qiitablog-vectorsearch-elasticsearch.ipynb

qiitablog-vectorsearch-elasticsearch.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/nobuhikosekiya/13478e388e3fd21c7987417a392f6044/qiitablog-vectorsearch-elasticsearch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-OxWKHLUOaZ"
      },
      "source": [
        "# このJupyter Notebookの使い方\n",
        "これはレシピ集として作成されています。最初に、初期設定セクションにて利用するElasticsearch環境やOpenAI環境、HuggingFace環境の接続情報を設定します。その後は、実行したいセクションA. B. C. ..から始めて順番にコマンドを実行してください。\n",
        "そのためにセクション間で重複したコードが繰り返しあります。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-71MkaMUOac"
      },
      "source": [
        "# 1.初期設定"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUcE-Tz0UOac"
      },
      "source": [
        "## ライブラリの有効化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_GXeQu2UOad",
        "outputId": "7a4f829d-e95f-4d49-ce40-0c3b76b82dba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m412.8/412.8 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.7/46.7 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "%pip install -q elasticsearch==8.11.1 langchain==0.0.352  openai==1.6.1 tiktoken jq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jQqsjx1HUOad"
      },
      "outputs": [],
      "source": [
        "from pprint import pprint\n",
        "import os, time\n",
        "from getpass import getpass\n",
        "from elasticsearch import Elasticsearch\n",
        "from elasticsearch.helpers import bulk\n",
        "from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
        "from langchain.embeddings.openai import OpenAIEmbeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnqgm395UOad"
      },
      "source": [
        "## Elasticsearchの設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TRbI2QAUOad",
        "outputId": "853895ae-78e3-40c2-82f9-a9bc96875193"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elastic deployment Cloud ID··········\n",
            "Elastic deployment API Key··········\n",
            "ObjectApiResponse({'name': 'instance-0000000036', 'cluster_name': '507a2cf6ba204071943512e0537eee58', 'cluster_uuid': 'oF-xDLtXRCet87gRuM3eJg', 'version': {'number': '8.11.2', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '76013fa76dcbf144c886990c6290715f5dc2ae20', 'build_date': '2023-12-05T10:03:47.729926671Z', 'build_snapshot': False, 'lucene_version': '9.8.0', 'minimum_wire_compatibility_version': '7.17.0', 'minimum_index_compatibility_version': '7.0.0'}, 'tagline': 'You Know, for Search'})\n"
          ]
        }
      ],
      "source": [
        "ELASTIC_CLOUD_ID = getpass(\"Elastic deployment Cloud ID\")\n",
        "ELASTIC_API_KEY = getpass(\"Elastic deployment API Key\")\n",
        "if ELASTIC_CLOUD_ID == '':\n",
        "  ELASTIC_URL = getpass(\"Elastic deployment URL. No need if Cloud ID is provided.\")\n",
        "if ELASTIC_API_KEY == '':\n",
        "  ELASTIC_USER = getpass(\"Elastic user. No need if API key is provided.\")\n",
        "  ELASTIC_PASSWORD = getpass(\"Elastic password. No need if API key is provided.\")\n",
        "\n",
        "if ELASTIC_CLOUD_ID != '' and ELASTIC_API_KEY != '':\n",
        "  es = Elasticsearch(\n",
        "    cloud_id=ELASTIC_CLOUD_ID,\n",
        "    api_key=ELASTIC_API_KEY,\n",
        "    request_timeout=300\n",
        "  )\n",
        "elif ELASTIC_URL != '' and ELASTIC_USER != '' and ELASTIC_PASSWORD != '':\n",
        "  es = Elasticsearch(\n",
        "    hosts = ELASTIC_URL,\n",
        "    basic_auth=(ELASTIC_USER, ELASTIC_PASSWORD),\n",
        "    request_timeout=300\n",
        "  )\n",
        "elif ELASTIC_URL != '' and ELASTIC_USER == '':\n",
        "  es = Elasticsearch(\n",
        "    hosts = ELASTIC_URL,\n",
        "    # request_timeout=300,\n",
        "    request_timeout=300\n",
        "  )\n",
        "else:\n",
        "  print(\"env needs to set either ELASTIC_CLOUD_ID or ELASTIC_URL\")\n",
        "\n",
        "\n",
        "pprint(es.info()) # should return cluster info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MPAeOZXUOae"
      },
      "source": [
        "## テスト名の設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "O_d6XJ_BUOae",
        "outputId": "807e8cc5-9e0a-4f13-88d1-61e837937677"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Set the Elasticsearch index name prefix (Optional):test1226\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "TEST_NAME=input(\"Set the test name (Optional):\")\n",
        "\n",
        "if TEST_NAME == '':\n",
        "    INDEX_PREFIX='test'\n",
        "else:\n",
        "    INDEX_PREFIX=TEST_NAME\n",
        "INDEX_PREFIX"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVqCWiSOUOae"
      },
      "source": [
        "## OpenAIかAzure OpenAIの有効化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2wX0iTaUOae",
        "outputId": "a4fc2727-6b29-4835-bbc4-2816da7b7558"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter number (or nothing to skip): 1: OpenAI 2: Azure OpenAI 1\n"
          ]
        }
      ],
      "source": [
        "num = input(\"Enter number (or nothing to skip): 1: OpenAI 2: Azure OpenAI \")\n",
        "USE_OPENAI = num == \"1\"\n",
        "USE_AZURE_OPENAI = num == \"2\"\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ymbpy5urUOae"
      },
      "source": [
        "### OpenAI APIの有効化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Czz0TciiUOae",
        "outputId": "9b790ce1-ed98-4c0f-90e9-54ce9b8998c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OpenAI API key: ··········\n"
          ]
        }
      ],
      "source": [
        "if USE_OPENAI:\n",
        "  os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API key: \")\n",
        "  # os.environ[\"OPENAI_API_TYPE\"] = \"open_ai\"\n",
        "  # os.environ[\"OPENAI_API_BASE\"] = \"https://api.openai.com/v1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfzMn7xzUOae"
      },
      "source": [
        "### Azure OpenAI APIの設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xge508rfUOae"
      },
      "outputs": [],
      "source": [
        "if USE_AZURE_OPENAI:\n",
        "    os.environ[\"OPENAI_API_BASE\"] = getpass(\"Azure OpenAI API endpoint: \")\n",
        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Azure OpenAI API key: \")\n",
        "    os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
        "    os.environ[\"OPENAI_API_VERSION\"] = \"2023-05-15\"\n",
        "    azureOpenAI_embedding_deployment = getpass(\"Name of Azure OpenAI deployment for embedding\")\n",
        "    azureOpenAI_chat_deployment = getpass(\"Name of Azure OpenAI deployment for chat\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueAXc1dDUOae"
      },
      "source": [
        "## Elastic 上のMLモデルの有効化"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTQbUenYUOae"
      },
      "source": [
        "### 有効化するMLモデルの設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mwim1WQEUOaf",
        "outputId": "cf3636ac-2c02-4f74-d65a-dc299b081c90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enable ML model intfloat/multilingual-e5-small? Enter any key for 'Yes'.··········\n",
            "Enable ML model cl-tohoku/bert-base-japanese-v3? Enter any key for 'Yes'.··········\n",
            "Enable ML model cl-tohoku/bert-base-japanese-v2? Enter any key for 'Yes'.··········\n"
          ]
        }
      ],
      "source": [
        "enable_multilinguale5small = getpass(\"Enable ML model intfloat/multilingual-e5-small? Enter any key for 'Yes'.\") != \"\"\n",
        "enable_bertbasejapanesev3 = getpass(\"Enable ML model cl-tohoku/bert-base-japanese-v3? Enter any key for 'Yes'.\") != \"\"\n",
        "enable_bertbasejapanesev2 = getpass(\"Enable ML model cl-tohoku/bert-base-japanese-v2? Enter any key for 'Yes'.\") != \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_bPVVw8UOaf",
        "outputId": "c52d246d-613f-422f-a978-7049a4557c6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/157.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/157.9 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.9/157.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m8.7 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/eland/ml/_optional.py:116: UserWarning: Eland requires version '1.3' or newer of 'sklearn' (version '1.2.2' currently installed). Use pip or conda to update sklearn.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        }
      ],
      "source": [
        "%pip install -q eland elasticsearch transformers sentence_transformers\n",
        "import tempfile\n",
        "from eland.ml.pytorch import PyTorchModel\n",
        "from eland.ml.pytorch.transformers import TransformerModel\n",
        "from eland.ml.pytorch.transformers import elasticsearch_model_id\n",
        "\n",
        "def load_model(es_connection, model_id, task_type):\n",
        "  with tempfile.TemporaryDirectory() as tmp_dir:\n",
        "    print(f\"Loading HuggingFace transformer tokenizer and model [{model_id}] for task [{task_type}]\" )\n",
        "\n",
        "    tm = TransformerModel(model_id=model_id, task_type=task_type)\n",
        "    model_path, config, vocab_path = tm.save(tmp_dir)\n",
        "\n",
        "    ptm = PyTorchModel(es_connection, tm.elasticsearch_model_id())\n",
        "    model_exists = es_connection.options(ignore_status=404).ml.get_trained_models(model_id=ptm.model_id).meta.status == 200\n",
        "\n",
        "    if model_exists:\n",
        "      print(\"Model has already been imported\")\n",
        "    else:\n",
        "      print(\"Importing model\")\n",
        "      ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)\n",
        "      print(f\"Model successfully imported with id '{ptm.model_id}'\")\n",
        "\n",
        "def start_model(es_connection, model_id=None, es_model_id=None):\n",
        "    if model_id:\n",
        "      es_model_id = elasticsearch_model_id(model_id)\n",
        "    if is_model_started(es_connection, es_model_id=es_model_id):\n",
        "      print(f\"Model '{es_model_id}' is already started\")\n",
        "    else:\n",
        "      print(f\"Starting model deployment '{es_model_id}'\")\n",
        "      es_connection.options(request_timeout=300).ml.start_trained_model_deployment(\n",
        "            model_id=es_model_id, timeout=\"300s\", wait_for=\"started\")\n",
        "      print(f\"Model successfully started with id '{es_model_id}'\")\n",
        "\n",
        "def stop_model(es_connection, model_id=None, es_model_id=None):\n",
        "    if model_id:\n",
        "      es_model_id = elasticsearch_model_id(model_id)\n",
        "    es_connection.options(request_timeout=300).ml.stop_trained_model_deployment(model_id=es_model_id, force=True)\n",
        "    print(\"Stopping model deployment\")\n",
        "\n",
        "def is_model_started(es_connection, model_id=None, es_model_id=None):\n",
        "    if model_id:\n",
        "      es_model_id = elasticsearch_model_id(model_id)\n",
        "    r = es_connection.ml.get_trained_models_stats(model_id=es_model_id)\n",
        "    if r['trained_model_stats'][0].get('deployment_stats') != None:\n",
        "      state = r['trained_model_stats'][0]['deployment_stats']['state']\n",
        "      if state == 'started':\n",
        "        print(\"Check: Model is started\")\n",
        "        return True\n",
        "      elif state == 'starting':\n",
        "        print(\"Check: Model is starting\")\n",
        "        return False\n",
        "      else:\n",
        "        print(f\"Check: Model is in {state} state\")\n",
        "        return False\n",
        "    else:\n",
        "      print(f\"Check: Model does not have deployment stats\")\n",
        "      return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1Jo82GXUOaf"
      },
      "source": [
        "### Elastic MLにE5モデルをアップロード"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821,
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          ]
        },
        "id": "z7rhNzEbUOaf",
        "outputId": "c5b15e79-2e52-4838-87a5-8e50187a9414"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading HuggingFace transformer tokenizer and model [intfloat/multilingual-e5-small] for task [text_embedding]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1042eb65111944229d59aeb6b72a681b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f84c17179c74539bbe049815eb8e2a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
