docs/pinecone-import.ipynb

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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/pinecone-import.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/pinecone-import.ipynb)\n",
        "\n",
        "# Import from object storage\n",
        "\n",
        "**Note:** This feature is in [public preview](https://docs.pinecone.io/release-notes/feature-availability) and available only on [Standard and Enterprise plans](https://www.pinecone.io/pricing/)."
      ]
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        "## Scenario: Ingesting Parquet Data from S3 to Pinecone Serverless\n",
        "\n",
        "In this scenario, we will be generating JSON data, embedding that data using the [Pinecone Inference API](https://docs.pinecone.io/guides/inference/understanding-inference), storing the data in AWS S3 as Parquet files, and ingesting the data from S3 into a Pinecone Serverless index.\n",
        "\n",
        "### Problem Overview\n",
        "\n",
        "The goal is to move the data from S3 to Pinecone so that it can be used for future tasks such as semantic search. This process ensures that the data is efficiently searchable and retrievable by applications.\n",
        "\n",
        "### Solution Steps\n",
        "\n",
        "1. **Generate data**: Begin by generating the data that needs to be processed.\n",
        "\n",
        "2. **Chunk data**: Split the generated data into smaller, manageable chunks that can be processed and embedded effectively.\n",
        "\n",
        "3. **Embed data**: Create vector embeddings from the chunked data. These embeddings are crucial for indexing and retrieval in Pinecone.\n",
        "\n",
        "4. **Create Parquet files**: Save the vector embeddings, along with metadata, into Parquet files.\n",
        "\n",
        "5. **Access S3 bucket**: Access the S3 bucket where the Parquet files will be stored.\n",
        "\n",
        "6. **Upload Parquet files**: Upload the Parquet files containing the embeddings to the S3 bucket.\n",
        "\n",
        "7. **Create Pinecone index**: Create a Pinecone index where the embeddings will be stored. This index will allow for efficient similarity search and other tasks.\n",
        "\n",
        "8. **Load S3 data into Pinecone index**: Load the embeddings from the S3 bucket into the Pinecone index.\n",
        "\n",
        "\n",
        "Please see our official [Understanding Imports in Pinecone documentation](https://docs.pinecone.io/guides/data/understanding-imports) for additional information.\n"
      ]
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      "source": [
        "The data flow for the notebook is outlined below:"
      ]
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        "![Pinecone101_flow.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5136421b",
      "metadata": {
        "id": "5136421b"
      },
      "source": [
        "## Import required libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "698bf926",
      "metadata": {
        "id": "698bf926"
      },
      "source": [
        "First, we need to import all the necessary libraries that will be used for phrase generation, text chunking, embedding, and uploading to S3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58502fd1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58502fd1",
        "outputId": "0df49eba-c481-43a1-cfd5-b11e64a8ee25"
      },
      "outputs": [],
      "source": [
        "!pip install pinecone\n",
        "!pip install pinecone_notebooks\n",
        "!pip install langchain\n",
        "!pip install boto3\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7407f360",
      "metadata": {
        "id": "7407f360"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import pandas as pd\n",
        "import boto3\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from pinecone import Pinecone, ServerlessSpec\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c641c107",
      "metadata": {
        "id": "c641c107"
      },
      "source": [
        "## Generate unique phrases"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a0f82e4",
      "metadata": {
        "id": "1a0f82e4"
      },
      "source": [
        "In this step, we create a list of adjectives, nouns, and verbs, and then randomly combine them to form 100 unique phrases. Each phrase will follow the structure: 'The [adjective] [noun] [verb] over the [adjective] [noun]'. These phrases will be stored in a Pandas DataFrame for further processing."
      ]
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          "text": [
            "Generating 100 unique phrases...\n",
            "Unique phrases generated.\n"
          ]
        },
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            "text/plain": [
              "    id                                             values\n",
              "0  id0        The brave tiger roars over the silent river\n",
              "1  id1  The bold mountain observes over the mysterious...\n",
              "2  id2          The bright tree roars over the quick bird\n",
              "3  id3         The bold lion observes over the lazy river\n",
              "4  id4     The mysterious bird climbs over the quick tree"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lists of words to combine into unique phrases\n",
        "adjectives = ['quick', 'lazy', 'energetic', 'bright', 'dark', 'mysterious', 'bold', 'silent', 'wild', 'brave']\n",
        "nouns = ['fox', 'dog', 'cat', 'tree', 'river', 'mountain', 'bird', 'sky', 'lion', 'tiger']\n",
        "verbs = ['jumps', 'runs', 'flies', 'sits', 'sleeps', 'roars', 'whispers', 'climbs', 'chases', 'observes']\n",
        "\n",
        "# Generate 100 unique phrases\n",
        "num_rows = 100\n",
        "print(f\"Generating {num_rows} unique phrases...\")\n",
        "\n",
        "# Create a DataFrame with unique phrases\n",
        "data = [{'id': f'id{i}', 'values': f\"The {random.choice(adjectives)} {random.choice(nouns)} {random.choice(verbs)} over the {random.choice(adjectives)} {random.choice(nouns)}\"} for i in range(num_rows)]\n",
        "df = pd.DataFrame(data)\n",
        "print(\"Unique phrases generated.\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c25b6ca",
      "metadata": {
        "id": "1c25b6ca"
      },
      "source": [
        "## Chunk text using LangChain"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ebfb42f",
      "metadata": {
        "id": "9ebfb42f"
      },
      "source": [
        "Here we chunk the generated phrases into smaller pieces using LangChain's `RecursiveCharacterTextSplitter`. This is useful when working with large texts, as smaller chunks can be processed more efficiently."
