[AI CoP_shared] 3주차 ostgresql_embed_openAI_ver1.00.ipynb의 사본

-ai-cop_shared-3-ostgresql_embed_openai_ver1-00-ipynb.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/tae0y/718f57c31e654004c8b283c4be6bbd51/-ai-cop_shared-3-ostgresql_embed_openai_ver1-00-ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PostgreSQL pgvector를 사용한 RAG 데모"
      ],
      "metadata": {
        "id": "5NTN4pdYi2nT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PostgreSQL 벡터 데이터베이스에 pgvector 사용  \n",
        "https://www.timescale.com/blog/postgresql-as-a-vector-database-create-store-and-query-openai-embeddings-with-pgvector/\n",
        "\n",
        "\n",
        "주피터 노트북 예제소스  \n",
        "https://github.com/timescale/vector-cookbook/tree/main/openai_pgvector_helloworld?ref=timescale.com\n",
        "  \n",
        "\n",
        "Azure Database for PostgreSQL - 유연한 서버에서 pgvector를 사용하도록 설정하고 사용하는 방법  \n",
        "https://learn.microsoft.com/ko-kr/azure/postgresql/flexible-server/how-to-use-pgvector\n",
        "  \n",
        "\n",
        "Azure Cosmos DB for PostgreSQL에서 pgVector를 사용하는 경우 성능을 최적화하는 방법  \n",
        "https://learn.microsoft.com/ko-kr/azure/cosmos-db/postgresql/howto-optimize-performance-pgvector"
      ],
      "metadata": {
        "id": "liV97C87iSxd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0부. 환경설정"
      ],
      "metadata": {
        "id": "5QADlwe3nDul"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "필요한 패키지를 설치하고 임포트합니다."
      ],
      "metadata": {
        "id": "jfcNXii4mdhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install openai pandas numpy tiktoken psycopg2 pgvector python-dotenv"
      ],
      "metadata": {
        "id": "JF_I-iKNiREE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json\n",
        "import tiktoken\n",
        "import psycopg2\n",
        "import ast\n",
        "import pgvector\n",
        "import math\n",
        "from psycopg2.extras import execute_values\n",
        "from pgvector.psycopg2 import register_vector"
      ],
      "metadata": {
        "id": "fE5we6RQkPKo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OpenAI의 API키를 발급받아 사용합시다  \n",
        "- 키발급 : https://platform.openai.com/api-keys\n",
        "- 제한설정 : https://platform.openai.com/settings/organization/limits"
      ],
      "metadata": {
        "id": "lSnaVfuAlG_F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "OpenAI에서는 보안을 위해 이렇게 권고하지만..\n",
        "- API키를 환경변수로 설정함으로써,\n",
        "- 팀 전체에서 API키를 일관되게 유지하며\n",
        "- 노출될 위험없이 코드를 커밋하고 공유  \n",
        "https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety?ref=timescale.com"
      ],
      "metadata": {
        "id": "XE4ldzUBm9xA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "코랩 환경에서는 잘 안되는것 같으니 생략합니다"
      ],
      "metadata": {
        "id": "M7-GYueUnAI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!export OPENAI_API_KEY=''\n",
        "#!echo $OPENAI_API_KEY\n",
        "\n",
        "from dotenv import load_dotenv, find_dotenv\n",
        "_ = load_dotenv(find_dotenv())\n",
        "#openai.api_key  = os.environ['OPENAI_API_KEY']\n",
        "openai.api_key = ''"
      ],
      "metadata": {
        "id": "JGDoXiT_k8Je"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1부. pgvector용 임베딩 생성"
      ],
      "metadata": {
        "id": "tIm5HkfHnF06"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "먼저 처리할 데이터를 파이썬에서 처리하기 좋게 변환해줍시다"
      ],
      "metadata": {
        "id": "65ZsfVcsvrmg"
      }
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      "cell_type": "code",
      "source": [
        "#미리 만들어둔 json을 플랫하게 펼쳐서 활용하자\n",
        "import json\n",
        "webscrap_results = json.load(open('WEBSCRAP_RESULTS.json', 'r'))\n",
        "#print(webscrap_results)\n",
        "\n",
        "webscrap_flatted = []\n",
        "for webscrap in webscrap_results:\n",
        "    webscrap_flatted.extend(webscrap_results[webscrap])\n",
        "\n",
        "print(webscrap_flatted[0])"
      ],
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        "df = pd.DataFrame(webscrap_flatted)\n",
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              "          + ' 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-56faaf1d-2103-42e0-b709-26fcdd32a782\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56faaf1d-2103-42e0-b709-26fcdd32a782')\"\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-56faaf1d-2103-42e0-b709-26fcdd32a782 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 922,\n  \"fields\": [\n    {\n      \"column\": \"isbn\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 867,\n        \"samples\": [\n          \"K692836515\",\n          \"8965706548\",\n          \"8934944072\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 867,\n        \"samples\": [\n          \"\\ube45\\ub370\\uc774\\ud130 \\uc0ac\\uc6a9\\uc124\\uba85\\uc11c\",\n          \"\\uc874 \\ub864\\uc2a4 \\uc815\\uc758\\ub860 - \\uacf5\\uc815\\ud55c \\uc138\\uc0c1\\uc744 \\ub9cc\\ub4dc\\ub294 \\uc6d0\\uce59\",\n          \"\\uc2a4\\uc704\\uce58\\ud06c\\ub798\\ud504\\ud2b8, \\uc804\\ud658\\uc758 \\uae30\\uc220\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"author\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 831,\n        \"samples\": [\n          \"KMAC (\\uc9c0\\uc740\\uc774)\",\n          \"\\ud61c\\ubbfc (\\uc9c0\\uc740\\uc774), \\uc774\\uc751\\uacac (\\uadf8\\ub9bc)\",\n          \"\\uc774\\ub098\\ubaa8\\ub9ac \\uac00\\uc988\\uc624 (\\uc9c0\\uc740\\uc774), \\uae40\\uc6b1\\uc1a1 (\\uc62e\\uae34\\uc774)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"\\uc628\\ud06c\\ubbf8\\ub514\\uc5b4\",\n          \"\\uc608\\ubb38\",\n          \"\\ubc18\\ub2c8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pubdate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 592,\n        \"samples\": [\n          \"2019-09-09\",\n          \"2015-05-18\",\n          \"2022-04-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 854,\n        \"samples\": [\n          \"\\ubbf8\\uad6d, \\uc720\\ub7fd, \\ud64d\\ucf69, \\ub3c4\\ucfc4, \\uc2f1\\uac00\\ud3ec\\ub974 \\ud68c\\uc0ac\\uc758 \\uc5ec\\ub7ec \\uc624\\ud53c\\uc2a4\\uc5d0\\uc11c \\uc7ac\\ubcf4\\ud5d8 \\uc5b8\\ub354\\ub77c\\uc774\\ud130\\ub85c \\uc77c\\ud574\\uc628 \\uc800\\uc790\\uac00 \\uacbd\\ud5d8\\ud55c \\ub2e4\\uc591\\ud55c \\uc5c5\\ubb34 \\uacf5\\uac04\\uacfc \\ud615\\ud0dc, \\uc5c5\\ubb34 \\ubc29\\uc2dd\\ub4e4\\uc744 \\uc18c\\uac1c\\ud55c\\ub2e4.\",\n          \"\\ucd5c\\uadfc \\uc18c\\ube44\\uc790\\uc758 \\ub2c8\\uc988\\uc640 \\ucde8\\ud5a5\\uc740 \\uc810\\uc810 \\ub354 \\ubcf5\\uc7a1\\ub2e4\\ub2e8\\ud558\\uac8c \\uc138\\ubd84\\ud654\\ub418\\uace0, \\uae30\\uc874 \\ubc29\\uc2dd\\uc73c\\ub85c \\uc774\\ub97c \\ub530\\ub77c \\uc7a1\\uae30\\uc5d4 \\ud2b8\\ub80c\\ub4dc \\ubcc0\\ud654 \\uc18d\\ub3c4\\uac00 \\uac08\\uc218\\ub85d \\ube68\\ub77c\\uc9c0\\uace0 \\uc788\\ub2e4. \\ub204\\uad6c\\ub098 \\uc54c\\uace0 \\uc788\\ub294 \\ub0a1\\uc740 \\uc9c0\\uc2dd \\ub300\\uc2e0 \\uc0ac\\uace0\\uc758 \\uc804\\ud658\\uc73c\\ub85c \\uc720\\uc758\\ubbf8\\ud55c \\ubcc0\\ud654\\ub97c \\uc774\\ub04c\\uc5b4 \\ub0bc \\ucd5c\\uc801\\uc758 \\ud601\\uc2e0\\uc548\\uc774 \\uc808\\uc2e4\\ud55c \\ub54c\\ub2e4. \\ud604\\uc7a5\\uc5d0\\uc11c \\uae30\\uc5c5\\ub4e4\\uc758 \\uba58\\ud1a0\\ub85c \\ud65c\\ub3d9 \\uc911\\uc778 \\uc800\\uc790\\uac00 \\u2018\\uacbd\\uc601 \\uc804\\ub7b5\\u2019, \\u2018\\uc131\\uacfc\\uc8fc\\uc758\\u2019, \\u2018\\uc870\\uc9c1 \\uc6b4\\uc601\\u2019, \\u2018\\uc18c\\ube44\\uc790\\uc758 \\ub9e5\\ub77d\\u2019\\uc774\\ub77c\\ub294 \\ub124 \\uac1c\\uc758 \\ud575\\uc2ec \\ud0a4\\uc6cc\\ub4dc\\ub97c \\uc81c\\uc2dc\\ud55c\\ub2e4.\",\n          \"\\uc0ac\\ub78c\\uc774 \\uc88b\\uc544 \\uad11\\uace0\\ub97c \\uc2dc\\uc791\\ud55c \\ucc9c\\uc0c1 \\uad11\\uace0\\uc778, \\uae40\\uc885\\uc12d \\uc18c\\uc7a5\\uc740 \\uc790\\uc2e0\\uc758 \\uc131\\uacf5 \\ube44\\uacb0\\ub85c \\u2018\\uae30\\uc874 \\uc544\\uc774\\ub514\\uc5b4 \\uc218\\uc900\\uc744 \\ub118\\ub294 \\ube45\\uc544\\uc774\\ub514\\uc5b4\\u2019 \\ub54c\\ubb38\\uc774\\ub77c\\uace0 \\ub9d0\\ud55c\\ub2e4. \\uc544\\uc774\\ub514\\uc5b4 \\ubc1c\\uc0c1\\uc744 \\uc5b4\\ub5bb\\uac8c \\uad11\\uace0\\uc640 \\uc811\\ubaa9\\ud574 \\uba54\\uc2dc\\uc9c0\\ub97c \\uc804\\ub2ec\\ud558\\ub294\\uc9c0\\ub97c \\uadf8\\uc758 \\uad11\\uace0 \\uc791\\uc5c5\\uc744 \\ubc14\\ud0d5\\uc73c\\ub85c \\uc54c\\uae30 \\uc27d\\uac8c \\uc124\\uba85\\ud55c\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"categoryname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 170,\n        \"samples\": [\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\ub2ec\\ub825/\\uae30\\ud0c0>\\ucd9c\\ud310\\uc0ac \\uc81c\\uc791 \\uc0c1\\ud488\",\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\uacbd\\uc81c\\uacbd\\uc601>CEO/\\ube44\\uc988\\ub2c8\\uc2a4\\ub9e8\\uc744 \\uc704\\ud55c \\ub2a5\\ub825\\uacc4\\ubc1c>\\uc131\\uacf5\\ud559/\\uacbd\\ub825\\uad00\\ub9ac\",\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\uacbd\\uc81c\\uacbd\\uc601>\\ub9c8\\ucf00\\ud305/\\uc138\\uc77c\\uc988>\\ub124\\ud2b8\\uc6cc\\ud06c \\ub9c8\\ucf00\\ud305\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"itempage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 103,\n        \"max\": 1012,\n        \"num_unique_values\": 162,\n        \"samples\": [\n          784,\n          180,\n          460\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviewrank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5,\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 데이터 임베딩 비용 계산"
