphStore(\n",
        "    space_name=space_name,\n",
        "    edge_types=edge_types,\n",
        "    rel_prop_names=rel_prop_names,\n",
        "    tags=tags,\n",
        ")\n",
        "storage_context = StorageContext.from_defaults(graph_store=graph_store)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJfnMtceXeIp"
      },
      "source": [
        "## LlamaIndex 🦙"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LlamaIndex is an open-source project designed to facilitate in-context learning. The toolkit offers data loaders that serialize diverse knowledge sources like PDFs, Wikipedia pages, and Twitter into a standardized format, eliminating the need for manual preprocessing. With a single code line, LlamaIndex aids in generating and storing embeddings, be it in memory or vector databases. In addition to VectorStoreIndex we have KnowledgeGraphIndex which automates the construction of knowledge graphs from raw text and enables precise entity-based querying. This capability enhances search efficiency, especially in contexts requiring broader, cross-node information."
      ],
      "metadata": {
        "id": "0K46xUX4_6r-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, the data is loaded into the system using LlamaIndex's SimpleDirectoryReader, which reads documents from a specified directory."
      ],
      "metadata": {
        "id": "KGLUCciyAPrz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2iolJnnXMY5"
      },
      "outputs": [],
      "source": [
        "from llama_index import SimpleDirectoryReader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iIF-5ZhBXMWQ",
        "outputId": "5bdfe3be-77a7-4871-844f-291109ac8572"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded 23 docs\n"
          ]
        }
      ],
      "source": [
        "reader = SimpleDirectoryReader(input_dir=\"../data/knowledge graphs/rebel_llamaindex/wiki/\")\n",
        "documents = reader.load_data()\n",
        "print(f\"Loaded {len(documents)} docs\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A Knowledge Graph index, kg_index, is then constructed using these documents. For each document, a maximum of 5 triplets is extracted. The include_embeddings=True  parameter ensures that semantic embeddings of the knowledge graph's  nodes and edges are also included in the index, facilitating  semantically-driven queries in the future."
      ],
      "metadata": {
        "id": "psLmpRJH__YI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bC5fuYAlXMPx"
      },
      "outputs": [],
      "source": [
        "kg_index = KnowledgeGraphIndex.from_documents(\n",
        "    documents,\n",
        "    storage_context=storage_context,\n",
        "    max_triplets_per_chunk=5,\n",
        "    service_context=service_context,\n",
        "    space_name=space_name,\n",
        "    edge_types=edge_types,\n",
        "    rel_prop_names=rel_prop_names,\n",
        "    tags=tags,\n",
        "    include_embeddings=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After connecting to the local instance of NebulaGraph, now we query  the llamaindex  space using Nebula Graph Query Language and fetch ten relationship triplets from the constructed knowledge  graph and also count all such relationships present in the database."
      ],
      "metadata": {
        "id": "1XFC8OlyAS5q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "avNva6quXMNU",
        "outputId": "817efb77-14e5-4d07-f484-405d361fe6a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Connection Pool Created\n",
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>llamaindex</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Name\n",
              "0  llamaindex"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%load_ext ngql\n",
        "%ngql --address 127.0.0.1 --port 9669 --user root --password nebula"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiBEp-s57hgP",
        "outputId": "2bd63d35-feb3-4f93-c92c-d001b8467867"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%ngql USE llamaindex;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "18JXUClD7hgP",
        "outputId": "c4d1af96-1fe4-4ca0-ac70-275b01feffc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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>m_entity</th>\n",
              "      <th>relationship</th>\n",
              "      <th>n_entity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Yale Law School faculty</td>\n",
              "      <td>is category of</td>\n",
              "      <td>people</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tyler and Huo</td>\n",
              "      <td>2002</td>\n",
              "      <td>is based on surveys of people in different eth...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tom R. Tyler</td>\n",
              "      <td>is</td>\n",
              "      <td>author or co-author of 9 books</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tom R. Tyler</td>\n",
              "      <td>is</td>\n",
              "      <td>known for contributions to understanding why p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tom R. Tyler</td>\n",
              "      <td>is</td>\n",
