chottokun · April 14, 2025 14:45
diff --git a/cl-nagoya-ruri-large-v2.ipynb b/cl-nagoya-ruri-large-v2.ipynb
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPVviaJM1Dy1uJEceRSrR1S",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/chottokun/f25bc29d8513f847890ffbb62bf007a8/cl-nagoya-ruri-large-v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN3Z4_Yq_T9U",
        "outputId": "397283cd-3074-47fa-ae2e-1bdaef8e6736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.4/47.4 MB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for unidic_lite (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q langchain_community sentence_transformers langchain_core langchain chromadb\n",
        "!pip install -q unidic_lite fugashi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List, Dict, Any, Optional\n",
        "from langchain_core.embeddings import Embeddings\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "class PrefixEmbeddings(Embeddings):\n",
        "    \"\"\"\n",
        "    LangChain用のPrefixEmbeddings埋め込みモデルラッパークラス\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        model_name: str = \"l-nagoya/ruri-v3-310m\",\n",
        "        query_prompt_name: str = \"検索クエリ: \",\n",
        "        document_prompt_name: str = \"検索文章: \",\n",
        "        cache_folder: Optional[str] = None,\n",
        "        model_kwargs: Optional[Dict[str, Any]] = None,\n",
        "        encode_kwargs: Optional[Dict[str, Any]] = None,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        初期化メソッド\n",
        "\n",
        "        Args:\n",
        "            model_name: Hugging Faceモデル名\n",
        "            query_prompt_name: クエリエンコード時のプロンプト名\n",
        "            document_prompt_name: ドキュメントエンコード時のプロンプト名\n",
        "            cache_folder: モデルキャッシュディレクトリ\n",
        "            model_kwargs: SentenceTransformerモデル初期化用の追加引数\n",
        "            encode_kwargs: encode関数用の追加引数\n",
        "        \"\"\"\n",
        "        self.model_name = model_name\n",
        "        self.query_prompt_name = query_prompt_name\n",
        "        self.document_prompt_name = document_prompt_name\n",
        "        self.cache_folder = cache_folder\n",
        "        self.model_kwargs = model_kwargs or {}\n",
        "        self.encode_kwargs = encode_kwargs or {}\n",
        "\n",
        "        # SentenceTransformerモデルの初期化\n",
        "        self._model = SentenceTransformer(\n",
        "            model_name_or_path=model_name,\n",
        "            cache_folder=cache_folder,\n",
        "            prompts={\n",
        "                query_prompt_name: query_prompt_name,\n",
        "                document_prompt_name: document_prompt_name,\n",
        "            },\n",
        "            **self.model_kwargs\n",
        "        )\n",
        "\n",
        "    def embed_documents(self, texts: List[str]) -> List[List[float]]:\n",
        "        \"\"\"\n",
        "        ドキュメントテキストをベクトル埋め込みに変換\n",
        "\n",
        "        Args:\n",
        "            texts: 埋め込むドキュメントのリスト\n",
        "\n",
        "        Returns:\n",
        "            埋め込みベクトルのリスト\n",
        "        \"\"\"\n",
        "        # ドキュメント用プロンプトを使用してエンコード\n",
        "        encode_kwargs = {**self.encode_kwargs, \"prompt_name\": self.document_prompt_name}\n",
        "        embeddings = self._model.encode(texts, **encode_kwargs)\n",
        "        return embeddings.tolist()\n",
        "\n",
        "    def embed_query(self, text: str) -> List[float]:\n",
        "        \"\"\"\n",
        "        クエリテキストをベクトル埋め込みに変換\n",
        "\n",
        "        Args:\n",
        "            text: 埋め込むクエリテキスト\n",
        "\n",
        "        Returns:\n",
        "            クエリの埋め込みベクトル\n",
        "        \"\"\"\n",
        "        # クエリ用プロンプトを使用してエンコード\n",
        "        encode_kwargs = {**self.encode_kwargs, \"prompt_name\": self.query_prompt_name}\n",