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            ],
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          "metadata": {}
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          "metadata": {}
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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          "metadata": {}
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        {
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          "metadata": {}
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        {
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            ],
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              "version_major": 2,
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fa81bc35cefc4f79a3ea0dd68db4f910"
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4d4a0827362446cab437ed37e5499d3"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eaca85d61178428598b512dbb6a4a04f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbf05482130748bc869fae970908de0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model has already been imported\n"
          ]
        }
      ],
      "source": [
        "if enable_multilinguale5small:\n",
        "    load_model(es, \"intfloat/multilingual-e5-small\", \"text_embedding\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRcF2qs9UOaf"
      },
      "source": [
        "### Elastic ML E5モデルの開始"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1RqEb6ZUOaf",
        "outputId": "a9ae9ac6-4bcc-4811-ab52-b779eb15fadb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model does not have deployment stats\n",
            "Starting model deployment 'intfloat__multilingual-e5-small'\n",
            "Model successfully started with id 'intfloat__multilingual-e5-small'\n"
          ]
        }
      ],
      "source": [
        "if enable_multilinguale5small:\n",
        "    start_model(es, model_id=\"intfloat/multilingual-e5-small\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1xU75LLUOaf"
      },
      "source": [
        "### Elastic MLにTohoku Bertモデルをアップロード"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "np1MgM3dUOaf"
      },
      "outputs": [],
      "source": [
        "if enable_bertbasejapanesev3:\n",
        "    %pip install -q fugashi ipadic unidic_lite\n",
        "    load_model(es, \"cl-tohoku/bert-base-japanese-v3\", \"text_embedding\")\n",
        "\n",
        "if enable_bertbasejapanesev2:\n",
        "    %pip install -q fugashi ipadic unidic_lite\n",
        "    load_model(es, \"cl-tohoku/bert-base-japanese-v2\", \"text_embedding\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5UxidkiUOag"
      },
      "source": [
        "### Elastic ML Tohoku Bertモデルの開始"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "AO7xrJixUOag"
      },
      "outputs": [],
      "source": [
        "if enable_bertbasejapanesev3:\n",
        "    start_model(es, model_id=\"cl-tohoku/bert-base-japanese-v3\")\n",
        "if enable_bertbasejapanesev2:\n",
        "    start_model(es, model_id=\"cl-tohoku/bert-base-japanese-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOh7ZLPQUOah"
      },
      "source": [
        "## テスト結果をまとめる関数"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "EktDn6pwUOah"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Initialize a list to store the results\n",
        "\n",
        "\n",
        "# Define the file path for data persistence\n",
        "file_path = f\"{INDEX_PREFIX}_results.json\"\n",
        "result_list = {}\n",
        "\n",
        "def add_result(query, search_logic, result):\n",
        "    global result_list\n",
        "    # クエリが存在しない場合、新しいクエリを追加\n",
        "    if query not in result_list[\"queries\"]:\n",
        "        result_list[\"queries\"][query] = {}\n",
        "\n",
        "    # ロジックが存在しない場合、新しいロジックを追加\n",
        "    if search_logic not in result_list[\"queries\"][query]:\n",
        "        result_list[\"queries\"][query][search_logic] = []\n",
        "\n",
        "    # 結果を追加\n",
        "    result_list[\"queries\"][query][search_logic] = result\n",
        "    save_results()\n",
        "\n",
        "def save_results():\n",
        "    global result_list\n",
        "    with open(file_path, 'w') as file:\n",
        "        json.dump(result_list, file, ensure_ascii=False, indent=2)\n",
        "\n",
        "def load_results():\n",
        "    global result_list\n",
        "    try:\n",
        "      if os.path.exists(file_path):\n",
        "        with open(file_path, 'r', encoding='utf-8') as file:\n",
        "            result_list = json.load(file)\n",
        "      else:\n",
        "        result_list = { \"queries\": {} }\n",
        "    except FileNotFoundError:\n",
        "        # If the file doesn't exist, start with an empty results list\n",
        "        result_list.clear()\n",
        "\n",
        "# Load existing results from the file (if any)\n",
        "load_results()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTHH4UKMUOag"
      },
      "source": [
        "# 2.検索ドキュメントのセットアップ\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41mhALLmUOaa"
      },
      "source": [
        "## Qiita記事のダウンロード"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Fg8mQrDUOab",
        "outputId": "2220e340-746a-4eb0-e379-ae171bba0940"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2023.11.17)\n",
            "200 OK\n",
            "total_count: 69\n"
          ]
        }
      ],
      "source": [
        "%pip install requests\n",
        "\n",
        "import json\n",
        "import requests\n",
        "import os\n",
        "\n",
        "# h = {'Authorization': 'Bearer xxxx'} # ユーザ認証する場合\n",
        "h = {}\n",
        "url = \"https://qiita.com/api/v2/items?\"\n",
        "\n",
        "# tag別に記事をPAGEだけ繰り返し取得\n",
        "query = \"&query=org%3Aelasticsearch_japan\"\n",
        "# 検索で指定した期間内に作成された記事数を取得\n",
        "res = requests.get(url=url + query, headers=h)\n",
        "# サーバーからの応答\n",
        "print(res.status_code, res.reason)\n",
        "# print(\"指定しているタグ: \" + tag_name)\n",
        "total_count = int(res.headers['Total-Count'])\n",
        "print(\"total_count: \" + str(total_count))\n",
        "\n",
        "page = f\"page=1&per_page={total_count}\"\n",
        "os.makedirs(\"qiita/elasticsearch_japan\", exist_ok=True)\n",
        "res = requests.get(url=url + page + query, headers=h)\n",
        "documents = json.loads(res.text)\n",
        "for doc in documents:\n",
        "    search_doc = {\n",
        "        \"title\": doc[\"title\"],\n",
        "        \"url\": doc[\"url\"],\n",
        "        \"body\": doc[\"body\"],\n",
        "        \"tags\": doc[\"tags\"]\n",
        "    }\n",
        "    title = doc[\"title\"].replace('/', '_')\n",
        "    filename = \"./qiita/\" + \"elasticsearch_japan\" + \"/\" + title + \".json\"\n",
        "    with open(filename, 'w') as f:\n",
        "        json.dump(search_doc, f, indent=2, ensure_ascii=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GPTによる質問の自動生成"
      ],
      "metadata": {
        "id": "ekoSaD3PZpFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "def generate_questions(context):\n",
        "  input_text='以下の情報に対する質問を10個作成してください。質問は以下に含まれる情報だけを使ってください。答えはJSON形式でquestionsキーのリストとして返してください。次のようなjson形式でお願いします。\\n' + \\\n",
        "  '''\n",
        "{\n",
        "  \"questions\": [\n",
        "    \"ElasticsearchのRPMインストール後に表示されるパスワードをどのように管理すべきですか？\",\n",
        "    \"Kibanaのネットワークインターフェースの設定を変更する目的は何ですか？\",\n",
        "  ]\n",
        "}\n",
        "''' + \"\\n\\n\" + context\n",
        "\n",
        "  client = OpenAI()\n",
        "\n",
        "  response = client.chat.completions.create(\n",
        "    model=\"gpt-3.5-turbo-1106\",\n",
        "    response_format={ \"type\": \"json_object\" },\n",
        "    messages=[\n",
        "      {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "      {\"role\": \"user\", \"content\": input_text},\n",
        "    ]\n",
        "  )\n",
        "\n",
        "  response_content = response.choices[0].message.content\n",
        "  # print(response_content)\n",
        "  response_content = json.loads(response_content)\n",
        "  return response_content\n",
        "  # response_content = json.loads(response_content)\n",
        "  # print(\"Received response:\" + json.dumps(response, ensure_ascii=False))"
      ],
      "metadata": {
        "id": "FuHbnFSxZscF"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# フォルダのパスを指定\n",
        "blog_path = './qiita/elasticsearch_japan/'\n",
        "questions_path = './qiita/questions/'\n",
        "os.makedirs(questions_path, exist_ok=True)\n",
        "documents = []\n",
        "file_list=os.listdir(blog_path)\n",
        "total = len(file_list)\n",
        "for i, filename in enumerate(file_list):\n",
        "    print(f\"Generating file for {i}/{total}: {filename}\")\n",
        "    file_path = os.path.join(blog_path, filename)\n",
        "    with open(file_path, 'r', encoding='utf-8') as f:\n",
        "        try:\n",
        "            data = json.load(f)\n",
        "            page_content = data.get('body')\n",
        "            if len(page_content) <= 100:\n",
        "                print(f\"Skipping because content length is short\")\n",
        "                continue\n",
        "\n",
        "            questions = generate_questions(page_content[:10000])\n",
        "            docs_per_question = []\n",
        "            try:\n",
        "                for q in questions['questions']:\n",
        "                    if not isinstance(q, str):\n",
        "                        raise Exception(\"bad format: \" + data.get('title'))\n",
        "                    doc = data.copy()\n",
        "                    del doc['body']\n",
        "                    doc['question'] = q\n",
        "                    docs_per_question.append(doc)\n",
        "            except Exception:\n",
        "                print(f\"Error at {file_path}\")\n",
        "                print(questions)\n",
        "                continue\n",
        "\n",
        "            new_file_name = filename\n",
        "            new_file_path = os.path.join(questions_path, new_file_name)\n",
        "\n",
        "            # \"questions.json\"としてデータを書き出す\n",
        "            with open(new_file_path, 'w', encoding='utf-8') as new_file:\n",
        "                json.dump(docs_per_question, new_file, ensure_ascii=False, indent=2)\n",
        "\n",
        "        except json.JSONDecodeError as e:\n",
        "            print(f'Error decoding JSON in file {file_path}: {e}')"
      ],
      "metadata": {
        "id": "BOtzipZBZ3q1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5db6d616-2284-42a5-e869-aac26d21f9fd"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating file for 0/69: AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）.json\n",
            "Generating file for 1/69: Elastic Cloud について　〜実際にデプロイメントを作ってみよう〜.json\n",
            "Generating file for 2/69: ElasticsearchでRAG (Retrieval Augmented Generation) を試す.json\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tp6TVu79UOag",
        "outputId": "0ae7ba71-1349-4462-a594-013fd869afb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 69/69 [00:00<00:00, 140.30it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'document count: 69'\n",