      ]
    },
    {
      "cell_type": "code",
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          "height": 206
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        "id": "8edf02bd",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61bae143-441d-48ff-a412-f4b15964fb3e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    id                                             values\n",
              "0  id0        The brave tiger roars over the silent river\n",
              "1  id1  The bold mountain observes over the mysterious...\n",
              "2  id2          The bright tree roars over the quick bird\n",
              "3  id3         The bold lion observes over the lazy river\n",
              "4  id4     The mysterious bird climbs over the quick tree"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Chunk the text column using LangChain's RecursiveCharacterTextSplitter\n",
        "chunk_size = 1000\n",
        "chunk_overlap = 100\n",
        "\n",
        "# Initialize the text splitter\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
        "\n",
        "# Perform the chunking process and store the chunked texts\n",
        "chunked_texts = []\n",
        "\n",
        "for text in df['values']:\n",
        "    if pd.isna(text) or text == '':\n",
        "        chunked_texts.append([text])  # Keep empty texts as they are\n",
        "    else:\n",
        "        chunked_text = text_splitter.split_text(text)\n",
        "        chunked_texts.append(chunked_text)\n",
        "\n",
        "# Flatten the DataFrame by exploding the chunked texts into separate rows\n",
        "df['values'] = chunked_texts\n",
        "df_chunked = df.explode('values').reset_index(drop=True)\n",
        "df_chunked.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "n6Y-EX3BgB0z",
      "metadata": {
        "id": "n6Y-EX3BgB0z"
      },
      "source": [
        "## Get your API key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb6be6ec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "cb6be6ec",
        "outputId": "12ffc9cf-47be-45ce-e31d-0670c5af09c4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<script type=\"text/javascript\" src=\"https://connect.pinecone.io/embed.js\"></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pinecone_notebooks.colab import Authenticate\n",
        "Authenticate()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63db8017",
      "metadata": {
        "id": "63db8017"
      },
      "source": [
        "## Initialize a Pinecone client"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "831325b5",
      "metadata": {
        "id": "831325b5"
      },
      "source": [
        "We load the Pinecone API key from a configuration file (`config.ini`) and initialize the Pinecone client. Pinecone will be used to embed the chunked text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "LKL1_B6GfA69",
      "metadata": {
        "id": "LKL1_B6GfA69"
      },
      "outputs": [],
      "source": [
        "api_key = os.getenv('PINECONE_API_KEY')\n",
        "\n",
        "# Configure Pinecone client\n",
        "pc = Pinecone(api_key=api_key)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5dc26203",
      "metadata": {
        "id": "5dc26203"
      },
      "source": [
        "## Embed text using the Pinecone Inference API"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21b7050c",
      "metadata": {
        "id": "21b7050c"
      },
      "source": [
        "Next, we embed the chunked text using Pinecone's embedding model. We process each chunk and store the embedding values back into the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd938b95",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "dd938b95",
        "outputId": "94c35fc7-9e9d-4f4b-c036-da7be34d12ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All embeddings generated.\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_chunked\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"id83\",\n          \"id53\",\n          \"id70\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"values\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_chunked"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-16b7d167-80b5-494e-88b8-52ba29ef76f2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id0</td>\n",
              "      <td>[-0.00984954833984375, 0.0060577392578125, -0....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id1</td>\n",
              "      <td>[0.0022792816162109375, 0.0202178955078125, -0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id2</td>\n",
              "      <td>[0.0230560302734375, 0.02764892578125, -0.0150...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3</td>\n",
              "      <td>[-0.01027679443359375, 0.0150299072265625, -0....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id4</td>\n",
              "      <td>[0.0034313201904296875, 0.016632080078125, -0....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16b7d167-80b5-494e-88b8-52ba29ef76f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-16b7d167-80b5-494e-88b8-52ba29ef76f2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-16b7d167-80b5-494e-88b8-52ba29ef76f2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-df403127-adae-4fcb-a106-a5f35bc29a8e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df403127-adae-4fcb-a106-a5f35bc29a8e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df403127-adae-4fcb-a106-a5f35bc29a8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    id                                             values\n",
              "0  id0  [-0.00984954833984375, 0.0060577392578125, -0....\n",
              "1  id1  [0.0022792816162109375, 0.0202178955078125, -0...\n",
              "2  id2  [0.0230560302734375, 0.02764892578125, -0.0150...\n",
              "3  id3  [-0.01027679443359375, 0.0150299072265625, -0....\n",
              "4  id4  [0.0034313201904296875, 0.016632080078125, -0...."
