RAG2_Experiment.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.2/173.2 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.1/177.1 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.6/98.6 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.0/153.0 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m36.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install -qU \\\n",
        "  transformers==4.31.0 \\\n",
        "  sentence-transformers==2.2.2 \\\n",
        "  pinecone-client==2.2.2 \\\n",
        "  datasets==2.14.0 \\\n",
        "  accelerate==0.21.0 \\\n",
        "  einops==0.6.1 \\\n",
        "  langchain==0.0.240 \\\n",
        "  xformers==0.0.20 \\\n",
        "  bitsandbytes==0.41.0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "embed_model = HuggingFaceEmbeddings(\n",
        "    model_name=embed_model_id,\n",
        "    model_kwargs={'device': device},\n",
        "    encode_kwargs={'device': device, 'batch_size': 32}\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589,
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        },
        "id": "LKeLbj8QneFd",
        "outputId": "117f048d-7131-4e3e-e599-b2aa17117fea"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs = [\n",
        "    \"this is one document\",\n",
        "    \"and another document\"\n",
        "]\n",
        "\n",
        "embeddings = embed_model.embed_documents(docs)\n",
        "\n",
        "print(f\"We have {len(embeddings)} doc embeddings, each with \"\n",
        "      f\"a dimensionality of {len(embeddings[0])}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wStP3J8inmIJ",
        "outputId": "2de52177-c00f-4f97-89ad-6b793c8363ab"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 2 doc embeddings, each with a dimensionality of 384.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "with open(\"/content/sample_data/path.txt\", \"r\") as f:\n",
        "    content = f.read()\n",
        "\n",
        "df=pd.read_excel(content)\n",
        "df.head()\n",
        "df.columns\n",
        "# df[\"ExternalReference\"].nunique\n",
        "# col=['CPIPrediction','CPIJustification','JobPrediction','JoblessJustification', 'EDU']\n",
        "# essentials=df[['CPIPrediction','CPIJustification','JobPrediction','JoblessJustification', 'EDU']].copy()\n",
        "# essentials.drop([0])\n",
        "# essentials.head()\n",
        "# #essentia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhsiZBtPntkr",
        "outputId": "bc38bc0a-984d-406a-b62b-8af97b7910f8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['StartDate', 'EndDate', 'Status', 'IPAddress', 'Progress',\n",
              "       'Duration (in seconds)', 'Finished', 'RecordedDate', 'ResponseId',\n",
              "       'RecipientLastName', 'RecipientFirstName', 'RecipientEmail',\n",
              "       'ExternalReference', 'LocationLatitude', 'LocationLongitude',\n",
              "       'DistributionChannel', 'UserLanguage', 'ProlificID', 'CPIPrediction',\n",
              "       'CPIJustification', 'TimeDelay_First Click', 'TimeDelay_Last Click',\n",
              "       'TimeDelay_Page Submit', 'TimeDelay_Click Count', 'JobPrediction',\n",
              "       'JoblessJustification', 'TimeDelay_First Click.1',\n",
              "       'TimeDelay_Last Click.1', 'TimeDelay_Page Submit.1',\n",
              "       'TimeDelay_Click Count.1', 'EDU'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "essentials=df[['CPIPrediction','CPIJustification','JobPrediction','JoblessJustification', 'EDU']]\n",
        "essentials.head()\n",
        "#essentia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "Do7zeTdHnwQi",
        "outputId": "ae178e3d-7e87-4f13-f37c-e38f55e1fa2d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       CPIPrediction  \\\n",
              "0  Inflation occurs when the price of goods and s...   \n",
              "1                                                3.6   \n",
              "2                                                  2   \n",
              "3                                                  4   \n",
              "4                                                0.5   \n",
              "\n",
              "                                    CPIJustification  \\\n",
              "0  Please write down 3-5 sentences to justify you...   \n",
              "1  I believe we will see a continued cooling of i...   \n",
              "2  I think inflation will go up slightly thanks t...   \n",
              "3  I was following the trend, and that seemed lik...   \n",