            "Document(page_content='# はじめに\\n前回の[パート1](https://qiita.com/nobuhikosekiya/items/f58a9c72d3972d3e24cc)ではAWS S3のバケットに入っているファイルをElasticの[S3 Connector Client](https://www.elastic.co/guide/en/enterprise-search/8.7/connectors-s3.html)を使ってElasticsearchにインデックスしてテキスト情報などを取り込みました。\\n\\n今回は、現在流行りのNLPの話題に乗っかるために、Elasticの[NLP Inference機能](https://www.elastic.co/guide/en/machine-learning/8.7/ml-nlp-inference.html)を試します。\\n具体的に何ができるようになるかというと、Elasticsearchにインデックスされたデータ（今回はS3バケット内にアップロードされているファイルに含まれるテキスト情報）に対してM/L学習モデルを使ってInference（推論）をかけ、その出力を同じElasticsearchのドキュメントに保存（インデックス）できます。\\nどのようなM/L学習モデルが使えるかは[こちら](https://www.elastic.co/guide/en/machine-learning/8.7/ml-nlp-model-ref.html)に書かれています。\\n\\n# MLノードの追加\\n最初に、ElasticsearchのMLノードが必要です。Elastic Cloudの場合Machine Learning Nodeを数クリックで簡単にデプロイできてしまいます :)\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/8f6c97b7-b99e-e7a4-0dc8-033372a8003f.png)\\nとりあえず低めのスペック（２GB RAM)からスタートしています。あとで簡単に拡張できるので。\\n\\n# MLモデルのデプロイ\\nサードパーティ学習モデルをM/Lノードにデプロイするには、ElandというElasticのpython製のツールを使います。以下の情報を見ながら今回の手順を実行していきました。\\nhttps://www.elastic.co/guide/en/enterprise-search/8.7/machine-learning-start.html\\n\\n1: 最初にElandのコードをダウンロードし、コンテナをビルドします。\\n```\\ngit clone git@github.com:elastic/eland.git\\ncd eland\\ndocker build -t elastic/eland .\\n```\\n\\n2: 次に、以下のパラメータを与えて学習モデルをMLノードにアップロードします。\\n- 学習モデル（--hub-model-idに指定)\\n- タスクタイプ（--task-typeに指定。fill_mask, ner, text_classification, text_embedding, zero_shot_classificationから選択）\\n- アップロード先のElasticsearchのURLとその認証のユーザーパスワード\\n```\\ndocker run -it --rm --network host \\\\\\n    elastic/eland \\\\\\n    eland_import_hub_model \\\\\\n      --url https://XXX.cloud.es.io:443 \\\\\\n      -u elastic -p <PASSWORD> \\\\\\n      --hub-model-id distilbert-base-uncased-finetuned-sst-2-english \\\\\\n      --task-type text_classification \\\\\\n      --start\\n```\\n \\n以下実行例です。\\n```\\n~/lab/projects/eland % docker run -it --rm --network host \\\\\\n    elastic/eland \\\\\\n    eland_import_hub_model \\\\\\n      --url https://XXX.es.asia-northeast1.gcp.cloud.es.io:443 \\\\\\n      -u elastic -p XXX \\\\\\n      --hub-model-id distilbert-base-uncased-finetuned-sst-2-english \\\\\\n      --task-type text_classification \\\\\\n      --start --clear-previous\\n2023-04-17 11:54:41,453 INFO : Establishing connection to Elasticsearch\\n2023-04-17 11:54:41,587 INFO : Connected to cluster named \\'b54f23853a2c4e6ea34aca4b2381d5a3\\' (version: 8.7.0)\\n2023-04-17 11:54:41,588 INFO : Loading HuggingFace transformer tokenizer and model \\'distilbert-base-uncased-finetuned-sst-2-english\\'\\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 48.0/48.0 [00:00<00:00, 80.6kB/s]\\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 629/629 [00:00<00:00, 415kB/s]\\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 226k/226k [00:00<00:00, 3.56MB/s]\\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 255M/255M [00:34<00:00, 7.70MB/s]\\n/usr/local/lib/python3.9/dist-packages/transformers/models/distilbert/modeling_distilbert.py:214: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\\n  scores = scores.masked_fill(mask, torch.tensor(-float(\"inf\")))  # (bs, n_heads, q_length, k_length)\\n2023-04-17 11:55:22,574 INFO : Stopping deployment for model with id \\'distilbert-base-uncased-finetuned-sst-2-english\\'\\n2023-04-17 11:55:22,593 INFO : Deleting model with id \\'distilbert-base-uncased-finetuned-sst-2-english\\'\\n/usr/local/lib/python3.9/dist-packages/eland/ml/pytorch/_pytorch_model.py:155: ElasticsearchWarning: The default [remove_binary] value of \\'false\\' is deprecated and will be set to \\'true\\' in a future release. Set [remove_binary] explicitly to \\'true\\' or \\'false\\' to ensure no behavior change.\\n  self._client.options(ignore_status=404).ml.delete_trained_model(\\n2023-04-17 11:55:22,902 INFO : Creating model with id \\'distilbert-base-uncased-finetuned-sst-2-english\\'\\n2023-04-17 11:55:22,930 INFO : Uploading model definition\\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [01:26<00:00,  1.36s/ parts]\\n2023-04-17 11:56:49,674 INFO : Uploading model vocabulary\\n2023-04-17 11:56:49,804 INFO : Starting model deployment\\n2023-04-17 11:56:59,317 INFO : Model successfully imported with id \\'distilbert-base-uncased-finetuned-sst-2-english\\'\\n```\\n \\n3: Trained ModelsメニューでMLモデルがロードされたことを確認します。Stateがstartedになっていない場合、ActionsからStartしてください。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/a8c67c1e-2ace-7dfa-265e-f342c4a7c538.png)\\n\\n\\n# NLP Inferenceのパイプラインを追加\\n今回は、S3からElasticsearchにデータを取り込む時のパイプライン処理にNLP Inference処理を追加します。最新のv8.7では、Enterprise Searchメニューから簡単にこれを追加できるようになりました。\\n\\n前回の[記事](https://qiita.com/nobuhikosekiya/items/f58a9c72d3972d3e24cc)で作ったEnterprise Searchメニューから該当Indexを開きます。PipelinesタブにMachine Learning Inference Pipelinesの項目が見えますが、最初は押せません。Ingest PipelinesをCopy and customizeしないといけないとあります。上にあるCopy and customizeリンクをクリックしましょう。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/edb7082a-2a9a-ee26-13cd-1a88f6752826.png)\\n\\nそうすると、Add Inference Pipelineが押せるようになりました。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/7420e68a-f94e-39ac-b3c3-9b5666cb1d1f.png)\\n\\n適当なPipeline名をつけ（test001)、先ほどのモデルを選択します。Source fieldとして、Inference対象のFieldとして、S3ファイルのテキストが抽出されたフィールド(=body)を選択します。Target fieldは今回は空のままにします。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/34f48d47-cdd7-a40c-9d11-06498dbba049.png)\\n\\n次の画面ではこのML Inferenceをテストできます。テストするために既にインデックスにあるDocumentのIDが必要なので、別のブラウザタブを開いてEnterprise Search > Content > Indiciesのメニューをたどり、IndexのDocumentを確認し、好きなDocument idをコピーします。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/bdf1d4af-8f45-8c69-8fe3-8aa114da33c6.png)\\n\\nそして、ここのDocument IDにペーストすると、そのDocument情報が左側に読み込まれます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/b8ff7946-7f7f-649a-9e70-6a49690cebd7.png)\\n\\nSimulate Pipelineをクリックすると、結果が右側に出てきます。Inference(推論）結果としてpredicted_value: POSITIVEという結果が得られました。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/d115bf75-cd96-8f8d-0944-5dd399d9a361.png)\\n\\n最終的なパイプライン定義が確認できるので、Create pipelineで確定します。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/c35fe4e0-655c-3670-dc9e-c0333de26107.png)\\n\\nIngest Pipelineの全体はこのようになっていました。\\n```\\n[\\n  {\\n    \"remove\": {\\n      \"field\": \"ml.inference.test001\",\\n      \"ignore_missing\": true\\n    }\\n  },\\n  {\\n    \"remove\": {\\n      \"field\": \"test001\",\\n      \"ignore_missing\": true\\n    }\\n  },\\n  {\\n    \"inference\": {\\n      \"field_map\": {\\n        \"body\": \"text_field\"\\n      },\\n      \"model_id\": \"distilbert-base-uncased-finetuned-sst-2-english\",\\n      \"on_failure\": [\\n        {\\n          \"append\": {\\n            \"field\": \"_source._ingest.inference_errors\",\\n            \"value\": [\\n              {\\n                \"message\": \"Processor \\'inference\\' in pipeline \\'ml-inference-test001\\' failed with message \\'{{ _ingest.on_failure_message }}\\'\",\\n                \"pipeline\": \"ml-inference-test001\",\\n                \"timestamp\": \"{{{ _ingest.timestamp }}}\"\\n              }\\n            ]\\n          }\\n        }\\n      ],\\n      \"target_field\": \"ml.inference.test001\"\\n    }\\n  },\\n  {\\n    \"append\": {\\n      \"field\": \"_source._ingest.processors\",\\n      \"value\": [\\n        {\\n          \"model_version\": \"8.7.0\",\\n          \"pipeline\": \"ml-inference-test001\",\\n          \"processed_timestamp\": \"{{{ _ingest.timestamp }}}\",\\n          \"types\": [\\n            \"pytorch\",\\n            \"text_classification\"\\n          ]\\n        }\\n      ]\\n    }\\n  },\\n  {\\n    \"set\": {\\n      \"copy_from\": \"ml.inference.test001.predicted_value\",\\n      \"description\": \"Copy the predicted_value to \\'test001\\' if the prediction_probability is greater than 0.5\",\\n      \"field\": \"test001\",\\n      \"if\": \"ctx?.ml?.inference != null && ctx.ml.inference[\\'test001\\'] != null && ctx.ml.inference[\\'test001\\'].prediction_probability > 0.5\"\\n    }\\n  }\\n]\\n```\\n\\n# S3ファイルに対してNLP Inferenceをかける\\ns3に新たなオブジェクトをアップロードし、Enterprise SearchのIndex画面のSyncボタンで即時同期させます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/f854866e-e41c-c6b5-e04d-c66d1e505c01.png)\\nそうすると、s3ファイルのテキスト抽出とともに、ML Inference処理が適用され、結果test001: \"NEGATIVE\"というフィウールどがDocumentに追加されるようになりました。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/bca57c7b-5470-4b62-e493-a7c5e68b8a5a.png)\\n\\n\\n# 2つ目のMLモデル(Named Entity Recognition)を適用\\n先ほどの学習モデルはtext_classificationだったので、今度はNamed Entity Recognitionタイプのモデルを使ったInferenceをパイプラインに追加してみたいと思います。\\n\\n新しいモデルをElandでアップします（ここでは実は間違えてtask-typeをtext_classificationのままにしてしまいました。後続手順で修正していますが、そのままのログを残します。）\\n```\\ndocker run -it --rm --network host \\\\\\n    elastic/eland \\\\\\n    eland_import_hub_model \\\\\\n      --url https://XXX.cloud.es.io:443 \\\\\\n      -u elastic -p <PASSWORD> \\\\\\n      --hub-model-id elastic/distilbert-base-uncased-finetuned-conll03-english \\\\\\n      --task-type text_classification \\\\\\n      --start\\n```\\n\\nしかし、キャパシティ不足で２つのMLモデルのアップロードは失敗してしまいました。\\n```\\nelasticsearch.ApiError: ApiError(429, \\'status_exception\\', \\'Could not start deployment because no ML nodes with sufficient capacity were found\\')\\n```\\n\\nよって、Elastic CloudでMLノードのメモリを増やします。2GBから4GBに変更。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/493481a7-31df-e4de-e078-bc9cd90eef90.png)\\n\\nモデルのアップロードはされていたので、Elandは再実行せずこの管理画面からstartを実行して、再トライします。startedになればOKです。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/c8601028-ef86-1ca8-01ef-2d7b4d0eb6e3.png)\\n\\nMemoryの使用状況はこの画面から確認できます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/f8ad9222-c459-ac61-a7e9-ebb77de5dd31.png)\\n\\ntest002として、新しくデプロイしたMLモデルのパイプラインを作ります。前と同じくbodyフィールドに対して分析します。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/bd2661c1-07f6-2805-8f56-d5a562ed8189.png)\\n\\nテストしてみたら、predicted valueに\"I_MISC\" ?? \\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/2e322175-0f93-86a6-9d9a-801ef36d99dc.png)\\n\\nどうやらMLモデルをアップロードするときにタイプを間違えていたようです。\\n--task-type text_classification\\u3000ではなく\\n--task-type ner\\u3000と設定すべきでした。\\n\\n先ほどアップロードしたモデルを画面からDeleteします。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/0744ca50-bcaf-f8d1-77f1-94cb8727a4b1.png)\\n\\n再び、Elandでアップロード。nerをtask-typeに指定。\\n```\\ndocker run -it --rm --network host \\\\\\n    elastic/eland \\\\\\n    eland_import_hub_model \\\\\\n      --url https://XXX.cloud.es.io:443 \\\\\\n      -u elastic -p <PASSWORD> \\\\\\n      --hub-model-id elastic/distilbert-base-uncased-finetuned-conll03-english \\\\\\n      --task-type ner \\\\\\n      --start\\n```\\n\\n今度は正しくなってます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/ded180a2-616a-d093-7735-5cdce89a8e30.png)\\n\\n今度はテスト結果もそれっぽい値になっています。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/0820f234-01e2-b948-912d-123ff43935b5.png)\\n\\n出来上がったPipeline定義はこうなりました。\\n```\\n{\\n  \"description\": \"\",\\n  \"processors\": [\\n    {\\n      \"remove\": {\\n        \"field\": \"ml.inference.test002\",\\n        \"ignore_missing\": true\\n      }\\n    },\\n    {\\n      \"inference\": {\\n        \"field_map\": {\\n          \"body\": \"text_field\"\\n        },\\n        \"model_id\": \"elastic__distilbert-base-uncased-finetuned-conll03-english\",\\n        \"on_failure\": [\\n          {\\n            \"append\": {\\n              \"field\": \"_source._ingest.inference_errors\",\\n              \"value\": [\\n                {\\n                  \"message\": \"Processor \\'inference\\' in pipeline \\'test002\\' failed with message \\'{{ _ingest.on_failure_message }}\\'\",\\n                  \"pipeline\": \"test002\",\\n                  \"timestamp\": \"{{{ _ingest.timestamp }}}\"\\n                }\\n              ]\\n            }\\n          }\\n        ],\\n        \"target_field\": \"ml.inference.test002\"\\n      }\\n    },\\n    {\\n      \"append\": {\\n        \"field\": \"_source._ingest.processors\",\\n        \"value\": [\\n          {\\n            \"model_version\": \"8.7.0\",\\n            \"pipeline\": \"test002\",\\n            \"processed_timestamp\": \"{{{ _ingest.timestamp }}}\",\\n            \"types\": [\\n              \"pytorch\",\\n              \"ner\"\\n            ]\\n          }\\n        ]\\n      }\\n    }\\n  ],\\n  \"version\": 1\\n}\\n```\\n\\n\\nS3にまた新たなファイルをアップし、Syncでオンデマンドで取り込みを行います。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/f854866e-e41c-c6b5-e04d-c66d1e505c01.png)\\n\\n結果は... どうなんでしょう。日本の文書だからでしょうか。今回は操作方法の実現目的なので出力結果にはこだわらないでおきます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/c0c9c145-afd4-4cb2-4c47-4417e54785e0.png)\\n\\n他いくつかの英語の文書で再トライ。\\n\\n文書１：\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/63c90453-bfe3-0adc-5dd5-2e9db3b9efa1.png)\\n\\n文書２：\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/5240ad2f-f78c-5700-16ed-90f84fa2f7d6.png)\\n\\n最後に一気に19個のファイルをS3にアップし、同期処理させてみます。\\n![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/fd05d4eb-d62b-5bf5-5431-1baa44e8e70f.png)\\n\\nConnector Clientのログを見る限り、57秒でS3からの取り込みとElasticsearchへのインデックス化が完了したようです。\\n```\\n[FMWK][13:21:15][INFO] Successfully connected to AWS Server.\\n[FMWK][13:21:15][INFO] Starting doc lookups\\n[FMWK][13:22:12][INFO] Job reporting task is stopped.\\n[FMWK][13:22:12][INFO] [p8Bgj4cBElFcAjC8BShT] Sync done: 19 indexed, 0 deleted. (57 seconds)\\n```\\n\\n# 終わり\\nNLP InferenceをElasticsearchのIngest Pipelineとして簡単に追加することができました。そしてパート１で作成したs3データの取り込み（インジェスト)にその処理を組み入れました。\\n\\n従来この辺のPipeline設定は自分でコードを書かなくてはなりませんでしたが、v8.7からだいぶ画面の設定ベースで進めることができ、大分使いやすくなったと感じます。\\n\\nどんなデータに対してどんなモデルを使うか、など今回は適当に選択しましたが、とりあえず一連のフローの実装方法のサンプルができたので、ぜひ皆さんもこれを元に使ってみてほしいと思います。（多分Elasticのs3コネクターを使ったNLP Inferenceのサンプル記事は世界初だと思います）\\n\\n他の使用例としては、例えばText EmbeddingsモデルのInferenceを使えば、ベクター値をElasticsearchに保存し、あとでElaticsearchのKNN-Searchクエリーにてセマンティック検索ができます。\\n\\nS3のデータの取り込みとそれに対するMLパイプラインの適用が１日で実装できると考えると、アイディア次第では面白いことができるんじゃないでしょうか :)\\n\\n', metadata={'source': '/content/qiita/elasticsearch_japan/AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）.json', 'seq_num': 1, 'title': 'AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）', 'url': 'https://qiita.com/nobuhikosekiya/items/765a7269da8809898936', 'tags': [{'name': 'S3', 'versions': []}, {'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}]})\n",