      ],
      "metadata": {
        "id": "L3NKRtKevwOX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "예상치 못한 상황을 피하기 위해, 먼저 비용을 추계해봅시다"
      ],
      "metadata": {
        "id": "y-FpCCgTv3xo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def num_tokens_from_string(string: str, encoding_name = \"cl100k_base\") -> int:\n",
        "    if not string:\n",
        "        return 0\n",
        "    encoding = tiktoken.get_encoding(encoding_name)\n",
        "    num_tokens = len(encoding.encode(string))\n",
        "    return num_tokens\n",
        "\n",
        "def get_desc_length(desc):\n",
        "    word_list = desc.split()\n",
        "    num_words = len(word_list)\n",
        "    return num_words\n",
        "\n",
        "def get_embedding_cost(num_tokens):\n",
        "    return num_tokens/1000000*0.1\n",
        "\n",
        "def get_total_embeddings_cost():\n",
        "    total_tokens = 0\n",
        "    for i in range(len(df.index)):\n",
        "        text = df['description'][i]\n",
        "        token_len = num_tokens_from_string(text)\n",
        "        total_tokens = total_tokens + token_len\n",
        "    total_cost = get_embedding_cost(total_tokens)\n",
        "    return total_cost"
      ],
      "metadata": {
        "id": "yZd-tDC1vz0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_cost = get_total_embeddings_cost()\n",
        "print(f\"비용을 대략 추산해보니 이만큼 드는데 : 미화 {total_cost}불, 한화로 {total_cost*1382.17}원\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7TkFMsRxLDl",
        "outputId": "fb32bacb-b43e-4c7f-aecc-82d4ffb4a784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "비용을 대략 추산해보니 이만큼 드는데 : 미화 0.0134202불, 한화로 18.548997834원\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 더 작은 콘텐츠 청크"
      ],
      "metadata": {
        "id": "JUWZ_2yKx8ZJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "한 번에 처리할 수 있을만큼 청크로 쪼개어 저장한다   \n",
        "맥락정보를 잃지 않게끔 각 청크는 일정부분 겹치게 설정"
      ],
      "metadata": {
        "id": "OjQecYPtx-kK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "우리가 임베딩할 데이터는 길이가 짧으므로 생략한다\n",
        "- 이 블로그에서 가정한 최적의 토큰크기는 512\n",
        "- 모델에서 처리할 수 있는 최대 토큰크기는 약 8000\n",
        "- 아래와 같이 확인하면 description의 최대길이는 264"
      ],
      "metadata": {
        "id": "revu1AFnyP2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max = -1\n",
        "for i in range(len(df.index)):\n",
        "    text = df['description'][i]\n",
        "    if len(text) > max:\n",
        "        max = len(text)\n",
        "print(max)"
      ],
      "metadata": {
        "id": "4vAgOMz8zOSl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 각 텍스트 조각에 대해 임베딩한다"
      ],
      "metadata": {
        "id": "mmLWZotXyakx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "블로그에서는 아래와 같이 설명하고 있지만  \n",
        "`openai.Embedding`는 openai 0.28까지만 쓸 수 있다\n",
        "\n",
        "```python\n",
        "def get_embeddings(text):\n",
        "   response = openai.Embedding.create(\n",
        "       model=\"text-embedding-ada-002\",\n",
        "       input = text.replace(\"\\n\",\" \")\n",
        "   )\n",
        "   embedding = response['data'][0]['embedding']\n",
        "   return embedding\n",
        "```"
      ],
      "metadata": {
        "id": "yB3ykJ7-z0Nc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "최신 버전에서는 이렇게 하자  \n",
        "> 마이그레이션 가이드 : https://github.com/openai/openai-python/discussions/742"
      ],
      "metadata": {
        "id": "FKIz5ao90Cge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "client = OpenAI(\n",
        "    api_key = openai.api_key\n",
        ")\n",
        "\n",
        "def get_embeddings(text):\n",
        "   response = client.embeddings.create(\n",
        "       model=\"text-embedding-ada-002\",\n",
        "       input = text.replace(\"\\n\",\" \")\n",
        "   )\n",
        "   embedding = response.data[0].embedding\n",
        "   return embedding"
      ],
      "metadata": {
        "id": "sqEe1URHyuUE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#실행하는 데 10분정도 소요\n",
        "token_num_list = []\n",
        "embedding_list = []\n",
        "for i in range(len(df.index)):\n",
        "    print(df['description'][i])\n",
        "    text = df['description'][i]\n",
        "    embedding = get_embeddings(text)\n",
        "    token_num = num_tokens_from_string(text)\n",
        "    token_num_list.append(token_num)\n",
        "    embedding_list.append(embedding)\n",
        "\n",
        "df['token_num'] = token_num_list\n",
        "df['embedding'] = embedding_list\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P1KcuZCqyyhf",
        "outputId": "f5aa9ebb-6a62-4f52-df7f-eb861459eeb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         isbn                                              title  \\\n",
              "0  K402831667           코끼리도 장례식장에 간다 - 동물들의 10가지 의례로 배우는 관계와 공존   \n",
              "1  K582831387  열두 발자국 (리커버 에디션) - 생각의 모험으로 지성의 숲으로 지도 밖의 세계로 ...   \n",
              "2  K342930036                             AI 사피엔스 - 전혀 다른 세상의 인류   \n",
              "3  K142831942                    디지털 헬스케어는 어떻게 비즈니스가 되는가 2 - 실전편   \n",
              "4  K962830273                                비즈니스 전략을 위한 AI 인사이트   \n",
              "\n",
              "                      author publisher     pubdate  \\\n",
              "0  케이틀린 오코넬 (지은이), 이선주 (옮긴이)      현대지성  2023-01-05   \n",
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              "\n",
              "                                         description  \\\n",
              "0  세계적으로 유명한 행동생태학자이자 코끼리 전문가인 저자 케이틀린 오코넬은 지난 30...   \n",
              "1  결정과 선택이 어려운 사람들을 위한 햄릿 증후군 처방전부터 제4차 산업혁명을 맞이하...   \n",
              "2  코로나 팬데믹을 거치면서 더욱 진화한 포노 사피엔스가 AI라는 전대미문의 신무기를 ...   \n",
              "3  코로나 팬데믹 이후 급변하는 디지털 헬스케어 시장에서 전작에 이어 다시 한번 국내 ...   \n",
              "4  IBM, 삼성전자, SK 텔레콤 등에서 기술 혁신을 주도하고 비즈니스와의 시너지를 ...   \n",
              "\n",
              "                     categoryname  itempage  reviewrank  token_num  \\\n",
              "0                 국내도서>인문학>인문 에세이       360          10        118   \n",
              "1                 국내도서>인문학>교양 인문학       408           9        116   \n",
              "2  국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반       480           9        167   \n",
              "3  국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반       296           0        117   \n",
              "4    국내도서>경제경영>트렌드/미래전망>인공지능/빅데이터       440          10        133   \n",