              "4          It is still going in an upward projection   \n",
              "\n",
              "                                       JobPrediction  \\\n",
              "0  Total nonfarm payroll measures employment in t...   \n",
              "1                                             210000   \n",
              "2                                             220000   \n",
              "3                                             210000   \n",
              "4                                             215000   \n",
              "\n",
              "                                JoblessJustification  \\\n",
              "0  Please write down 3-5 sentences to justify you...   \n",
              "1  I believe we will continue to see an improveme...   \n",
              "2   I think the number of jobless claims will go up.   \n",
              "3  It falls in line with the graph, and it's simi...   \n",
              "4             It may be going down but not by a lot.   \n",
              "\n",
              "                                                 EDU  \n",
              "0  What is the highest level of education you hav...  \n",
              "1  Graduate or professional degree (MA, MS, MBA, ...  \n",
              "2                                  Bachelor’s degree  \n",
              "3                         High school diploma or GED  \n",
              "4                                  Prefer not to say  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1565f03-70de-4c72-befc-5b0db119d567\" 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>CPIPrediction</th>\n",
              "      <th>CPIJustification</th>\n",
              "      <th>JobPrediction</th>\n",
              "      <th>JoblessJustification</th>\n",
              "      <th>EDU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Inflation occurs when the price of goods and s...</td>\n",
              "      <td>Please write down 3-5 sentences to justify you...</td>\n",
              "      <td>Total nonfarm payroll measures employment in t...</td>\n",
              "      <td>Please write down 3-5 sentences to justify you...</td>\n",
              "      <td>What is the highest level of education you hav...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.6</td>\n",
              "      <td>I believe we will see a continued cooling of i...</td>\n",
              "      <td>210000</td>\n",
              "      <td>I believe we will continue to see an improveme...</td>\n",
              "      <td>Graduate or professional degree (MA, MS, MBA, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>I think inflation will go up slightly thanks t...</td>\n",
              "      <td>220000</td>\n",
              "      <td>I think the number of jobless claims will go up.</td>\n",
              "      <td>Bachelor’s degree</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>I was following the trend, and that seemed lik...</td>\n",
              "      <td>210000</td>\n",
              "      <td>It falls in line with the graph, and it's simi...</td>\n",
              "      <td>High school diploma or GED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.5</td>\n",
              "      <td>It is still going in an upward projection</td>\n",
              "      <td>215000</td>\n",
              "      <td>It may be going down but not by a lot.</td>\n",
              "      <td>Prefer not to say</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-d1565f03-70de-4c72-befc-5b0db119d567')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-d1565f03-70de-4c72-befc-5b0db119d567 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-d1565f03-70de-4c72-befc-5b0db119d567');\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-9f487d24-dea1-4a60-8960-9beaf76e0efc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f487d24-dea1-4a60-8960-9beaf76e0efc')\"\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-9f487d24-dea1-4a60-8960-9beaf76e0efc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#essentia\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CPIPrediction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.6,\n          0.5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPIJustification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"I believe we will see a continued cooling of inflation\",\n          \"It is still going in an upward projection\",\n          \"I think inflation will go up slightly thanks to all the political stuff going on\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobPrediction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          210000,\n          215000,\n          \"Total nonfarm payroll measures employment in the private and government sectors (excluding farm employees and unpaid volunteers). In August 2023, the total nonfarm payroll employment increased by 187,000. \\n\\n Please predict the change in nonfarm payroll employment in September, 2023. If your answer is negative, please use a '-' in front of your estimate.