        "        embedding = self._model.encode(text, **encode_kwargs)\n",
        "        return embedding.tolist()\n"
      ],
      "metadata": {
        "id": "tCIKCjXmAMbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain_community.document_loaders import TextLoader\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "\n",
        "# カスタムAMBER埋め込みモデルのインスタンス化\n",
        "embeddings = PrefixEmbeddings(\n",
        "    model_name=\"cl-nagoya/ruri-large-v2\",\n",
        "    model_kwargs={\"device\": \"cuda\"},\n",
        "    query_prompt_name=\"クエリ: \",\n",
        "    document_prompt_name=\"文章: \"\n",
        ")\n",
        "\n",
        "# ドキュメントのロードと分割\n",
        "loader = TextLoader(\"japanese_documents.txt\")\n",
        "documents = loader.load()\n",
        "text_splitter = CharacterTextSplitter(\n",
        "    separator = \"\\n\",  # セパレータ\n",
        "    chunk_size=100,\n",
        "    chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)\n",
        "\n",
        "# ChromaDBベクトルストアの作成\n",
        "vectordb = Chroma.from_documents(\n",
        "    documents=docs,\n",
        "    embedding=embeddings,\n",
        "    persist_directory=\"vectorstore\"\n",
        ")\n",
        "\n",
        "# クエリの実行\n",
        "query = \"劉備と曹操について教えてください\"\n",
        "results = vectordb.similarity_search(query, k=1)\n",
        "\n",
        "# 結果の表示\n",
        "for doc in results:\n",
        "    print(doc.page_content)\n",
        "    print(\"---\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OV_ShAgeANrJ",
        "outputId": "9e53af4d-cc18-48f8-c49e-741ce9feac2c"
      },
      "execution_count": null,
      "outputs": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "「これは県楼桑村の生れで、それがしとは幼少からの朋友です。劉備字は玄徳といって、つい先頃までは、平原県の令を勤めていた者です。どうかよろしく」\n",
            "曹操は、眼をみはって、\n",
            "---\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# クエリの実行\n",
        "query = \"孔明はいつ劉備と会いましたか？\"\n",
        "results = vectordb.similarity_search(query, k=10)\n",
        "\n",
        "# 結果の表示\n",
        "for doc in results:\n",
        "    print(doc.page_content)\n",
        "    print(\"---\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUbL33SAD3IQ",
        "outputId": "fef6bb0d-1931-49d6-a10e-a685383d0ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "このとき孔明は二十七歳、劉備玄徳は四十七であった。\n",
            "新野に帰ってからも、ふたりは寝るにも、室を共にし、食事をするにも、卓をべつにしたことがない。\n",
            "---\n",
            "このときの孔明は実に果断速決であった。その日に朝へ出て、後主劉禅に謁し、\n",
            "---\n",
            "孔明の病状はこの時から精神的にもふたたび恢復を望み得なくなっていた。翌日、彼はその重態にもかかわらず姜維を身近く招いていった。\n",
            "---\n",
            "孔明は成都に還ると、すぐ参内して、天機を奉伺し、帝劉禅へこう奏した。\n",
            "「いったい如何なる大事が出来て、かくにわかに、臣をお召し還し遊ばされましたか」\n",
            "---\n",
            "始終を聞き取ってから孔明ははじめて彼に親しみを見せた。そして心からその恭順を歓迎し、また贈り物を眺めては、あらゆる随喜と満足を表明した。かつ席をあらためて、酒宴をひらき、成都の美酒、四川の佳肴、下へもおかずもてなした。\n",
            "---\n",
            "「いま呼んでおひきあわせ致そうと考えていたところだ。誰か、孔明を召し連れてこい」\n",
            "玄徳の命にひとりが立ち去って行くと、やがて孔明もここへ姿をあらわして、物やわらかに席に着いた。\n",
            "---\n",
            "孟建などが噂するせいか、襄陽の名士のあいだには、いつか、孔明の存在とその人物は、無言のうちに認められていた。\n",
            "---\n",
            "孔明はにわかに厳かにいって、さらに彼を一堂に入れ、密談数刻に及んでいたが、やがて酒を饗応して帰した。\n",
            "あくる日、孔明は、初めて朝にのぼった。そして後主劉禅に奏して、\n",
            "---\n",
            "（汝、国を憂い、南陽諸道の軍馬を糾合して、日を期し、長安に出るあらば、朕また鸞駕を備えて長安へむかい、相会してともに孔明をやぶらん）と、伝えさせた。\n",
            "この日頃。\n",
            "一方、祁山の陣にある孔明は、\n",
            "---\n",
            "蜀の馬良は、漢中に着いた。ときに孔明は漢中に来ていた。\n",
            "---\n",
            "CPU times: user 31.8 ms, sys: 1.03 ms, total: 32.8 ms\n",
            "Wall time: 33.2 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# クエリの実行\n",
        "query = \"曹操の愛馬の名前は？\"\n",
        "results = vectordb.similarity_search(query, k=10)\n",
        "\n",
        "# 結果の表示\n",
        "for doc in results:\n",
        "    print(doc.page_content)\n",
        "    print(\"---\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukAA9u4C7KYF",