              "      <td>professor of psychology and law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Time to Get Alone</td>\n",
              "      <td>is song written by</td>\n",
              "      <td>Brian Wilson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Time to Get Alone</td>\n",
              "      <td>is produced by</td>\n",
              "      <td>Carl Wilson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Time to Get Alone</td>\n",
              "      <td>is released on</td>\n",
              "      <td>20/20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>best known for</td>\n",
              "      <td>work on motion picture and animated TV series</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>gained experience writing music for</td>\n",
              "      <td>motion pictures</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  m_entity                         relationship  \\\n",
              "0  Yale Law School faculty                       is category of   \n",
              "1            Tyler and Huo                                 2002   \n",
              "2             Tom R. Tyler                                   is   \n",
              "3             Tom R. Tyler                                   is   \n",
              "4             Tom R. Tyler                                   is   \n",
              "5        Time to Get Alone                   is song written by   \n",
              "6        Time to Get Alone                       is produced by   \n",
              "7        Time to Get Alone                       is released on   \n",
              "8            Sébastien Pan                       best known for   \n",
              "9            Sébastien Pan  gained experience writing music for   \n",
              "\n",
              "                                            n_entity  \n",
              "0                                             people  \n",
              "1  is based on surveys of people in different eth...  \n",
              "2                     author or co-author of 9 books  \n",
              "3  known for contributions to understanding why p...  \n",
              "4                    professor of psychology and law  \n",
              "5                                       Brian Wilson  \n",
              "6                                        Carl Wilson  \n",
              "7                                              20/20  \n",
              "8      work on motion picture and animated TV series  \n",
              "9                                    motion pictures  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%ngql MATCH (m)-[e]->(n) RETURN m.entity.name AS m_entity,e.relationship AS relationship,n.entity.name AS n_entity LIMIT 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nZAhBEIn7hgQ",
        "outputId": "75244b64-4609-4089-acb3-666a736932a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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>COUNT(*)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   COUNT(*)\n",
              "0        92"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%ngql MATCH (m)-[e]->(n) RETURN COUNT(*)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ysJ5XSfB7hgQ"
      },
      "outputs": [],
      "source": [
        "from llama_index.query_engine import KnowledgeGraphQueryEngine\n",
        "\n",
        "from llama_index.storage.storage_context import StorageContext\n",
        "from llama_index.graph_stores import NebulaGraphStore\n",
        "\n",
        "query_engine = KnowledgeGraphQueryEngine(\n",
        "    storage_context=storage_context,\n",
        "    service_context=service_context,\n",
        "    llm=llm,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now run a simple query."
      ],
      "metadata": {
        "id": "TiWOIgGgAWvz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U4PGsklA7hgQ",
        "outputId": "1030efe9-9330-47cb-810f-c3a492904073"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;33mGraph Store Query:\n",
            "```\n",
            "MATCH (p:`entity`)-[:relationship]->(m:`entity`) WHERE p.`entity`.`name` == 'Sébastien Pan'\n",
            "RETURN m.`entity`.`name`;\n",
            "```\n",
            "\u001b[0m\u001b[1;3;33mGraph Store Response:\n",
            "{'m.entity.name': ['work on motion picture and animated TV series', 'motion pictures', 'composer', 'musician']}\n",
            "\u001b[0m\u001b[1;3;32mFinal Response: \n",
            "\n",
            "Sébastien Pan is a composer and musician who works on motion pictures and animated TV series.\n",
            "\u001b[0m"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>\n",
              "\n",
              "Sébastien Pan is a composer and musician who works on motion pictures and animated TV series.</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "response = query_engine.query(\n",
        "    \"Tell me about Sébastien Pan?\",\n",
        ")\n",
        "display(Markdown(f\"<b>{response}</b>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "plntdvAh7hgR",
        "outputId": "673c3047-1208-468e-8750-bfd6d4deb66e"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "\n",