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JoblessJustification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"I believe we will continue to see an improvement in the number of jobless claims\",\n          \"It may be going down but not by a lot.\",\n          \"I think the number of jobless claims will go up.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EDU\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Graduate or professional degree (MA, MS, MBA, PhD, JD, MD, DDS etc.)\",\n          \"Prefer not to say\",\n          \"Bachelor\\u2019s degree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "essentials = essentials.drop(essentials.index[0])\n",
        "\n",
        "# Display the DataFrame after dropping the 0th row\n",
        "print(\"\\nDataFrame after dropping the 0th row:\")\n",
        "essentials.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "cTshxy2fnzkW",
        "outputId": "28ab6eb8-e360-448b-b987-22916ae03d67"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "DataFrame after dropping the 0th row:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  CPIPrediction                                   CPIJustification  \\\n",
              "1           3.6  I believe we will see a continued cooling of i...   \n",
              "2             2  I think inflation will go up slightly thanks t...   \n",
              "3             4  I was following the trend, and that seemed lik...   \n",
              "4           0.5          It is still going in an upward projection   \n",
              "5           4.1  The economy is still the same but doesn't get ...   \n",
              "\n",
              "  JobPrediction                               JoblessJustification  \\\n",
              "1        210000  I believe we will continue to see an improveme...   \n",
              "2        220000   I think the number of jobless claims will go up.   \n",
              "3        210000  It falls in line with the graph, and it's simi...   \n",
              "4        215000             It may be going down but not by a lot.   \n",
              "5        209000  The trend  is going down so I expect the numbe...   \n",
              "\n",
              "                                                 EDU  \n",
              "1  Graduate or professional degree (MA, MS, MBA, ...  \n",
              "2                                  Bachelor’s degree  \n",
              "3                         High school diploma or GED  \n",
              "4                                  Prefer not to say  \n",
              "5                                  Bachelor’s degree  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ada76b03-832c-4e14-a072-faac87f81226\" 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>CPIPrediction</th>\n",
              "      <th>CPIJustification</th>\n",
              "      <th>JobPrediction</th>\n",
              "      <th>JoblessJustification</th>\n",
              "      <th>EDU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.6</td>\n",
              "      <td>I believe we will see a continued cooling of i...</td>\n",
              "      <td>210000</td>\n",
              "      <td>I believe we will continue to see an improveme...</td>\n",
              "      <td>Graduate or professional degree (MA, MS, MBA, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>I think inflation will go up slightly thanks t...</td>\n",
              "      <td>220000</td>\n",
              "      <td>I think the number of jobless claims will go up.</td>\n",
              "      <td>Bachelor’s degree</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>I was following the trend, and that seemed lik...</td>\n",
              "      <td>210000</td>\n",
              "      <td>It falls in line with the graph, and it's simi...</td>\n",
              "      <td>High school diploma or GED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.5</td>\n",
              "      <td>It is still going in an upward projection</td>\n",
