Llama_Index_Embeddings.ipynb

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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m276.3/276.3 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q llama-index\n",
        "!pip install -q openai\n",
        "!pip install -q transformers\n",
        "!pip install -q accelerate\n",
        "!pip install -q optimum[exporters]\n",
        "!pip install -q InstructorEmbedding\n",
        "!pip install -q sentence_transformers\n",
        "!pip install -q pypdf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "import os\n",
        "openai.api_key = \"Enter Your OpenAI Key\""
      ],
      "metadata": {
        "id": "8uDbCiyiL7x-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms import OpenAI\n",
        "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
        "from IPython.display import Markdown, display"
      ],
      "metadata": {
        "id": "h-RAu2KfMMfs"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader(\"data\").load_data()\n",
        "index = VectorStoreIndex.from_documents(documents)\n",
        "query_engine = index.as_query_engine()\n",
        "response = query_engine.query(\"What did the author do growing up? \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DefQYC5aNQrM",
        "outputId": "d33a97ee-d945-4834-88ac-8a54223426ff"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /tmp/llama_index...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import ServiceContext, set_global_service_context\n",
        "\n",
        "llm = OpenAI(\n",
        "    model = \"gpt-3.5-turbo\",\n",
        "    temperature = 0,\n",
        "    max_tokens = 256\n",
        ")\n",
        "service_context = ServiceContext.from_defaults(\n",
        "    llm = llm,\n",
        "    chunk_size = 800,\n",
        "    chunk_overlap = 20\n",
        ")\n",
        "index = VectorStoreIndex.from_documents(\n",
        "    documents,\n",
        "    service_context = service_context\n",
        ")"
      ],
      "metadata": {
        "id": "gXyigF4tMavO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Customize Embeddings model"
      ],
      "metadata": {
        "id": "yg9bgjR2Nqaa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import ServiceContext\n",
        "from llama_index.embeddings import OpenAIEmbedding"
      ],
      "metadata": {
        "id": "qD5wOoyUNkNs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "openai_embed = OpenAIEmbedding()\n",
        "service_context = ServiceContext.from_defaults(embed_model=openai_embed)"
      ],
      "metadata": {
        "id": "O2HNOvX6NwdJ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_embeddings = openai_embed.get_text_embedding(\"AI is awesome!\")"
      ],
      "metadata": {
        "id": "K6PynvECN-oy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(text_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMLHEVwGODnk",
        "outputId": "bb0c06bf-da53-403b-e8c2-f4f1e1ca8cc8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1536"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Local embeddings from hugging face\n",
        "Massive Text Embedding Benchmark (MTEB) [Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)"
      ],
      "metadata": {
        "id": "HD4Q_7WMOIiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### bge embedding"
      ],
      "metadata": {
        "id": "0iAzWEk0PKb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings import HuggingFaceEmbedding"
      ],
      "metadata": {
        "id": "Gl1chtwROFke"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embed_model_bge = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
        "text_embeddings = embed_model_bge.get_text_embedding(\"AI is awesome!\")\n",
        "print(len(text_embeddings))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
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        },
        "id": "ew1733jCOvFT",
        "outputId": "3fb20e9b-8702-4ae2-dc97-9a42382469f9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afab2e8db1524f0eba880d494a048ae9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/134M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ada325cef2cc4f4c9f8e41e32f6e4414"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f86aa10a17c0486e83970d451d36055c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f763aa91a8af43b788c22df7ff5a0a7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e6f0b61c1f44aea92764f4e692114a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7976722f1ef84839a5c4b02e2bdc15c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Instructor embedding"
      ],
      "metadata": {
        "id": "eeIHYchkPOkT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings import InstructorEmbedding"
      ],
      "metadata": {
        "id": "N3pVcnW2PD5S"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embed_model_inst = InstructorEmbedding(model_name=\"hkunlp/instructor-base\")\n",
        "text_embeddings = embed_model_inst.get_text_embedding(\"AI is awesome!\")\n",
        "print(len(text_embeddings))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517,
          "referenced_widgets": [
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        "id": "Oz67ZHCoPcwo",
        "outputId": "1e121b03-0bda-4970-f2a1-5ff696498027"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n",
            "768\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Speed Benchmarking"
      ],
      "metadata": {
        "id": "zD4iCHNzPyyt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzy0NpTAPl2G",