            "{'seq_num': 1,\n",
            " 'source': '/content/qiita/elasticsearch_japan/AWS s3 '\n",
            "           'をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）.json',\n",
            " 'tags': [{'name': 'S3', 'versions': []},\n",
            "          {'name': 'NLP', 'versions': []},\n",
            "          {'name': 'Elasticsearch', 'versions': []}],\n",
            " 'title': 'AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）',\n",
            " 'url': 'https://qiita.com/nobuhikosekiya/items/765a7269da8809898936'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from langchain.document_loaders import DirectoryLoader\n",
        "from langchain.document_loaders import JSONLoader\n",
        "\n",
        "DRIVE_FOLDER = \"./qiita/elasticsearch_japan/\"\n",
        "\n",
        "def metadata_func(record: dict, metadata: dict) -> dict:\n",
        "\n",
        "    metadata[\"title\"] = record.get(\"title\")\n",
        "    metadata[\"url\"] = record.get(\"url\")\n",
        "    metadata[\"tags\"] = record.get(\"tags\")\n",
        "\n",
        "    return metadata\n",
        "\n",
        "loader_kwargs = {\n",
        "    'jq_schema':'.',\n",
        "    'text_content': True,\n",
        "    'content_key': \"body\",\n",
        "    'metadata_func': metadata_func\n",
        "}\n",
        "\n",
        "loader = DirectoryLoader(DRIVE_FOLDER, glob='**/*.json', show_progress=True, loader_cls=JSONLoader, loader_kwargs = loader_kwargs)\n",
        "\n",
        "json_docs = loader.load()\n",
        "\n",
        "pprint(f'document count: {len(json_docs)}')\n",
        "pprint(json_docs[0] if len(json_docs) > 0 else None)\n",
        "pprint(json_docs[0].metadata if len(json_docs) > 0 else None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "3YpW8gsAUOag"
      },
      "outputs": [],
      "source": [
        "if False: #質問検索する場合はTrueに変えてください\n",
        "    from langchain.document_loaders import DirectoryLoader\n",
        "    from langchain.document_loaders import JSONLoader\n",
        "\n",
        "    DRIVE_FOLDER = \"./qiita/questions/\"\n",
        "\n",
        "    def metadata_func(record: dict, metadata: dict) -> dict:\n",
        "\n",
        "        metadata[\"title\"] = record.get(\"title\")\n",
        "        metadata[\"url\"] = record.get(\"url\")\n",
        "        metadata[\"tags\"] = record.get(\"tags\")\n",
        "\n",
        "        return metadata\n",
        "\n",
        "    loader_kwargs = {\n",
        "        'jq_schema':'.[]',\n",
        "        'text_content': True,\n",
        "        'content_key': \"question\",\n",
        "        'metadata_func': metadata_func\n",
        "    }\n",
        "\n",
        "    loader = DirectoryLoader(DRIVE_FOLDER, glob='**/*.json', show_progress=True, loader_cls=JSONLoader, loader_kwargs = loader_kwargs)\n",
        "\n",
        "    json_docs = loader.load()\n",
        "\n",
        "    pprint(f'document count: {len(json_docs)}')\n",
        "    pprint(json_docs[0] if len(json_docs) > 0 else None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-NmshkmUOag"
      },
      "source": [
        "# 3.検索の質問の設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "V6-9-qUaUOag"
      },
      "outputs": [],
      "source": [
        "questions = [\n",
        "    \"Frozen tierの使い方について教えてください\",\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmRjQygUUOah"
      },
      "source": [
        "## Run Allする際にストップしますか？"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "kwBI-QBqUOah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af258600-3db4-4471-f6c1-6248e6e7b4d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Auto run the tests? Enter any key for true, no key for falsed\n"
          ]
        }
      ],
      "source": [
        "AUTO_RUN=input(\"Auto run the tests? Enter any key for true, no key for false\") != \"\"\n",
        "if not AUTO_RUN:\n",
        "    raise UserWarning('Exit to stop here.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwtJI68nUOah"
      },
      "source": [
        "# 3.様々なサーチを試そう"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRujnWkoUOaz"
      },
      "source": [
        "## Elasticsearchのキーワード検索 (BM25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZ2A50GpUOaz"
      },
      "source": [
        "### インデックス作成\n",
        "Kuromojiをアナライザーとして設定したElasticsearchのインデックスを作成します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KY_cwSJEUOaz",
        "outputId": "6fb9744a-2d92-425b-fa09-effcda6f7145"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_bm25'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8vzhm90UOaz",
        "outputId": "104e9e03-028f-4b3a-b13d-ccf0c23eb460"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index 'test1226_bm25' deleted successfully.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'test1226_bm25'})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "if es.indices.exists(index=INDEX_NAME):\n",
        "    # If it exists, delete the index\n",
        "    es.indices.delete(index=INDEX_NAME)\n",
        "    print(f\"Index '{INDEX_NAME}' deleted successfully.\")\n",
        "else:\n",
        "    print(f\"Index '{INDEX_NAME}' does not exist.\")\n",
        "\n",
        "es.indices.create(\n",
        "  index=INDEX_NAME,\n",
        "  settings={\n",
        "      \"index\": {\n",
        "          \"number_of_shards\": 1,\n",
        "          \"number_of_replicas\": 0\n",
        "      }\n",
        "  }\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqXboKfIUOaz"
      },
      "source": [
        "### Kuromojiアナライザの設定"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxBHFx5bUOaz",
        "outputId": "f9e7fd4c-03be-4eb4-bfe8-7de393577088"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-77f437a2cdf9>:65: DeprecationWarning: Importing from the 'elasticsearch.client' module is deprecated. Instead use 'elasticsearch' module for importing the client.\n",
            "  from elasticsearch.client import SynonymsClient\n",
            "<ipython-input-23-77f437a2cdf9>:120: DeprecationWarning: Passing transport options in the API method is deprecated. Use 'Elasticsearch.options()' instead.\n",
            "  es.indices.open(index=INDEX_NAME, request_timeout=60)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "es.indices.close(index=INDEX_NAME)\n",
        "\n",
        "add_settings = {\n",
        "  \"index\": {\n",
        "    \"analysis\": {\n",
        "      \"char_filter\": {\n",
        "        \"normalize\": {\n",
        "          \"mode\": \"compose\",\n",
        "          \"name\": \"nfkc\",\n",
        "          \"type\": \"icu_normalizer\"\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "  }\n",
        "}\n",
        "es.indices.put_settings(index=INDEX_NAME, body=add_settings)\n",
        "\n",
        "add_settings = {\n",
        "  \"index\": {\n",
        "    \"analysis\": {\n",
        "      \"filter\": {\n",
        "        \"ja_index_synonym\": {\n",
        "          \"type\": \"synonym\",\n",
        "          \"lenient\": \"false\",\n",
        "          \"synonyms\": []\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "  }\n",
        "}\n",
        "es.indices.put_settings(index=INDEX_NAME, body=add_settings)\n",
        "\n",
        "add_settings = {\n",
        "  \"index\": {\n",
        "    \"analysis\": {\n",
        "      \"tokenizer\": {\n",
        "        \"ja_kuromoji_tokenizer\": {\n",
        "          \"mode\": \"search\",\n",
        "          \"discard_compound_token\": \"true\",\n",
        "          \"type\": \"kuromoji_tokenizer\"\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "es.indices.put_settings(index=INDEX_NAME, body=add_settings)\n",
        "\n",
        "# Define a synonym set\n",
        "synonyms_set = [\n",
        "    # {\n",
        "    #   \"id\": \"test-1\",\n",
        "    #   \"synonyms\": \"foo, bar\"\n",
        "    # },\n",
        "    # {\n",
        "    #   \"id\": \"test-2\",\n",
        "    #   \"synonyms\": \"test => check\"\n",
        "    # }\n",
        "  ]\n",
        "\n",
        "# Define the identifier for the synonym set\n",
        "synonym_id = \"ja_search_synonym-set\"\n",
        "\n",
        "from elasticsearch.client import SynonymsClient\n",
        "# Call the put_synonym function to add or update the synonym set\n",
        "response = SynonymsClient(es).put_synonym(id=synonym_id, synonyms_set=synonyms_set)\n",
        "\n",
        "# Define the new settings you want to apply\n",
        "add_settings = {\n",
        "  \"index\": {\n",
        "    \"analysis\": {\n",
        "      \"filter\": {\n",
        "        \"ja_search_synonym\": {\n",
        "          \"type\": \"synonym_graph\",\n",
        "          \"synonyms_set\": \"ja_search_synonym-set\",\n",
        "          \"updateable\": \"true\"\n",
        "        }\n",
        "      },\n",
        "      \"analyzer\": {\n",
        "        \"ja_kuromoji_index_analyzer\": {\n",
        "          \"filter\": [\n",
        "            \"kuromoji_baseform\",\n",
        "            \"kuromoji_part_of_speech\",\n",
        "            \"ja_index_synonym\",\n",
        "            \"cjk_width\",\n",
        "            \"ja_stop\",\n",
        "            \"kuromoji_stemmer\",\n",
        "            \"lowercase\"\n",
        "          ],\n",
        "          \"char_filter\": [\n",
        "            \"normalize\"\n",
        "          ],\n",
        "          \"type\": \"custom\",\n",
        "          \"tokenizer\": \"ja_kuromoji_tokenizer\"\n",
        "        },\n",
        "        \"ja_kuromoji_search_analyzer\": {\n",
        "          \"filter\": [\n",
        "            \"kuromoji_baseform\",\n",
        "            \"kuromoji_part_of_speech\",\n",
        "            \"ja_search_synonym\",\n",
        "            \"cjk_width\",\n",
        "            \"ja_stop\",\n",
        "            \"kuromoji_stemmer\",\n",
        "            \"lowercase\"\n",
        "          ],\n",
        "          \"char_filter\": [\n",
        "            \"normalize\"\n",
        "          ],\n",
        "          \"type\": \"custom\",\n",
        "          \"tokenizer\": \"ja_kuromoji_tokenizer\"\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "es.indices.put_settings(index=INDEX_NAME, body=add_settings)\n",
        "\n",
        "es.indices.open(index=INDEX_NAME, request_timeout=60)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDCe07WfUOa0"
      },
      "source": [
        "### Mapping定義"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0vu2HzuUOa0",
        "outputId": "0f725600-4ade-47c2-9bcf-08cd2746f4d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-b38c42e6f337>:2: DeprecationWarning: Passing transport options in the API method is deprecated. Use 'Elasticsearch.options()' instead.\n",
            "  es.indices.close(index=INDEX_NAME, request_timeout=60)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "es.indices.close(index=INDEX_NAME, request_timeout=60)\n",
        "\n",
        "# Define the new mapping\n",
        "add_mapping = {\n",
        "  \"properties\": {\n",
        "    \"page_content\": {\n",
        "      \"type\": \"text\",\n",
        "      \"search_analyzer\": \"ja_kuromoji_search_analyzer\",\n",
        "      \"analyzer\": \"ja_kuromoji_index_analyzer\"\n",
        "    },\n",
        "    \"metadata\": {\n",
        "      \"properties\": {\n",
        "        \"seq_num\": {\n",
        "          \"type\": \"long\"\n",
        "        },\n",
        "        \"source\": {\n",
        "          \"type\": \"keyword\"\n",
        "        },\n",
        "        \"tags\": {\n",
        "          \"properties\": {\n",
        "            \"name\": {\n",
        "              \"type\": \"text\",\n",