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  \"column\": \"author\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 831,\n        \"samples\": [\n          \"KMAC (\\uc9c0\\uc740\\uc774)\",\n          \"\\ud61c\\ubbfc (\\uc9c0\\uc740\\uc774), \\uc774\\uc751\\uacac (\\uadf8\\ub9bc)\",\n          \"\\uc774\\ub098\\ubaa8\\ub9ac \\uac00\\uc988\\uc624 (\\uc9c0\\uc740\\uc774), \\uae40\\uc6b1\\uc1a1 (\\uc62e\\uae34\\uc774)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"\\uc628\\ud06c\\ubbf8\\ub514\\uc5b4\",\n          \"\\uc608\\ubb38\",\n          \"\\ubc18\\ub2c8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pubdate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 592,\n        \"samples\": [\n          \"2019-09-09\",\n          \"2015-05-18\",\n          \"2022-04-26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 854,\n        \"samples\": [\n          \"\\ubbf8\\uad6d, \\uc720\\ub7fd, \\ud64d\\ucf69, \\ub3c4\\ucfc4, \\uc2f1\\uac00\\ud3ec\\ub974 \\ud68c\\uc0ac\\uc758 \\uc5ec\\ub7ec \\uc624\\ud53c\\uc2a4\\uc5d0\\uc11c \\uc7ac\\ubcf4\\ud5d8 \\uc5b8\\ub354\\ub77c\\uc774\\ud130\\ub85c \\uc77c\\ud574\\uc628 \\uc800\\uc790\\uac00 \\uacbd\\ud5d8\\ud55c \\ub2e4\\uc591\\ud55c \\uc5c5\\ubb34 \\uacf5\\uac04\\uacfc \\ud615\\ud0dc, \\uc5c5\\ubb34 \\ubc29\\uc2dd\\ub4e4\\uc744 \\uc18c\\uac1c\\ud55c\\ub2e4.\",\n          \"\\ucd5c\\uadfc \\uc18c\\ube44\\uc790\\uc758 \\ub2c8\\uc988\\uc640 \\ucde8\\ud5a5\\uc740 \\uc810\\uc810 \\ub354 \\ubcf5\\uc7a1\\ub2e4\\ub2e8\\ud558\\uac8c \\uc138\\ubd84\\ud654\\ub418\\uace0, \\uae30\\uc874 \\ubc29\\uc2dd\\uc73c\\ub85c \\uc774\\ub97c \\ub530\\ub77c \\uc7a1\\uae30\\uc5d4 \\ud2b8\\ub80c\\ub4dc \\ubcc0\\ud654 \\uc18d\\ub3c4\\uac00 \\uac08\\uc218\\ub85d \\ube68\\ub77c\\uc9c0\\uace0 \\uc788\\ub2e4. \\ub204\\uad6c\\ub098 \\uc54c\\uace0 \\uc788\\ub294 \\ub0a1\\uc740 \\uc9c0\\uc2dd \\ub300\\uc2e0 \\uc0ac\\uace0\\uc758 \\uc804\\ud658\\uc73c\\ub85c \\uc720\\uc758\\ubbf8\\ud55c \\ubcc0\\ud654\\ub97c \\uc774\\ub04c\\uc5b4 \\ub0bc \\ucd5c\\uc801\\uc758 \\ud601\\uc2e0\\uc548\\uc774 \\uc808\\uc2e4\\ud55c \\ub54c\\ub2e4. \\ud604\\uc7a5\\uc5d0\\uc11c \\uae30\\uc5c5\\ub4e4\\uc758 \\uba58\\ud1a0\\ub85c \\ud65c\\ub3d9 \\uc911\\uc778 \\uc800\\uc790\\uac00 \\u2018\\uacbd\\uc601 \\uc804\\ub7b5\\u2019, \\u2018\\uc131\\uacfc\\uc8fc\\uc758\\u2019, \\u2018\\uc870\\uc9c1 \\uc6b4\\uc601\\u2019, \\u2018\\uc18c\\ube44\\uc790\\uc758 \\ub9e5\\ub77d\\u2019\\uc774\\ub77c\\ub294 \\ub124 \\uac1c\\uc758 \\ud575\\uc2ec \\ud0a4\\uc6cc\\ub4dc\\ub97c \\uc81c\\uc2dc\\ud55c\\ub2e4.\",\n          \"\\uc0ac\\ub78c\\uc774 \\uc88b\\uc544 \\uad11\\uace0\\ub97c \\uc2dc\\uc791\\ud55c \\ucc9c\\uc0c1 \\uad11\\uace0\\uc778, \\uae40\\uc885\\uc12d \\uc18c\\uc7a5\\uc740 \\uc790\\uc2e0\\uc758 \\uc131\\uacf5 \\ube44\\uacb0\\ub85c \\u2018\\uae30\\uc874 \\uc544\\uc774\\ub514\\uc5b4 \\uc218\\uc900\\uc744 \\ub118\\ub294 \\ube45\\uc544\\uc774\\ub514\\uc5b4\\u2019 \\ub54c\\ubb38\\uc774\\ub77c\\uace0 \\ub9d0\\ud55c\\ub2e4. \\uc544\\uc774\\ub514\\uc5b4 \\ubc1c\\uc0c1\\uc744 \\uc5b4\\ub5bb\\uac8c \\uad11\\uace0\\uc640 \\uc811\\ubaa9\\ud574 \\uba54\\uc2dc\\uc9c0\\ub97c \\uc804\\ub2ec\\ud558\\ub294\\uc9c0\\ub97c \\uadf8\\uc758 \\uad11\\uace0 \\uc791\\uc5c5\\uc744 \\ubc14\\ud0d5\\uc73c\\ub85c \\uc54c\\uae30 \\uc27d\\uac8c \\uc124\\uba85\\ud55c\\ub2e4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"categoryname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 170,\n        \"samples\": [\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\ub2ec\\ub825/\\uae30\\ud0c0>\\ucd9c\\ud310\\uc0ac \\uc81c\\uc791 \\uc0c1\\ud488\",\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\uacbd\\uc81c\\uacbd\\uc601>CEO/\\ube44\\uc988\\ub2c8\\uc2a4\\ub9e8\\uc744 \\uc704\\ud55c \\ub2a5\\ub825\\uacc4\\ubc1c>\\uc131\\uacf5\\ud559/\\uacbd\\ub825\\uad00\\ub9ac\",\n          \"\\uad6d\\ub0b4\\ub3c4\\uc11c>\\uacbd\\uc81c\\uacbd\\uc601>\\ub9c8\\ucf00\\ud305/\\uc138\\uc77c\\uc988>\\ub124\\ud2b8\\uc6cc\\ud06c \\ub9c8\\ucf00\\ud305\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"itempage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 103,\n        \"max\": 1012,\n        \"num_unique_values\": 162,\n        \"samples\": [\n          784,\n          180,\n          460\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviewrank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5,\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"token_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 0,\n        \"max\": 275,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          242,\n          68,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "지금까지의 작업을 CSV로 저장해둡시다"
      ],
      "metadata": {
        "id": "OsRAsO0e6aVr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('WEBSCRAP_RESULTS_and_embeddings.csv', index=False)"
      ],
      "metadata": {
        "id": "X9cjGJaJ6c7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#이어서 진행해보자\n",
        "df.load_csv('WEBSCRAP_RESULTS_and_embeddings.csv', index=False)"
      ],
      "metadata": {
        "id": "Pos52fnhQeXj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2부 : pgvector로 PostgreSQL에 저장"
      ],
      "metadata": {
        "id": "R5DFvNXK6ymR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.0 PostgreSQL 설치/구성"
      ],
      "metadata": {
        "id": "SOp2Bh6D7Vu3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PostgreSQL은 오픈소스 RDBMS  \n",
        "간단한 웹앱부터 복잡한 데이터 웨어하우스, 지리공간 데이터분석에 이르기까지 폭넓게 지원함  \n",
        "pgvector는 그러한 postgresql에 벡터 데이터를 잘 저장하기 위한 확장  \n",
        "https://rfriend.tistory.com/837\n",
        "\n",
        "pinecon이라는 SaaS형 벡터DB도 있고 이외에도 다양한 대안이 있음  \n",
        "이번 실습땐 RDB와의 통합도 되고, 오픈소스라서 선택함  \n",
        "https://medium.com/@jeffreyip54/why-we-replaced-pinecone-with-pgvector-2f679d253eba"
      ],
      "metadata": {
        "id": "bXxnQBOp7bib"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "pgvector의 주요요소\n",
        "- **벡터 데이터타입**\n",
        "- **벡터용 인덱싱**\n",
        "- PostgreSQL과 통합\n",
        "- 머신러닝 파이프라인에서의 사용"
      ],
      "metadata": {
        "id": "NRQ0FCR88Ay2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "pgvector에서 제공되는 연산자\n",
        "- `+` : 요소별 더하기\n",
        "- `-` : 요소별 빼기\n",
        "- `*` : 요소별 곱하기\n",
        "- `<->` : 유클리드 거리\n",
        "- `<#>` : 음의 내적\n",
        "- `<=>` : 코사인 거리"
      ],
      "metadata": {
        "id": "n4t7xIEjBsrJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "먼저 postgreSQL부터 설치한다"
      ],
      "metadata": {
        "id": "xKWXHRLHApmV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get -y -qq update\n",
        "!sudo apt-get -y -qq install postgresql"
      ],
      "metadata": {
        "id": "mOFoWK7w7n75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo service postgresql start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrgmcNenADEc",
        "outputId": "dbab70f7-2261-4c61-bf27-3d056388cb9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Starting PostgreSQL 14 database server\n",
            "   ...done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo -u postgres psql -U postgres -c \"ALTER USER postgres PASSWORD 'postgres';\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUZRcgU_AG0c",
        "outputId": "804df6a6-820c-4607-be96-1420d48ae4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ALTER ROLE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo -u postgres psql -U postgres -c \"DROP DATABASE IF EXISTS dev;\"\n",
        "!sudo -u postgres psql -U postgres -c \"CREATE DATABASE dev;\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2g-zIgHAOc_",
        "outputId": "9dc97f53-9ed6-482e-f3be-b05e7aedc5e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ERROR:  database \"dev\" is being accessed by other users\n",
            "DETAIL:  There is 1 other session using the database.\n",
            "ERROR:  database \"dev\" already exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "%env DATABASE_URL=postgresql://postgres:postgres@localhost:5432/dev\n",
        "출처: https://rfriend.tistory.com/837 [R, Python 분석과 프로그래밍의 친구 (by R Friend):티스토리]"
      ],
      "metadata": {
        "id": "ymdPWd7uATt_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이어서 pgvector 확장을 설치하자"
      ],
      "metadata": {
        "id": "WZXWBJmtAser"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install postgresql-server-dev-14"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJqPs-03BUk4",
        "outputId": "9826f6e4-7449-44ca-80ab-0fc9d431124f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osqR1khjAu1q",
        "outputId": "22960f3c-ec2b-45b8-9f76-c985f7f5fa8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#디렉토리 이동을 계속 유지\n",
        "!pwd\n",
        "%cd /content/pgvector\n",
        "!make\n",
