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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hoangcuongnguyen2001/RAG_lessons/blob/main/Final_Exam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bài thi cuối khóa**"
      ],
      "metadata": {
        "id": "ef5leVtE3YTu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Đây là bài kiểm tra khả năng của các bạn về việc xây dựng và sử dụng hệ thống RAG. Các bạn có thể dùng bất kỳ LLM nào cho bài này, miễn sao các bạn có thể trả về kết quả từ LLM là được.\n",
        "\n",
        "Note: Tài liệu các bạn sẽ dùng trong bài test này là một bài báo về kinh tế học: *Trade, Growth and the Size of Countries*, của Alberto Alesina, Enrico Spolaore, Romain Wacziarg (2005). Với những bạn muốn tìm hiểu thêm, các bạn có thể tham khảo trên đây: https://www.sciencedirect.com/science/article/abs/pii/S1574068405010233\n"
      ],
      "metadata": {
        "id": "YvvXz3Vv3fwn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGANGGuisnFs",
        "outputId": "7d37349a-505c-45a7-f038-d74f8a16f7ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading huggingface_hub-0.23.5-py3-none-any.whl.metadata (12 kB)\n",
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            "\u001b[?25hInstalling collected packages: huggingface_hub\n",
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            "\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",
            "peft 0.14.0 requires huggingface-hub>=0.25.0, but you have huggingface-hub 0.23.5 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed huggingface_hub-0.23.5\n",
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            "Collecting huggingface-hub>=0.17.0 (from peft==0.12)\n",
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            "\u001b[?25hInstalling collected packages: huggingface-hub, peft\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.23.5\n",
            "    Uninstalling huggingface-hub-0.23.5:\n",
            "      Successfully uninstalled huggingface-hub-0.23.5\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.14.0\n",
            "    Uninstalling peft-0.14.0:\n",
            "      Successfully uninstalled peft-0.14.0\n",
            "Successfully installed huggingface-hub-0.27.0 peft-0.12.0\n",
            "Collecting llama-index\n",
            "  Downloading llama_index-0.12.8-py3-none-any.whl.metadata (11 kB)\n",
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            "Installing collected packages: striprtf, filetype, dirtyjson, pypdf, mypy-extensions, minijinja, marshmallow, typing-inspect, tiktoken, huggingface-hub, tokenizers, openai, llama-cloud, dataclasses-json, bitsandbytes, transformers, text-generation, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-llms-huggingface, llama-index-embeddings-huggingface, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index\n",
            "  Attempting uninstall: huggingface-hub\n",
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            "    Found existing installation: openai 1.57.4\n",
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            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.47.1\n",
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            "Successfully installed bitsandbytes-0.45.0 dataclasses-json-0.6.7 dirtyjson-1.0.8 filetype-1.2.0 huggingface-hub-0.23.5 llama-cloud-0.1.6 llama-index-0.12.8 llama-index-agent-openai-0.4.1 llama-index-cli-0.4.0 llama-index-core-0.12.8 llama-index-embeddings-huggingface-0.4.0 llama-index-embeddings-openai-0.3.1 llama-index-indices-managed-llama-cloud-0.6.3 llama-index-llms-huggingface-0.4.1 llama-index-llms-openai-0.3.12 llama-index-multi-modal-llms-openai-0.4.1 llama-index-program-openai-0.3.1 llama-index-question-gen-openai-0.3.0 llama-index-readers-file-0.4.1 llama-index-readers-llama-parse-0.4.0 llama-parse-0.5.18 marshmallow-3.23.2 minijinja-2.5.0 mypy-extensions-1.0.0 openai-1.58.1 pypdf-5.1.0 striprtf-0.0.26 text-generation-0.7.0 tiktoken-0.8.0 tokenizers-0.20.3 transformers-4.46.3 typing-inspect-0.9.0\n",
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            "Collecting llama-index-postprocessor-colbert-rerank\n",
