01_ChromaDB_Complete_Tutorial-ja.ipynb

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ChromaDB"],"metadata":{"id":"Gvachtup0Gaf"}},{"cell_type":"markdown","source":["## ChromaDB 概要"],"metadata":{"id":"oQrzoUCLnWTi"}},{"cell_type":"markdown","source":["このチュートリアルでは、ChromaDBの基本的な使い方から中級の利用方法までを紹介します。ChromaDBの構築方法、ベクトルの生成、検索、更新、削除などのトピックを探求します。さらに、データの保存と読み込みの技術についても取り上げます。また、実際のプロジェクト内でChromaDBを活用した実践的なアプリケーションにも触れていきます。\n","\n","\n","\n","\n"],"metadata":{"id":"LiNwEkF-4xZX"}},{"cell_type":"markdown","source":["### ChromaDBとは\n","\n","ChromaDBは、ベクトル埋め込みを格納し、大規模な言語モデル（LLM）アプリケーションを開発・構築するために設計されたオープンソースのベクトルデータベースです。ChromaDBは、LLMアプリケーションを構築するための強力なツールです。高速で効率的で使いやすな特徴を持っています。"],"metadata":{"id":"fyrKvMkl1BfC"}},{"cell_type":"markdown","source":["### ChromaDBの利点\n","\n","- **高速で効率的**: ChromaDBは、人気のあるインメモリデータストアであるRedisの上に構築されています。これにより、埋め込みの格納とクエリが非常に高速に行えます。\n","- **使いやすさ**: ChromaDBにはシンプルで直感的なAPIが備わっており、初めてでも簡単に始めることができます。\n","- **複数の埋め込みフォーマットのサポート**: ChromaDBは、Word2VecやGloVe、FastTextなど、さまざまな埋め込みフォーマットをサポートしています。\n","- **最近傍探索のサポート**: ChromaDBは、指定された埋め込みの最近傍を見つけるために使用できます。これは、セマンティック検索や質問応答などのアプリケーションに役立ちます。\n","- **増分更新のサポート**: ChromaDBは増分で更新することが可能です。これにより、頻繁に更新が必要なアプリケーションに適しています。\n","- **レプリケーションのサポート**: ChromaDBは複数のサーバーにレプリケーションでき、高い可用性と耐障害性を提供します。\n","- **ハイブリッドクラウドのサポート**: ChromaDBはローカルおよびクラウドの両方で使用することができます。"],"metadata":{"id":"-nUCY23h1b-_"}},{"cell_type":"markdown","source":["### 言語のサポート\n","\n","| Language     | In-Memory | Client & Server |\n","|--------------|:---------:|:---------------:|\n","| Python       | ✔       | ✔             |\n","| Javascript   | -        | ✔              |\n","| Ruby         | -        | from @mariochavez |\n","| C#           | -        | from Microsoft (coming soon) |\n","| Other?       | ?        | ?              |\n"],"metadata":{"id":"uIqfmUXe47cj"}},{"cell_type":"markdown","source":["## 環境構築"],"metadata":{"id":"EPz2haqnAZ82"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"UH3YlhRB0FxA","executionInfo":{"status":"ok","timestamp":1692371060031,"user_tz":-540,"elapsed":106827,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"outputs":[],"source":["%%capture\n","%pip install chromadb\n","%pip install datasets\n","%pip install sentence-transformers\n","%pip install openai\n","%pip install tiktoken"]},{"cell_type":"code","source":["import chromadb\n","import datasets\n","import transformers\n","import openai\n","\n","print(\"chromadb version: \", chromadb.__version__)\n","print(\"datasets version: \", datasets.__version__)\n","print(\"transformers version: \", transformers.__version__)\n","print(\"openai version: \", openai.__version__)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"citJlB6sVupf","executionInfo":{"status":"ok","timestamp":1692371062499,"user_tz":-540,"elapsed":2474,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"61d95e18-b2c0-46b9-9e2b-4aa0f99bf2a4"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["chromadb version:  0.4.6\n","datasets version:  2.14.4\n","transformers version:  4.31.0\n","openai version:  0.27.8\n"]}]},{"cell_type":"code","source":["import json\n","import numpy as np\n","import pandas as pd\n","import pprint"],"metadata":{"id":"RZjfvLAWdSUc","executionInfo":{"status":"ok","timestamp":1692372263726,"user_tz":-540,"elapsed":282,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":52,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XU_JzsJNNHtE","executionInfo":{"status":"ok","timestamp":1692371084617,"user_tz":-540,"elapsed":22124,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"ff86b224-a5a7-418c-d822-e65fc11ba636"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["## Dataset\n","\n","- 多言語のレビュー、画像、感情データの**「unstructured-data-multilingual」**のデータセットを使用します。\n","\n","- language: ['en', 'ja', 'th']\n","\n","- https://huggingface.co/datasets/jingwora/unstructured-data-multilingual"],"metadata":{"id":"xElp6MHX7sAx"}},{"cell_type":"code","source":["from datasets import load_dataset\n","\n","# データセットの読み込み\n","dataset = load_dataset(\"jingwora/unstructured-data-multilingual\")\n","\n","# スプリットの選択\n","dataset = dataset[\"ja\"]\n","\n","dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":397,"referenced_widgets":["185a8c9e3d0248fa9095f8c2a2b455a6","49557ec578c74e57818049e9a1256450","da9408db1a824987b48af10b81a1a90a","d0220b2bcddc412b90aae7ec6cd4a54f","75df8a7a2f8d4b93ad0082fddc5052fa","781a8608527c48f289e451400092f72a","f8f3b7f08e3844a8b9730e319baa77a9","985083b5e4d34cbaadc8dc94f46d540d","7004348b91b7424ab827dd640404c187","5975121acc5945e585023121f707bb80","0568fdd477004845a366332c60dbd704","e20592fa01644f21ae4e100e4a398356","43cc423ed039440ea12217e8e25bb3a2","1fb6b3b614064adc9b4d7bde6e0797ce","d28f509d01c2413ab0036e386bf384ed","6369946831ba4fc4b4b1d85dfc13bee8","70eab164d966476ca5005a1d8e80c1a2","b3dd703aa24d4c5db5d40fb987acd3eb","16e03334f9c64005898d41d3597c9fc7","9c2cc08bd0ed475d8886026b852fa0f8","e6c4bf1bf29e4b66ae431e440b69c9c6","76b17b4f47a44f9589d5fad8dc3e3b74","16c6ca1e20064ff6ba89dd8a79a92a9a","cc97702d6feb431a86303cc7dedd0488","65aac1bc593d4c8898549684789605e2","3de3bab6b2ea48daa2d0cca471de49c5","8ad12d7bd24e4b69a7f19ab50dee1c3b","a61b39508e7e485baf6728fc134c4780","90aad082a06047a7a4844e5c4262583f","fd3f46fb9e5342f79a81222473f0a284","3a17bc597d084d40bb169ae82c32f5a4","55a5383e524642eaa961a9794a570536","017c870277a94f6590e2a43f93831abc","67c3bbd74b1541d3a23e342abdcabfff","63e01a8efb3647d4a83fa4cfde8a6c2c","f712828fe8ea4dd4acac551168cd28c7","278c3f619e5a4100a2e732baac6d5940","09f609a301264b6bb4b4fa2d42759d44","da50ef14365c4f448822bc226fb7ed50","cf461b4bb5844365ac3003839c2903bb","e97f46c485384472afd15a2c16cdba02","31a1a7c4735b4c6f8f1af2d322388970","bd573e36790d477b8101bc9a07d1cfb4","02cc00693f8647baa5d5c395fad37ab1","df1c3a75e8334f65b4759d4fe46d0822","4c5dd687c2854f1998bfc01aaf202da4","aab285a6b6ae4c1a8605fc80dd0a152d","e113a8f5040840729271aae1f3348109","ff3a1813c438404a9963c3062f6fc097","e813a1b2160d47aabb1f4562cba80985","0f554875bf6245b4a13667261d6e972a","61fba7eaf3c749ddb867e1e6aa7df585","b3ddda34342b4af2956baa352a58c9d4","84ea0fd4f93447158619e9631b4a2b5a","b052275c4e3742b38ea941d5c2f0888f","1c9e3