              "```cypher\n",
              "```\n",
              "MATCH (p:`entity`)-[:relationship]->(m:`entity`) \n",
              "  WHERE p.`entity`.`name` == 'Sébastien Pan'\n",
              "\n",
              "RETURN m.`entity`.`name`;\n",
              "```\n",
              "```\n"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "graph_query = query_engine.generate_query(\n",
        "    \"Tell me about Sébastien Pan?\",\n",
        ")\n",
        "\n",
        "graph_query = graph_query.replace(\"WHERE\", \"\\n  WHERE\").replace(\"RETURN\", \"\\nRETURN\")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        f\"\"\"\n",
        "```cypher\n",
        "{graph_query}\n",
        "```\n",
        "\"\"\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-MrJvPX7hgR",
        "outputId": "d66571c6-df74-4c43-dd92-be9805a7d375"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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>p.entity.name</th>\n",
              "      <th>e.relationship</th>\n",
              "      <th>m.entity.name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>best known for</td>\n",
              "      <td>work on motion picture and animated TV series</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>gained experience writing music for</td>\n",
              "      <td>motion pictures</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>is</td>\n",
              "      <td>composer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sébastien Pan</td>\n",
              "      <td>is</td>\n",
              "      <td>musician</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   p.entity.name                       e.relationship  \\\n",
              "0  Sébastien Pan                       best known for   \n",
              "1  Sébastien Pan  gained experience writing music for   \n",
              "2  Sébastien Pan                                   is   \n",
              "3  Sébastien Pan                                   is   \n",
              "\n",
              "                                   m.entity.name  \n",
              "0  work on motion picture and animated TV series  \n",
              "1                                motion pictures  \n",
              "2                                       composer  \n",
              "3                                       musician  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%ngql\n",
        "MATCH (p:`entity`)-[e:relationship]->(m:`entity`)\n",
        "  WHERE p.`entity`.`name` == 'Sébastien Pan'\n",
        "RETURN p.`entity`.`name`, e.relationship, m.`entity`.`name`;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5twJei4F7hgS"
      },
      "source": [
        "## REBEL + LlamaIndex 🦙"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's establish a new space rebel_llamaindex, which leverages the capabilities of both REBEL and LlamaIndex to build a knowledge graph."
      ],
      "metadata": {
        "id": "oqPrO9LxAZ2W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2orlDszF7hgS"
      },
      "outputs": [],
      "source": [
        "os.environ[\"NEBULA_USER\"] = \"root\"\n",
        "os.environ[\"NEBULA_PASSWORD\"] = \"nebula\"  # default is \"nebula\"\n",
        "os.environ[\n",
        "    \"NEBULA_ADDRESS\"\n",
        "] = \"127.0.0.1:9669\"\n",
        "\n",
        "space_name = \"rebel_llamaindex\"\n",
        "edge_types, rel_prop_names = [\"relationship\"], [\n",
        "    \"relationship\"\n",
        "]  # default, could be omit if create from an empty kg\n",
        "tags = [\"entity\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XsP_abtg7hgS"
      },
      "outputs": [],
      "source": [
        "graph_store = NebulaGraphStore(\n",
        "    space_name=space_name,\n",
        "    edge_types=edge_types,\n",
        "    rel_prop_names=rel_prop_names,\n",
        "    tags=tags,\n",
        ")\n",
        "storage_context = StorageContext.from_defaults(graph_store=graph_store)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3vMRbM4o7hgT",
        "outputId": "8ec01f16-9989-4965-8a94-4cfbe280b6b8",
        "colab": {
          "referenced_widgets": [
            "cbb7f5be235d4115957727c8a7aa7bd5",
            "84555ec792ab475887d3c9108490e38d",
            "e66c2e0ebfe44b6f84459cf8faab3bda",
            "7d5adb77b4ec48028b4b0d5822408a97",
            "835e1b32a0c44524b3dcea9da8361177",
            "cdea483db95649c38840323611aa107d",
            "af9720935cef4611b24d5f97d5392f65",
            "b3c823527aee4f58bec10e2406beb87b"
          ]
        }
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbb7f5be235d4115957727c8a7aa7bd5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "84555ec792ab475887d3c9108490e38d",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e66c2e0ebfe44b6f84459cf8faab3bda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d5adb77b4ec48028b4b0d5822408a97",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cdea483db95649c38840323611aa107d",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af9720935cef4611b24d5f97d5392f65",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/123 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3c823527aee4f58bec10e2406beb87b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/344 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "triplet_extractor = pipeline('text2text-generation', model='Babelscape/rebel-large', tokenizer='Babelscape/rebel-large')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CQObtpG27hgT"