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            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.13.0,>=0.12.0->llama-index-postprocessor-colbert-rerank) (1.0.7)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->llama-index-core<0.13.0,>=0.12.0->llama-index-postprocessor-colbert-rerank) (0.14.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<3.0.0,>=2.2.0->llama-index-postprocessor-colbert-rerank) (3.0.2)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->llama-index-core<0.13.0,>=0.12.0->llama-index-postprocessor-colbert-rerank) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->llama-index-core<0.13.0,>=0.12.0->llama-index-postprocessor-colbert-rerank) (1.2.2)\n",
            "Downloading llama_index_postprocessor_colbert_rerank-0.3.0-py3-none-any.whl (3.5 kB)\n",
            "Installing collected packages: llama-index-postprocessor-colbert-rerank\n",
            "Successfully installed llama-index-postprocessor-colbert-rerank-0.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade huggingface_hub==0.23.5\n",
        "!pip install --upgrade peft==0.12\n",
        "!pip install llama-index bitsandbytes accelerate llama-index-llms-huggingface llama-index-embeddings-huggingface\n",
        "!pip install --upgrade sentence-transformers\n",
        "!pip install llama-index-postprocessor-colbert-rerank"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas scikit-learn"
      ],
      "metadata": {
        "id": "DIDIvoudy9dZ",
        "outputId": "5d726c52-b8ec-46e0-a8f9-73017d82da45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.6.0)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYfvzzxWv3ea",
        "outputId": "6ee2b78a-48a0-49e2-d71a-db8b030fee27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pypdf in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
            "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from pypdf) (4.12.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "hf_token = userdata.get('HF_TOKEN')"
      ],
      "metadata": {
        "id": "U7zZedmktEFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
        "\n",
        "reader = SimpleDirectoryReader(input_files=[\"/content/Size_of_Countries.pdf\"])\n",
        "data = reader.load_data()\n"
      ],
      "metadata": {
        "id": "_O4I5cRO74Mz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Đánh giá cho test\n",
        "\n",
        "\n",
        "from llama_index.core import PromptTemplate\n",
        "from llama_index.postprocessor.colbert_rerank import ColbertRerank\n",
        "\n",
        "system_prompt = \"Please answer the questions in a truthful and reliable way.\"\n",
        "\n",
        "query_wrapper_prompt = PromptTemplate(\"<|USER|>{query_str}<|ASSISTANT|>\")"
      ],
      "metadata": {
        "id": "cImxu71u65NZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Đánh giá cho test\n",
        "\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "\n",
        "embed_model = HuggingFaceEmbedding(model_name=\"intfloat/multilingual-e5-base\", trust_remote_code=True)"
      ],
      "metadata": {
        "id": "kpikAOM68Q_Q",
        "outputId": "5b8cc729-a93b-460e-ff5a-58c2db74d688",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
          "referenced_widgets": [
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/387 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "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": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Đánh giá cho test\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    \"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
        "    token=hf_token,\n",
        ")\n",
        "\n",
        "stopping_ids = [\n",
        "    tokenizer.eos_token_id,\n",
        "    tokenizer.convert_tokens_to_ids(\"<|eot_id|>\"),\n",
        "]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "_uYeRzNC8gFZ",
        "outputId": "83d723e4-9c1d-4797-9dc4-8c45f160f174"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/51.0k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
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        {
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    {
      "cell_type": "code",
      "source": [
        "# Đánh giá cho test\n",
        "\n",
        "import torch\n",
        "from llama_index.llms.huggingface import HuggingFaceLLM\n",
        "from transformers import BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "\n",
        "llm = HuggingFaceLLM(\n",
        "    model_name=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
        "    system_prompt=system_prompt,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    context_window=8192,\n",
        "    max_new_tokens=512,\n",
        "    model_kwargs={\n",
        "        \"token\": hf_token,\n",