        "!make install\n",
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RXUCsilAxz1",
        "outputId": "dc04752f-9fe6-49da-f5e8-496ae4c5199d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 PostgreSQL 데이터베이스 생성 및 pgvector 설치"
      ],
      "metadata": {
        "id": "bVr7mSW364p1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "connection_string = 'postgresql://postgres:postgres@localhost:5432/dev'\n",
        "\n",
        "conn = psycopg2.connect(connection_string)\n",
        "cur = conn.cursor()\n",
        "\n",
        "cur.execute(\"CREATE EXTENSION IF NOT EXISTS vector\")\n",
        "conn.commit()"
      ],
      "metadata": {
        "id": "u8QtyhNC7Lqk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 데이베이스에 테이블을 만들고"
      ],
      "metadata": {
        "id": "AsEjC3NMCD6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "register_vector(conn)"
      ],
      "metadata": {
        "id": "3poHJfRbB-n5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "테이블 설계\n",
        "- isbn : 책별로 고유한 키값\n",
        "- title : 책 제목\n",
        "- author : 저자\n",
        "- publisher : 출판사\n",
        "- pubdate : 출판일자\n",
        "- description : 설명\n",
        "- categoryname : 카테고리(`>`로 구분)\n",
        "- itempage : 쪽수\n",
        "- reviewrank : 리뷰점수\n",
        "- token_num : 설명의 토큰수\n",
        "- embedding : 설명의 임베딩 벡터"
      ],
      "metadata": {
        "id": "Y_kUgxqsCMoh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table_create_command = \"\"\"\n",
        "CREATE TABLE tb_books (\n",
        "            id serial primary key,\n",
        "            isbn text,\n",
        "            title text,\n",
        "            author text,\n",
        "            publisher text,\n",
        "            pubdate text,\n",
        "            description text,\n",
        "            categoryname text,\n",
        "            itempage integer,\n",
        "            reviewrank integer,\n",
        "            token_num integer,\n",
        "            embedding vector(1536)\n",
        "            );\n",
        "            \"\"\"\n",
        "\n",
        "cur.execute(table_create_command)\n",
        "cur.close()\n",
        "conn.commit()"
      ],
      "metadata": {
        "id": "LCS0G1MvDAT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "실행중인 트랜잭션이 있다고하면, colab에 pgadmin을 띄우긴 어려우니까 그냥 재기동하자"
      ],
      "metadata": {
        "id": "2dCRZSzHDxQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo service postgresql stop\n",
        "!sudo service postgresql start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyYqZ-U6DlkG",
        "outputId": "2f9435cb-f13f-4c90-ba9f-f36321906011"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Stopping PostgreSQL 14 database server\n",
            "   ...done.\n",
            " * Starting PostgreSQL 14 database server\n",
            "   ...done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 pgvector로 데이터를 저장하고 ~"
      ],
      "metadata": {
        "id": "Ydh_UGJrD43z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "register_vector(conn)\n",
        "cur = conn.cursor()\n",
        "data_list = [(row['isbn'], row['title'], row['author'], row['publisher'], row['pubdate'],row['description'],row['categoryname'],int(row['itempage']),int(row['reviewrank']),int(row['token_num']), np.array(row['embedding'])) for index, row in df.iterrows()]\n",
        "execute_values(cur, \"INSERT INTO tb_books (isbn, title, author, publisher, pubdate, description, categoryname, itempage, reviewrank, token_num, embedding) VALUES %s\", data_list)\n",
        "conn.commit()"
      ],
      "metadata": {
        "id": "DxXN4AdSEAjg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cur.execute(\"SELECT COUNT(*) as cnt FROM tb_books;\")\n",
        "num_records = cur.fetchone()[0]\n",
        "print(\"Number of vector records in table: \", num_records,\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjUvBX_2FUYf",
        "outputId": "82ad887e-42d9-4720-8fb5-f64385e025de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of vector records in table:  922 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.4 벡터 데이터의 인덱스 생성"
      ],
      "metadata": {
        "id": "hPKrrG0LFaRp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "'Pgvector는 **대략적인 최근접 이웃(ANN) 검색**의 속도를 높이기 위해 ivfflat 인덱스 유형을 지원'한다고함  \n",
        "정리하면, 벡터 데이터 타입에 알맞은 인덱스를 지원함"
      ],
      "metadata": {
        "id": "Bt7NL8UqFcvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_lists = num_records / 1000\n",
        "if num_lists < 10:\n",
        "   num_lists = 10\n",
        "if num_records > 1000000:\n",
        "   num_lists = math.sqrt(num_records)\n",
        "\n",
        "cur.execute(f'CREATE INDEX ON tb_books USING ivfflat (embedding vector_cosine_ops) WITH (lists = {num_lists});')\n",
        "conn.commit()"
      ],
      "metadata": {
        "id": "nP5qFMzCGH6W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3부 pgvector를 사용한 최근접 이웃 검색"
      ],
      "metadata": {
        "id": "l7v3xrPwGa3k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 사용자 질문에 대한 임베딩 벡터를 만든다\n",
        "- pgvector로 벡터 유사성 검색을 수행해서 질문 임베딩에 가장 가까운 이웃을 검색\n",
        "- 검색된 컨텐츠를 모델에 추가 컨텍스트로 제공하고 답변을 생성하는 등 완료처리"
      ],
      "metadata": {
        "id": "7GQ1IRLGGqt6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 질문을 정의하기"
      ],
      "metadata": {
        "id": "TxYudJA-HHM7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 데이터베이스에서 관련성 높은 컨텐츠 찾기"
      ],
      "metadata": {
        "id": "wATwifDJHJxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top3_similar_docs(query_embedding, conn):\n",
        "    \"\"\"\n",
        "    임베딩 값을 코사인 유사도를 기준으로 검색\n",
        "    \"\"\"\n",
        "    embedding_array = np.array(query_embedding)\n",
        "    register_vector(conn)\n",
        "    cur = conn.cursor()\n",
        "    cur.execute(\"SELECT json_build_object('title',title, 'itemPage',itemPage,'categoryname',categoryname, 'description',description) FROM tb_books ORDER BY embedding <=> %s LIMIT 3\", (embedding_array,))\n",
        "    top3_docs = cur.fetchall()\n",
        "\n",
        "    for doc in top3_docs:\n",
        "        print(doc)\n",
        "\n",
        "    return top3_docs\n"
      ],
      "metadata": {
        "id": "UEX13JS4HPMZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 openAI에서 질문임베딩, 답변완성"
      ],
      "metadata": {
        "id": "1ACyHgCeIBYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#def get_completion_from_messages(messages, model=\"gpt-3.5-turbo\", temperature=1, max_tokens=500):\n",
        "def get_completion_from_messages(messages, model=\"gpt-3.5-turbo\"):\n",
        "    \"\"\"\n",
        "    답변은 좋은 모델로\n",
        "    \"\"\"\n",
        "    response = client.chat.completions.create(\n",
        "        model=model,\n",
        "        messages=messages,\n",
        "        #temperature=temperature,\n",
        "        #max_tokens=max_tokens\n",
        "    )\n",
        "    return response.choices[0].message.content\n",
        "\n",
        "def get_embeddings(text):\n",
        "    \"\"\"\n",
        "    임베딩은 싼걸로\n",
        "    - 임베딩/답변용 백본 모델이 같은 계열이면 괜찮다고함\n",
        "    \"\"\"\n",
        "    response = client.embeddings.create(\n",
        "        model=\"text-embedding-ada-002\",\n",
        "        input = text.replace(\"\\n\",\" \")\n",
        "    )\n",
        "    embedding = response.data[0].embedding\n",
        "    return embedding"
      ],
      "metadata": {
        "id": "VGfLYcYyIFzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 질문을 임베딩해 RAG로 답변을 얻어보자"
      ],
      "metadata": {
        "id": "c1htJMj4Io16"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#https://platform.openai.com/docs/guides/text-generation/chat-completions-api\n",
        "def process_input_with_retrieval(user_input):\n",
        "    delimiter = \"```\"\n",
        "\n",
        "    # 1단계 : 관련문서 조회하기\n",
        "    related_docs = get_top3_similar_docs(get_embeddings(user_input), conn)\n",
        "\n",
        "    # 2단계: 답변 완성하기\n",
        "    system_message = f\"\"\"\n",
        "    You are a friendly chatbot. \\\n",
        "    You MUST answer IN KOREAN. \\\n",