              "      <td>215000</td>\n",
              "      <td>It may be going down but not by a lot.</td>\n",
              "      <td>Prefer not to say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.1</td>\n",
              "      <td>The economy is still the same but doesn't get ...</td>\n",
              "      <td>209000</td>\n",
              "      <td>The trend  is going down so I expect the numbe...</td>\n",
              "      <td>Bachelor’s degree</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-ada76b03-832c-4e14-a072-faac87f81226')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-ada76b03-832c-4e14-a072-faac87f81226 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-ada76b03-832c-4e14-a072-faac87f81226');\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-30a1e634-0055-4795-b4d8-144e752f68f6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30a1e634-0055-4795-b4d8-144e752f68f6')\"\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-30a1e634-0055-4795-b4d8-144e752f68f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "essentials",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs=[]\n",
        "print(len(essentials))\n",
        "for i in range(1,len(essentials)):\n",
        "    d=\"My CPI Prediction i \" + str(essentials.CPIPrediction[i])+\". I think so because \" + essentials.CPIJustification[i] +\". My Jobs Prediction is \"+ str(essentials.JobPrediction[i])+\". I think so because \"+ essentials.JoblessJustification[i]+\".\"+\" My education is \"+ essentials.EDU[i]+\"\\n\"\n",
        "    docs.append(d)\n",
        "\n",
        "# for i in docs:\n",
        "#   print(i)\n",
        "\n",
        "embeddings = embed_model.embed_documents(docs)\n",
        "\n",
        "print(f\"We have {len(embeddings)} doc embeddings, each with \"\n",
        "      f\"a dimensionality of {len(embeddings[0])}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxszyiMZn104",
        "outputId": "97b7a361-aa8d-4964-d2be-fb467c1f2105"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21\n",
            "We have 20 doc embeddings, each with a dimensionality of 384.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pinecone\n",
        "#os.environ.get('PINECONE_API_KEY') or\n",
        "# get API key from app.pinecone.io and environment from console\n",
        "pinecone.init(\n",
        "    api_key='35ff28eb-f787-4950-8d45-70e26d9a71fd',\n",
        "    environment=os.environ.get('PINECONE_ENVIRONMENT') or 'gcp-starter'\n",
        ")"
      ],
      "metadata": {
        "id": "WU8nr3p_n4r5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "index_name = 'llama-2-rag'\n",
        "\n",
        "if index_name not in pinecone.list_indexes():\n",
        "    pinecone.create_index(\n",
        "        index_name,\n",
        "        dimension=len(embeddings[0]),\n",
        "        metric='cosine'\n",
        "    )\n",
        "    # wait for index to finish initialization\n",
        "    while not pinecone.describe_index(index_name).status['ready']:\n",
        "        time.sleep(1)"
      ],
      "metadata": {
        "id": "vULRhO78n708"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = pinecone.Index(index_name)\n",
        "index.describe_index_stats()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKNpOGUDn-Mx",
        "outputId": "632bb28c-81e9-40eb-d5bb-b4d8c926e462"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'dimension': 384,\n",
              " 'index_fullness': 0.0486,\n",
              " 'namespaces': {'': {'vector_count': 4860}},\n",
              " 'total_vector_count': 4860}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\n",
        "    'jamescalam/llama-2-arxiv-papers-chunked',\n",
        "    split='train'\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "a3cf618868e944b99b283303c99019d7",
            "d1dadf9614ff4f9da6c636eb4e76d603",
            "674074966fd34dd8bf9120f166a6d70a",
            "fc60f820f0ef42258c74ef84d83b9b4b",
            "4ceb49f6836b4303868e6b578609d4d0",
            "b469de0993df4e1b82934331be6a3bc8",
            "7bacb8a968014224a291b06b932eb38d",
            "0bdffe03a3944e6aba6011f05e563dbd",
            "7053d626f6c248189a704e9902b89eb0",
            "a9a2daf2e94545169839cb5a8f99a316",
            "fbb2eb3216d44b588650b8a0f9f8ea9d",
            "7f7d7ef651a54895a3c2acda2025dd91",
            "9033d94e543d4799bc9a8e4e2e665bd7",
            "a1282064e1cc4cc5acacc7e80fbae4bf",
            "4441607d0a0a4855be87a15608ac3348",