      },
      "outputs": [],
      "source": [
        "def extract_triplets(input_text):\n",
        "    text = triplet_extractor.tokenizer.batch_decode([triplet_extractor(input_text, return_tensors=True, return_text=False)[0][\"generated_token_ids\"]])[0]\n",
        "\n",
        "    triplets = []\n",
        "    relation, subject, relation, object_ = '', '', '', ''\n",
        "    text = text.strip()\n",
        "    current = 'x'\n",
        "    for token in text.replace(\"<s>\", \"\").replace(\"<pad>\", \"\").replace(\"</s>\", \"\").split():\n",
        "        if token == \"<triplet>\":\n",
        "            current = 't'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "                relation = ''\n",
        "            subject = ''\n",
        "        elif token == \"<subj>\":\n",
        "            current = 's'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "            object_ = ''\n",
        "        elif token == \"<obj>\":\n",
        "            current = 'o'\n",
        "            relation = ''\n",
        "        else:\n",
        "            if current == 't':\n",
        "                subject += ' ' + token\n",
        "            elif current == 's':\n",
        "                object_ += ' ' + token\n",
        "            elif current == 'o':\n",
        "                relation += ' ' + token\n",
        "    if subject != '' and relation != '' and object_ != '':\n",
        "        triplets.append((subject.strip(), relation.strip(), object_.strip()))\n",
        "\n",
        "    return triplets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTv3xiLh7hgT"
      },
      "outputs": [],
      "source": [
        "rebel_kg_index = KnowledgeGraphIndex.from_documents(\n",
        "    documents,\n",
        "    kg_triplet_extract_fn=extract_triplets,\n",
        "    storage_context=storage_context,\n",
        "    max_triplets_per_chunk=5,\n",
        "    service_context=service_context,\n",
        "    space_name=space_name,\n",
        "    edge_types=edge_types,\n",
        "    rel_prop_names=rel_prop_names,\n",
        "    tags=tags,\n",
        "    include_embeddings=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_c6lUrA77hgU",
        "outputId": "41cba725-8d00-4d4a-bf58-b2a1e2e3f232"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The ngql extension is already loaded. To reload it, use:\n",
            "  %reload_ext ngql\n",
            "Connection Pool Created\n",
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>rebel_llamaindex</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Name\n",
              "0  rebel_llamaindex"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%load_ext ngql\n",
        "%ngql --address 127.0.0.1 --port 9669 --user root --password nebula"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rmtorjxG7hgU",
        "outputId": "57d6ce86-1dc3-4777-fa34-cc9176b8e22b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        }
      ],
      "source": [
        "%ngql USE rebel_llamaindex;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WQZcqckU7hgU",
        "outputId": "103388b9-ff52-457f-db44-5e4cc29bad79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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>m_entity</th>\n",
              "      <th>relationship</th>\n",
              "      <th>n_entity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WiiWare</td>\n",
              "      <td>has part</td>\n",
              "      <td>WiiWare games (North America)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Union–Republican</td>\n",
              "      <td>replaces</td>\n",
              "      <td>People's Commissariat of Defense of the Soviet...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>head</td>\n",
              "      <td>type</td>\n",
              "      <td>tail</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tom Tyler</td>\n",
              "      <td>date of birth</td>\n",
              "      <td>March 3, 1950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sir Nathan Wright</td>\n",
              "      <td>position held</td>\n",
              "      <td>Lord Keeper of the Great Seal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Savoy Hotel</td>\n",
              "      <td>located in or next to body of water</td>\n",
              "      <td>River Thames</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Savoy Hospital</td>\n",
              "      <td>inception</td>\n",
              "      <td>1512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Union–Republican People's Commissariat of the ...</td>\n",
              "      <td>replaces</td>\n",
              "      <td>People's Commissariat of the Navy of the Soviet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Somerset County, Pennsylvania</td>\n",