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Function to get embeddings for a given text\n",
        "def get_embedding(text):\n",
        "    res = pc.inference.embed(\n",
        "        model=\"multilingual-e5-large\",\n",
        "        inputs=text,\n",
        "        parameters={\n",
        "            \"input_type\": \"query\",  # or \"passage\"\n",
        "            \"truncate\": \"END\"\n",
        "        }\n",
        "    )\n",
        "    embedding = res.data[0]['values']\n",
        "    return embedding\n",
        "\n",
        "# Embed the chunked phrases in the DataFrame\n",
        "for index, row in df_chunked.iterrows():\n",
        "    #print(f\"Processing record {index + 1}/{len(df_chunked)}: {row['id']}\")\n",
        "    df_chunked.at[index, 'values'] = get_embedding(row['values'])\n",
        "\n",
        "print(\"All embeddings generated.\")\n",
        "df_chunked.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f627ebe",
      "metadata": {
        "id": "4f627ebe"
      },
      "source": [
        "## Upload embedded data to S3"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0110506c",
      "metadata": {
        "id": "0110506c"
      },
      "source": [
        "Now, we can upload the embedded DataFrame to an Amazon S3 bucket as Parquet files in chunks. This step uses the `boto3` library to interact with S3.\n",
        "\n",
        "Be sure to replace the bucket name and folder name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70dc8a47",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "70dc8a47",
        "outputId": "32a50183-1ff4-4e45-ce65-dcf16381ee44"
      },
      "outputs": [],
      "source": [
        "# Function to convert the DataFrame to Parquet in-memory and upload to S3 in chunks\n",
        "def upload_to_s3(df, bucket, folder, chunk_size=10):\n",
        "    s3_client = boto3.client('s3', config=Config(signature_version=UNSIGNED))\n",
        "    total_rows = len(df)\n",
        "    total_chunks = (total_rows + chunk_size - 1) // chunk_size  # Calculate the total number of chunks\n",
        "\n",
        "    for i, start in enumerate(range(0, total_rows, chunk_size)):\n",
        "        chunk = df.iloc[start:start + chunk_size]\n",
        "\n",
        "        # Save the chunk as a Parquet file in memory\n",
        "        parquet_buffer = BytesIO()\n",
        "        chunk.to_parquet(parquet_buffer, index=False)\n",
        "        parquet_buffer.seek(0)\n",
        "\n",
        "        object_name = f\"{folder}/part-{i}.parquet\"\n",
        "        try:\n",
        "            # Upload the Parquet file from memory to S3\n",
        "            s3_client.put_object(Body=parquet_buffer, Bucket=bucket, Key=object_name)\n",
        "            print(f\"Part-{i}.parquet successfully uploaded to {bucket}/{object_name}\")\n",
        "        except ClientError as e:\n",
        "            print(f\"Failed to upload part-{i}.parquet: {e}\")\n",
        "\n",
        "bucket_name = \"rjbucketaws\"  # Replace with your bucket name\n",
        "folder_name = \"1-1024-1/namespace1\"  # Specify the folder name\n",
        "\n",
        "# Start the upload process\n",
        "upload_to_s3(df_chunked, bucket_name, folder_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c5e2cdc0",
      "metadata": {
        "id": "c5e2cdc0"
      },
      "source": [
        "## Create a serverless index\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8dcb7acb",
      "metadata": {
        "collapsed": true,
        "id": "8dcb7acb"
      },
      "outputs": [],
      "source": [
        "# Get cloud and region settings\n",
        "cloud = os.getenv('PINECONE_CLOUD', 'aws')\n",
        "region = os.getenv('PINECONE_REGION', 'us-east-1')\n",
        "\n",
        "# Define serverless specifications\n",
        "spec = ServerlessSpec(cloud=cloud, region=region)\n",
        "\n",
        "index_name = \"pinecone-import1-1024-1\"\n",
        "dimension = 1024\n",
        "\n",
        "# List all existing indexes\n",
        "existing_indexes = pc.list_indexes()\n",
        "\n",
        "# Check if the index exists\n",
        "if index_name not in existing_indexes.names():\n",
        "    # Create the index if it doesn't exist\n",
        "    pc.create_index(\n",
        "        name=index_name,\n",
        "        dimension=dimension,\n",
        "        metric=\"cosine\",\n",
        "        spec=ServerlessSpec(cloud=\"aws\", region=\"us-west-2\")\n",
        "    )\n",
        "    print(f\"Index '{index_name}' created successfully.\")\n",
        "else:\n",
        "    print(f\"Index '{index_name}' already exists.\")\n",
        "\n",
        "# Connect to the index\n",
        "index = pc.Index(name=index_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfc374ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfc374ca",