fdf53644326976749a50e4fe5ef","90561bed5d2e4494a320ba04f551503f","5137d900fbc74b4c991d89d92ccc1afa","989e23cdc7da48699c0f1885745765bb","4a8a1b2ddd8545eb892735385866e521","3a4a5a40d3a24fed91ad7a98b6d00ab7","03dedf27fe624efebb4962ff71f2eb4d","22358c6a6ac74f39a2b81721a8ad7a60","3b7df112a3984d3d8eee9158486a3a04","2557e702c7f3431698a7bd060303b8df","204c07d756c945f0a0f44285b94f78b0","c628c6984045437cbfee29056a31ae60","bbaeac5bf2fd47e28ef21bc46e7a0b52","0a4859e179fd4326b4a5a28676463c82","29adf1d6d3b14de88e905bf5567ebdb5","ffcb3cb74fa240c689e3435a4f3d628d","f1993ec9e0754f2d8f1bb11f27a13d6c","dfc217fadce245959a02cf434a7af0c0","2521a2e9ea784ef98c43d53b20203aec","ad416799b00c436bb755b2dbe34e459d","968fae4503524411a25e33c60c276c00","bef56316943943b99dd3b43252d60c54","14e23044e3c1432f9e93ddc08db7d30f","9357c0194f584ddba857accf4992907c","398b9479c89a4007b1dc52cdb9f0ab58","6a5a3622b59540c782fa1c8ff14901fd","0d374694a47449478f1d65b78b5fd99d","dab0136504a145078e2da712239397eb","0929003e293c43f7ae072fdab5a6966f","cae87f2e540c4ff198c7e0ebab39dd20","88d08527494844a59fd3eaefb3aff413","99d333672b424a289a99efcd6df023e6","3bb554a3d27349f0b54a41e432d3c819","feeaf72e456a4e97a1995857e1c0a97f","6e116ae061684ce8bd9aac253415cc28","1a7e2bda019d422e8b30ceaef0f965bc","0efdca5acd75484cb3c17fb2ce684475","4bebf51420404810b00ebb63b0c4d9b7","a0b9455edbe145839790882acb9e5b08","9dc8952e84ad41a5a93e8c3dad23f4f9","4b784e6ff9f54cc3bc115d3f78e66887","c365a0e47fea4074a01b2cd1d0260488","8026d70de4d744c7b23308c032fdbaf0","6b97c7a13b82436087fe7d04fb47bdc0"]},"id":"Nrmp6fozQfdL","executionInfo":{"status":"ok","timestamp":1692371090603,"user_tz":-540,"elapsed":5995,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"df58c187-7347-4ac1-94b0-2f32cbc9f1de"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/1.00k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"185a8c9e3d0248fa9095f8c2a2b455a6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e20592fa01644f21ae4e100e4a398356"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/12.5k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"16c6ca1e20064ff6ba89dd8a79a92a9a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 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examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c628c6984045437cbfee29056a31ae60"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating ja split:   0%|          | 0/24 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"14e23044e3c1432f9e93ddc08db7d30f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating th split:   0%|          | 0/24 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"feeaf72e456a4e97a1995857e1c0a97f"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['language', 'id', 'product_id', 'category', 'sub_category', 'product_name', 'product_detail', 'image_files', 'review', 'star', 'sentiment'],\n","    num_rows: 24\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# データセットをPandasのDataFrameに変換する\n","\n","df = pd.DataFrame(dataset)"],"metadata":{"id":"TA--g90CENth","executionInfo":{"status":"ok","timestamp":1692371094649,"user_tz":-540,"elapsed":273,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# category_labelとsentiment_labelの列を追加する\n","\n","df['category_label'] = df['category'].map({'Electronics': 0, 'Health and Beauty': 1})\n","df['sentiment_label'] = df['sentiment'].map({'positive': 0, 'neutral': 1, 'negative': 2})\n","df['product_name_label'] = df['product_name'].map({'Smart watch': 0, 'Wall Lamps': 1, 'Supplements': 2, 'Sunblock': 3})"],"metadata":{"id":"TggoEvlXQqW7","executionInfo":{"status":"ok","timestamp":1692371097562,"user_tz":-540,"elapsed":1,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# 埋め込みのためのすべてのテキスト列\n","\n","df['text'] = df['category'] + \", \" + df['sub_category'] + \", \" + df['product_detail'] + \", \" + df['review'] + \", \" + df['sentiment']+ \", \" + df['star']\n"],"metadata":{"id":"uPB5NU1-ZpvI","executionInfo":{"status":"ok","timestamp":1692371104259,"user_tz":-540,"elapsed":256,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":670},"id":"4wTlBtID_rM2","executionInfo":{"status":"ok","timestamp":1692371105585,"user_tz":-540,"elapsed":373,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"8894a184-7047-4228-b8ac-37b8334000d8"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  language      id product_id     category      sub_category product_name  \\\n","0       ja  ja-001     ja-001  Electronics  Wearable devices  Smart watch   \n","1       ja  ja-002     ja-001  Electronics  Wearable devices  Smart watch   \n","\n","                                      product_detail  \\\n","0  [ja-001] スマートウォッチ 体表面温度検知 1.9インチ超大画面 腕時計 レディース...   \n","1  [ja-001] スマートウォッチ 体表面温度検知 1.9インチ超大画面 腕時計 レディース...   \n","\n","                    image_files  \\\n","0  ja-002-01.jpg, ja-002-02.jpg   \n","1  ja-002-01.jpg, ja-002-02.jpg   \n","\n","                                              review star sentiment  \\\n","0  軽くてストレス無し！！\\n薄型でとても軽い。\\nカッコイイと思います。\\nすぐにスマホとペア...  4-5  positive   \n","1   大変使いやすく、お手頃な価格で変えたので、とてもよかったです！機会があればまた、購入したいです！  4-5  positive   \n","\n","   category_label  sentiment_label  product_name_label  \\\n","0               0                0                   0   \n","1               0                0                   0   \n","\n","                                                text  \n","0  Electronics, Wearable devices, [ja-001] スマートウォ...  \n","1  Electronics, Wearable devices, [ja-001] スマートウォ...  "],"text/html":["\n","  <div id=\"df-ac96cc61-8892-4bf4-80b4-5bd6caefbfd4\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>language</th>\n","      <th>id</th>\n","      <th>product_id</th>\n","      <th>category</th>\n","      <th>sub_category</th>\n","      <th>product_name</th>\n","      <th>product_detail</th>\n","      <th>image_files</th>\n","      <th>review</th>\n","      <th>star</th>\n","      <th>sentiment</th>\n","      <th>category_label</th>\n","      <th>sentiment_label</th>\n","      <th>product_name_label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ja</td>\n","      <td>ja-001</td>\n","      <td>ja-001</td>\n","      <td>Electronics</td>\n","      <td>Wearable devices</td>\n","      <td>Smart watch</td>\n","      <td>[ja-001] スマートウォッチ 体表面温度検知 1.9インチ超大画面 腕時計 レディース...