            "50097fb75acf4fb6aa7bbab63bba2b88",
            "85fb1a145db74b7ca32b63606b1dd33a",
            "93d7fa0dc8ae4d8c8563c4fe59741bcc",
            "a7b7fe6f978c495a808a44d86defaee3",
            "9363ceaa27254d3f932377d6499d531f",
            "a43dbdcf9d674732953232a95e42b68e",
            "2bb2bb5132964658b75f0c9a5cf5d3de",
            "f5b6080995514312b09f4189c1be8352",
            "2b742ac659844d13ae483725cd4b5ed6",
            "e06c771bea764720a0009b4d7b311bd4",
            "e621730e892a45a7b9694d19d8391ce7",
            "ebc92d2100c744efb806a2b93230e5f7",
            "3ba93d472a8d4a75b88664bd9bbb2569",
            "ca1b026c5b1441538e54b4279213ab74",
            "5560bb70739b4488ba3eca3e69b3de7a",
            "d530827d73e24e3d913129370450c330",
            "8995cb4fecf8422f98beacd303971f1f",
            "ad437bfe04034d8e8dae603e7e58a6b6",
            "5737d84e6c1f4e61afd6f2c93667eb32",
            "0ac4082b3a5748119fdf067e93122b18",
            "35312b77444d40019132ae94ea3640e6",
            "8d255bbab3f6426598dd22a073fc42d8",
            "89307e469b144bbe9c0a14f7c61519d3",
            "d239cc3bc5c241909626bcf82ccad1b7",
            "9ac5dd53bc254cb881a04f8227892d9c",
            "7c55cc2285c04696b00ad15e42c59aa0",
            "ce6174554d544555b85707d7f4305482",
            "b38a19f4a2f34adc81c39b3ab7eb5b0d",
            "68ad9ba4a2db47f09cd48ddd292ad6e0",
            "634f7720fc8f4835abd2680d561fa0f7",
            "2f829b1ac6454c92b189bddac75d93aa",
            "8da5e2beaf9d45dcbd4203500d099ef9",
            "184115228ed245ea8ad1a13a0b0a1c92",
            "3e53684e497c4405ab95b2a3a9bfc705",
            "e3383f0e8919418497b6e32f5f629f97",
            "45b974f35a754a51a6a53f4679b82ac3",
            "f5305aa6f31f430590f4d48fe54e9c3b",
            "2dc6a55e7f0f47d5a5d2d2917e866086",
            "61228b1536fd46228b8a7a85b812e20d",
            "540fc5c85ab749c6a48ffcacd53b7337"
          ]
        },
        "id": "9P_Vw1--oA2V",
        "outputId": "747b0e31-3a2c-41ac-9d91-ef895685f6b8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/409 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3cf618868e944b99b283303c99019d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f7d7ef651a54895a3c2acda2025dd91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5b6080995514312b09f4189c1be8352"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5737d84e6c1f4e61afd6f2c93667eb32"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "634f7720fc8f4835abd2680d561fa0f7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs_df=pd.DataFrame(docs)\n",
        "batch_size = 1\n",
        "# Add a new column 'id' filled with the index of each row\n",
        "docs_df\n",
        "docs_df['id'] = docs_df.reset_index().index\n",
        "docs_df = docs_df.rename(columns={0: 'Text'})\n",
        "docs_df = pd.concat([docs_df, essentials['EDU']], axis=1)\n",
        "docs_df['EDU'] = docs_df['EDU'].fillna(\"Prefer not to say\")\n",
        "docs_df['Text'] = docs_df['Text'].fillna(\"Prefer not to say\")\n",
        "docs_df['id'] = docs_df['id'].fillna(\"0\")\n",
        "\n",
        "docs_df.head()\n",
        "docs_df.columns\n",
        "\n",
        "for i in range(0, len(docs_df), batch_size):\n",
        "      i_end = min(len(data), i+batch_size)\n",
        "      batch = docs_df.iloc[i:i_end]\n",
        "      ids = [f\"{x['id']}\" for i, x in batch.iterrows()]\n",
        "      texts = [str(x['Text']) for i, x in batch.iterrows()]\n",
        "      # print(type(texts))\n",
        "      # print(\"This is an example text:\\n\",texts,\"\\n\")\n",
        "      embeds = embed_model.embed_documents(texts)\n",
        "      # get metadata to store in Pinecone\n",
        "      metadata = [\n",
        "          {'text': x['Text'],\n",
        "          'id': x['id']} for i, x in batch.iterrows()\n",
        "      ]\n",
        "      # add to Pinecone\n",
        "      index.upsert(vectors=zip(ids, embeds, metadata))\n"
      ],
      "metadata": {
        "id": "aggAdYVJoCqO"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index.describe_index_stats()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOmFq3N5q7gT",
        "outputId": "01cab7ea-02b2-4a0f-9b49-367de7344fdb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'dimension': 384,\n",
              " 'index_fullness': 0.0486,\n",
              " 'namespaces': {'': {'vector_count': 4860}},\n",
              " 'total_vector_count': 4860}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "# begin initializing HF items, need auth token for these\n",
