04_UpstageEmbeddings.ipynb

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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU langchain_upstage langchain_openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjGnulhLpzO4",
        "outputId": "8cab4413-2301-41cc-8f34-44feb6c48b80"
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      "outputs": [
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          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/54.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/411.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m409.6/411.9 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m411.9/411.9 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/295.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m46.8 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",
            "transformers 4.47.1 requires tokenizers<0.22,>=0.21, but you have tokenizers 0.19.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_upstage import UpstageEmbeddings\n",
        "\n",
        "query_embeddings = UpstageEmbeddings(model = \"solar-embedding-1-large-query\")\n",
        "passage_embeddings = UpstageEmbeddings(model=\"solar-embedding-1-large-passage\")"
      ],
      "metadata": {
        "id": "aCwqYfTzp3Dy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "texts = [\n",
        "    \"안녕, 만나서 반가워.\",\n",
        "    \"LangChain simplifies the process of building applications with large language models\",\n",
        "    \"랭체인 한국어 튜토리얼은 LangChain의 공식 문서, cookbook 및 다양한 실용 예제를 바탕으로 하여 사용자가 LangChain을 더 쉽고 효과적으로 활용할 수 있도록 구성되어 있습니다. \",\n",
        "    \"LangChain은 초거대 언어모델로 애플리케이션을 구축하는 과정을 단순화합니다.\",\n",
        "    \"Retrieval-Augmented Generation (RAG) is an effective technique for improving AI responses.\",\n",
        "]"
      ],
      "metadata": {
        "id": "kJwSdiCtqUyG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 쿼리 임베딩\n",
        "embedded_query = query_embeddings.embed_query(\"LangChain 에 대해서 상세히 알려주세요.\")\n",
        "# 문서 임베딩\n",
        "embedded_documents = passage_embeddings.embed_documents(texts)"
      ],
      "metadata": {
        "id": "CSxan4UAqXH6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# 질문(embedded_query): LangChain 에 대해서 알려주세요.\n",
        "similarity = np.array(embedded_query) @ np.array(embedded_documents).T\n",
        "\n",
        "# 유사도 기준 내림차순 정렬\n",
        "sorted_idx = (np.array(embedded_query) @ np.array(embedded_documents).T).argsort()[::-1]\n",
        "\n",
        "# 결과 출력\n",
        "print(\"[Query] LangChain 에 대해서 알려주세요.\\n====================================\")\n",
        "for i, idx in enumerate(sorted_idx):\n",
        "    print(f\"[{i}] 유사도: {similarity[idx]:.3f} | {texts[idx]}\")\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uH1W2o4tqgsO",
        "outputId": "a29a3234-a380-48d7-b85a-8c1622f118cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Query] LangChain 에 대해서 알려주세요.\n",
            "====================================\n",
            "[0] 유사도: 0.487 | LangChain은 초거대 언어모델로 애플리케이션을 구축하는 과정을 단순화합니다.\n",
            "\n",
            "[1] 유사도: 0.465 | LangChain simplifies the process of building applications with large language models\n",
            "\n",
            "[2] 유사도: 0.432 | 랭체인 한국어 튜토리얼은 LangChain의 공식 문서, cookbook 및 다양한 실용 예제를 바탕으로 하여 사용자가 LangChain을 더 쉽고 효과적으로 활용할 수 있도록 구성되어 있습니다. \n",
            "\n",
            "[3] 유사도: 0.194 | Retrieval-Augmented Generation (RAG) is an effective technique for improving AI responses.\n",
            "\n",
            "[4] 유사도: 0.151 | 안녕, 만나서 반가워.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oRW4LIYsqf0l"
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      "execution_count": null,
      "outputs": []
    }
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}