        "    Firstly, write pros and cons for each books given context. \\\n",
        "    Secondly, pick up only one book most proper for user's need. \\\n",
        "    Thirdly, suggest the book for the user with kind message.\n",
        "    \"\"\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": system_message},\n",
        "        {\"role\": \"user\", \"content\": f\"{delimiter}{user_input}{delimiter}\"},\n",
        "        {\"role\": \"assistant\", \"content\": f\"Relevant Books information: \\n {related_docs[0][0]} \\n {related_docs[1][0]} {related_docs[2][0]}\"}\n",
        "    ]\n",
        "\n",
        "    final_response = get_completion_from_messages(messages)\n",
        "    return final_response\n"
      ],
      "metadata": {
        "id": "hCyeyLoaIqlx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input = \"지치고 힘들 때 읽을 수 있는 책이 있을까?\"\n",
        "response = process_input_with_retrieval(input)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5P3DXah5JkSn",
        "outputId": "f4069c55-9dec-4453-ff4f-84f96739dd04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "({'title': '어른의 어휘력 (양장) - 말에 품격을 더하고 세상을 올바르게 이해하는 힘', 'itemPage': 308, 'categoryname': '국내도서>인문학>책읽기/글쓰기>글쓰기', 'description': '작가는 글을 쓰고 책을 읽고 사람을 만나면서 어휘력 부족이 단순히 국어능력 문제에 국한되는 게 아니며 얼마나 일상에 커다란 불편을 가져오는지 깨닫는다. 그리고 어휘력의 쓸모에 대해 새로운 시각이 필요하다는 절실함에 이 책을 집필했다.'},)\n",
            "({'title': '어른의 어휘력 (양장) - 말에 품격을 더하고 세상을 올바르게 이해하는 힘', 'itemPage': 308, 'categoryname': '국내도서>인문학>책읽기/글쓰기>글쓰기', 'description': '작가는 글을 쓰고 책을 읽고 사람을 만나면서 어휘력 부족이 단순히 국어능력 문제에 국한되는 게 아니며 얼마나 일상에 커다란 불편을 가져오는지 깨닫는다. 그리고 어휘력의 쓸모에 대해 새로운 시각이 필요하다는 절실함에 이 책을 집필했다.'},)\n",
            "({'title': '스티커 메시지 - 스킵되지 않고 착착 달라붙는 말과 글을 만드는 법', 'itemPage': 252, 'categoryname': '국내도서>경제경영>마케팅/세일즈>마케팅/브랜드', 'description': '수많은 상품과 콘텐츠가 쏟아지지만 주의력 결핍의 시대다. 당신의 말은 제대로 전달되고 있는가? 당신이 쓴 글은 읽히고 있는가? 아니면 이미 묻혀서 사라졌는가? 이 책은 주목받기 어려운 때 우리의 메시지가 1초 만에 스티커처럼 착 달라붙어 상대의 마음을 사로잡는 법을 이야기한다.'},)\n",
            "**어른의 어휘력 (양장) - 말에 품격을 더하고 세상을 올바르게 이해하는 힘**\n",
            "\n",
            "장점:\n",
            "- 어휘력 향상을 통해 더 깊은 이해와 의사소통 능력 향상 가능\n",
            "- 일상에서 발생하는 커다란 불편에 대한 새로운 시각 제시\n",
            "- 말과 글을 더욱 품격 있게 다듬을 수 있는 지친 마음에 힘이 되는 책\n",
            "\n",
            "단점:\n",
            "- 단순한 휘어진 어휘 학습이 아닌, 시간과 노력이 필요한 깊이있는 학습이 요구됨\n",
            "- 즉시 해소되는 지친 감정에 대한 심리적 위로가 부족함\n",
            "\n",
            "**스티커 메시지 - 스킵되지 않고 착착 달라붙는 말과 글을 만드는 법**\n",
            "\n",
            "장점:\n",
            "- 즉각적인 메시지 전달 능력 향상을 통해 주변에 주목받을 수 있음\n",
            "- 짧고 강렬한 메시지 전달 방법을 배움으로써 빠른 소통 능력 증진\n",
            "- 간단하게 학습할 수 있는 실용적인 메시지 전달 방법 제시\n",
            "\n",
            "단점:\n",
            "- 깊이 있는 의사소통과 이해를 위한 어휘력 증진에 한계가 있을 수 있음\n",
            "- 지친 마음을 즉각적으로 힘들게 해소하기보다는 주변에 주목받는 능력 강화에 초점이 맞춰져 있음\n",
            "\n",
            "주어진 맥락에서, **어른의 어휘력 (양장) - 말에 품격을 더하고 세상을 올바르게 이해하는 힘**이 가장 적합할 것입니다. 이 책은 지친 마음을 어휘력 향상을 통해 새로운 시각과 품격 있는 의사소통 능력 향상으로 치유하고자 하는 분들에게 적합합니다. 읽으면서 지치고 힘든 마음을 조금이나마 치유할 수 있기를 바랍니다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.+ RDB 일반 쿼리와 조합해서 RAG를 해보자"
      ],
      "metadata": {
        "id": "jK1PtOpkPrl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#https://platform.openai.com/docs/guides/text-generation/chat-completions-api\n",
        "def process_input_with_retrieval_withUserFileter(user_input):\n",
        "    delimiter = \"```\"\n",
        "\n",
        "    # 1단계 : 관련문서 조회하기\n",
        "    related_docs = get_top3_similar_docs_withUserFilter(get_embeddings(user_input), conn)\n",
        "\n",
        "    # 2단계: 답변 완성하기\n",
        "    # 프롬프트 엔지니어링을 응용 1)단계를 나눠 처리 2)각 정보를 장단점으로 먼저 해석하고 3)취합해서 최종결과를 도출\n",
        "    system_message = f\"\"\"\n",
        "    You are a friendly chatbot. \\\n",
        "    You MUST answer IN KOREAN. \\\n",
        "    Firstly, write pros and cons for each books given context. \\\n",
        "    Secondly, pick up only one book most proper for user's need. \\\n",
        "    Thirdly, suggest the book for the user with kind message.\n",
        "    \"\"\"\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": system_message},\n",
        "        {\"role\": \"user\", \"content\": f\"{delimiter}{user_input}{delimiter}\"},\n",
        "        {\"role\": \"assistant\", \"content\": f\"Relevant Books information: \\n {related_docs[0][0]} \\n {related_docs[1][0]} {related_docs[2][0]}\"}\n",
        "    ]\n",
        "\n",
        "    final_response = get_completion_from_messages(messages)\n",
        "    return final_response\n"
      ],
      "metadata": {
        "id": "d8RVLgDmPcDH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top3_similar_docs_withUserFilter(query_embedding, conn):\n",
        "    \"\"\"\n",
        "    임베딩 값을 코사인 유사도를 기준으로 검색하되, 사용자의 제약조건에 따라 조회\n",
        "    \"\"\"\n",
        "    embedding_array = np.array(query_embedding)\n",
        "    register_vector(conn)\n",
        "    cur = conn.cursor()\n",
        "    cur.execute(\"SELECT json_build_object('title',title, 'itemPage',itemPage,'categoryname',categoryname, 'description',description) FROM tb_books WHERE itempage < 150 ORDER BY embedding <=> %s LIMIT 3\", (embedding_array,))\n",
        "    top3_docs = cur.fetchall()\n",
        "\n",
        "    for doc in top3_docs:\n",
        "        print(doc)\n",
        "\n",
        "    return top3_docs\n"
      ],
      "metadata": {
        "id": "4ESQDWQEHuTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input = \"지치고 힘들 때 읽을 수 있는 책이 있을까?\"\n",
        "response = process_input_with_retrieval_withUserFileter(input)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL_JblB9Pg-t",
        "outputId": "0895a752-882f-493e-e3c4-7b10e772db04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "({'title': '민주주의 언박싱 - 시민이 되려는 청소년을 위한 민주주의 가이드북', 'itemPage': 128, 'categoryname': '국내도서>청소년>청소년 인문/사회', 'description': '청소년 참정권과 우리 사회를 작동하는 원리인 민주주의를 다각도에서 살펴볼 수 있도록 잡지형 도서로 기획되었다. 칼럼, 기사는 물론 인포그래픽, 만화 등 독자의 시선을 잡아끄는 다채로운 꼭지들이 들어 있다.'},)\n",
            "({'title': '랜덤 가족 - 당신을 위한 맞춤 가족 구독 서비스', 'itemPage': 104, 'categoryname': '국내도서>어린이>동화/명작/고전>국내창작동화', 'description': '『안 본 눈 삽니다』, 『사춘기 대 갱년기』 등 유쾌하지만, 따뜻한 이야기로 많은 사랑을 받았던 제성은 작가가 『랜덤 가족』으로 돌아왔다. ‘랜덤 가족’은 AI가 취향을 반영해, 딱 맞는 가족을 보내 주는 구독 서비스다. 말 그대로 어떤 가족이 올지는 모른다. 서비스 기간은 일주일, 반품 기회는 단 두 번뿐이다.'},)\n",
            "({'title': '한경무크 : 궁금한 중대재해처벌법 - 광장 변호사들이 알려주는', 'itemPage': 144, 'categoryname': '국내도서>경제경영>기업 경영>경영 일반', 'description': '각 산업에 걸쳐 중대재해처벌법이 어떤 영향을 미칠 수 있을지 체계적으로 담았다. 구체적인 사례를 통해 주요 쟁점과 궁금한 점을 조목조목 짚어봤다. 국내대표 로펌 중 한 곳인 법무법인 광장의 중대재해팀 변호사들이 참여해 전문성을 강화했다.'},)\n",
            "**민주주의 언박싱 - 시민이 되려는 청소년을 위한 민주주의 가이드북**\n",
            "\n",
            "장점:\n",
            "1. 청소년을 위한 민주주의에 대한 이해를 높일 수 있는 책\n",
            "2. 다양한 시각으로 민주주의에 대한 내용을 다루어 집중력 및 이해도 향상\n",
            "\n",
            "단점:\n",
            "1. 민주주의 이론에 대한 깊은 내용을 다루지 않을 수 있음\n",
            "\n",
            "**랜덤 가족 - 당신을 위한 맞춤 가족 구독 서비스**\n",
            "\n",
            "장점:\n",
            "1. 유쾌하고 따뜻한 이야기로 힐링을 할 수 있는 책\n",
            "2. 특이한 소재와 전개로 흥미를 끌 수 있음\n",
            "\n",
            "단점:\n",
            "1. 너무 가벼운 소재로 인해 깊이 있는 여운을 남기기 어려울 수 있음\n",
            "\n",
            "**한경무크 : 궁금한 중대재해처벌법 - 광장 변호사들이 알려주는**\n",
            "\n",
            "장점:\n",
            "1. 중대재해처벌법에 대한 실무적인 정보를 얻을 수 있는 책\n",
            "2. 구체적인 사례를 통해 중대재해처벌법에 대한 이해를 높일 수 있음\n",
            "\n",
            "단점:\n",
            "1. 전문적인 법률 용어가 포함되어 있어 이해하기 어려울 수 있음\n",
            "\n",
            "사용자의 지친 마음을 달래줄 책으로는 **민주주의 언박싱 - 시민이 되려는 청소년을 위한 민주주의 가이드북**이 가장 적합할 것으로 판단됩니다. 민주주의에 대한 다양한 시각과 이해를 통해 지친 마음을 달래고, 새로운 에너지를 얻을 수 있을 것입니다. 힘든 마음을 달래줄 이 책을 읽어보시기 바랍니다. 힘내세요!\n"
          ]
        }
      ]
    }
  ]
}

embeddings.csv

isbn	title	author	publisher	pubdate	description	categoryname	itempage	reviewrank	token_num	embedding