        "hf_auth = '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'\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345,
          "referenced_widgets": [
            "f515088fc7e94f4c8377717369941d7a",
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            "9aba2febd3704033ad426b7d67067c34",
            "093d7876189b40208c5fc94ca0d9df83",
            "3a7b54bafa384a0cad9be3917c62a4ba",
            "2fbc695bbb9d44b78b63ad5bd7330993",
            "79a3ec3b76414c319e4d8169e92dee7e",
            "5a81323d523d4e3eb7d84546d4cc7e46",
            "cd349c2ae5a747e7b77565cc6f7bcfef",
            "af28ed7589534a059c88926e17bbd028"
          ]
        },
        "id": "QYxpQdq7-CxL",
        "outputId": "99c0ca81-a06e-488d-c7ea-b70cb36a04fb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/587 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f515088fc7e94f4c8377717369941d7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:2193: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/33.4k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f479e73743b8430ea5897b5c0093f5e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e9e4b165c5845efa5bb60de175ae87f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14a27c6cd09e4344ab9c290f7ad4bed9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4029880f3c4546bebee2b995fcb3e0ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a94d3aec36e14942990e49cd6f7e0d9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a02f890ac4f94fe79cf634842d18c113"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "b8c0a872e12b467bb5b4d3a6a2d713ed",
            "9724e28163ed41bcb6c5a6a820a10c76",
            "1606af995f904e3a9a551047c17fbd26",
            "33ba806ae7dd49c8a412559bc672f465",
            "f62b0165928a4de48d7519ccb792cc55",
            "e6de10ce80964f46a80bebc578c39049",
            "f96ad1dcf90f465784c6e1518af8d6bd",
            "ac15d1e32f3449f29e45a56d74321017",
            "105092925c4648f4abcea2b6d4be604e",
            "53b8066581684325ab2a20c0454b28a0",
            "f4f66919365c4a439d2a198c9bdc674c",
            "54363a9a3bc84461927938a81fe6a11b",
            "1d54fae3b3e04862b8d38b52a0a08767",
            "55e7d84fe0ae4506a73ffd405060b989",
            "c69b8426bc4e4ba1ab07f6a346586476",
            "fa6f6b00e22145caa0f0e47bc8b0e38c",
            "fa3314a104884b4cb62894b65ec6d723",
            "9e037ca771574ab383cc5d43f0018e59",
            "9d0b0d01a85a4b3eb73547c1aba26de3",
            "f46e45bbbb404d17b2ecf078305e31a0",
            "3c5b668faab64f8c9c3f3e18553aede9",
            "9c5056f32fd446feb5c49bb40d8cf991",
            "1b08ead8426c4026bac8225dc602c6ed",
            "32402c54727b453a9487aa390be31115",
            "411f714c0dab4c9c8236c51ede4ddc08",
            "6dbeacd5e22a49f0995d689eaa6ca676",
            "e7c54342a0dd48e7b92fa58312151954",
            "f13de3267dd84b329e515b42e932157b",
            "f3a3da64b1874bf581c7855ab86899bd",
            "2ff981a50a0c47568a1ac74bdf7ed0b2",
            "48c1c37ca32a46e3be4a9e56a7d29464",
            "7d2472318a824eeba4c100407d3b8341",
            "e219b878a1e94b01942d5c7d39b3f3e8",
            "f965c734ba1844a0a94f682ef7eb5947",
            "ed178f884c154b089f334bfb37e88c73",
            "aa4685f716ad416aad4e1e6ba567f2f9",
            "d94e10f74c6b41cc9a72d6d55adff8c4",
            "80d77a7f0ffd49788e47bcd91be18c04",
            "8ecb203491004dfbb39636c0bc2f1469",
            "e856609c0d184215aa65b00898c5d3a8",
            "f75b116490eb41a2819002fa0c87e007",
            "fe7cfb17cbc04d6db492a0d91440a9c7",
            "14a6fdaeb8584382aa7db3706448b5af",
            "bc1466fb6dfa4c86a9e20b787a8a162f"
          ]
        },
        "id": "9SCiYRWk-8TP",
        "outputId": "81c6fd9c-afe0-4e24-a50e-b6948fa23c11"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8c0a872e12b467bb5b4d3a6a2d713ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1714: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54363a9a3bc84461927938a81fe6a11b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b08ead8426c4026bac8225dc602c6ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f965c734ba1844a0a94f682ef7eb5947"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate_text = transformers.pipeline(\n",
        "    model=model, tokenizer=tokenizer,\n",
        "    return_full_text=True,  # langchain expects the full text\n",
        "    task='text-generation',\n",
        "    # we pass model parameters here too\n",
        "    temperature=0.0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max\n",