</td>\n","      <td>ja-002-01.jpg, ja-002-02.jpg</td>\n","      <td>軽くてストレス無し！！\\n薄型でとても軽い。\\nカッコイイと思います。\\nすぐにスマホとペア...</td>\n","      <td>4-5</td>\n","      <td>positive</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Electronics, Wearable devices, [ja-001] スマートウォ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ja</td>\n","      <td>ja-002</td>\n","      <td>ja-001</td>\n","      <td>Electronics</td>\n","      <td>Wearable devices</td>\n","      <td>Smart watch</td>\n","      <td>[ja-001] スマートウォッチ 体表面温度検知 1.9インチ超大画面 腕時計 レディース...</td>\n","      <td>ja-002-01.jpg, ja-002-02.jpg</td>\n","      <td>大変使いやすく、お手頃な価格で変えたので、とてもよかったです！機会があればまた、購入したいです！</td>\n","      <td>4-5</td>\n","      <td>positive</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Electronics, Wearable devices, [ja-001] スマートウォ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac96cc61-8892-4bf4-80b4-5bd6caefbfd4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-ac96cc61-8892-4bf4-80b4-5bd6caefbfd4 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# ラベル確認\n","\n","display(df.groupby(['sentiment_label']).size())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"GG8i_UjSgLr-","executionInfo":{"status":"ok","timestamp":1692371112393,"user_tz":-540,"elapsed":230,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"ca93b5cd-1ed0-4f17-94c0-27a68d075997"},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":["sentiment_label\n","0    8\n","1    8\n","2    8\n","dtype: int64"]},"metadata":{}}]},{"cell_type":"markdown","source":["## 埋め込み（エンベディング）\n","\n","Chromaはデフォルトで文のトランスフォーマーを使用します。\n","\n","https://huggingface.co/intfloat/multilingual-e5-large\n","\n","https://huggingface.co/spaces/mteb/leaderboard"],"metadata":{"id":"SyR-QiQMYZTT"}},{"cell_type":"code","source":["from sentence_transformers import SentenceTransformer\n","\n","# キャッシュディレクトリのパスを指定\n","cache_dir = \"/content/drive/MyDrive/models/intfloat_multilingual-e5-large/\"\n","\n","# SentenceTransformerモデルを読み込む\n","model = SentenceTransformer(\"intfloat/multilingual-e5-large\", cache_folder=cache_dir)"],"metadata":{"id":"thdWu2G7R1br","executionInfo":{"status":"ok","timestamp":1692371195129,"user_tz":-540,"elapsed":71688,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# 埋め込み（エンベディング）\n","\n","# 埋め込みを生成するための関数\n","def generate_embeddings(text):\n","    embeddings = model.encode(text)\n","    return embeddings.tolist()\n","\n","df['embeddings'] = df['text'].apply(generate_embeddings)\n","\n","# 埋め込みの確認\n","sample_embeddings = df[\"embeddings\"][0]\n","print(type(sample_embeddings))\n","embeddings_array = np.array(df['embeddings'].tolist())\n","\n","print(\"embeddings length: \", len(sample_embeddings))\n","print(\"embeddings max:\", embeddings_array.max())\n","print(\"embeddings min:\", embeddings_array.min())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"obZjbep-ZLx6","executionInfo":{"status":"ok","timestamp":1692371223665,"user_tz":-540,"elapsed":28542,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"a488950d-df2e-46e0-f18d-37fa9dd63cbc"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'list'>\n","embeddings length:  1024\n","embeddings max: 0.19916462898254395\n","embeddings min: -0.13209502398967743\n"]}]},{"cell_type":"code","source":["# 散布図を作成する\n","\n","import numpy as np\n","from ast import literal_eval\n","import json\n","from sklearn.manifold import TSNE\n","from sklearn.decomposition import PCA\n","import matplotlib.pyplot as plt\n","import matplotlib\n","\n","def get_scatter_plot(input_df, method=\"TSNE\", label_col=\"label\"):\n","\n","    df = input_df.copy()\n","\n","    # numpy array作成\n","    matrix = np.array(df[\"embeddings\"].apply(lambda x: x).tolist())\n","\n","    # TSNE modelの計算\n","    if method == \"TSNE\":\n","\n","        tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)\n","        vis_dims = tsne.fit_transform(matrix)\n","\n","    # PCA modelの計算\n","    elif method == \"PCA\":\n","        pca = PCA(n_components=2)\n","        pca.fit(matrix)\n","        vis_dims = pca.transform(matrix)\n","    else:\n","        print(\"please specify method as 'TSNE' or 'PCA'\")\n","\n","    # 散布図の作成\n","    x = [x for x, y in vis_dims]\n","    y = [y for x, y in vis_dims]\n","    color_indices = df[label_col].values\n","\n","    unique_labels = np.unique(color_indices)\n","    num_labels = len(unique_labels)\n","\n","    colormap = matplotlib.colormaps['tab10']\n","\n","    plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.5)\n","    for idx, label in enumerate(unique_labels):\n","        avg_x = np.mean(np.array(x)[df[label_col] == label])\n","        avg_y = np.mean(np.array(y)[df[label_col] == label])\n","        color = colormap(idx % num_labels)\n","        plt.scatter(avg_x, avg_y, marker='x', color=color, s=100)\n","\n","    plt.title(f\"Scatter Plot: {method} | {label_col}\")"],"metadata":{"id":"pCp7rRBSeqet","executionInfo":{"status":"ok","timestamp":1692371223665,"user_tz":-540,"elapsed":3,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["# 散布図\n","\n","get_scatter_plot(df, method=\"PCA\", label_col=\"sentiment_label\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"1K4S3219H9jb","executionInfo":{"status":"ok","timestamp":1692371224516,"user_tz":-540,"elapsed":853,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"a5e9782b-34e3-4caa-a7e0-5fd05aaaab02"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 散布図\n","\n","get_scatter_plot(df, method=\"PCA\", label_col=\"category_label\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"lzOij1TfabZ7","executionInfo":{"status":"ok","timestamp":1692371224981,"user_tz":-540,"elapsed":471,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"fc93e40c-c234-4360-9ea4-be883cbd7dd5"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 散布図\n","\n","get_scatter_plot(df, method=\"PCA\", label_col=\"product_name_label\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"ik7NadTfcoI6","executionInfo":{"status":"ok","timestamp":1692371225277,"user_tz":-540,"elapsed":298,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"36772e3e-5811-4d05-fdc4-52024e20540d"},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# ChromaDBの関数"],"metadata":{"id":"MbV1XLHaU48L"}},{"cell_type":"markdown","source":["## Clientのメソッド"],"metadata":{"id":"gvvQdkOhmdmO"}},{"cell_type":"markdown","source":["### EphemeralClient\n","- Chromaをローカルマシンに保存およびロードするように設定できます。