K402831667	코끼리도 장례식장에 간다 - 동물들의 10가지 의례로 배우는 관계와 공존	케이틀린 오코넬 (지은이), 이선주 (옮긴이)	현대지성	2023-01-05	세계적으로 유명한 행동생태학자이자 코끼리 전문가인 저자 케이틀린 오코넬은 지난 30여 년간 코끼리, 원숭이, 얼룩말, 코뿔소, 사자, 고래, 홍학 등 수많은 동물을 관찰하고 연구했다.	국내도서>인문학>인문 에세이	360	10	118	[-0.009768545627593994, -0.016815371811389923, 0.03441222757101059, -0.037888482213020325, -0.00013705437595490366, 0.023929568007588387, -0.02926521562039852, 0.018216652795672417, -0.015467986464500427, 0.010233393870294094, -0.017529485747218132, 0.0005280066980049014, 0.0027402453124523163, -0.004840482491999865, -0.005231224466115236, 0.0014745451044291258, 0.0348164439201355, 0.011250670067965984, 0.006154183764010668, -0.001360859489068389, -0.03063954785466194, 0.02182764559984207, -0.008663689717650414, -0.0122679453343153, 0.012887743301689625, 0.012914691120386124, 0.00956643745303154, -0.01917329616844654, 0.005399647634476423, -0.022380074486136436, 0.01377028040587902, 0.005551228765398264, -0.005089749116450548, -0.02918437123298645, 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K582831387	열두 발자국 (리커버 에디션) - 생각의 모험으로 지성의 숲으로 지도 밖의 세계로 이끄는 열두 번의 강의	정재승 (지은이)	어크로스	2023-01-18	결정과 선택이 어려운 사람들을 위한 햄릿 증후군 처방전부터 제4차 산업혁명을 맞이하기 위한 자세까지, 좀 더 현명하게 내 삶을 가꾸고 다가올 새로운 세상을 준비하기 위한 뇌과학의 지혜를 모아냈다.	국내도서>인문학>교양 인문학	408	9	116	[0.007406338583678007, -0.0230783149600029, 0.012698529288172722, -0.04743875935673714, -0.009520486928522587, 0.005544524174183607, -0.0015677091432735324, 0.005578623618930578, -0.021196041256189346, 0.01181195117533207, -0.02361026220023632, 0.01438984740525484, -0.004068030510097742, -0.021018724888563156, -0.005254681222140789, 0.0001999063533730805, 0.03780915588140488, -0.009875117801129818, 0.002842165296897292, -0.010823074728250504, -0.0021840515546500683, 0.00043156754691153765, 0.013141818344593048, -0.007924646139144897, 0.0010476968018338084, 0.015221867710351944, 0.027934035286307335, -0.024674156680703163, 0.001440689666196704, -0.010863994248211384, -0.00039235351141542196, 0.011914248578250408, 0.003822516417130828, -0.014539884403347969, 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K342930036	AI 사피엔스 - 전혀 다른 세상의 인류	최재붕 (지은이)	쌤앤파커스	2024-06-01	코로나 팬데믹을 거치면서 더욱 진화한 포노 사피엔스가 AI라는 전대미문의 신무기를 장착하고 어떻게 천지개벽급 신문명을 만들고 있는지를 세세하게 포착했다. 지금 우리가 유심히 관찰할 것은, 일상의 변화부터 각 산업의 구조적 진화까지 이끌어가는 거대한 축의 전환과 새로운 문명의 시작이다.	국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반	480	9	167	[0.007687531411647797, -0.015525798313319683, 0.024624766781926155, -0.019486041739583015, -0.011243528686463833, 0.010448738932609558, -0.02654322236776352, 0.025063270702958107, -0.013189392164349556, 0.008571391925215721, -0.0013763216556981206, 0.01355252880603075, -0.003686178009957075, -0.002447745529934764, 0.01693723537027836, -0.00614591408520937, 0.028475383296608925, 0.005347698926925659, 0.0029890246223658323, -0.022240398451685905, -0.00749568548053503, 0.0030249955598264933, -0.005018820520490408, -0.01241515763103962, -0.009475807659327984, 0.028036879375576973, 0.013401792384684086, -0.02984570898115635, 0.0007725215400569141, -0.00026207492919638753, 0.027022836729884148, -0.0029736084397882223, 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K142831942	디지털 헬스케어는 어떻게 비즈니스가 되는가 2 - 실전편	김치원 (지은이)	클라우드나인	2023-02-20	코로나 팬데믹 이후 급변하는 디지털 헬스케어 시장에서 전작에 이어 다시 한번 국내 디지털 헬스케어의 비즈니스 모델의 가능성을 내다보고 디지털 헬스케어가 주류 의료로 진입하기 위한 과정과 방법을 다루고 있다.	국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반	296	0	117	[0.006565560586750507, -0.005259097088128328, 0.01563766971230507, -0.04334932193160057, -0.012472902424633503, 0.00714067043736577, -0.01728653907775879, 0.0007737388368695974, -0.006814885418862104, 0.015451506711542606, -0.025690456852316856, 0.02382882870733738, -0.023416612297296524, -0.006961156148463488, 0.002508210251107812, -0.011994198895990849, 0.017685459926724434, -0.005990450736135244, 0.02131563238799572, -0.00937462318688631, -0.010571383871138096, 0.009746948257088661, -0.004610851872712374, 0.012060685083270073, -0.0011036790674552321, 0.03773784264922142, 0.0004965724656358361, -0.015079180710017681, -0.02057098224759102, 0.015757344663143158, 0.01570415496826172, -0.0015075856354087591, -0.005648044403642416, -0.017791837453842163, 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K962830273	비즈니스 전략을 위한 AI 인사이트	이호수 (지은이)	한빛비즈	2022-11-18	IBM, 삼성전자, SK 텔레콤 등에서 기술 혁신을 주도하고 비즈니스와의 시너지를 고민해온 저자는 지난 70여 년 AI 역사에 대한 통찰을 바탕으로 비즈니스 현장에서 체험한 좌절과 성공의 사례를 꼼꼼하게 분석하고 대안들을 정리했다.	국내도서>경제경영>트렌드/미래전망>인공지능/빅데이터	440	10	133	[0.00018861712305806577, -0.033555567264556885, 0.02125806361436844, -0.02375744841992855, -0.0042343297973275185, 0.008408834226429462, -0.035230688750743866, 0.020393915474414825, -0.025605397298932076, -0.005218130070716143, -0.014796888455748558, 0.021231474354863167, 0.004792702849954367, -0.02143089286983013, -0.007936875335872173, 0.011779014952480793, 0.0093926340341568, 0.0015047824708744884, 0.004506869241595268, -0.010177015326917171, -0.01909104362130165, 0.0041180020198225975, 0.008947265334427357, -0.0030378159135580063, -0.00202742638066411, -0.0010020803892984986, 0.01919740065932274, -0.030285095795989037, 0.0030876705422997475, 0.0060955737717449665, 0.026575902476906776, 0.0006626526592299342, -0.022348221391439438, -0.002863324247300625, 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K352839716	2023 트렌드 노트 - 새로운 소비주체의 등장	이원희, 박현영, 최재연, 정석환, 신수정, 신예은, 심우연 (지은이)	북스톤	2022-10-24	빅데이터는 소비 트렌드를 어떻게 연구할까? 어떤 항목에 더 많은 소비를 하는지, 어떤 채널에 대해 더 많이 말하는지, 어떤 방법을 더 많이 쓰는지, 어떤 식의 경제적 혜택을 더 선호하는지 등 시간과 장소, 품목과 수단의 변화를 관측한다. 소비에 대한 죄책감과 합리화도 연구한다. 소비를 기록하는 이유와 주체도 살펴본다.	국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반	304	10	194	[-0.0035242608282715082, -0.019949892535805702, 0.030070168897509575, -0.04626789689064026, -0.015946701169013977, 0.008904785849153996, -0.006100571248680353, 0.009241688065230846, -0.01056287344545126, -0.026542603969573975, -0.018100233748555183, 0.02766561135649681, -0.003781891893595457, -0.013376996852457523, -0.005892484448850155, 0.008026198484003544, 0.028035543859004974, 0.0033459006808698177, 0.012683374807238579, -0.021931668743491173, -0.026384063065052032, 0.006648863200098276, -0.011210253462195396, -0.0012856780085712671, -0.005806607659906149, 0.019447842612862587, 0.02777130715548992, -0.008191346190869808, -0.008316858671605587, 0.008924604393541813, 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K002830471	머니 트렌드 2023 - 45가지 키워드로 전망하는 대한민국 돈의 흐름	부동산읽어주는남자(정태익), 김도윤, 김경민, 김상균, 전영수, 최준철, 홍춘욱 (지은이)	북모먼트	2022-11-15	장기 불황에도 꿋꿋하게 성장하기 위해 전국민이 꼭 알아야 할 부의 트렌드를 핵심만 골라 담은 책이다. 극심한 경기 불황으로 주식과 부동산 시장이 무너진 지금같은 시기일수록, 다음 상승장을 위해 꼼꼼히 준비하고 치열하게 공부해야만 부에 한걸음 더 가까워질 수 있을 것이다.	국내도서>경제경영>경제학/경제일반>경제사/경제전망>한국 경제사/경제전망	336	9	158	[-0.009794140234589577, -0.015569144859910011, 0.014006889425218105, -0.0248358566313982, -0.02201845496892929, -0.012611540965735912, -0.03084453195333481, -0.0037620982620865107, -0.021951692178845406, -0.01519527193158865, -0.007657722570002079, 0.013365963473916054, -0.006733054295182228, 0.006656276527792215, -0.002348390407860279, 0.026785338297486305, 0.02420828491449356, -0.009934342466294765, 0.008759313262999058, -0.03199285641312599, -0.008532318286597729, 0.002269943943247199, 0.005307662766426802, -0.006909976247698069, 0.003965725656598806, 0.0026805365923792124, 0.02793366275727749, -0.021097125485539436, -0.00821185577660799, -0.0040725464932620525, 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K822839859	유난한 도전 - 경계를 부수는 사람들, 토스팀 이야기	정경화 (지은이)	북스톤	2022-11-13	간편송금으로 시작해 뱅킹, 증권, 보험, 결제 등을 아우르는 종합 금융 플랫폼으로 성장한 토스팀, 이들은 어떻게 이런 성과를 이루었을까? 세간에 화제가 되는 그들의 독특한 기업문화는 어떻게 만들어졌을까? 창업자 등 35명을 인터뷰하고 회의록 등 내부자료를 샅샅이 뒤져 토스가 달려온 11년의 유난한 도전사를 정리했다.	국내도서>경제경영>기업 경영>경영전략/혁신	336	10	198	[0.007365711964666843, -0.02525578998029232, 0.02278263121843338, -0.04330716282129288, -0.01414001639932394, 0.017150819301605225, -0.01620994322001934, 0.012043207883834839, -0.014314750209450722, -0.014771747402846813, -0.0009316348587162793, 0.015457242727279663, -0.007802546955645084, -0.030349958688020706, 0.00645843893289566, 0.018777189776301384, 0.018535248935222626, -0.007244742475450039, 0.0029553575441241264, -0.003928155638277531, -0.03596832975745201, 0.0012617814354598522, -0.030403723940253258, 0.03645221143960953, 0.01552444789558649, 0.0069288769736886024, 0.007298506796360016, -0.006337469443678856, -0.015605093911290169, 0.020699264481663704, -0.0069288769736886024, -0.006132492795586586, 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K542830726	개발자 원칙 - 테크 리더 9인이 말하는 더 나은 개발자로 살아가는 원칙과 철학	박성철, 강대명, 공용준, 김정, 박미정, 박종천, 이동욱(네피림), 이동욱(향로), 장동수 (지은이)	골든래빗(주)	2022-12-20	“나도 테크 리더가 될 수 있을까? 어떻게 선배 개발자들처럼 성장할 수 있을까? 개발자로 살아가면서 하루에도 천 번을 되묻는 물음에 마켓컬리, 레몬트리, 카카오, 코드스쿼드, 무신사, 몰로코, 데이블, 인프런, 패스트캠퍼스 테크 리더 9명이 답한다.	