        "    max_new_tokens=512,  # mex number of tokens to generate in the output\n",
        "    repetition_penalty=1.1  # without this output begins repeating\n",
        ")"
      ],
      "metadata": {
        "id": "LYmFLEHMECiD"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "res = generate_text(\"Explain to me the meaning of CPI.\")\n",
        "print(res[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPkFP_FPEGAP",
        "outputId": "fbbbf423-b2e1-442b-da98-a7124f6f7cec"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explain to me the meaning of CPI.\n",
            "CPI stands for Consumer Price Index, which is a measure of the average change in prices of a basket of goods and services that are commonly consumed by households. It is calculated by the Bureau of Labor Statistics (BLS) and is widely used as an indicator of inflation.\n",
            "The CPI measures the price changes of a fixed basket of goods and services over time, providing a comprehensive view of the rate of inflation. The basket of goods and services includes items such as food, housing, transportation, clothing, and healthcare. The CPI is calculated using a formula that takes into account the weights of each item in the basket, based on their importance to household consumption expenditures.\n",
            "The CPI is expressed as a percentage change from a specific reference period, usually the previous year. For example, if the CPI for a particular month is 2.5% higher than the same month in the previous year, it means that the average price of the goods and services in the basket increased by 2.5% over that period.\n",
            "The CPI is widely used by policymakers, businesses, and investors to understand the impact of inflation on the economy and make informed decisions. It is also used as a deflator to adjust for the effects of inflation on nominal economic data, such as GDP, to obtain real economic growth.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=generate_text)"
      ],
      "metadata": {
        "id": "-h1kvcXHEJI9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kQ4OP0AribqO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm(prompt=\"Explain to me the difference between Micro and Macro Economics.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "_mvZ5-ryE0__",
        "outputId": "e1650060-78ff-4c56-a26c-d9b4902261fd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\nMicroeconomics is the study of individual economic units such as households, firms, and markets. It focuses on understanding the behavior and decision-making processes of these units, and how they interact with each other in the market. Microeconomics examines the supply and demand for specific goods and services, and how prices are determined in a market economy. It also studies the various factors that affect consumer behavior and firm decision-making, such as income, preferences, technology, and government policies.\\n\\nMacroeconomics, on the other hand, is the study of the entire economy as a whole. It looks at the overall performance of the economy, including issues such as inflation, unemployment, and economic growth. Macroeconomics examines the relationships between different sectors of the economy, such as households, firms, and governments, and how they interact with each other. It also studies the impact of various economic policies, such as monetary and fiscal policy, on the overall performance of the economy.\\n\\nIn summary, microeconomics focuses on the individual units of the economy, while macroeconomics looks at the economy as a whole. Microeconomics examines the behavior of households and firms in specific markets, while macroeconomics examines the overall performance of the economy and the interactions between different sectors.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import Pinecone\n",
        "\n",
        "text_field = 'text'  # field in metadata that contains text content\n",
        "\n",
        "vectorstore = Pinecone(\n",
        "    index, embed_model.embed_query, text_field\n",
        ")"
      ],
      "metadata": {
        "id": "vIU4mutqE-38"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'What makes CPI special'\n",
        "#Just a similarity search: doesn't actually use the LLM\n",
        "vectorstore.similarity_search(\n",
        "    query,  # the search query\n",
        "    k=3  # returns top 3 most relevant chunks of text\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ig2CtkBqFRQV",