データは自動的に永続化され、存在する場合には起動時にロードされます。\n","\n","- Create chroma.sqlite3"],"metadata":{"id":"g4hCbIXmn4zj"}},{"cell_type":"code","source":["import chromadb\n","from chromadb.config import Settings\n","\n","chroma_client = chromadb.EphemeralClient()\n","chroma_client = chromadb.PersistentClient(path=\"/content/drive/MyDrive/share_KM/VectorDB/chromadb\")"],"metadata":{"id":"Qtill77cU4Qd","executionInfo":{"status":"ok","timestamp":1692371255678,"user_tz":-540,"elapsed":1005,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["### create_collection\n","\n","- コレクションは、埋め込み、ドキュメント、および追加のメタデータを格納する場所です。"],"metadata":{"id":"tm0OuBQlWu5m"}},{"cell_type":"code","source":["review_cl = chroma_client.create_collection(name=\"review\")"],"metadata":{"id":"cvIPJqtFmfLp","executionInfo":{"status":"ok","timestamp":1692371457955,"user_tz":-540,"elapsed":529,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["test_cl = chroma_client.create_collection(name=\"test\")"],"metadata":{"id":"9ejipOYKI4Yn","executionInfo":{"status":"ok","timestamp":1692371462842,"user_tz":-540,"elapsed":232,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["type(review_cl)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1h0dKUpdoKPM","executionInfo":{"status":"ok","timestamp":1692371463881,"user_tz":-540,"elapsed":2,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"6254ce8b-48aa-4d26-bd17-aaf4c20c600a"},"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["chromadb.api.models.Collection.Collection"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","source":["### heartbeat\n","- ナノ秒単位のハートビートを返します。クライアントが接続されたままであることを確認するのに役立ちます。"],"metadata":{"id":"27NAQENvrw9t"}},{"cell_type":"code","source":["chroma_client.heartbeat()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bNYahd7Trvyr","executionInfo":{"status":"ok","timestamp":1692371466919,"user_tz":-540,"elapsed":235,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"58c37f50-725d-4dc3-8be1-f644ca2c319f"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1692371466218689789"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","source":[" ### reset\n","\n","- データベースを空にして完全にリセットします。\n","- ⚠️ 元に戻すことはできません。"],"metadata":{"id":"OdNrT1yAr9M7"}},{"cell_type":"code","source":["# chroma_client.reset()"],"metadata":{"id":"PyYym-VWr9_M","executionInfo":{"status":"ok","timestamp":1692371469094,"user_tz":-540,"elapsed":254,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":["### get_collection\n","- 既存のコレクションからコレクションオブジェクトを取得します\n","- ```name```: コレクション名"],"metadata":{"id":"h_aPSxoIj7Kw"}},{"cell_type":"code","source":["review_cl = chroma_client.get_collection(name=\"review\")\n","\n","print(type(review_cl))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dvs2ATbyj9fD","executionInfo":{"status":"ok","timestamp":1692371470126,"user_tz":-540,"elapsed":5,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"41ab7c13-ab43-4419-9c32-8365af1b38b3"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'chromadb.api.models.Collection.Collection'>\n"]}]},{"cell_type":"markdown","source":["### get_or_create_collection\n","\n","- 既存のコレクションからコレクションオブジェクトを名前で取得し、存在しない場合は作成します。"],"metadata":{"id":"j1QjQyHplsQs"}},{"cell_type":"code","source":["review_cl = chroma_client.get_or_create_collection(name=\"review\")\n","\n","print(type(review_cl))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cXBZlwRJllPA","executionInfo":{"status":"ok","timestamp":1692371473600,"user_tz":-540,"elapsed":230,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"658f5a86-2d54-47a8-e8bf-8d4886fb3eea"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'chromadb.api.models.Collection.Collection'>\n"]}]},{"cell_type":"markdown","source":["### list_collections\n","- すべてのコレクションをリストするために使います。"],"metadata":{"id":"qwEyWOh4X9b1"}},{"cell_type":"code","source":["collections = chroma_client.list_collections()\n","\n","for collection in collections:\n","  print(collection.name)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dFM2WR8cmtbR","executionInfo":{"status":"ok","timestamp":1692371475014,"user_tz":-540,"elapsed":2,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"b8c4c9d5-869d-4985-892e-9a2146c0ce85"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["test\n","review\n"]}]},{"cell_type":"markdown","source":["### delete_collection\n","- コレクションと関連するすべての埋め込み、ドキュメント、およびメタデータを削除します。⚠️元に戻せません。"],"metadata":{"id":"oQ257ZByobfn"}},{"cell_type":"code","source":["chroma_client.delete_collection(name=\"test\")"],"metadata":{"id":"dBnZ5ux4ofcX","executionInfo":{"status":"ok","timestamp":1692371480535,"user_tz":-540,"elapsed":229,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":["## Methods on Collection"],"metadata":{"id":"_om-a3GqmqTq"}},{"cell_type":"markdown","source":["### add\n","\n","- コレクションにテキストドキュメントを追加します。\n","\n","- パラメータ:\n","  - ```documents```: ドキュメントのリスト。\n","  - ```embedding```: ドキュメントの埋め込みのオプションのリスト。\n","  - ```ids```: ドキュメントのオプションのIDのリスト。\n","  - ```collection_name```: 作成するコレクションの名前。\n","  - ```persist_directory```: コレクションデータを保存するディレクトリ。\n","  - ```client```: オプションのClientオブジェクト。"],"metadata":{"id":"DlsWSY-gKiZ9"}},{"cell_type":"code","source":["# ドキュメント、埋め込み、およびメタデータを追加します。