        "              \"search_analyzer\": \"ja_kuromoji_search_analyzer\",\n",
        "              \"analyzer\": \"ja_kuromoji_index_analyzer\"\n",
        "            }\n",
        "          }\n",
        "        },\n",
        "        \"title\": {\n",
        "          \"type\": \"text\",\n",
        "          \"search_analyzer\": \"ja_kuromoji_search_analyzer\",\n",
        "          \"analyzer\": \"ja_kuromoji_index_analyzer\"\n",
        "        },\n",
        "        \"url\": {\n",
        "          \"type\": \"text\",\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "  }\n",
        "}\n",
        "\n",
        "es.indices.put_mapping(index=INDEX_NAME, body=add_mapping)\n",
        "\n",
        "es.indices.open(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlBoXPeSUOa0"
      },
      "source": [
        "### インジェスト"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Q8rpoFpUOa0",
        "outputId": "72f14b5f-b913-4a44-b495-5246a1934410"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "metadata: {'source': '/content/qiita/elasticsearch_japan/AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）.json', 'seq_num': 1, 'title': 'AWS s3 をElasticsearchに取り込んで検索してみる（パート２: NLP Inferenceを追加）', 'url': 'https://qiita.com/nobuhikosekiya/items/765a7269da8809898936', 'tags': [{'name': 'S3', 'versions': []}, {'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜.json', 'seq_num': 1, 'title': 'Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜', 'url': 'https://qiita.com/tomo_s_el/items/3584d0b1fabb0bafa4fa', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/ElasticsearchでRAG (Retrieval Augmented Generation) を試す.json', 'seq_num': 1, 'title': 'ElasticsearchでRAG (Retrieval Augmented Generation) を試す', 'url': 'https://qiita.com/takeo-furukubo/items/e5d43fa734e4338b895f', 'tags': [{'name': 'Python', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'OpenAI', 'versions': []}, {'name': '生成AI', 'versions': []}, {'name': 'ChatGPT', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/GKE上のOpenTelemetry DemoアプリケーションをさくっとElasticでAPM・ログ・インフラ+eBPFプロファイリング監視する.json', 'seq_num': 1, 'title': 'GKE上のOpenTelemetry DemoアプリケーションをさくっとElasticでAPM・ログ・インフラ+eBPFプロファイリング監視する', 'url': 'https://qiita.com/nobuhikosekiya/items/9c3ae25b39827b1ef9d1', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'GKE', 'versions': []}, {'name': 'ElasticStack', 'versions': []}, {'name': 'opentelemetry', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Stackに対してTerraformのImportをやってみた.json', 'seq_num': 1, 'title': 'Elastic Stackに対してTerraformのImportをやってみた', 'url': 'https://qiita.com/nobuhikosekiya/items/44027ee0cb6653d29858', 'tags': [{'name': 'Terraform', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elasticsearchにカスタム時系列データを取り込む (Elastic Agent編).json', 'seq_num': 1, 'title': 'Elasticsearchにカスタム時系列データを取り込む (Elastic Agent編)', 'url': 'https://qiita.com/yukshimizu/items/abec12ac46749db46b40', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'beats', 'versions': []}, {'name': 'elasticcloud', 'versions': []}, {'name': 'ElasticStack', 'versions': []}, {'name': 'ElasticAgent', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Stack コードリーディング: Painless スクリプト.json', 'seq_num': 1, 'title': 'Elastic Stack コードリーディング: Painless スクリプト', 'url': 'https://qiita.com/ijokarumawak@github/items/84b619a799d78421eca5', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}, {'name': 'Painless', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Cloudに対してTerraformのImportをやってみた.json', 'seq_num': 1, 'title': 'Elastic Cloudに対してTerraformのImportをやってみた', 'url': 'https://qiita.com/nobuhikosekiya/items/c474a21b7114f3371d85', 'tags': [{'name': 'Terraform', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/[v8.7] Elasticsearch multinode_dedicaded masterをdocker-composeでインストールする手順（試用用途）.json', 'seq_num': 1, 'title': '[v8.7] Elasticsearch multinode/dedicaded masterをdocker-composeでインストールする手順（試用用途）', 'url': 'https://qiita.com/takeo-furukubo/items/13214d8cf822376c6072', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'docker-compose', 'versions': []}]}\n",
            "metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 前編.json', 'seq_num': 1, 'title': 'Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 前編', 'url': 'https://qiita.com/ijokarumawak@github/items/9b0c2d650536488718a5', 'tags': [{'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}]}\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "from elasticsearch import Elasticsearch, helpers\n",
        "\n",
        "# 前の実行で残っているDocumentはクリアしてからインジェストします\n",
        "if es.indices.exists(index=INDEX_NAME):\n",
        "    es.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "\n",
        "docs_json = [doc.to_json()[\"kwargs\"] for doc in json_docs]\n",
        "# print(docs_json)\n",
        "\n",
        "index_docs = []\n",
        "for doc_json in docs_json:\n",
        "    index_docs.append({\n",
        "        \"_index\": INDEX_NAME,\n",
        "        \"_source\": doc_json,\n",
        "    })\n",
        "\n",
        "helpers.bulk(es, index_docs)\n",
        "es.indices.refresh(index=INDEX_NAME)\n",
        "\n",
        "response = es.search(index=INDEX_NAME, query={\"match_all\": {}}, fields=[\"metadata\"])\n",
        "for hit in response['hits']['hits']:\n",
        "    metadata = hit['_source']['metadata']\n",
        "    print(f\"metadata: {metadata}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28N__yHJUOa0"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "gcEOCL8uUOa0"
      },
      "outputs": [],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "def do_search(query_text, filter=None, subfield=None):\n",
        "    subfield=\"prefix\"\n",
        "    query = {\n",
        "        \"bool\": {\n",
        "            \"must\": [\n",
        "                {\n",
        "                    \"match\": {\n",
        "                        \"page_content\": {\n",
        "                            \"query\": query_text,\n",
        "                            \"analyzer\": \"ja_kuromoji_search_analyzer\"\n",
        "                        }\n",
        "                    }\n",
        "                }\n",
        "            ]\n",
        "        }\n",
        "    }\n",
        "\n",
        "    # フィルタが指定されている場合はフィルタを追加\n",
        "    if filter is not None:\n",
        "        query[\"bool\"][\"filter\"] = filter\n",
        "\n",
        "    highlight = {\n",
        "        \"fields\": {\n",
        "            \"page_content\": {}\n",
        "        }\n",
        "    }\n",
        "    fields = [\"metadata\"]\n",
        "    response = es.search(index=INDEX_NAME, query=query, fields=fields, highlight=highlight)\n",
        "\n",
        "    results = []\n",
        "    # Iterate through the search results and access the fields you want\n",
        "    for hit in response['hits']['hits']:\n",
        "        metadata = hit['_source']['metadata']\n",
        "        score = hit['_score']\n",
        "        highlight = hit['highlight']\n",
        "        print(f\"_score: {score}, metadata: {metadata}\")\n",
        "        pprint(f\"{highlight}\")\n",
        "        r = {\n",
        "            \"metadata\": metadata,\n",
        "            \"highlight\": highlight\n",
        "        }\n",
        "        results.append(json.dumps(r, ensure_ascii=False))\n",
        "\n",
        "    return {\"query\": query_text, \"result\": results}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WW7mKv9YUOa0",
        "outputId": "bf006c3d-854c-4164-a83a-e3bb894f1166"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frozen tierの使い方について教えてください\n",
            "_score: 10.334702, metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜.json', 'seq_num': 1, 'title': 'Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜', 'url': 'https://qiita.com/tomo_s_el/items/3584d0b1fabb0bafa4fa', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]}\n",
            "(\"{'page_content': ['- クラスターのデプロイ\\\\n    - Elasticsearch ノード (Hot, Warm, Cold, \"\n",
            " \"<em>Frozen</em>, Machine Learning, Coordinating)\\\\n    -', '[[Pricing \"\n",
            " 'Calculator]](https://cloud.elastic.co/pricing/) '\n",
            " \"というツールですが、構成をポチポチと選択すると、その金額を<em>教え</em>てくれるので金額感やサイズ', '```\\\\n- Hot \"\n",
            " '<em>tier</em> node 8GB RAM x 2zones\\\\n- Master (tie breaker) node 1GB RAM x '\n",
            " \"1zone (自動追加)\\\\n- Integration', '＊ただし、Machine Learning 自体はplatinum \"\n",
            " \"ライセンスの機能なのでご注意<em>ください</em>。', \"\n",
            " \"'少し長くなったので、いったんここで区切りましたが、まだまだ色々と便利な機能があるので、<em>使い方</em>や tips \"\n",
            " \"含め書いていきたいと思います。']}\")\n",
            "_score: 8.751479, metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方.json', 'seq_num': 1, 'title': 'Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方', 'url': 'https://qiita.com/nobuhikosekiya/items/3c03932c3efec0a9f04d', 'tags': [{'name': 'Elasticsearch', 'versions': []}]}\n",
            "(\"{'page_content': ['基本（必須）のHot \"\n",
            " \"<em>Tier</em>以外に、よりコストの安いWarm/Cold/<em>Frozen</em>の<em>Tier</em>を選択的に設けることができます。', \"\n",
            " \"'しかし、Warm/Cold/<em>Frozen</em>はそれらのノードを立ち上げただけではデータはデフォルトではそちらに移動してくれません。', \"\n",
            " \"'前提： 事前に使用したい<em>Tier</em>をElastic \"\n",
            " \"Cloudの管理画面で作成して<em>ください</em>。あるいはオートスケールを有効にしておくと、<em>Tier</em>が必要となったときに自動的に作成されます。', \"\n",
            " \"'このようなケースでは、0000001のロールオーバーされたインデックスがWarm/Cold/<em>Frozen</em> \"\n",
            " \"の<em>Tier</em>に移動できます。', \"\n",
            " \"'qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/53166964-686b-7723-30d5-839041b6e8bc.png)\\\\n\\\\n\\\\n# \"\n",
            " \"最後に\\\\nインデックスがどの<em>Tier</em>']}\")\n",
            "_score: 8.153202, metadata: {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)', 'url': 'https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70', 'tags': [{'name': 'Elasticsearch', 'versions': []}]}\n",
            "(\"{'page_content': \"\n",
            " \"['（<em>Frozen</em>を使うにはEnterpriseライセンスが必要です。）\\\\n\\\\n（Opensearchでは同じ機能は存在しないので、ご注意<em>ください</em>）\\\\n\\\\n!', \"\n",
            " \"'44a0ee84-8fc4-3294-cc9f-bb39fb837f82.png)\\\\n引用: \"\n",
            " \"https://www.elastic.co/blog/introducing-elasticsearch-<em>frozen</em>-<em>tier</em>-searchbox-on-s3', \"\n",
            " \"'このブログでは、<em>Frozen</em>データティアを使うための必要な最低限の設定と、実際にデータが<em>Frozen</em>に入る様子を確認していきます。', \"\n",
            " \"'以下のように<em>Frozen</em> data <em>tier</em>を設定するだけです。\\\\n!', \"\n",
            " \"'実際に<em>Frozen</em>データティアを使いたいと思っても、<em>Frozen</em>ノードをプロビジョンしただけではデフォルトではデータは入っていきませんので、今回の手順を参考にILMの設定、Data \"\n",
            " \"Streamの設定をして<em>ください</em>']}\")\n",
            "_score: 7.010372, metadata: {'source': '/content/qiita/elasticsearch_japan/Elasticsearch v8.9 で実装した日本語NLP、ベクトル検索（セマンティック検索）を使ってみる.json', 'seq_num': 1, 'title': 'Elasticsearch v8.9 で実装した日本語NLP、ベクトル検索（セマンティック検索）を使ってみる', 'url': 'https://qiita.com/daixque/items/931b8be343075b835097', 'tags': [{'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'ベクトル検索', 'versions': []}, {'name': 'VectorSearch', 'versions': []}, {'name': 'VectorStore', 'versions': []}]}\n",
            "(\"{'page_content': ['御託はいいからベクトル検索の<em>使い方</em>だけ<em>教えろ</em>！', \"\n",
            " \"'より丁寧な説明を読みたい方は上記ブログを参照して<em>ください</em>。', \"\n",
            " \"'Elandは以下のようにインストールして<em>ください</em>。', \"\n",
            " \"'ここでword_tokenizer_typeの値がmecabとなっていることを確認して<em>ください</em>。', \"\n",
            " \"'こちらも合わせて確認して<em>ください</em>。\\\\n\\\\nベクトル検索を使ったセマンティック検索を実現する流れについては以上になります。']}\")\n",
            "_score: 6.7907996, metadata: {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)', 'url': 'https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736', 'tags': [{'name': 'Elasticsearch', 'versions': []}]}\n",
            "(\"{'page_content': ['（Hotで設定できる一番小さい構成）\\\\n    - <em>Frozen</em> <em>Tier</em>: \"\n",
            " \"6.25 TB Storage | 4 GB RAM | Up to 2.5 vcpu を1 Availability Zone', \"\n",
            " \"'を有効化\\\\n<em>Frozen</em>を有効化し、HotのIndexのロールオーバが500 MB溜まったら行われる条件としています。', \"\n",
            " \"'ロールオーバー後すぐに<em>Frozen</em>に移動させるために<em>frozen</em>のmin_ageは0dです。', \"\n",
            " \"'に対する検索\\\\n<em>Frozen</em>にあるデータを検索した際どうなるかを確認してみます。', '### 性能値 テスト1: \"\n",
            " \"Hotのみの場合の結果(<em>Frozen</em>は使わない）\\\\n!']}\")\n",