        "outputId": "25cdef47-fd5b-4f18-b135-715d7cd0eea4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='My CPI Prediction i 4.3. I think so because Observation  based on rising oil prices lately. Also, the CPI has been manipulated by every presidential administration dating back to Carter. One aspect of the \"basket of goods\" coming in too hot? Substitute something that\\'s stable or dropping in price instead. The CPI is tied to Social Security payouts, so there is a reason they cheat with this data. \\n\\nInflation is ALWAYS higher than reported. But the phony headline number can only be massaged so much, so I picked 4.3% (it\\'s actually more like 7-8% in reality). The basket of goods sometimes will have electronics or phones instead of things people really need like gasoline or meat. A total farce, just like our government. Shadow Stats by John Williams has been tracking the unsubstituted number for years now. He understands the game. \\n\\nI\\'m a former Wall St. worker. I\\'ve seen this first hand. An old WS saw goes, \"don\\'t believe the rumor until you hear an official governmental denial.\". My Jobs Prediction is 75000. I think so because Automation is destroying jobs. REAL unemployment iand underemployment is massive. More like 25% true unemployment based on those who have dropped out of the workforce because of an inabiity to find a good job. U3 number does not count people who were on unemployment and couldn\\'t find a job when U/I runs out. Again, the nonfarm payroll numbers are a joke. . My education is Some college, but no degree\\n', metadata={'id': 10.0}),\n",
              " Document(page_content='My CPI Prediction i 0.2. I think so because Inflation seems to continue to climb. It is slowing but not completely going to stop by September. For this reason I selected 0.2% as it is small but still shows inflation.. My Jobs Prediction is 100000. I think so because I think it would be somewhat on par with what occurred in August. It is a bit lower because the economy seems to be dragging. Overall it will still increase but just not by as much.. My education is Graduate or professional degree (MA, MS, MBA, PhD, JD, MD, DDS etc.)\\n', metadata={'id': 14.0}),\n",
              " Document(page_content='My CPI Prediction i 0.5. I think so because It is still going in an upward projection. My Jobs Prediction is 215000. I think so because It may be going down but not by a lot.. My education is Prefer not to say\\n', metadata={'id': 3.0})]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "rag_pipeline = RetrievalQA.from_chain_type(\n",
        "    llm=llm, chain_type='stuff',\n",
        "    retriever=vectorstore.as_retriever()\n",
        ")"
      ],
      "metadata": {
        "id": "JDQDDm9pFXKx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "start=time.time()\n",
        "rag_pipeline('what is the overall consensus on Joblessness claims?')\n",
        "end=time.time()\n",
        "\n",
        "# # torch.cuda.memory_summary(device=None, abbreviated=False)\n",
        "# import torch\n",
        "# torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "T7N07_HHFebH"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Time for a prompt response: \",end-start)\n"
      ],
      "metadata": {
        "id": "sKI1n3hrFhYK"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rag_pipeline('what is the overall consensus on Joblessness claims?')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AP-s6n4rw5NB",
        "outputId": "24db6e39-616a-404e-8d73-75c34f4b364c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'query': 'what is the overall consensus on Joblessness claims?',\n",
              " 'result': ' According to the Bureau of Labor Statistics, the seasonally adjusted number of initial claims was 217,000 for the week ending August 29th. This represents a decrease of 1,000 from the previous week. The four-week moving average was 220,750, a decrease of 2,750 from the previous week. These numbers suggest that the labor market remains strong and that the economy is continuing to recover from the COVID-19 pandemic. However, it is important to note that the number of continuing claims, which measures the number of people who have been receiving benefits for more than a week, increased by 11,000 to 1.7 million for the week ending August 22nd. This suggests that while the labor market is improving, there are still many people who are struggling to find employment.'}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hZ8UytF2w5rQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}