\n","\n","review_cl.add(\n","    documents=df[\"review\"].tolist(),\n","    embeddings=df[\"embeddings\"].tolist(),\n","    metadatas=[\n","        {\n","            \"language\": language,\n","            \"product_id\": product_id,\n","            \"category\": category,\n","            \"sub_category\": sub_category,\n","            \"product_name\": product_name,\n","            \"image_files\": image_files,\n","            \"sentiment\": sentiment\n","        }\n","        for language, product_id, category, sub_category, product_name, image_files, sentiment\n","        in zip(\n","            df[\"language\"].tolist(),\n","            df[\"product_id\"].tolist(),\n","            df[\"category\"].tolist(),\n","            df[\"sub_category\"].tolist(),\n","            df[\"product_name\"].tolist(),\n","            df[\"image_files\"].tolist(),\n","            df[\"sentiment\"].tolist()\n","        )\n","    ],\n","    ids=df[\"id\"].tolist()\n",")"],"metadata":{"id":"xLwhFVvqR1Vg","executionInfo":{"status":"ok","timestamp":1692371484192,"user_tz":-540,"elapsed":248,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":42,"outputs":[]},{"cell_type":"markdown","source":["### peek\n","\n","- 指定しない限り、コレクション内の最初の5つのアイテムのリストを返します。"],"metadata":{"id":"7dXqEiMli7v_"}},{"cell_type":"code","source":["print(review_cl.peek(1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Qs9WQ2pDiw0x","executionInfo":{"status":"ok","timestamp":1692372339067,"user_tz":-540,"elapsed":259,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"6d801861-30e3-461f-b0db-6c9feafc9117"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["{'ids': ['ja-001'], 'embeddings': [[-0.0033082470763474703, -0.021322360262274742, -0.045499153435230255, -0.04512368515133858, 0.037261173129081726, -0.0547495074570179, -0.02079579420387745, 0.04622034355998039, 0.06176404654979706, -0.02581595629453659, 0.04663693159818649, 0.038380734622478485, -0.016167711466550827, -0.024133270606398582, -0.04513375833630562, 0.0036329992581158876, -0.02700548619031906, 0.021578505635261536, 0.009692693129181862, -0.016088120639324188, 0.04648549109697342, 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-0.018892547115683556, 0.00026351501583121717, -0.029082396999001503, 0.04435869678854942, -0.023831592872738838, 0.03140626847743988, -0.007146212738007307, -0.04786182940006256, 0.00017410831060260534, -0.014507578685879707, 0.0219411738216877, 0.008785612881183624, -0.014105760492384434, 0.0288928784430027, -0.0143909165635705, 0.0210239440202713, -0.04126371070742607, 0.022627029567956924, 0.0009250112343579531, 0.017461219802498817, -0.03337319940328598, -0.028127191588282585, -8.94431141205132e-05, 0.02273327112197876, -0.04002239927649498, -0.0041539641097188, 0.06332691013813019, 0.0603121779859066, 0.021242475137114525, -0.06435034424066544, -0.013043203391134739, 0.015437573194503784, 0.004247404169291258, -0.020069045946002007, 0.021640721708536148, 0.021279748529195786, 0.012056061998009682, 0.04035098850727081, -0.013226436451077461, 0.052693724632263184, -0.028815308585762978, -0.018247194588184357, -0.07581265270709991, 0.027709057554602623, -0.0036129371728748083, -0.041713763028383255, -0.019049707800149918, -0.0419980026781559, -0.04870489239692688, 0.02926258184015751, 0.022008107975125313, -0.05333385989069939, 0.011069727130234241, -0.01058595534414053, 0.05476003140211105, -0.01200979296118021, 0.031614288687705994, 0.03557831794023514, 0.0420730859041214, -0.008135841228067875, 0.007844898849725723, 0.03954249992966652, 0.013414333574473858, -0.04392768815159798, -0.018084054812788963, 0.01921943575143814, 0.01347343809902668, 0.014042713679373264, 0.04374835640192032, -0.0006904486217536032, -0.03145363926887512, -0.05042719095945358, -0.017225952818989754, -0.01921364851295948, 0.0048248195089399815, 0.01031009666621685, -0.032644592225551605, 0.013989138416945934, -0.02617032639682293, -0.013174003921449184, 0.0032493143808096647, 0.0317779965698719, -0.003949232865124941, 0.022578328847885132, -0.03360193222761154, 0.006954021751880646, 0.0072623612359166145, 0.042804934084415436, 8.209534280467778e-05, -0.011386068537831306, 0.023099243640899658, -0.016030844300985336, -0.018786318600177765, -0.02593669295310974, 0.031151536852121353, 0.01639862172305584, 0.019761065021157265, 0.03833410143852234, 0.0020673121325671673, -0.026050981134176254, 0.029864804819226265, 0.03419587016105652, 0.011116637848317623, -0.03262089192867279, -0.022358344867825508, -0.022827764973044395, -0.02770271524786949, -0.010050958022475243, -0.06005778908729553, -0.016553759574890137, -0.0071516395546495914, -0.004394050221890211, -0.025450285524129868, -0.01159267034381628, 0.035832468420267105, -0.014956473372876644, 0.02381722442805767, -0.020780861377716064, -0.023883581161499023, 0.00922136940062046, 0.034508563578128815, 0.011987429112195969, -0.029775163158774376, -0.04005952924489975, 0.05679705739021301, 0.014698479324579239, -0.04074029251933098, -0.003370059421285987, -0.012538542971014977, -0.03332456573843956, -0.03669340908527374, 0.024753650650382042, -0.05204206332564354, 0.007430837489664555, 0.0007443689391948283, -0.013825833797454834, -0.0028670388273894787, 0.0008918116800487041, -0.021813828498125076, -0.03406095132231712, -0.01269292552024126, -0.025297807529568672, -0.018464243039488792, -0.00937943160533905, 0.021705295890569687, -0.022061780095100403, -0.00798439048230648, 0.03657485544681549, 0.006189728155732155, -0.027527855709195137, 0.03723904490470886, -0.019383277744054794, -0.05526531860232353, -0.023899927735328674, -0.02094145305454731, -0.015938671305775642, -0.020994946360588074, 0.010551439598202705, -0.021861659362912178, 0.024261821061372757, -0.013150297105312347, 0.011613551527261734, -0.02645796351134777, -0.04902330040931702, 0.042939912527799606, -0.02053345926105976, 0.015665538609027863, -0.011930765584111214, 0.0561809279024601, -0.0356258787214756, -0.01254808995872736, -0.023189490661025047, 0.032123904675245285, -0.06162339821457863, 0.025418005883693695, -0.003388012293726206, -0.021375497803092003, -0.03663597255945206, -0.0040866918861866, 0.10210278630256653, 0.005606742575764656, -0.0009673493332229555, -0.019013315439224243, -0.011111397296190262, -0.043333280831575394, -0.044370848685503006, -0.027994604781270027, 0.0057490915060043335, -0.05352802947163582, -0.07115201652050018, -0.033866140991449356, -0.006694809999316931, 0.026617346331477165, -0.032199498265981674, -0.028105594217777252, 0.013220624066889286, 0.01386634074151516, 0.021864766255021095, -0.006222200579941273, 0.03329752758145332, 0.03205886110663414, 