        "        \"torch_dtype\": torch.bfloat16,\n",
        "        \"quantization_config\": quantization_config\n",
        "    },\n",
        "    generate_kwargs={\n",
        "        \"do_sample\": False,\n",
        "        \"temperature\": 0.05,\n",
        "        \"top_p\": 0.3,\n",
        "\n",
        "    },\n",
        "    tokenizer_name=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
        "    tokenizer_kwargs={\"token\": hf_token},\n",
        "    stopping_ids=stopping_ids,\n",
        ")"
      ],
      "metadata": {
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        "outputId": "b2b21936-14d0-41f6-8fff-5bb21fc6895b"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/654 [00:00<?, ?B/s]"
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        {
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        {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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        {
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from llama_index.core import Settings\n",
        "\n",
        "\n",
        "Settings.embed_model = embed_model\n",
        "\n",
        "Settings.chunk_size = 1024\n",
        "\n",
        "Settings.llm = llm"
      ],
      "metadata": {
        "id": "e8mfxeRo9lwO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Đánh giá cho test\n",
        "\n",
        "index = VectorStoreIndex.from_documents(data)\n",
        "\n",
        "colbert_reranker = ColbertRerank(\n",
        "    top_n=3,\n",
        "    model=\"colbert-ir/colbertv2.0\",\n",
        "    tokenizer=\"colbert-ir/colbertv2.0\",\n",
        "    keep_retrieval_score=True,\n",
        ")"
      ],
      "metadata": {
        "id": "b6257jKl-EQE",
        "outputId": "c7f9e6a2-cbf1-4f8e-f736-e5a89963f879",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/405 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c3784db5f5346c6aa6344b54b4c4c09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7a2bb5f8ce6849afa2eda7ae68ac7a05"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Đánh giá cho test\n",
        "\n",
        "query_engine = index.as_query_engine(similarity_top_k=3, streaming=True, node_postprocessors=[colbert_reranker],)\n",
        "\n"
      ],
      "metadata": {
        "id": "-lebn10R-S4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(r'''\n",
        "According to this article, what are the benefits of size in terms of population for countries?\n",
        "''')"
      ],
      "metadata": {
        "id": "_WqaYMBb-XSB",
        "outputId": "bb6ef5d6-8c48-4388-a332-49cb4d9e70eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:590: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.05` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:595: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.3` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Đánh giá cho test\n",
        "print(response)"
      ],
      "metadata": {
        "id": "icFTHUrgAUxs",
        "outputId": "5b553966-6979-4280-8b82-c8a651c0c285",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "According to the article, the main benefits of size in terms of population for countries are:\n",
            "\n",
            "1. Economies of scale in the production of public goods, where the per capita cost of many public goods is lower in larger countries.\n",
            "2. A larger country is less subject to foreign aggression, and smaller countries may have to spend proportionally more for defense.\n",
            "3. Larger countries can better internalize cross-regional externalities by centralizing the provision of public goods that involve strong externalities.\n",
            "4. Larger countries can provide insurance to regions affected by imperfectly correlated shocks.\n",
            "5. Larger countries can build redistributive schemes from richer to poorer regions, achieving distributions of after-tax income that would not be available to individual regions acting independently.\n",
            "6. The role of market size, where larger countries can create a limit on specialization, and the extent of the market can create a limit on the accumulation of human capital and the transmission of knowledge.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lưu ý, sau khi có câu trả lời ở trên các bạn cần copy và dán câu trả lời này xuống mục *user_result* ở cell dưới. Nếu không, hệ thống sẽ không thể tính toán độ chính xác của câu trả lời mà LLM trả về."