            "_score: 5.4528594, metadata: {'source': '/content/qiita/elasticsearch_japan/Upgrade Assistantを使ってElastic Stackをバージョンアップ.json', 'seq_num': 1, 'title': 'Upgrade Assistantを使ってElastic Stackをバージョンアップ', 'url': 'https://qiita.com/yukshimizu/items/50e471379e1c693b61bc', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}, {'name': 'elasticcloud', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]}\n",
            "(\"{'page_content': \"\n",
            " \"['www.elastic.co/guide/en/kibana/7.17/batch-start-resume-reindex.html)が、Experimentalながらもガイドされるので、興味があれば試してみて<em>ください</em>', \"\n",
            " \"'local\\\\n```\\\\n\\\\nDeprecation logにログが出力されるので、以下のように<em>教え</em>てくれます。\\\\n!', \"\n",
            " \"'アプリケーションの対応後、再度テストをして、Deprecation \"\n",
            " \"logが出なくなったことを確認したりといった<em>使い方</em>ができますね。\\\\n!']}\")\n",
            "_score: 4.744902, metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Cloudに対してTerraformのImportをやってみた.json', 'seq_num': 1, 'title': 'Elastic Cloudに対してTerraformのImportをやってみた', 'url': 'https://qiita.com/nobuhikosekiya/items/c474a21b7114f3371d85', 'tags': [{'name': 'Terraform', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]}\n",
            "(\"{'page_content': ['なお、Elastic \"\n",
            " \"Stackの方のTerraform化については以下の記事を参照<em>ください</em>。', \"\n",
            " \"'items/44027ee0cb6653d29858)\\\\n\\\\n# 手順\\\\n## Elastic \"\n",
            " \"CloudのAPIキーの取得\\\\n最初にElastic Cloudの管理画面のこのページからAPIキーを作成して<em>ください</em>', \"\n",
            " \"'xxxのところは上の手順で取得した値を入れて<em>ください</em>。', \"\n",
            " \"'このリソースを自動的に生成してくれる-generate-config-outオプションをコマンドを<em>教え</em>てくれます。', \"\n",
            " '\\'\"0g\"\\\\n      size_resource    = \"memory\"\\\\n      zone_count       = '\n",
            " \"2\\\\n    }\\\\n    extension = null\\\\n    <em>frozen</em>']}\")\n",
            "_score: 4.572545, metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic と AWS：すぐに使用できる統合プラットフォームでログとメトリックをシームレスに取り込む.json', 'seq_num': 1, 'title': 'Elastic と AWS：すぐに使用できる統合プラットフォームでログとメトリックをシームレスに取り込む', 'url': 'https://qiita.com/shosuz/items/5fc76be33f7332cf3e76', 'tags': [{'name': 'AWS', 'versions': []}, {'name': 'log', 'versions': []}, {'name': 'metrics', 'versions': []}, {'name': 'ElasticStack', 'versions': []}, {'name': 'observability', 'versions': []}]}\n",
            "(\"{'page_content': \"\n",
            " \"['トレードオフやバックアップからデータを[再水和する必要なしに](https://www.elastic.co/blog/whats-new-elastic-7-12-0-schema-on-read-<em>frozen</em>-<em>tier</em>-autoscaling', \"\n",
            " \"'90分と長時間ですが、AWS と Elastic \"\n",
            " \"合計3名のスピーカー陣により、今回記述した内容の一部について、デモなどを含めてご紹介していますので、ぜひこちら登録の上ご覧<em>ください</em>。', \"\n",
            " \"'33599/e1dd0d0b-730f-4641-3b57-1c61a917c452.png)\\\\n\\\\n今後、さらなる AWS さんとの協業や、共同 \"\n",
            " \"Webinar なども色々予定していますので、ぜひご期待<em>ください</em>']}\")\n",
            "_score: 4.2220697, metadata: {'source': '/content/qiita/elasticsearch_japan/Elastic Observability による AWS サービスメトリクスの高速な監視.json', 'seq_num': 1, 'title': 'Elastic Observability による AWS サービスメトリクスの高速な監視', 'url': 'https://qiita.com/shosuz/items/489863f7b4d4269c9628', 'tags': [{'name': 'AWS', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'elasticcloud', 'versions': []}, {'name': 'observability', 'versions': []}, {'name': 'AWSIntegration', 'versions': []}]}\n",
            "(\"{'page_content': ['# \"\n",
            " \"前提条件と構成\\\\nこのブログを見てやってみようと思われる方は、このデモをセットアップするために使用したコンポーネントと詳細を参照して<em>ください</em>。', \"\n",
            " \"'[aws-three-<em>tier</em>-web-architecture-workshop]( \"\n",
            " \"https://github.com/aws-samples/aws-three-<em>tier</em>-web-architecture-workshop', \"\n",
            " \"'## ステップ\\\\u30000:AWS Three <em>Tier</em> アプリケーションをロードし、クレデンシャルを取得する\\\\n[AWS の \"\n",
            " \"Three <em>Tier</em> app](https://github.com/aws-samples', \"\n",
            " \"'/aws-three-<em>tier</em>-web-architecture-workshop) に記載されている手順と、git \"\n",
            " \"上のワークショップのリンクに記載されている手順にしたがって<em>ください</em>。', \"\n",
            " \"'以下のアーキテクチャ図を参照して<em>ください</em>。\\\\n!']}\")\n",
            "_score: 3.4045644, metadata: {'source': '/content/qiita/elasticsearch_japan/PandasのData FrameとElasticsearchのindexを相互変換する.json', 'seq_num': 1, 'title': 'PandasのData FrameとElasticsearchのindexを相互変換する', 'url': 'https://qiita.com/daixque/items/a9708a6e438a8f84fac4', 'tags': [{'name': 'Python', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'pandas', 'versions': []}, {'name': 'DataFrame', 'versions': []}, {'name': 'eland', 'versions': []}]}\n",
            "(\"{'page_content': \"\n",
            " \"['eland\\\\n```\\\\n\\\\nElandの詳細については[ドキュメント](https://eland.readthedocs.io/en/v8.11.0/reference/index.html)を確認して<em>ください</em>', \"\n",
            " \"'ドキュメント](https://eland.readthedocs.io/en/v8.11.0/reference/api/eland.DataFrame.html#eland.DataFrame)を確認して<em>ください</em>', \"\n",
            " \"'さらに進んだ<em>使い方</em>として、ElasticsearchのデータをPandasで読み込んだ後にScikit \"\n",
            " \"LearnのXGBoostなどで学習させて、出来上がった予測モデルをElasticsearchにアップロード', \"\n",
            " \"'して利用する、などの<em>使い方</em>もできます。', '機械学習が得意な方はぜひ試してみて<em>ください</em>。']}\")\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_bm25\"\n",
        "for query in questions:\n",
        "    print(query)\n",
        "    result = do_search(query)\n",
        "    add_result(search_logic=\"bm25\", query=query, result=result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa0uJv0DUOa0"
      },
      "source": [
        "## （Open AI Embeddingを利用） Elasticsearchのセマンティック検索\n",
        "LangchainのElasticsearchStoreというクラスを使って色々なAI/MLモデルを切り替えながらElasticsearchのベクトル検索をテストしていくことができます。\n",
        "\n",
        "参考：https://python.langchain.com/docs/integrations/vectorstores/elasticsearch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VX9zX3wDUOa0"
      },
      "source": [
        "### インジェスト\n",
        "補足：ElasticsearchStoreの中で自動的にインデックスの作成も行われています。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ER_zyLbmUOa0",
        "outputId": "4d53367d-e518-4b19-d040-505473d4d250"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_openai'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_openai\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "qInbxruLUOa0"
      },
      "outputs": [],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_openai\"\n",
        "\n",
        "if os.environ[\"OPENAI_API_KEY\"]  == \"\":\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "    if USE_AZURE_OPENAI:\n",
        "        embedding = OpenAIEmbeddings(deployment=azureOpenAI_embedding_deployment)\n",
        "    else:\n",
        "        embedding = OpenAIEmbeddings(openai_api_type=\"open_ai\")\n",
        "\n",
        "\n",
        "    db_openai = ElasticsearchStore(\n",
        "        es_connection=es,\n",
        "        index_name=INDEX_NAME,\n",
        "        embedding=embedding,\n",
        "        strategy=ElasticsearchStore.ApproxRetrievalStrategy()\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "OTa5OSQlUOa1"
      },
      "outputs": [],
      "source": [
        "if os.environ[\"OPENAI_API_KEY\"]  == \"\":\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    # 前の実行で残っているDocumentはクリアしてからインジェストします\n",
        "    if es.indices.exists(index=INDEX_NAME):\n",
        "        db_openai.client.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "    db_openai.add_documents(json_docs)\n",
        "    db_openai.client.indices.refresh(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX1oloKuUOa1"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPXbLZ7PUOa1",
        "outputId": "b893cded-e787-4bee-effa-0bb28ff176a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frozen tierの使い方について教えてください\n",
            "0.92430925 {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)', 'url': 'https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトをしている関屋です。\n",
            "Elasticsearchバージョン7.12からFroze\n",
            "0.910676 {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)', 'url': 'https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトの関屋です。\n",
            "前回の１回目の記事では、Frozenティアを使うための設定を確認し\n",
            "0.908291 {'source': '/content/qiita/elasticsearch_japan/Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方.json', 'seq_num': 1, 'title': 'Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方', 'url': 'https://qiita.com/nobuhikosekiya/items/3c03932c3efec0a9f04d', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} # はじめに\n",
            "Elasticソリューションアーキテクトの関屋です。\n",
            "Elastic Cloudでは、データを格納するノードを選択できます。基本（必須）のHot Tier以外に、よりコストの安いWarm/Cold/FrozenのTierを選択的に設けることができます。\n",
            "\n",
            "しかし、Warm/Cold/Frozenはそれらのノードを立ち上げただけではデータはデフォルトではそちらに移動してくれません。\n",
            "Index Lifecycle Management (ILM）を使ってライフサイクルを設定する必要があります。\n",
            "\n",
            "ILMですが、インデックスの作られ方によって、状況は少し異なります。\n",
            "1. Fileb\n",
            "0.8984802 {'source': '/content/qiita/elasticsearch_japan/オンプレミスから Elastic on AWS への Elastic ワークロード移行を自動化するための10のステップ.json', 'seq_num': 1, 'title': 'オンプレミスから Elastic on AWS への Elastic ワークロード移行を自動化するための10のステップ', 'url': 'https://qiita.com/shosuz/items/a2714b98adfd35149c57', 'tags': [{'name': 'AWS', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'Terraform', 'versions': []}, {'name': 'Vault', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]} \n",
            "皆様こんにちは！\n",
            "Elastic テクニカルプロダクトマーケティングマネージャー/エバンジェリストの鈴木章太郎です。\n",
            "Elastic の Qiita の Organization もでき、個人のエントリとは別に、その中のエントリとしても、ブログを定期的に書いていきたいと思います。こちらの組織は、メインは弊社のソリューションアーキテクト4名、コンサルタント3名、ですが、僕も技術マーケッターとしてブログを書いていきますので、よろしくお願いします。\n",
            "\n",
            "自己管理の Elastic ワークロードを Elasticon Amazon Web Services（AWS） に移行して、コスト、時間、スケール\n",
            "0.89588165 {'source': '/content/qiita/elasticsearch_japan/AzureのログをさくっとElastic Cloudに送る方法.json', 'seq_num': 1, 'title': 'AzureのログをさくっとElastic Cloudに送る方法', 'url': 'https://qiita.com/nobuhikosekiya/items/9751fda90ba59dde1561', 'tags': [{'name': 'Azure', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]} # この記事について\n",
            "AzureのログをElastic Stackに送りたいけど、具体的にどうすればいいのか？　\n",
            "基本的にはElastic Agentを使うことになりますが、どちらかというとログの転送のためのAzure側の設定が大変なので、そこをTerraformでさくっと作ってみます。\n",
            "\n",
            "他のクラウド版の記事と含め最終的にはこのように3つのクラウドからログを集めることができます。\n",
            "![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/012d3e8d-48bd-1ea1-54ca-9c5\n"
          ]
        }
      ],
      "source": [
        "if os.environ[\"OPENAI_API_KEY\"]  == \"\":\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    # for query in questions:\n",
        "    for query in questions:\n",
        "        print(query)\n",
        "        results = db_openai.similarity_search_with_score(query, k=5)\n",
        "        [print(score, element.metadata, element.page_content[:300]) for element, score in results]\n",
        "        add_result(search_logic=\"vector_openai_embeddings\", query=query, result=[(f\"score: {score}\", element.metadata, element.page_content[:100]) for element, score in results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "re9CjfxlUOa1"
      },
      "source": [
        "## (ElasticsearchにアップしたE5モデルを利用） Elasticsearchのセマンティック検索"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twGgXF90UOa1"
      },
      "source": [
        "### インジェスト"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "S08LHWidUOa1",
        "outputId": "015b1a71-7eb5-4566-fbd3-57011cdef010"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_esml_e5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMe_XJS2UOa1",
        "outputId": "013464bb-a898-4fd4-a156-d0903f48eafe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5\"\n",
        "if not is_model_started(es, model_id=\"intfloat/multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
        "    from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings\n",
        "\n",
        "    ES_MODEL_ID=elasticsearch_model_id(\"intfloat/multilingual-e5-small\")\n",
        "\n",
        "    embedding = ElasticsearchEmbeddings.from_es_connection(\n",
        "        es_connection=es,\n",
        "        model_id=ES_MODEL_ID\n",
        "    )\n",
        "\n",
        "    db_esml_e5 = ElasticsearchStore(\n",
        "        es_connection=es,\n",
        "        index_name=INDEX_NAME,\n",
        "        embedding=embedding,\n",
        "        strategy=ElasticsearchStore.ApproxRetrievalStrategy()\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xIDRGZMUOa1",
        "outputId": "11849a09-5ff7-43cd-d132-1940710bd382"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "....................................................................."