0.0277873408049345, -0.04236481711268425, -0.012160567566752434, -0.04009679704904556, 0.02200443297624588, -0.017482440918684006, -0.019739603623747826, 0.02083166502416134, 0.01575646735727787, -0.060158755630254745, -0.018400579690933228, 0.026178967207670212, 0.020480619743466377, -0.06032905355095863, 0.002356662880629301, -0.014985733665525913, 0.01470713410526514, 0.07383695244789124, -0.025321099907159805, 0.05749056860804558, 0.01700153388082981, 0.020571393892169, 0.005392399616539478, -0.036537475883960724, 0.041026074439287186, 0.010198486968874931, 0.09160435944795609, 0.04437331482768059, -0.014158939011394978, -0.025549443438649178, 0.045255985110998154, -0.0315384604036808, 0.023394407704472542, -0.062114063650369644, 0.04313299059867859, 0.04249565675854683, -0.01678851805627346, 0.010283774696290493, 0.016354909166693687, -0.035103991627693176, -0.016372347250580788, 0.024014024063944817, 0.0025038612075150013, 0.012355886399745941, 0.0025368083734065294, -0.02611907757818699, 0.0025056658778339624, -0.011312800459563732, 0.009867207147181034, -0.036612432450056076, 0.013974854722619057, 0.06224728748202324, 0.0055151889100670815, 0.06362585723400116, 0.028646014630794525, 0.03997424617409706, 0.010780157521367073, 0.038575656712055206, -0.0611577183008194, -0.004049391020089388, 0.06241174042224884, 0.016443314030766487, 0.03622456640005112, -0.04852660745382309, -0.009094590321183205, -0.0018606216181069613, 0.019846182316541672, 0.007059358526021242, 0.03319787606596947, -0.036202434450387955, -0.009552361443638802, -0.030468063428997993, 0.040036093443632126, 0.013205887749791145, 0.017550785094499588, -0.03305141627788544, -0.028543543070554733, 0.035051897168159485, -0.01917787455022335, 0.0025794191751629114, 0.015650572255253792, -0.001865291385911405, 0.002341028768569231, -0.03685411810874939, -0.009967643767595291]], 'metadatas': [{'category': 'Electronics', 'image_files': 'ja-002-01.jpg, ja-002-02.jpg', 'language': 'ja', 'product_id': 'ja-001', 'product_name': 'Smart watch', 'sentiment': 'positive', 'sub_category': 'Wearable devices'}], 'documents': ['軽くてストレス無し！！\\n薄型でとても軽い。\\nカッコイイと思います。\\nすぐにスマホとペアリング出来ました。\\n商品が来るのも早かったので助かりました。\\n軽くて良い&#11093;&#65039;\\n写真追加しました。']}\n"]}]},{"cell_type":"markdown","source":["### count\n","- コレクション内のアイテムの数を返します。"],"metadata":{"id":"hvkawODSjMil"}},{"cell_type":"code","source":["review_cl.count()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0TAyizLEi0ry","executionInfo":{"status":"ok","timestamp":1692371490923,"user_tz":-540,"elapsed":229,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"9fecbd0a-adea-49c1-c9e6-707c37bf5efb"},"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["24"]},"metadata":{},"execution_count":44}]},{"cell_type":"markdown","source":["### modify\n","- コレクションの名前を変更します。"],"metadata":{"id":"bNEmWFAsjUmR"}},{"cell_type":"code","source":["review_cl.modify(name=\"review\")"],"metadata":{"id":"7_NSyXAri2q2","executionInfo":{"status":"ok","timestamp":1692371494240,"user_tz":-540,"elapsed":242,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":["### get\n","- パラメータ:\n","  - ```ids```: IDによってアイテムを取得します\n","  - ```where```: メタデータの条件"],"metadata":{"id":"63s9mckjc4kq"}},{"cell_type":"code","source":["# get ids\n","\n","results = review_cl.get(\n","    ids=[\"ja-001\"],\n",")\n","\n","print(type(results))\n","pprint.pprint(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7mvew7kYccwy","executionInfo":{"status":"ok","timestamp":1692372235509,"user_tz":-540,"elapsed":262,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"908ede0c-83f4-49c0-ce78-de6f3c99bfc5"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'dict'>\n","{'documents': ['軽くてストレス無し！！\\n'\n","               '薄型でとても軽い。\\n'\n","               'カッコイイと思います。\\n'\n","               'すぐにスマホとペアリング出来ました。\\n'\n","               '商品が来るのも早かったので助かりました。\\n'\n","               '軽くて良い&#11093;&#65039;\\n'\n","               '写真追加しました。'],\n"," 'embeddings': None,\n"," 'ids': ['ja-001'],\n"," 'metadatas': [{'category': 'Electronics',\n","                'image_files': 'ja-002-01.jpg, ja-002-02.jpg',\n","                'language': 'ja',\n","                'product_id': 'ja-001',\n","                'product_name': 'Smart watch',\n","                'sentiment': 'positive',\n","                'sub_category': 'Wearable devices'}]}\n"]}]},{"cell_type":"code","source":["# get ids + where\n","\n","results = review_cl.get(\n","    ids=[\"ja-001\", \"ja-002\", \"ja-003\"],\n","    where={\"sentiment\": \"positive\"}\n",")\n","\n","pprint.pprint(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gi8LspVhdFN9","executionInfo":{"status":"ok","timestamp":1692372357556,"user_tz":-540,"elapsed":2,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"cfddc036-c7f9-4468-f9e0-53541c0eace1"},"execution_count":55,"outputs":[{"output_type":"stream","name":"stdout","text":["{'documents': ['軽くてストレス無し！！\\n'\n","               '薄型でとても軽い。\\n'\n","               'カッコイイと思います。\\n'\n","               'すぐにスマホとペアリング出来ました。\\n'\n","               '商品が来るのも早かったので助かりました。\\n'\n","               '軽くて良い&#11093;&#65039;\\n'\n","               '写真追加しました。',\n","               '大変使いやすく、お手頃な価格で変えたので、とてもよかったです！機会があればまた、購入したいです！'],\n"," 'embeddings': None,\n"," 'ids': ['ja-001', 'ja-002'],\n"," 'metadatas': [{'category': 'Electronics',\n","                'image_files': 'ja-002-01.jpg, ja-002-02.jpg',\n","                'language': 'ja',\n","                'product_id': 'ja-001',\n","                'product_name': 'Smart watch',\n","                'sentiment': 'positive',\n","                'sub_category': 'Wearable devices'},\n","               {'category': 'Electronics',\n","                'image_files': 'ja-002-01.jpg, ja-002-02.jpg',\n","                'language': 'ja',\n","                'product_id': 'ja-001',\n","                'product_name': 'Smart watch',\n","                'sentiment': 'positive',\n","                'sub_category': 'Wearable devices'}]}\n"]}]},{"cell_type":"markdown","source":["### query\n","\n","- Query the collection\n","- Parameters:\n","  - ```query_texts```: input text (defualt embeddings)\n","  - ```generate_embeddings```: input embedding\n","  - ```n_results```: number of returned ids"],"metadata":{"id":"IJowvdc1LGUN"}},{"cell_type":"code","source":["# query\n","\n","query_embeddings = generate_embeddings(\"Smart watch\")\n","results = review_cl.query(query_embeddings=query_embeddings,\n","                          n_results=2,\n","                          )\n","\n","pprint.pprint(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7QOF6XWjR1TB","executionInfo":{"status":"ok","timestamp":1692372367692,"user_tz":-540,"elapsed":845,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"ec4199bc-a2a4-4b2e-9606-07b53d5c741e"},"execution_count":56,"outputs":[{"output_type":"stream","name":"stdout","text":["{'distances': [[0.36229462681363606, 0.36564063800894087]],\n"," 'documents': [['大変使いやすく、お手頃な価格で変えたので、とてもよかったです！