        "outputId": "51441002-6cd2-4711-82bc-400e2246a059"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'dimension': 1024,\n",
              " 'index_fullness': 0.0,\n",
              " 'namespaces': {},\n",
              " 'total_vector_count': 0}"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "index.describe_index_stats()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9719a558",
      "metadata": {
        "id": "9719a558"
      },
      "source": [
        "## Start import task\n",
        "\n",
        "Each file contains:\n",
        "\n",
        "*   **id**: Unique identifier\n",
        "*   **Values**: Embedded vectors\n",
        "*   **metadata**: JSON-formatted dictionary with metadata\n",
        "\n",
        "***Note***: *This task may take 10 minutes or more to complete. Each import request can import up 1TB of data, or 100,000,000 records into a maximum of 100 namespaces, whichever limit is met first.*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73197a39",
      "metadata": {
        "id": "73197a39"
      },
      "source": [
        "## Specify AWS S3 folder and start task"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b979e842",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "b979e842",
        "outputId": "a40bbc72-3eab-462e-aafe-39f5b5a59e28"
      },
      "outputs": [],
      "source": [
        "# Specify S3 data URI\n",
        "root = \"[YOUR S3 URI HERE]\"\n",
        "\n",
        "# Start import\n",
        "op = index.start_import(uri=root, error_mode=\"ABORT\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7a908310",
      "metadata": {
        "id": "7a908310"
      },
      "source": [
        "## Check the status of the import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "936032f7",
      "metadata": {
        "id": "936032f7"
      },
      "outputs": [],
      "source": [
        "index.describe_import(id=op.id)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a56aced9",
      "metadata": {
        "id": "a56aced9"
      },
      "source": [
        "## List import operations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eeac0925",
      "metadata": {
        "id": "eeac0925"
      },
      "outputs": [],
      "source": [
        "imports = list(index.list_imports())\n",
        "if imports:\n",
        "    for i in imports:\n",
        "        print(i)\n",
        "else:\n",
        "    print(\"No imports found.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c0f1908",
      "metadata": {
        "id": "8c0f1908"
      },
      "source": [
        "## Describe a specific import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a2dc3916",
      "metadata": {
        "id": "a2dc3916"
      },
      "outputs": [],
      "source": [
        "index.describe_import(\"1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9899dbd",
      "metadata": {
        "id": "e9899dbd"
      },
      "source": [
        "## Cancel the Import (if needed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "49121d9b",
      "metadata": {
        "id": "49121d9b"
      },
      "outputs": [],
      "source": [
        "# Check if operation status and cancel running instance\n",
        "op_status = index.describe_import(op.id)\n",
        "print(f\"Operation status: {op_status}\")\n",
        "\n",
        "if op_status in ['in_progress', 'pending']:\n",
        "    try:\n",
        "        cancel_response = index.cancel_import(op.id)\n",
        "        print(f\"Import operation {op.id} cancelled.\")\n",
        "    except Exception as e:\n",
        "        print(f\"Error cancelling import: {e}\")\n",
        "else:\n",
        "    print(f\"Cannot cancel operation {op.id} because its status is: {op_status}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "28a43542",
      "metadata": {
        "id": "28a43542"
      },
      "source": [
        "## Delete the index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0afe1809",
      "metadata": {
        "id": "0afe1809"
      },
      "outputs": [],
      "source": [
        "pc.delete_index(index_name)\n",
        "print(f\"Index '{index_name}' deleted.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a139b974",
      "metadata": {
        "id": "a139b974"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a9e55ea6",
      "metadata": {
        "id": "a9e55ea6"
      },
      "source": [
        "In this notebook, we successfully generated random phrases, chunked them, embedded the chunked texts using Pinecone, and uploaded the final embedded data to Amazon S3. You can further customize this notebook for you use case by updating it to use your S3 bucket, changing the chunk size or embedding model as needed."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}