        "outputId": "4309433c-4517-4aa8-b9e5-da9aa4228d69"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 20.7M  100 20.7M    0     0   249k      0  0:01:25  0:01:25 --:--:--  257k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import VectorStoreIndex, SimpleDirectoryReader"
      ],
      "metadata": {
        "id": "ieCOO8ZjP47m"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader(\n",
        "    input_files = [\"IPCC_AR6_WGII_Chapter03.pdf\"]\n",
        ").load_data()"
      ],
      "metadata": {
        "id": "aRUn0HW8QDpy"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of documents: {len(documents)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5CX3FfhQRwa",
        "outputId": "99a0b991-0dea-4413-b5de-7dc375c1a09d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of documents: 172\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### OpenAI embeddings"
      ],
      "metadata": {
        "id": "fYDXrfH-QbXW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embed_model_openai = OpenAIEmbedding()\n",
        "service_context = ServiceContext.from_defaults(embed_model=embed_model_openai)"
      ],
      "metadata": {
        "id": "VbKrQECZQZ0_"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit -r 2 -n 2\n",
        "index = VectorStoreIndex.from_documents(\n",
        "    documents, service_context=service_context, show_progress=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
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            "c67832ec46154009b19c5be442e14b0e",
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            "bb86534dd7484beda5838ce03fb2dd30",
            "e2acf60ca37541ac8c5d92501550a88b",
            "737de1ab668541088cd4d17677edc4a2"
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        },
        "id": "Hp3IxgDwQhiV",
        "outputId": "943cdddd-c8c3-47ee-9cea-b86cf0f4a027"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2ff360774c2482e8092bd3476cd388d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51398f75aea14d708c419af072702cb1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1423bfa132144470892d6795e6e9bbf5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0b54759e88e4c2e8462994697f389cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a42147e4d5bc47c5bba9779c2d40a267"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3aa4325dba3e47e2bf0e75471624ba28"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20a65e972cc840b2be49f2304ebcb33c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a40652c97d4241feb50d15b9867e66e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18.2 s ± 697 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BGE embeddings"
      ],
      "metadata": {
        "id": "60Cd5-gmQlNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "service_context = ServiceContext.from_defaults(embed_model=embed_model_bge)"
      ],
      "metadata": {
        "id": "LejYtEUzQkU5"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit -r 2 -n 2\n",
        "index = VectorStoreIndex.from_documents(\n",
        "    documents, service_context=service_context, show_progress=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
          "referenced_widgets": [
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        },
        "id": "YXgO7rVnQrA8",
        "outputId": "ed53ad74-c32a-45e1-c716-67786c5e9f81"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bccb1403fe8746ba84be8ca078a67fb1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5bedc1badb1f4d658d9172e4671e7ff6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4eea63d519464788aa0bb6c8cc825b66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4baad4f039df4990bea14fc24d43750c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3015c19f1f44fc2bd4d7c4cb64eae78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1af3ffcac20e4bdda2c2ac94c95140e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6db9efe6b94547d79cf56d9f8047f507"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65a1a1949de4432e82e0b49fb402f1aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8.61 s ± 40.2 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Instructor embeddings"
      ],
      "metadata": {
        "id": "-kLMLpt4QsXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "service_context = ServiceContext.from_defaults(embed_model=embed_model_inst)"
      ],
      "metadata": {
        "id": "BGwI7-88QxoZ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit -r 2 -n 2\n",
        "index = VectorStoreIndex.from_documents(\n",
        "    documents, service_context=service_context, show_progress=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
          "referenced_widgets": [
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        "id": "-L00B7eqQ0YL",
        "outputId": "f7f04d85-7b23-46f7-b5ee-cbd36c89d064"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70d31f55ee374f90847ca424413294ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a9578adacf046e6a0e956e69e320f5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c54cc0f492e94466993221c227e0f334"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f26bf32c9f834216bfbfd77780fac176"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ba67dad7d044edbad8bcfb390830cd8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d998f9bc16c408386c451edeb8e4711"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing documents into nodes:   0%|          | 0/172 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7103e51f382e458e9f675be9d55a130c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/428 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9eddfce4ebfa40d1889f8526eb9beba6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18.8 s ± 648 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WiAFHs3eRFok"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}