機会があればまた、購入したいです！',\n","                'LINEや電話の通知設定がなかなかうまく行きません。ここ2週間くらい使用してますが、通知がうまくいかないのが不満なくらいで、他の機能は満足してます。']],\n"," 'embeddings': None,\n"," 'ids': [['ja-002', 'ja-004']],\n"," 'metadatas': [[{'category': 'Electronics',\n","                 'image_files': 'ja-002-01.jpg, ja-002-02.jpg',\n","                 'language': 'ja',\n","                 'product_id': 'ja-001',\n","                 'product_name': 'Smart watch',\n","                 'sentiment': 'positive',\n","                 'sub_category': 'Wearable devices'},\n","                {'category': 'Electronics',\n","                 'image_files': 'ja-002-01.jpg, ja-002-02.jpg',\n","                 'language': 'ja',\n","                 'product_id': 'ja-001',\n","                 'product_name': 'Smart watch',\n","                 'sentiment': 'neutral',\n","                 'sub_category': 'Wearable devices'}]]}\n"]}]},{"cell_type":"code","source":["# query + where\n","\n","query_embeddings = generate_embeddings(\"The worst and terrify review\")\n","results = review_cl.query(query_embeddings=query_embeddings,\n","                          where={\"sentiment\": \"negative\"},\n","                          n_results=1,\n","                          )\n","pprint.pprint(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"igl6z0OBR1Qo","executionInfo":{"status":"ok","timestamp":1692372375739,"user_tz":-540,"elapsed":322,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"014ee544-ca71-40a8-810d-441cf1e97173"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stdout","text":["{'distances': [[0.4398985927543045]],\n"," 'documents': [['不良品\\n到着後段ボールを開封すると角が潰れて漏れていたようです。']],\n"," 'embeddings': None,\n"," 'ids': [['ja-018']],\n"," 'metadatas': [[{'category': 'Health and Beauty',\n","                 'image_files': 'ja-003-01.jpg, ja-003-02.jpg',\n","                 'language': 'ja',\n","                 'product_id': 'ja-003',\n","                 'product_name': 'Supplements',\n","                 'sentiment': 'negative',\n","                 'sub_category': 'Health'}]]}\n"]}]},{"cell_type":"code","source":["# query + where + where_document\n","\n","results = review_cl.query(query_embeddings=query_embeddings,\n","                          where={\"sentiment\": \"negative\"},\n","                          where_document={\"$contains\":\"不良品\"},\n","                          n_results=1,\n","                          )\n","pprint.pprint(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YLMyWOEwR1Kk","executionInfo":{"status":"ok","timestamp":1692372423156,"user_tz":-540,"elapsed":393,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"5837c1a8-1fcb-4c17-ff47-ce220d9ccf96"},"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["{'distances': [[0.4398985927543045]],\n"," 'documents': [['不良品\\n到着後段ボールを開封すると角が潰れて漏れていたようです。']],\n"," 'embeddings': None,\n"," 'ids': [['ja-018']],\n"," 'metadatas': [[{'category': 'Health and Beauty',\n","                 'image_files': 'ja-003-01.jpg, ja-003-02.jpg',\n","                 'language': 'ja',\n","                 'product_id': 'ja-003',\n","                 'product_name': 'Supplements',\n","                 'sentiment': 'negative',\n","                 'sub_category': 'Health'}]]}\n"]}]},{"cell_type":"markdown","source":["### delete\n","\n","- ドキュメントを削除します。"],"metadata":{"id":"ggVolU0gvsh3"}},{"cell_type":"code","source":["review_cl.delete(ids=[\"ja-020\"])"],"metadata":{"id":"Esp-5Wd1tlK7","executionInfo":{"status":"ok","timestamp":1692372453387,"user_tz":-540,"elapsed":227,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":62,"outputs":[]},{"cell_type":"code","source":["review_cl.get(\n","    ids=[\"ja-020\"],\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4YsWn_iptmdV","executionInfo":{"status":"ok","timestamp":1692372454330,"user_tz":-540,"elapsed":2,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"96d213de-703b-4ae8-fecb-ff7fe6383485"},"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'ids': [], 'embeddings': None, 'metadatas': [], 'documents': []}"]},"metadata":{},"execution_count":63}]},{"cell_type":"markdown","source":["### update\n","\n","- ドキュメントを更新します。"],"metadata":{"id":"edVqHhSLwTVU"}},{"cell_type":"code","source":["review_cl.get(\n","    ids=[\"ja-021\"],\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2BgNsdJ2tmZ_","executionInfo":{"status":"ok","timestamp":1692372594910,"user_tz":-540,"elapsed":251,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"5e169c3f-006e-4079-ac7a-0d94e079954b"},"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'ids': ['ja-021'],\n"," 'embeddings': None,\n"," 'metadatas': [{'category': 'Health and Beauty',\n","   'image_files': 'ja-004-01.jpg, ja-004-02.jpg',\n","   'language': 'ja',\n","   'product_id': 'ja-004',\n","   'product_name': 'Sunblock',\n","   'sentiment': 'neutral',\n","   'sub_category': 'Beauty'}],\n"," 'documents': ['ローズを買いました。\\nトーンアップはしますが、脂性肌なのでヨレてしまうのが残念です。']