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5\"\n",
        "if not is_model_started(es, model_id=\"intfloat/multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    if db_esml_e5.client.indices.exists(index=INDEX_NAME):\n",
        "        db_esml_e5.client.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "\n",
        "    for doc in json_docs:\n",
        "        db_esml_e5.add_documents([doc])\n",
        "        print('.', end='')\n",
        "\n",
        "    db_esml_e5.client.indices.refresh(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lY3SR8DUOa1"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GK8qdzlUOa1",
        "outputId": "25aa6c1d-0efc-4af2-90e0-f0917cd2dec3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "Frozen tierの使い方について教えてください\n",
            "0.95075536 {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)', 'url': 'https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトをしている関屋です。\n",
            "Elasticsearchバージョン7.12からFroze\n",
            "0.942831 {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)', 'url': 'https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトの関屋です。\n",
            "前回の１回目の記事では、Frozenティアを使うための設定を確認し\n",
            "0.93683434 {'source': '/content/qiita/elasticsearch_japan/Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方.json', 'seq_num': 1, 'title': 'Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方', 'url': 'https://qiita.com/nobuhikosekiya/items/3c03932c3efec0a9f04d', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} # はじめに\n",
            "Elasticソリューションアーキテクトの関屋です。\n",
            "Elastic Cloudでは、データを格納するノードを選択できます。基本（必須）のHot Tier以外に、よりコストの安いWarm/Cold/FrozenのTierを選択的に設けることができます。\n",
            "\n",
            "しかし、Warm/Cold/Frozenはそれらのノードを立ち上げただけではデータはデフォルトではそちらに移動してくれません。\n",
            "Index Lifecycle Management (ILM）を使ってライフサイクルを設定する必要があります。\n",
            "\n",
            "ILMですが、インデックスの作られ方によって、状況は少し異なります。\n",
            "1. Fileb\n",
            "0.9219451 {'source': '/content/qiita/elasticsearch_japan/Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 後編.json', 'seq_num': 1, 'title': 'Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 後編', 'url': 'https://qiita.com/ijokarumawak@github/items/6cc714060090160cf2d5', 'tags': [{'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}]} 先日 [Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた](https://qiita.com/ijokarumawak@github/items/9b0c2d650536488718a5) を書いたところ、「これ、ちゃんと日本語で処理できるのかな？中の動きが知りたい」とコメントいただきました。確かに、モデル側では fugashi などを使っているのに Elasticsearch 側では使ってないはずですね。\n",
            "\n",
            "今回は Elastic Stack 8.0.1 を使って、 inference で判定させるテキストをどうやって tokenize しているかを調査\n",
            "0.9216387 {'source': '/content/qiita/elasticsearch_japan/Elastic における OpenTelemetry による独立性.json', 'seq_num': 1, 'title': 'Elastic における OpenTelemetry による独立性', 'url': 'https://qiita.com/shosuz/items/1eee784c5dd8f009e204', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'apm', 'versions': []}, {'name': 'GKE', 'versions': []}, {'name': 'observability', 'versions': []}, {'name': 'opentelemetry', 'versions': []}]} ※ このブログは、[Independence with OpenTelemetry on Elastic](https://www.elastic.co/jp/blog/opentelemetry-observability) を日本語訳し加筆等したものです。\n",
            "![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/33599/3822357c-7d4d-47e7-4001-2673d09e28cc.png)\n",
            " \n",
            "より速く、よりスケーラブルなサービスを求める動きが活発化しています。私たちの日常生活は、お気\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5\"\n",
        "if not is_model_started(es, \"intfloat__multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    # for query in questions:\n",
        "    for query in questions:\n",
        "        print(query)\n",
        "        results = db_esml_e5.similarity_search_with_score(query, k=5)\n",
        "        [print(score, element.metadata, element.page_content[:300]) for element, score in results]\n",
        "        add_result(search_logic=\"vector_elastic_e5\", query=query, result=[(f\"score: {score}\", element.metadata, element.page_content[:100]) for element, score in results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2e_PxHBUOa1"
      },
      "source": [
        "## Elasticsearch RRF ハイブリッド検索"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TghmnUJdUOa1"
      },
      "source": [
        "### インジェスト"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zHfiIvjLUOa1",
        "outputId": "c51ed428-be89-4244-e9cb-b06bf7ed86c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_esml_e5_hybrid'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5_hybrid\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYJepDsTUOa1",
        "outputId": "efc37a55-eea1-420f-fba7-545f2817d40c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5_hybrid\"\n",
        "if not is_model_started(es, model_id=\"intfloat/multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
        "    from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings\n",
        "\n",
        "    ES_MODEL_ID=elasticsearch_model_id(\"intfloat/multilingual-e5-small\")\n",
        "\n",
        "    embedding = ElasticsearchEmbeddings.from_es_connection(\n",
        "        es_connection=es,\n",
        "        model_id=ES_MODEL_ID\n",
        "    )\n",
        "\n",
        "    db_esml_e5_hybrid = ElasticsearchStore(\n",
        "        es_connection=es.options(request_timeout=3600),\n",
        "        index_name=INDEX_NAME,\n",
        "        embedding=embedding,\n",
        "        strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True)\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "il_ICmCDUOa1",
        "outputId": "5a8bc30f-c5d9-4602-f12d-d1e21958926f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "....................................................................."
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5_hybrid\"\n",
        "if not is_model_started(es, \"intfloat__multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    if db_esml_e5_hybrid.client.indices.exists(index=INDEX_NAME):\n",
        "        db_esml_e5_hybrid.client.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "\n",
        "    for doc in json_docs:\n",
        "        db_esml_e5_hybrid.add_documents([doc])\n",
        "        print(\".\", end=\"\")\n",
        "    db_esml_e5_hybrid.client.indices.refresh(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FurjiCChUOa2"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMPYsSsRUOa2",
        "outputId": "65162bd0-ce06-4295-aa57-04346fa4c484"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "Frozen tierの使い方について教えてください\n",
            "None {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)', 'url': 'https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトをしている関屋です。\n",
            "Elasticsearchバージョン7.12からFroze\n",
            "None {'source': '/content/qiita/elasticsearch_japan/Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方.json', 'seq_num': 1, 'title': 'Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方', 'url': 'https://qiita.com/nobuhikosekiya/items/3c03932c3efec0a9f04d', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} # はじめに\n",
            "Elasticソリューションアーキテクトの関屋です。\n",
            "Elastic Cloudでは、データを格納するノードを選択できます。基本（必須）のHot Tier以外に、よりコストの安いWarm/Cold/FrozenのTierを選択的に設けることができます。\n",
            "\n",
            "しかし、Warm/Cold/Frozenはそれらのノードを立ち上げただけではデータはデフォルトではそちらに移動してくれません。\n",
            "Index Lifecycle Management (ILM）を使ってライフサイクルを設定する必要があります。\n",
            "\n",
            "ILMですが、インデックスの作られ方によって、状況は少し異なります。\n",
            "1. Fileb\n",
            "None {'source': '/content/qiita/elasticsearch_japan/ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2).json', 'seq_num': 1, 'title': 'ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)', 'url': 'https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} シリーズ\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (1)](https://qiita.com/nobuhikosekiya/items/dd2ce836b184730f8d70)\n",
            "[ElasticsearchのFrozenデータティアにデータが入るのをテストしてみた (2)](https://qiita.com/nobuhikosekiya/items/8ab82e70953b6b5d1736)\n",
            "\n",
            "# はじめに\n",
            "こんにちは。Elasticのソリューションアーキテクトの関屋です。\n",
            "前回の１回目の記事では、Frozenティアを使うための設定を確認し\n",
            "None {'source': '/content/qiita/elasticsearch_japan/Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜.json', 'seq_num': 1, 'title': 'Elastic Cloud について\\u3000〜実際にデプロイメントを作ってみよう〜', 'url': 'https://qiita.com/tomo_s_el/items/3584d0b1fabb0bafa4fa', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'elasticcloud', 'versions': []}]} :::note warn\n",
            "*(情報は投稿時点（2022年3月頃）の話です)*\n",
            ":::\n",
            "\n",
            "\n",
            "# Elastic Cloud について 〜実際にデプロイメントを作ってみよう〜\n",
            "\n",
            "Elastic Cloud は一言でいうと、Elastic が提供する Elasticsearch のマネージドサービスです。簡単にElasitcsearch のクラスターがデプロイ・管理できるサービスです。\n",
            "\n",
            ":::note info\n",
            "前書きが長いので、実際の作成の流れを見たい方はこちらへ [[先に飛ぶ]](#実際に使ってみる)\n",
            ":::\n",
            "\n",
            "## メリット\n",
            "\n",
            "ご存知の方もいらっしゃると思いますが、Elasticsearch\n",
            "None {'source': '/content/qiita/elasticsearch_japan/Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 後編.json', 'seq_num': 1, 'title': 'Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた - 後編', 'url': 'https://qiita.com/ijokarumawak@github/items/6cc714060090160cf2d5', 'tags': [{'name': 'NLP', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}]} 先日 [Elastic Stack 8.0 の NLP で日本語センチメント分析を試してみた](https://qiita.com/ijokarumawak@github/items/9b0c2d650536488718a5) を書いたところ、「これ、ちゃんと日本語で処理できるのかな？中の動きが知りたい」とコメントいただきました。確かに、モデル側では fugashi などを使っているのに Elasticsearch 側では使ってないはずですね。\n",
            "\n",
            "今回は Elastic Stack 8.0.1 を使って、 inference で判定させるテキストをどうやって tokenize しているかを調査\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_e5_hybrid\"\n",
        "if not is_model_started(es, \"intfloat__multilingual-e5-small\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    for query in questions:\n",
        "        print(query)\n",
        "        results = db_esml_e5_hybrid.similarity_search_with_score(query, k=5)\n",
        "        [print(score, element.metadata, element.page_content[:300]) for element, score in results]\n",
        "        add_result(search_logic=\"hybrid_elastic_bm25_e5\", query=query, result=[(f\"score: {score}\", element.metadata, element.page_content[:100]) for element, score in results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6se1deaUOa2"
      },
      "source": [
        "## (ElasticsearchにアップしたTohoku BERT Japanese v3モデルを利用) Elasticのセマンティック検索"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlV1J373UOa2"
      },
      "source": [
        "### インジェスト"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "l7ChiwtpUOa2",
        "outputId": "0ba61430-bfa7-419c-c3c0-19b2de05bbb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_esml_tohokubertv3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv3\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNFSVwaBUOa2",
        "outputId": "3997419b-d32f-411f-f45e-7471638407bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model does not have deployment stats\n",
            "Skipping because model is not enabled\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv3\"\n",
        "if not is_model_started(es, model_id=\"cl-tohoku/bert-base-japanese-v3\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
        "    from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings\n",
        "\n",
        "    ES_MODEL_ID=elasticsearch_model_id(\"cl-tohoku/bert-base-japanese-v3\")\n",
        "\n",
        "    embedding = ElasticsearchEmbeddings.from_es_connection(\n",
        "        es_connection=es,\n",
        "        model_id=ES_MODEL_ID\n",
        "    )\n",
        "\n",
        "    db_esml_tohokubertv3 = ElasticsearchStore(\n",
        "        es_connection=es,\n",
        "        index_name=INDEX_NAME,\n",
        "        embedding=embedding,\n",
        "        strategy=ElasticsearchStore.ApproxRetrievalStrategy()\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZWGkZFFUOa2",
        "outputId": "c9242de7-fc76-4dc6-cb10-036718e71638"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model does not have deployment stats\n",
            "Skipping because model is not enabled\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv3\"\n",
        "if not is_model_started(es, \"cl-tohoku__bert-base-japanese-v3\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    if db_esml_tohokubertv3.client.indices.exists(index=INDEX_NAME):\n",
        "        db_esml_tohokubertv3.client.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "    for doc in json_docs:\n",
        "        db_esml_tohokubertv3.add_documents([doc])\n",
        "        print(\".\", end='')\n",
        "    db_esml_tohokubertv3.client.indices.refresh(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7MqBcLZUOa2"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fF8UnV7MUOa2",
        "outputId": "b481e905-21ed-4d43-cafd-83816c921047"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model does not have deployment stats\n",
            "Skipping because model is not enabled\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv3\"\n",
        "if not is_model_started(es, \"cl-tohoku__bert-base-japanese-v3\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    for query in questions:\n",
        "        print(query)\n",
        "\n",
        "        results = db_esml_tohokubertv3.similarity_search_with_score(query, k=10)\n",
        "        [print(score, element.metadata, element.page_content[:300]) for element, score in results]\n",
        "        add_result(search_logic=\"esml_tohokubertv3\", query=query, result=[(f\"score: {score}\", element.metadata, element.page_content[:100]) for element, score in results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhZy_qbxUOa2"
      },
      "source": [
        "## (ElasticsearchにアップしたTohoku BERT Japanese v2モデルを利用) Elasticのセマンティック検索"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLWeUJAhUOa2"
      },
      "source": [
        "### インジェスト"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ntIvnloDUOa2",
        "outputId": "c24da679-465f-4180-af7d-b116dfe3dfe2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'test1226_esml_tohokubertv2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv2\"\n",
        "INDEX_NAME"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SaxtowZ3UOa2",
        "outputId": "3ffb8aca-f8aa-406a-95d1-f265c22df200"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv2\"\n",
        "if not is_model_started(es, model_id=\"cl-tohoku/bert-base-japanese-v2\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
        "    from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings\n",
        "\n",
        "    ES_MODEL_ID=elasticsearch_model_id(\"cl-tohoku/bert-base-japanese-v2\")\n",
        "\n",
        "    embedding = ElasticsearchEmbeddings.from_es_connection(\n",
        "        es_connection=es,\n",
        "        model_id=ES_MODEL_ID\n",
        "    )\n",
        "\n",
        "    db_esml_tohokubertv2 = ElasticsearchStore(\n",
        "        es_connection=es,\n",
        "        index_name=INDEX_NAME,\n",
        "        embedding=embedding,\n",
        "        strategy=ElasticsearchStore.ApproxRetrievalStrategy()\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "je4Jv4EBUOa2",
        "outputId": "2669dd76-5100-4c88-f2ca-172f99ffc40e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "....................................................................."