      ],
      "metadata": {
        "id": "WeGrzdRI5BhW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Đánh giá cho test\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import pandas as pd\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Hãy copy câu trả lời (response) ở trên và dán vào đây:\n",
        "user_result = r'''\n",
        "\n",
        " '''\n",
        "actual_result = r'''\n",
        "The main beneﬁts from size in terms of population are the following:\n",
        "(1) There are economies of scale in the production of public goods. The per capita\n",
        "cost of many public goods is lower in larger countries, where more taxpayers pay\n",
        "for them.\n",
        "\n",
        "(2) A larger country (both in terms of population and national product) is less subject\n",
        "to foreign aggression. Thus, safety is a public good that increases with country\n",
        "size. Also, and related to the size of government argument above, smaller coun-\n",
        "tries may have to spend proportionally more for defense than larger countries\n",
        "given economies of scale in defense spending.\n",
        "\n",
        "(3) Larger countries can better internalize cross-regional externalities by centralizing\n",
        "the provision of those public goods that involve strong externalities.\n",
        "(4) Larger countries are better able to provide insurance to regions affected by im-\n",
        "perfectly correlated shocks.\n",
        "\n",
        "(5) Larger countries can build redistributive schemes from richer to poorer regions,\n",
        "therefore achieving distributions of after tax income which would not be avail-\n",
        "able to individual regions acting independently.\n",
        "\n",
        "(6) Finally, the role of market size is the issue on which we focus most in this arti-\n",
        "cle. Adam Smith (1776) already had the intuition that the extent of the market\n",
        "creates a limit on specialization. More recently, a well established literature from\n",
        "Romer (1986), Lucas (1988) to Grossman and Helpman (1991) has emphasized\n",
        "the beneﬁts of scale in light of positive externalities in the accumulation of hu-\n",
        "man capital and the transmission of knowledge, or in light of increasing returns\n",
        "to scale embedded in technology or knowledge creation.\n",
        "\n",
        "'''\n",
        "\n",
        "documents = [user_result, actual_result]\n",
        "count_vectorizer = CountVectorizer(stop_words=\"english\")\n",
        "count_vectorizer = CountVectorizer()\n",
        "sparse_matrix = count_vectorizer.fit_transform(documents)\n",
        "\n",
        "doc_term_matrix = sparse_matrix.todense()\n",
        "df = pd.DataFrame(\n",
        "   doc_term_matrix,\n",
        "   columns=count_vectorizer.get_feature_names_out(),\n",
        "   index=[\"user_result\", \"actual_result\"],\n",
        ")\n",
        "\n",
        "print(cosine_similarity(df, df))"
      ],
      "metadata": {
        "id": "IkelKfjKzCOZ",
        "outputId": "b1d7a76e-7bfc-4ff5-e253-ea17b9693950",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'StreamingResponse' object has no attribute 'lower'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-71a4ef58338f>\u001b[0m in \u001b[0;36m<cell line: 42>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mcount_vectorizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstop_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"english\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0mcount_vectorizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0msparse_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcount_vectorizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0mdoc_term_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtodense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1387\u001b[0m                 )\n\u001b[1;32m   1388\u001b[0m             ):\n\u001b[0;32m-> 1389\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, raw_documents, y)\u001b[0m\n\u001b[1;32m   1374\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1376\u001b[0;31m         \u001b[0mvocabulary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count_vocab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfixed_vocabulary_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_count_vocab\u001b[0;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[1;32m   1261\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mraw_documents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m             \u001b[0mfeature_counter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0manalyze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1264\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m                     \u001b[0mfeature_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvocabulary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_analyze\u001b[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpreprocessor\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m             \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m             \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_preprocess\u001b[0;34m(doc, accent_function, lower)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \"\"\"\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maccent_function\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccent_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'StreamingResponse' object has no attribute 'lower'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity_matrix = cosine_similarity(df, df)\n",
        "\n",
        "# Access the desired element and assign it to a variable\n",
        "similarity_score = similarity_matrix[0][1]\n",
        "\n",
        "# Print the variable to verify\n",
        "print(similarity_score)\n",
        "\n",
        "assert similarity_score > 0.75, \"Similarity score is below the threshold.\""
      ],
      "metadata": {
        "id": "l4wz4ZAitfoX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}