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Created March 10, 2025 13:47
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langhain_amber_embeddinngs.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"authorship_tag": "ABX9TyNzjV9wRAMp48AEOl5tprft",
"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/527005772ee0b41d9726888ed02cd937/langhain_amber_embeddinngs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vN3Z4_Yq_T9U",
"outputId": "00f8a1ad-5ed1-4c9c-a1ac-33a91fcf5f34"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m42.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m415.4/415.4 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/65.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.8/77.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.6/452.6 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install -q langchain_community sentence_transformers langchain_core langchain chromadb"
]
},
{
"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 AMBEREmbeddings(Embeddings):\n",
" \"\"\"\n",
" LangChain用のAMBER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 = \"retrieva-jp/amber-large\",\n",
" query_prompt_name: str = \"Retrieval-query\",\n",
" document_prompt_name: str = \"Retrieval-passage\",\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",
" **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": 2,
"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 = AMBEREmbeddings(\n",
" model_name=\"retrieva-jp/amber-large\",\n",
" query_prompt_name=\"Retrieval-query\",\n",
" document_prompt_name=\"Retrieval-passage\"\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=\"amber_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": "d0b321fc-03f7-4d6b-b213-c0fd52b3c808"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
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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": [
"# 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": "c53a0210-45ea-4759-fca5-8803a05ab59d"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"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",
"「お見えです、劉皇叔新野の将軍が、もう久しいこと、階下にたって、お待ちになっておられます」\n",
"「劉皇叔が」\n",
"孔明は切れの長い眼を、しずかに玄徳のほうへ向けた。\n",
"「なんで早く告げなかったか」\n",
"---\n",
"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",
"で孔明は、劉の館へ出向いた。すぐ帰ろうとしたが、劉が礼を篤くして、酒をすすめるので、帰ろうにも帰れなかった。\n",
"酒、半酣の頃、\n",
"---\n",
"と、型のごとく、酒宴にうつり、重礼厚遇、至らざるなしであった。\n",
"その日まで、孔明は何も知らなかったが、ふと、江岸の兵から、今日のお客は、夏口の劉皇叔であると聞いて、\n",
"「さては?」\n",
"---\n",
"隆中の彼の住居へ、或る日、友人の孟建が、ぶらりと訪ねてきて云った。\n",
"「近日、故郷へ帰ろうと思う。きょうはお別れにやって来た」\n",
"孔明は、そういう先輩の面を、しばらく無言で見まもっていたが、\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",
"「先生、いよいよ日限は、こよい限りですな」\n",
"魯粛が、様子を見に来ると、孔明は待っていたように、\n",
"「そうです、こよい一夜となりました。ついては、大儀ながら粛兄にも、一緒に来ていただけますまいか」\n",
"---\n",
"「えっ、ほんとですか」\n",
"は、剣をおいて、孔明の前にひれ伏し、急に眼をかがやかした。\n",
"孔明は、ねんごろに話した。\n",
"---\n",
"と、平常のごとくそんなことを語ったりしていた。しかし黄昏れ近く、にわかに容態が改まったと思うと、\n",
"「諸臣はみな詰めておるか」\n",
"と問い、孔明が臣下みな一睡もせず詰めておりますと答えると、\n",
"---\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 = AMBEREmbeddings(\n",
" model_name=\"retrieva-jp/amber-large\",\n",
" query_prompt_name=\"Retrieval-query\",\n",
" document_prompt_name=\"Retrieval-passage\"\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=\"amber_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"
},
"execution_count": null,
"outputs": []
}
]
}
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