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv2\"\n",
        "if not is_model_started(es, \"cl-tohoku__bert-base-japanese-v2\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    if db_esml_tohokubertv2.client.indices.exists(index=INDEX_NAME):\n",
        "        db_esml_tohokubertv2.client.delete_by_query(index=INDEX_NAME, body={\"query\": {\"match_all\": {}}})\n",
        "    for doc in json_docs:\n",
        "        db_esml_tohokubertv2.add_documents([doc])\n",
        "        print(\".\", end='')\n",
        "    db_esml_tohokubertv2.client.indices.refresh(index=INDEX_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znLB-fc8UOa2"
      },
      "source": [
        "### サーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wbjxzm2HUOa3",
        "outputId": "1d9d50c8-5cae-4754-abc5-fe9bd837d802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "Frozen tierの使い方について教えてください\n",
            "0.7157447 {'source': '/content/qiita/elasticsearch_japan/delika のデータを Elastic Stack で分析じゃ.json', 'seq_num': 1, 'title': 'delika のデータを Elastic Stack で分析じゃ', 'url': 'https://qiita.com/ijokarumawak@github/items/2d9022c59b34cc89fab8', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}, {'name': 'delika', 'versions': []}, {'name': 'Qiitadelika', 'versions': []}]} データの Github になる！を目指す [delika](https://delika.io/) 。お恥ずかしながら今まで存じ上げず、ふと Qiita で何か書こうかな、と眺めていたら Qiita で delika の記事投稿キャンペーンをやっていたので、やってみました！\n",
            "\n",
            "この記事で利用している Elastic Stack は ver 8.1.0 です。\n",
            "\n",
            "## まずはデータを取り込み！\n",
            "\n",
            "delika には creative commons などでライセンスされたパブリックなデータセットが公開されています。さっそくいくつか取り込んでみましょう。\n",
            "\n",
            "## 品川区避難所\n",
            "\n",
            "CC 2.1 JP\n",
            "0.7097299 {'source': '/content/qiita/elasticsearch_japan/[小ネタ] ElasticのAPMでコードに手を入れずにメソッド監視する方法.json', 'seq_num': 1, 'title': '[小ネタ] ElasticのAPMでコードに手を入れずにメソッド監視する方法', 'url': 'https://qiita.com/nobuhikosekiya/items/5dd6d910aec83dbe3f15', 'tags': [{'name': 'apm', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]} # はじめに\n",
            "本記事はElasticのAPM Java Agentにおいて使用できる技になります。\n",
            "執筆時点のJava Agentのバージョン1.34.1で試しています。\n",
            "\n",
            "\n",
            "# やりたいこと\n",
            "Elastic APM Java Agentをデフォルトの状態で使用すると、トレースの中のスパンとして可視化されるのは[サポートされているテクノロジー](https://www.elastic.co/guide/en/apm/agent/java/current/supported-technologies-details.html)のポイントとなるメソッドです。\n",
            "\n",
            "例としてこの簡単なHello Wor\n",
            "0.70762074 {'source': '/content/qiita/elasticsearch_japan/Upgrade Assistantを使ってElastic Stackをバージョンアップ.json', 'seq_num': 1, 'title': 'Upgrade Assistantを使ってElastic Stackをバージョンアップ', 'url': 'https://qiita.com/yukshimizu/items/50e471379e1c693b61bc', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}, {'name': 'elasticcloud', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]} Elastic Stackの8.0がリリースされてから少し経ちましたが、そろそろ7系から8系にアップグレードしようかと考えている方々も多いのではないでしょうか？そこで、メジャーアップグレード時の心強い味方、Upgrade Assistantを使って、8系へのバージョンアップについて触れておきたいと思います。\n",
            "\n",
            ":::note info\n",
            "こちらの情報は記事投稿時(2022年4月)の話です。\n",
            ":::\n",
            "\n",
            "## 8系の新機能って？\n",
            "\b[8.0のGAは今年の2月](https://www.elastic.co/jp/blog/whats-new-elastic-8-0-0)ですが、Elastic Sta\n",
            "0.7066405 {'source': '/content/qiita/elasticsearch_japan/Elasticsearch Python Client (elasticsearch-py)でProxyを設定する方法.json', 'seq_num': 1, 'title': 'Elasticsearch Python Client (elasticsearch-py)でProxyを設定する方法', 'url': 'https://qiita.com/takeo-furukubo/items/9595abde7ffac324cecd', 'tags': [{'name': 'Python', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'proxy', 'versions': []}, {'name': 'docker-compose', 'versions': []}]} # 概要\n",
            "Elasticsearch Python Client (elasticsearch-py)をプロキシサーバを通して利用する方法がググってもあまり出てこないため、簡単に設定方法をまとめました。\n",
            "elasticsearch-pyのバージョンによって設定方法が少し異なりますので、v7とv8で分けて記載します。\n",
            "[elasticsearch-pyのドキュメント](https://www.elastic.co/guide/en/elasticsearch/client/python-api/8.5/index.html)\n",
            "[elasticsearch-pyのGitHub](https://g\n",
            "0.70257676 {'source': '/content/qiita/elasticsearch_japan/ビジネス・オブザーバビリティ？Elasticでビジネストレンドのレポート自動化に挑戦.json', 'seq_num': 1, 'title': 'ビジネス・オブザーバビリティ？Elasticでビジネストレンドのレポート自動化に挑戦', 'url': 'https://qiita.com/nobuhikosekiya/items/4c17467e5880c793d945', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}]} # はじめに。BIツールで作るレポートの課題。\n",
            "何かのビジネスのトランザクションのデータに対して分析を行う時、長期的なトレンドを見ながら様々な面に切り分けて観測したいと思います。\n",
            "\n",
            "BIツールや今回使うKibanaなどを触りながら分析をして、洞察を得ることができるかもしれません。しかし、これはそのツールを使いなれていないと厳しいです。物事を判断する上の人たちがそういったツールを操作することはないですよね。\n",
            "結局は、BIで作ったデータをパワーポイントなどに貼り付けて報告しているケースが多いんじゃないかと。\n",
            "しかし、分析する面が増えるほどpptに貼り付けるグラフを作成しなければいけないので、結構大\n",
            "0.70076776 {'source': '/content/qiita/elasticsearch_japan/さくっとOpenTelemetryをElastic Observabilityで試す方法.json', 'seq_num': 1, 'title': 'さくっとOpenTelemetryをElastic Observabilityで試す方法', 'url': 'https://qiita.com/nobuhikosekiya/items/5f770ee4bc9be37b5733', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'ElasticStack', 'versions': []}, {'name': 'opentelemetry', 'versions': []}]} # はじめに\n",
            "オブザーバビリティ界隈では、ますますOpenTelemetryの人気が上がっていますが、本記事ではさくっとバックエンドの監視ツールとしてElasticを試してみる方法を紹介します。\n",
            "\n",
            "OpenTelemetryのDemoアプリケーションを使います。Demoアプリケーションのバージョン1.5.0で動作確認しているので、それを使う前提です。\n",
            "https://opentelemetry.io/docs/demo\n",
            "\n",
            "# 前提\n",
            "* 4 GB RAM のパソコンがあればかろうじて動くと思います。（公式的な推奨は6GBか8GBだったとかだったと思います）\n",
            "* Docker Compose v2\n",
            "0.69838417 {'source': '/content/qiita/elasticsearch_japan/[v8.7] Elasticsearch_Kibana_Fleet_Elastic Agentをdocker-composeでインストールする手順（試用用途）.json', 'seq_num': 1, 'title': '[v8.7] Elasticsearch/Kibana/Fleet/Elastic Agentをdocker-composeでインストールする手順（試用用途）', 'url': 'https://qiita.com/takeo-furukubo/items/591617ea57e537046fb8', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'Kibana', 'versions': []}, {'name': 'docker-compose', 'versions': []}]} # はじめに\n",
            "下記の記事にて`docker-compose`を使ってElasticsearchとKibanaを構成する方法を説明しました。\n",
            "https://qiita.com/takeo-furukubo/items/c2f194679afadc06a4e9\n",
            "\n",
            "今回は更に一歩進んでFleetを設定する方法を説明します。\n",
            "これにより手元環境でElastic Solutionsを試すことができるようになります。\n",
            "\n",
            "# Fleetとは？\n",
            "https://www.elastic.co/guide/en/fleet/current/fleet-server.html\n",
            "Elastic Agentの管理プレ\n",
            "0.69712543 {'source': '/content/qiita/elasticsearch_japan/Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方.json', 'seq_num': 1, 'title': 'Elastic CloudでHotに溜まっているデータを別のティアに移動するやり方', 'url': 'https://qiita.com/nobuhikosekiya/items/3c03932c3efec0a9f04d', 'tags': [{'name': 'Elasticsearch', 'versions': []}]} # はじめに\n",
            "Elasticソリューションアーキテクトの関屋です。\n",
            "Elastic Cloudでは、データを格納するノードを選択できます。基本（必須）のHot Tier以外に、よりコストの安いWarm/Cold/FrozenのTierを選択的に設けることができます。\n",
            "\n",
            "しかし、Warm/Cold/Frozenはそれらのノードを立ち上げただけではデータはデフォルトではそちらに移動してくれません。\n",
            "Index Lifecycle Management (ILM）を使ってライフサイクルを設定する必要があります。\n",
            "\n",
            "ILMですが、インデックスの作られ方によって、状況は少し異なります。\n",
            "1. Fileb\n",
            "0.6971147 {'source': '/content/qiita/elasticsearch_japan/AzureのログをさくっとElastic Cloudに送る方法.json', 'seq_num': 1, 'title': 'AzureのログをさくっとElastic Cloudに送る方法', 'url': 'https://qiita.com/nobuhikosekiya/items/9751fda90ba59dde1561', 'tags': [{'name': 'Azure', 'versions': []}, {'name': 'Elasticsearch', 'versions': []}, {'name': 'ElasticStack', 'versions': []}]} # この記事について\n",
            "AzureのログをElastic Stackに送りたいけど、具体的にどうすればいいのか？　\n",
            "基本的にはElastic Agentを使うことになりますが、どちらかというとログの転送のためのAzure側の設定が大変なので、そこをTerraformでさくっと作ってみます。\n",
            "\n",
            "他のクラウド版の記事と含め最終的にはこのように3つのクラウドからログを集めることができます。\n",
            "![image.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/109197/012d3e8d-48bd-1ea1-54ca-9c5\n",
            "0.6951545 {'source': '/content/qiita/elasticsearch_japan/GKE上のOpenTelemetry DemoアプリケーションをさくっとElasticでAPM・ログ・インフラ+eBPFプロファイリング監視する.json', 'seq_num': 1, 'title': 'GKE上のOpenTelemetry DemoアプリケーションをさくっとElasticでAPM・ログ・インフラ+eBPFプロファイリング監視する', 'url': 'https://qiita.com/nobuhikosekiya/items/9c3ae25b39827b1ef9d1', 'tags': [{'name': 'Elasticsearch', 'versions': []}, {'name': 'GKE', 'versions': []}, {'name': 'ElasticStack', 'versions': []}, {'name': 'opentelemetry', 'versions': []}]} # はじめに\n",
            "こちらのOpenTelemetry Demoを使うと、簡単にKubernetes上でアプリケーションを動かして、そしてElasticオブザーバビリティ機能を色々試すことができます。\n",
            "https://opentelemetry.io/docs/demo/kubernetes-deployment/\n",
            "\n",
            "本記事はGKEを使い、そのやり方をまとめました。\n",
            "\n",
            "# 手順\n",
            "\n",
            "## GKEクラスタ作成とOtel Demoアプリのデプロイ\n",
            "以下でGKEクラスタ作ります。Otel Demoアプリのデプロイは上のDemoアプリドキュメントの通りに行います。\n",
            "```:sh\n",
            "CLUSTER_NAME=no\n"
          ]
        }
      ],
      "source": [
        "INDEX_NAME=f\"{INDEX_PREFIX}_esml_tohokubertv2\"\n",
        "if not is_model_started(es, \"cl-tohoku__bert-base-japanese-v2\"):\n",
        "    print(\"Skipping because model is not enabled\")\n",
        "else:\n",
        "\n",
        "    for query in questions:\n",
        "        print(query)\n",
        "\n",
        "        results = db_esml_tohokubertv2.similarity_search_with_score(query, k=10)\n",
        "        [print(score, element.metadata, element.page_content[:300]) for element, score in results]\n",
        "        add_result(search_logic=\"esml_tohokubertv2\", query=query, result=[(f\"score: {score}\", element.metadata, element.page_content[:100]) for element, score in results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUAdu6lEUOa3"
      },
      "source": [
        "# 終了処理"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9h6bPOEUOa3"
      },
      "source": [
        "## Elastic ML E5モデルの停止"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od4r1eOaUOa3",
        "outputId": "060bf419-8090-44dc-c792-5b318e2d0512"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model is started\n",
            "Stopping model deployment\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-17415ad073d6>:38: ElasticsearchWarning: The default [remove_binary] value of 'false' is deprecated and will be set to 'true' in a future release. Set [remove_binary] explicitly to 'true' or 'false' to ensure no behavior change.\n",
            "  es_connection.options(request_timeout=300).ml.stop_trained_model_deployment(model_id=es_model_id, force=True)\n"
          ]
        }
      ],
      "source": [
        "if is_model_started(es, model_id=\"intfloat/multilingual-e5-small\"):\n",
        "    stop_model(es, model_id=\"intfloat/multilingual-e5-small\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qU_jJQ3wUOa3"
      },
      "source": [
        "## Elastic ML Tohoku BERT モデルの停止"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpHW-J6TUOa3",
        "outputId": "75ac54d8-544d-426d-86df-4bab9f299c32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Check: Model does not have deployment stats\n",
            "Check: Model is started\n",
            "Stopping model deployment\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-17415ad073d6>:38: ElasticsearchWarning: The default [remove_binary] value of 'false' is deprecated and will be set to 'true' in a future release. Set [remove_binary] explicitly to 'true' or 'false' to ensure no behavior change.\n",
            "  es_connection.options(request_timeout=300).ml.stop_trained_model_deployment(model_id=es_model_id, force=True)\n"
          ]
        }
      ],
      "source": [
        "if is_model_started(es, model_id=\"cl-tohoku/bert-base-japanese-v3\"):\n",
        "    stop_model(es, model_id=\"cl-tohoku/bert-base-japanese-v3\")\n",
        "if is_model_started(es, model_id=\"cl-tohoku/bert-base-japanese-v2\"):\n",
        "    stop_model(es, model_id=\"cl-tohoku/bert-base-japanese-v2\")"
      ]
    }
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