국내도서>컴퓨터/모바일>프로그래밍 개발/방법론>프로그래밍 기초/개발 방법론	252	10	153	[0.00916200503706932, -0.034078504890203476, 0.01566365547478199, -0.06665164232254028, -0.0013423440977931023, 0.014378897845745087, -0.02919902466237545, 0.004269546363502741, -0.010537603870034218, 0.005486172158271074, 0.007935645990073681, 0.008467717096209526, 0.009051697328686714, -0.028913524001836777, 0.009804383851587772, -0.007221892010420561, 0.010732264257967472, -0.016688864678144455, -0.00504494272172451, -0.016208702698349953, 0.011368153616786003, -0.012497182935476303, 0.018687376752495766, 0.007416552398353815, -0.007663121912628412, 0.004736730828881264, 0.01719498075544834, -0.01034943200647831, -0.010518137365579605, 0.004639400634914637, 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K902830613	제품의 탄생 - CEO, 경영진, 실무자가 꼭 읽어야 할 프로덕트 관리의 모든 것	오이카와 다쿠야, 소네하라 하루키, 고시로 구미코 (지은이), 강경민 (옮긴이)	책만	2022-12-08	비즈니스 전략, IT 개발, UX 디자인, 마케팅, 고객 성공, 보안, 지적재산 법률, 조직 운영 등 세계 유수기업에서 B2C, B2B 프로덕트를 기획하고 출시하며 체득한 다양한 지식과 노하우를 풍부한 그림과 표, 사례와 함께 자세히 설명한다.	국내도서>컴퓨터/모바일>프로그래밍 개발/방법론>프로그래밍 기초/개발 방법론	468	7	139	[0.0056196944788098335, -0.015580812469124794, 0.020747575908899307, -0.031054263934493065, -0.005545883439481258, 0.014735342003405094, -0.02086835727095604, 0.006562461145222187, -0.007555553689599037, -0.009447797201573849, -0.024290500208735466, 0.0034036897122859955, -0.028021305799484253, -0.028638634830713272, -0.0012220739154145122, 0.012829679064452648, 0.011165577918291092, -0.0034137547481805086, 0.015097687020897865, -0.01717781275510788, -0.015124526806175709, -0.016439704224467278, 0.004143476020544767, 0.0025951245333999395, 0.0073139904998242855, -0.006653047166764736, 0.01688256859779358, -0.012943750247359276, 0.007522003259509802, 0.002256265375763178, 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K652830483	천 원을 경영하라 - 국민가게 다이소 창업주 박정부 회장의 본질 경영	박정부 (지은이)	쌤앤파커스	2022-12-01	전국 1,500개 매장에 하루 100만 명의 고객이 찾아오고, 1시간에 42만 개씩 상품이 팔리는 곳, 매월 600종의 신상품이 나오고, 1,000원짜리 상품이 주력인데 연매출이 3조나 되는 곳, 25년간 단 한 번도 당기순손실을 낸 적이 없고, 2030이 가장 좋아하는 라이프스타일숍 1위로 뽑힌 곳…, 바로 균일가 생활용품점 다이소 이야기다.	국내도서>경제경영>기업/경영자 스토리>국내 기업/경영자	276	9	198	[-0.0008807635167613626, -0.0015658017946407199, 0.0038509354926645756, -0.022697433829307556, -0.012881061993539333, 0.003837552620097995, -0.022336095571517944, 0.008498154580593109, 0.00352305406704545, 0.0023236097767949104, -0.011462472379207611, 0.020074382424354553, -0.029897445812821388, -0.019311554729938507, -0.01696954481303692, 0.030486294999718666, 0.014292960055172443, -0.009207449853420258, -0.013904855586588383, -0.012312287464737892, 0.013208943419158459, 0.004500007256865501, -0.016367312520742416, 0.003228629706427455, 0.01650114171206951, 0.031101908534765244, 0.018441665917634964, -0.0024741676170378923, 0.01398515235632658, 0.0074074468575417995, 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8954689523	디컨슈머 - 소비하지 않는 소비자들이 온다	J. B. 매키넌 (지은이), 김하현 (옮긴이)	문학동네	2022-12-05	환경문제를 고민하면서도 경기 침체의 우려 앞에 우리는 어떤 선택을 할 수 있을까? 소비하지 않는 삶은 실현 가능할까? 『뉴요커』 『내셔널 지오그래픽』 저널리스트 J. B. 매키넌은 이 딜레마를 해결하기 위해 어느 날 소비의 25퍼센트가 사라지면 어떤 일이 벌어질지 분석한다.	국내도서>사회과학>사회학>사회학 일반	400	9	159	[0.023438431322574615, -0.0016322003211826086, 0.019416609779000282, -0.010221020318567753, -0.017099400982260704, -0.0012135381111875176, -0.02314545027911663, -0.008216767571866512, -0.028658809140324593, 0.013343924656510353, 0.0006671129958704114, 0.013137506321072578, -0.0031661856919527054, -0.003945247270166874, 0.008836021646857262, 0.008456478826701641, 0.022279826924204826, -0.0011611012741923332, 0.018337909132242203, -0.007690735161304474, -0.008449820801615715, 0.0010079523781314492, 0.007197995204478502, -0.01931007020175457, 0.006175893358886242, 0.009894746355712414, 0.023158768191933632, -0.01946987770497799, -0.008842680603265762, 0.0036855600774288177, 0.02514304406940937, -0.016912957653403282, -0.015794306993484497, -0.01747228391468525, -0.0178984384983778, 0.018457764759659767, 0.009082391858100891, 0.004464621655642986, 0.006175893358886242, -0.018004976212978363, 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K432737052	데일 카네기 자기관리론 - 국내최초 초판 무삭제 완역본	데일 카네기 (지은이), 임상훈 (옮긴이)	현대지성	2021-01-13	현대지성에서는 출간 즉시 베스트셀러가 된 『데일 카네기 인간관계론』에 이어 저자의 또 다른 역작 『데일 카네기 자기관리론』을 펴냈다. 이 책은 국내에서 유일하게 1948년 초판의 10부까지 고스란히 담은 ‘오리지널’ 초판 완역본이다.	국내도서>자기계발>성공>성공학	424	9	143	[-0.006994121707975864, -0.011746883392333984, 0.027301378548145294, -0.04507022723555565, -0.01652665063738823, 0.013556174002587795, -0.030271854251623154, 0.010389916598796844, -0.026869308203458786, 0.0018970542587339878, -0.021508948877453804, 0.019483624026179314, -0.02577563375234604, 0.004725757986307144, -0.009991602040827274, 0.011098779737949371, 0.029866790398955345, -0.02198152430355549, 0.015392468310892582, -0.007426190655678511, -0.009957847185432911, 0.0007464165682904422, 0.0002860771201085299, -0.004681875929236412, 0.008344338275492191, 0.007027877029031515, 0.020145229995250702, -0.03208114579319954, 0.00766247883439064, -0.01078823022544384, 0.005633778404444456, 0.0073991864919662476, 0.0093772541731596, -0.017215261235833168, 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8984012564	물류가 온다	박철홍 (지은이)	영림카디널	2022-11-25	코로나 팬데믹 시기와 맞물려 더욱 강화된 비대면 사회를 겪으며 변화된 물류업의 위상과 급하게 바뀌고 있는 이커머스 시장 동향, 그리고 4차 산업혁명 시대의 첨단과학 기술이 결합되며 어떻게 물류업의 혁신이 이루어지고 있는지 쉽고 자세하게 알려주고 있다.	국내도서>경제경영>경제학/경제일반>경제이야기	176	9	156	[0.01997750997543335, -0.008782641030848026, 0.012447628192603588, -0.039875056594610214, -0.019684311002492905, -0.004124775994569063, -0.02630794234573841, -0.0019940859638154507, -0.039715129882097244, 0.012347673997282982, -0.022656282410025597, 0.020923743024468422, -0.013513806276023388, -0.014579984359443188, 0.016952229663729668, 0.0003219357749912888, 0.0239756777882576, -0.010675107128918171, 0.004164757672697306, -0.023349298164248466, -0.016339177265763283, 0.01294739916920662, -0.0015776102663949132, -0.01629919558763504, 0.002818708075210452, 0.040674690157175064, 0.007783099543303251, -0.021190287545323372, 0.002338927937671542, 0.02013743668794632, 0.019777601584792137, 0.0033118154387921095, -0.009468993172049522, -0.005863978993147612, 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K222830659	배터리 전쟁 - 리튬부터 2차 전지까지, 누가 새로운 경제 영토를 차지할 것인가	루카스 베드나르스키 (지은이), 안혜림 (옮긴이)	위즈덤하우스	2023-01-02	배터리 분야의 세계적인 애널리스트 루카스 베드나르스키의 첫 책이다. 리튬 채굴과 가공, 배터리 제조와 재활용 등 배터리 산업의 전 분야를 아우르는 폭넓은 시각으로 시장 흐름과 전망을 제시한다. 또한 배터리 강국 한·중·일을 시작으로 유럽과 북미, 오스트레일리아와 라틴아메리카 국가들을 넘나들며 에너지 패권 변화에 대응하는 각국의 전략을 살펴본다.	국내도서>경제경영>경제학/경제일반>경제사/경제전망>세계 경제사/경제전망	384	7	205	[0.02234325371682644, -0.01501250546425581, 0.009551571682095528, -0.030133014544844627, -0.004755535162985325, -0.001841124496422708, -0.012420418672263622, 0.005825446452945471, -0.023801302537322044, -0.011185127310454845, -0.029646998271346092, 0.014458986930549145, -0.03334612399339676, -0.008350031450390816, 0.009342314675450325, -0.00029194733360782266, 0.014512988738715649, 0.0190221406519413, 0.003959008492529392, -0.012697177939116955, -0.020885203033685684, 0.0019052516436204314, 0.005869322922080755, -0.019481156021356583, 0.00583557179197669, -0.00590307405218482, 0.022937273606657982, -0.023153280839323997, -0.0012690427247434855, 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K512830134	자막 만들기 100가지 - 세계 프로들의 노하우	연희승 (지은이)	박영사	2023-01-15	자세한 설명을 위해 세계 광고 100편을 예제로 사용한다. 힐튼 호텔은 왜 글자에 이미지 요소를 추가했는지, 샤넬은 무얼 기준으로 글씨 크기를 변형했는지, 나이키는 글자 간격을 어떻게 처리했는지, 스타벅스는 왜 한 문장을 두 줄로 표현했는지 등을 살펴본다.	국내도서>대학교재/전문서적>사회과학계열>언론정보학	304	0	154	[0.012419921346008778, 0.0076014455407857895, -0.0013180732494220138, -0.023838773369789124, 0.003161707427352667, 0.013467705808579922, -0.012880412861704826, 0.007928460836410522, 0.0017852385062724352, -0.0011695815483108163, -0.015589971095323563, 0.028243474662303925, -0.015870269387960434, -0.018112663179636, 0.005332357250154018, 0.017805669456720352, 0.023958902806043625, 0.019207164645195007, 0.007441274356096983, -0.007921786978840828, -0.007314472459256649, -0.00638014217838645, -0.006440206430852413, 0.011685804463922977, -0.021409515291452408, 0.019794458523392677, 0.04006942734122276, -0.026761893182992935, -0.0033368943259119987, -0.005188870709389448, 0.0071209329180419445, -0.0025327028706669807, -0.02302457205951214, 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K732831216	지구를 위한 소비 수업 - 결제 버튼을 누르기 전에 알아야 할 환경 이야기	선보라, 전진현, 최혜연 (지은이), 대안사회교사모임 (기획)	휴머니스트	2023-02-20	곰곰문고 22권. 