}"]},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["review_cl.update(\n","    ids=[\"ja-021\"],\n","    metadatas=[{\"product_name\": \"Sunblock_new\"}],\n",")"],"metadata":{"id":"7eBsoc2AwdX8","executionInfo":{"status":"ok","timestamp":1692372609091,"user_tz":-540,"elapsed":257,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":65,"outputs":[]},{"cell_type":"code","source":["review_cl.get(\n","    ids=[\"ja-021\"],\n",")"],"metadata":{"id":"N5IBVynDxKbc","executionInfo":{"status":"ok","timestamp":1692372616934,"user_tz":-540,"elapsed":230,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"14a04542-1263-4a4d-e16f-b6f68dcc6e98","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":67,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'ids': ['ja-021'],\n"," 'embeddings': None,\n"," 'metadatas': [{'category': 'Health and Beauty',\n","   'image_files': 'ja-004-01.jpg, ja-004-02.jpg',\n","   'language': 'ja',\n","   'product_id': 'ja-004',\n","   'product_name': 'Sunblock_new',\n","   'sentiment': 'neutral',\n","   'sub_category': 'Beauty'}],\n"," 'documents': ['ローズを買いました。\\nトーンアップはしますが、脂性肌なのでヨレてしまうのが残念です。']}"]},"metadata":{},"execution_count":67}]},{"cell_type":"markdown","source":["### upsert\n","\n","- 新しいアイテムは追加され、既存のアイテムは更新されます。"],"metadata":{"id":"WhS7-lAP8fjs"}},{"cell_type":"code","source":["# upsert items\n","\n","review_cl.upsert(\n","    documents=df[\"review\"].tolist(),\n","    embeddings=df[\"embeddings\"].tolist(),\n","    metadatas=[\n","        {\n","            \"language\": language,\n","            \"product_id\": product_id,\n","            \"category\": category,\n","            \"sub_category\": sub_category,\n","            \"product_name\": product_name,\n","            \"image_files\": image_files,\n","            \"sentiment\": sentiment\n","        }\n","        for language, product_id, category, sub_category, product_name, image_files, sentiment\n","        in zip(\n","            df[\"language\"].tolist(),\n","            df[\"product_id\"].tolist(),\n","            df[\"category\"].tolist(),\n","            df[\"sub_category\"].tolist(),\n","            df[\"product_name\"].tolist(),\n","            df[\"image_files\"].tolist(),\n","            df[\"sentiment\"].tolist()\n","        )\n","    ],\n","    ids=df[\"id\"].tolist()\n",")"],"metadata":{"id":"WRilDHZx8g_R","executionInfo":{"status":"ok","timestamp":1692372665339,"user_tz":-540,"elapsed":282,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}}},"execution_count":68,"outputs":[]},{"cell_type":"markdown","source":["## embedding\n","- デフォルトでは、Chromaは埋め込みを作成するためにSentence Transformersの```all-MiniLM-L6-v2```モデルを使用します。\n","- https://www.sbert.net/docs/pretrained_models.html"],"metadata":{"id":"KxudqeF1s77_"}},{"cell_type":"markdown","source":["### DefaultEmbeddingFunction"],"metadata":{"id":"wo7u9vB5uJyE"}},{"cell_type":"code","source":["from chromadb.utils import embedding_functions\n","\n","default_ef = embedding_functions.DefaultEmbeddingFunction()\n","\n","default_ef"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U2LbPqxP69dx","executionInfo":{"status":"ok","timestamp":1692366582178,"user_tz":-540,"elapsed":329,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"147ae537-be42-4001-c652-a493a70d34ae"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<chromadb.utils.embedding_functions.ONNXMiniLM_L6_V2 at 0x7c6b56d7efb0>"]},"metadata":{},"execution_count":120}]},{"cell_type":"markdown","source":["### SentenceTransformerEmbeddingFunction"],"metadata":{"id":"iFmUIdxeuH8U"}},{"cell_type":"code","source":["# SentenceTransformerを読み込みます。\n","sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=\"intfloat/multilingual-e5-large\")\n","\n","sentence_transformer_ef"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pyMVX9H8tjba","executionInfo":{"status":"ok","timestamp":1692366589211,"user_tz":-540,"elapsed":244,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"f5847030-5792-47d5-f359-a0d81c17b539"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction at 0x7c6b56daf370>"]},"metadata":{},"execution_count":121}]},{"cell_type":"markdown","source":["### OpenAI"],"metadata":{"id":"5xWA3ZMguE0d"}},{"cell_type":"code","source":["import os\n","import getpass\n","import openai\n","\n","APIKEY = getpass.getpass(\"APIKEY: \")\n","os.environ[\"OPENAI_API_KEY\"] = APIKEY\n","openai.api_key = os.environ[\"OPENAI_API_KEY\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HiTflDoG0Gan","executionInfo":{"status":"ok","timestamp":1692365180719,"user_tz":-540,"elapsed":58112,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"a0c2c822-6e92-4b46-dc63-b4ce992ac273"},"execution_count":null,"outputs":[{"name":"stdout","output_type":"stream","text":["APIKEY: ··········\n"]}]},{"cell_type":"code","source":["from chromadb.utils import embedding_functions\n","\n","def text_embedding(text):\n","    response = openai.Embedding.create(model=\"text-embedding-ada-002\", input=text)\n","    return response[\"data\"][0][\"embedding\"]\n","\n","openai_ef = embedding_functions.OpenAIEmbeddingFunction(\n","                api_key=os.environ[\"OPENAI_API_KEY\"],\n","                model_name=\"text-embedding-ada-002\"\n","            )"],"metadata":{"id":"tKeE_kBptjVr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["embeddings = text_embedding(\"test\")\n","print(embeddings)"],"metadata":{"id":"gGw0PZqqtjNx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1692366359957,"user_tz":-540,"elapsed":572,"user":{"displayName":"Jing Wora","userId":"14098338404840441752"}},"outputId":"f2cc1974-6fa9-440c-8556-0fa2ec3e85e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[-0.0017262666951864958, -0.013922297395765781, 0.0016728137852624059, -0.018408838659524918, -0.0071258884854614735, 0.018563061952590942, -0.00954090990126133, -0.029190557077527046, -0.006235590670257807, -0.01467940118163824, 0.011791190132498741, 0.007153929676860571, 0.0013932812726125121, -0.01178418006747961, -0.007090837694704533, 0.01302498858422041, 0.046940434724092484, 0.0009928224608302116, 0.0170208141207695, -0.02868582122027874, 0.010585431940853596, 0.007311659399420023, -0.003193155163899064, 0.0016386389033868909, -0.03011590614914894, -0.014791564084589481, 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https://docs.trychroma.com/api-reference\n","- datasets: https://huggingface.co/datasets/jingwora/unstructured-data-multilingual\n"],"metadata":{"id":"fJZF0FTF0T2J"}}]}