        "outputId": "0cdfef79-f87d-4310-89c5-74643063d7bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "「馬を見給え」と促すと、曹操は、\n",
            "「はっ、有難く拝領いたします」\n",
            "と、急いで庭上へ出て、呂布がひいて来た駿馬の鬣をなでながら、\n",
            "---\n",
            "曹操は急に、侍臣をどこかへ走らせて、一頭の馬を、そこへ曳かせた。\n",
            "見ると、全身の毛は、炎のように赤く、眼は、二つの鑾鈴をはめこんだようだった。\n",
            "「美髯公、君はこの馬に見おぼえはないかね」\n",
            "---\n",
            "などと、警蹕のあいだにも、ささやく声が流れる。\n",
            "この日。\n",
            "曹操は、「爪黄飛電」と名づける名馬にまたがって、狩装束も華やかに、ひたと天子のお側に寄り添っていた。\n",
            "---\n",
            "曹操は、駿馬にまたがり、錦袍金冠のまばゆき姿を、すこし左右にうごかして、\n",
            "---\n",
            "関羽の愛馬は世にも名高い駿足赤兎である。孫権は、馬忠にそれを与え、また潘璋にはこれも関羽の遺物となった青龍の偃月刀を与えた。\n",
            "---\n",
            "曹操は、一嘆、大きく空へさけんで、落ち行く馬の背へ飛び乗った。\n",
            "---\n",
            "そこで曹操主従はまた一団になって、東北へ東北へとさして落ちのびた。\n",
            "すると、一彪の軍馬が、山に拠って控えていた。\n",
            "---\n",
            "「召しのお使いをうけたので、すぐ拝領のこれに乗って、快足を試してきました」\n",
            "馬の鞍を叩きながら云った。\n",
            "曹操はここ数日の惨敗を、ことばも飾らず彼に告げて、\n",
            "「ともかく、戦場を一望してくれ給え」\n",
            "---\n",
            "洛陽の都をあとに、黄馬に鞭をつづけ、日夜をわかたず、南へ南へと風の如く逃げてきた曹操は、早くも中牟県（河南省中牟・開封鄭州の中間）の附近までかかっていた。\n",
            "「待てっ」\n",
            "「馬をおりろ」\n",
            "---\n",
            "朝門を辞して帰る折、曹操はまた、彼がみすぼらしい痩馬を用いているのを見て、\n",
            "「なぜもっと良い飼糧をやって、充分に馬を肥やさせないのか」と、武人のたしなみを咎めた。\n",
            "---\n",
            "CPU times: user 36.8 ms, sys: 777 µs, total: 37.6 ms\n",
            "Wall time: 37.4 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve & Re-Rankパターンの実装\n",
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "# カスタムAMBER埋め込みモデルのインスタンス化\n",
        "embeddings = PrefixEmbeddings(\n",
        "    model_name=\"cl-nagoya/ruri-large-v2\",\n",
        "    query_prompt_name=\"クエリ: \",\n",
        "    document_prompt_name=\"文章: \"\n",
        ")\n",
        "\n",
        "# ベクトルストアの初期化とレトリーバーの作成\n",
        "vectorstore = Chroma(\n",
        "    persist_directory=\"vectorstore\",\n",
        "    embedding_function=embeddings\n",
        ")\n",
        "base_retriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n",
        "\n",
        "# コンテキスト圧縮レトリーバーの作成（最も関連性の高い部分のみを抽出）\n",
        "embeddings_filter = EmbeddingsFilter(\n",
        "    embeddings=embeddings,\n",
        "    similarity_threshold=0.76\n",
        ")\n",
        "compression_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=embeddings_filter,\n",
        "    base_retriever=base_retriever\n",
        ")\n",
        "\n",
        "# LLMとRAGチェーンの定義\n",
        "llm = ChatOpenAI(temperature=0)\n",
        "template = \"\"\"\n",
        "以下の情報に基づいて質問に答えてください：\n",
        "\n",
        "{context}\n",
        "\n",
        "質問: {question}\n",
        "\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "# RAGチェーンの構築\n",
        "rag_chain = (\n",
        "    {\"context\": compression_retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")\n",
        "\n",
        "# クエリの実行\n",
        "response = rag_chain.invoke(\"劉備玄徳とは？\")\n",
        "print(response)\n"
      ],
      "metadata": {
        "id": "RwKD41zGITzM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "84ed2df1-b17c-4c97-b4cf-d29ac16f5844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'langchain_openai'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-3727d0091656>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprompts\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mChatPromptTemplate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunnables\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRunnablePassthrough\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain_openai\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mChatOpenAI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# カスタムAMBER埋め込みモデルのインスタンス化\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'langchain_openai'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    }
  ]
 }
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