사회 교사가 청소년의 눈높이에 맞춰 집필한 책으로, 일상의 익숙한 장면에서 출발해 생활 속 소비재들이 품고 있는 환경문제와 그 대안을 구체적인 자료와 함께 살펴본다.	국내도서>청소년>청소년 인문/사회	224	10	113	[0.01737941987812519, 0.001089573954232037, 0.004240685608237982, -0.03852236270904541, -0.013689820654690266, 0.01042361930012703, -0.01455677580088377, 0.002997378585860133, -0.03026949055492878, -0.006414793431758881, -0.008273033425211906, 0.003706399817019701, -0.00676426338031888, -0.011875264346599579, 0.007419520057737827, 0.009590267203748226, 0.02825331687927246, -0.009919575415551662, -0.007688343059271574, -0.019852591678500175, -0.014166981913149357, 0.0054705520160496235, 0.007406078744679689, -0.02914043329656124, 0.006300543434917927, 0.001994332065805793, 0.03634489327669144, -0.015403568744659424, 0.009939737617969513, 0.008380562998354435, 0.02673446573317051, -0.024879585951566696, -0.006942358799278736, -0.017433183267712593, 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K492830721	뉴그레이 - 마케터들을 위한 시니어 탐구 리포트	정지원, 유지은, 염선형 (지은이)	미래의창	2022-12-09	스타벅스, 애플, 윌라, 츠타야 T 사이트… 이들의 공통점은 뭘까? 젊은 세대가 주로 찾는 브랜드라 생각했던 이 브랜드들의 이용자 상당수가 중장년층이라는 것이다. 문제는 기업과 브랜드가 시니어 시장을 웬만해서는 움직이지 않는 시장이라 단정하며 시니어를 고객으로 인지하지 못하거나, 인지하면서도 외면하고 있다는 것이다.	국내도서>경제경영>마케팅/세일즈>마케팅/브랜드	248	9	178	[0.015654560178518295, -0.010856539011001587, 0.008010255172848701, -0.03675771877169609, -0.015302162617444992, -0.003115664003416896, -0.027920685708522797, 0.014570261351764202, -0.011195382103323936, -0.015234393998980522, -0.03038746491074562, 0.009487612172961235, -0.03160730004310608, -0.014976873062551022, 0.004774301778525114, 0.020140845328569412, 0.023759691044688225, 0.003795044729486108, 0.007895048707723618, -0.00414744159206748, -0.001707770163193345, -0.0034595897886902094, -0.005980583839118481, -0.007088601589202881, -0.0076104202307760715, 0.02755473554134369, 0.010605794377624989, -0.02111671306192875, -0.023176880553364754, 0.0113241421058774, 0.02786647155880928, 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K152832225	비전공자도 이해할 수 있는 AI 지식 (챗GPT 수록 개정판) - 챗GPT부터 유튜브 추천, 파파고 번역과 내비게이션까지 일상을 움직이는 인공지능 이해하기	박상길 (지은이), 정진호 (그림)	반니	2023-04-10	2022년 과학 분야 베스트셀러, 《비전공자도 이해할 수 있는 AI 지식》이 2023년의 가장 거대한 화두, 챗GPT를 다룬 개정판을 출간했다. 챗GPT에 관한 AI 지식을 집중적으로 다룬 내용을 덧붙이고 챗GPT가 어떤 과정을 바탕으로 개발되었는지 그 배경과 원리를 친절하게 전한다.	국내도서>과학>기초과학/교양과학	464	9	148	[0.006752301473170519, -0.013531558215618134, 0.028626523911952972, -0.01599796861410141, -0.006085158325731754, 0.01587666943669319, -0.031187277287244797, 0.020755577832460403, -0.032831549644470215, -0.0009232189040631056, 0.016739238053560257, 0.029057810083031654, -0.01827569119632244, -0.007877685129642487, 0.010943850502371788, 0.0074464003555476665, 0.0253245010972023, 0.008052894845604897, -0.003601901466026902, -0.00018974000704474747, 0.014798457734286785, 0.002099143574014306, -0.005495510995388031, -0.01622708886861801, 0.01877436414361, 0.012291614897549152, 0.02222464233636856, -0.035985320806503296, 0.007614871021360159, 0.007877685129642487, 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8934999969	말 하자니 일이 커지고 안 하자니 속이 터지고 - 좋은 관계를 위한 표현의 기술	김지윤 (지은이)	김영사	2019-12-26	《직장생활도 연애처럼》개정판. 기업, 기관, 방송을 오가며 속 시원한 강의로 열띤 반응을 불러일으키고 있는 소통 전문가 김지윤 소장. 관계에 대한 예리한 통찰에 위트를 겸한 그가 그간 쌓아온 소통 노하우를 한 권의 책에 담았다.	국내도서>자기계발>인간관계>인간관계	208	10	136	[-0.015328217297792435, -0.01453001331537962, 0.02724715694785118, -0.020469190552830696, -0.007156775798648596, 0.011513073928654194, -0.02720656991004944, 0.01837221346795559, -0.01872396469116211, -0.012642734684050083, -0.013210947625339031, 0.019292177632451057, 0.0001681597059359774, -0.004122924990952015, -0.004701284691691399, 0.0004756245471071452, 0.011141030117869377, 0.00309473043307662, -0.003629120998084545, -0.03563506156206131, -0.005506252869963646, 0.002042860258370638, -0.015436448156833649, -0.0007149165612645447, -0.0007500071078538895, 0.010708105750381947, 0.013068894855678082, -0.022769099101424217, -0.014773532748222351, -0.003859112039208412, 0.006507389713078737, -0.010593109764158726, -0.005749772302806377, 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K272830938	그릿 GRIT (골드 에디션) - IQ, 재능, 환경을 뛰어넘는 열정적 끈기의 힘	앤절라 더크워스 (지은이), 김미정 (옮긴이)	비즈니스북스	2022-12-15	2016년 출간 즉시 아마존 베스트셀러 1위에 오르며 교육, 산업, 방송할 것 없이 전세계적으로 신드롬을 일으킨 책 《그릿》이 국내 50만 부 돌파, 150쇄를 기념한 골드 에디션으로 다시 우리를 찾아왔다. 이번 리커버는 고급스러운 하드커버로 소장 가치를 높였다.	국내도서>자기계발>창의적사고/두뇌계발	416	9	154	[0.0010968385031446815, -0.018864281475543976, -0.016972485929727554, -0.02361389435827732, -0.01100192777812481, 0.01683831587433815, -0.03821157291531563, -0.0075202202424407005, -0.015000189654529095, -0.011115972883999348, -0.0059101819060742855, 0.01622113585472107, -0.02417740784585476, -0.019414378330111504, -0.01740182936191559, 0.016422390937805176, 0.02263445407152176, -0.005413753446191549, -0.03558184579014778, -0.007003666367381811, -0.018314184620976448, -0.017213992774486542, 0.01086104940623045, -0.0004620474355760962, 0.0011915960349142551, 0.0015295363264158368, 0.020434068515896797, -0.020058393478393555, 0.004115660209208727, -0.0020595071837306023, 0.009445557370781898, 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K582830503	하마터면 또 회계를 모르고 일할 뻔했다! - 투자에서 비즈니스까지 한칼로 끝내는	김수헌, 이재홍 (지은이)	어바웃어북	2022-11-29	2018년 출간된 이후 부동의 베스트셀러! 독자들에게 “인생 회계책”, “왜 이제야 봤을까?”, “진심 쉽고 재미있는 회계책” 등의 찬사를 받은 『하마터면 회계를 모르고 일할 뻔했다!』가 주식투자에게 꼭 필요한 회계를 담고 새롭게 돌아왔다.	국내도서>경제경영>기업 경영>세무/재무/회계	458	9	140	[-0.008365515619516373, -0.029837442561984062, 0.004474959801882505, -0.04360048845410347, -0.020894089713692665, 0.0030139493755996227, -0.022942787036299706, -0.0027233888395130634, -0.006474409718066454, -0.001856632181443274, -0.005430361721664667, 0.013401896692812443, -0.013283702544867992, -0.0016194232739508152, -0.0030582721810787916, 0.0164027139544487, 0.027237171307206154, -0.02382267639040947, -0.012338150292634964, -0.016481509432196617, -0.0001143979316111654, -0.010236921720206738, -0.020421313121914864, 0.00937673170119524, 0.003480159444734454, 0.006802726536989212, 0.03409243002533913, -0.02214169315993786, 0.002396713476628065, 0.017926104366779327, 0.0022982184309512377, -0.0002948696492239833, 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8937427362	회복력 시대 - 재야생화되는 지구에서 생존을 다시 상상하다	제러미 리프킨 (지은이), 안진환 (옮긴이)	민음사	2022-11-01	죽어 가는 진보의 시대를 해체하고 부상하는 새로운 문명의 서사를 제시한다. 8년의 집필 기간 끝에 완성되어 전 세계 주요 국가에서 2022년 11월 1일 동시 출간되는 이 책은 그가 50년에 걸쳐 글로벌 경제와 사회, 거버넌스 혁신, 기후변화 등에 대해 연구한 결과가 집대성되어 있다.	국내도서>사회과학>사회학>사회학 일반	432	8	148	[-0.01983555033802986, -0.03264849632978439, -0.0036636805161833763, -0.013124323450028896, -0.010984416119754314, 0.0054756454192101955, -0.045130182057619095, 0.007605615071952343, -0.03344350680708885, 0.00430300273001194, -0.001367255114018917, 0.03248949348926544, -0.017728768289089203, -0.01200468186289072, 0.013051447458565235, 0.013270076364278793, 0.025493387132883072, -0.010242405347526073, -0.006264032330363989, -0.03153547644615173, 0.01611224375665188, 0.0018533718539401889, 0.007075607310980558, -0.008798133581876755, 0.008798133581876755, 0.01968979835510254, 0.018682783469557762, -0.01794077269732952, 0.023731108754873276, 0.00506157660856843, 0.013965711928904057, -0.010838664136826992, 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K332830578	모두 거짓말을 한다 - 구글 트렌드로 밝혀낸 충격적인 인간의 욕망, 개정판	세스 스티븐스 다비도위츠 (지은이), 이영래 (옮긴이)	더퀘스트	2022-11-15	전 구글 데이터과학자 세스 스티븐스 다비도위츠는 검색 데이터를 이용해 사람들의 숨겨진 진짜 욕망과 생각을 탐사한다. 인종주의뿐 아니라 정신질환, 성생활, 아동학대, 낙태, 광고, 종교, 건강 등 다양한 주제에 걸쳐 인간의 충격적인 본성을 거침없이 보여준다.	국내도서>경제경영>트렌드/미래전망>트렌드/미래전망 일반	388	10	147	[-0.004290993325412273, 0.008540460839867592, 0.024597080424427986, -0.03355279937386513, -0.01745465397834778, 0.02873581275343895, -0.02430640161037445, -0.0018582770135253668, -0.02981548383831978, 0.006394964177161455, -0.03095052018761635, 0.022493110969662666, -0.0059693255461752415, -0.004962326027452946, 0.008042152039706707, -0.006699486635625362, 0.037594638764858246, -0.017925279214978218, 0.016859451308846474, -0.03266691789031029, -0.007405424024909735, -0.006758314557373524, -0.007689183112233877, 0.010263779200613499, -0.009758549742400646, 0.016582613810896873, 0.03178103640675545, -0.012360828928649426, -0.016693348065018654, -0.009177189320325851, 0.01767612434923649, 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