              "      <td>located in the administrative territorial entity</td>\n",
              "      <td>Pennsylvania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Ministry of Defense of the Russian Federation</td>\n",
              "      <td>replaces</td>\n",
              "      <td>Ministry of Defense of the Soviet Union</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            m_entity  \\\n",
              "0                                            WiiWare   \n",
              "1                                   Union–Republican   \n",
              "2                                               head   \n",
              "3                                          Tom Tyler   \n",
              "4                                  Sir Nathan Wright   \n",
              "5                                        Savoy Hotel   \n",
              "6                                     Savoy Hospital   \n",
              "7  Union–Republican People's Commissariat of the ...   \n",
              "8                      Somerset County, Pennsylvania   \n",
              "9      Ministry of Defense of the Russian Federation   \n",
              "\n",
              "                                       relationship  \\\n",
              "0                                          has part   \n",
              "1                                          replaces   \n",
              "2                                              type   \n",
              "3                                     date of birth   \n",
              "4                                     position held   \n",
              "5               located in or next to body of water   \n",
              "6                                         inception   \n",
              "7                                          replaces   \n",
              "8  located in the administrative territorial entity   \n",
              "9                                          replaces   \n",
              "\n",
              "                                            n_entity  \n",
              "0                      WiiWare games (North America)  \n",
              "1  People's Commissariat of Defense of the Soviet...  \n",
              "2                                               tail  \n",
              "3                                      March 3, 1950  \n",
              "4                      Lord Keeper of the Great Seal  \n",
              "5                                       River Thames  \n",
              "6                                               1512  \n",
              "7    People's Commissariat of the Navy of the Soviet  \n",
              "8                                       Pennsylvania  \n",
              "9            Ministry of Defense of the Soviet Union  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%ngql MATCH (m)-[e]->(n) RETURN m.entity.name AS m_entity,e.relationship AS relationship,n.entity.name AS n_entity LIMIT 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eTAUEOdZ7hgV",
        "outputId": "14b1cce2-0e84-4270-f0f8-dc392134999c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:nebula3.logger:Get connection to ('127.0.0.1', 9669)\n"
          ]
        },
        {
          "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>COUNT(*)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   COUNT(*)\n",
              "0        22"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%ngql MATCH (m)-[e]->(n) RETURN COUNT(*)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OooZ6k1d7hgV"
      },
      "outputs": [],
      "source": [
        "from llama_index.query_engine import KnowledgeGraphQueryEngine\n",
        "\n",
        "from llama_index.storage.storage_context import StorageContext\n",
        "from llama_index.graph_stores import NebulaGraphStore\n",
        "\n",
        "query_engine = KnowledgeGraphQueryEngine(\n",
        "    storage_context=storage_context,\n",
        "    service_context=service_context,\n",
        "    llm=llm,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pq1FlKy87hgV",
        "outputId": "7cc9f1bf-2abb-4bae-dedd-276b73ae70f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;33mGraph Store Query:\n",
            "\n",
            "```\n",
            "MATCH (e:`entity`)-[:relationship]->(h:`entity`) WHERE e.`entity`.`name` == 'Savoy Hotel'\n",
            "RETURN h.`entity`.`name`;\n",
            "```\n",
            "\u001b[0m\u001b[1;3;33mGraph Store Response:\n",
            "{'h.entity.name': ['River Thames']}\n",
            "\u001b[0m\u001b[1;3;32mFinal Response: \n",
            "\n",
            "The Savoy Hotel is located on the River Thames.\n",
            "\u001b[0m"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "<b>\n",
              "\n",
              "The Savoy Hotel is located on the River Thames.</b>"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "response = query_engine.query(\n",
        "    \"Tell me about Savoy Hotel?\",\n",
        ")\n",
        "display(Markdown(f\"<b>{response}</b>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GcuVo9G97hgV"
      },
      "outputs": [],
      "source": []
    }
  ],
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