RAG_on_PDF_Q&A_using_LLM.ipynb

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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/Relieq/RAG-on-PDF-Q-A-using-LLM/blob/master/RAG_on_PDF_Q%26A_using_LLM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Chương trình RAG"
      ],
      "metadata": {
        "id": "hckNJ508BUjw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1. Cài đặt các gói thư viện cần thiết"
      ],
      "metadata": {
        "id": "YbpCR7n7BWtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers==4.41.2\n",
        "!pip install -q bitsandbytes==0.43.1\n",
        "!pip install -q accelerate==0.31.0\n",
        "!pip install -q langchain==0.2.5\n",
        "!pip install -q langchainhub==0.1.20\n",
        "!pip install -q langchain-chroma==0.1.1\n",
        "!pip install -q langchain-community==0.2.5\n",
        "!pip install -q langchain_huggingface==0.0.3\n",
        "!pip install -q python-dotenv==1.0.1\n",
        "!pip install -q pypdf==4.2.0\n",
        "!pip install -q numpy==1.24.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9Nfl8gTBf0L",
        "outputId": "f5c44d54-640e-4b99-fcc7-525dcb92214f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\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",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m64.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m17.5 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[31m8.0 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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.4/290.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m55.9 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",
            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.24.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. Xây dựng vector database"
      ],
      "metadata": {
        "id": "mg0Qy2MVBW13"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import các thư viện cần thiết"
      ],
      "metadata": {
        "id": "5OO4f7NsBW45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from transformers import BitsAndBytesConfig\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_huggingface.llms import HuggingFacePipeline\n",
        "\n",
        "from langchain. memory import ConversationBufferMemory\n",
        "from langchain_community.chat_message_histories import ChatMessageHistory\n",
        "from langchain_community.document_loaders import PyPDFLoader, TextLoader\n",
        "from langchain. chains import ConversationalRetrievalChain\n",
        "\n",
        "from langchain_chroma import Chroma\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain import hub"
      ],
      "metadata": {
        "id": "bOZQ-HY9BoBY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Đọc file pdf"
      ],
      "metadata": {
        "id": "Mq7zYVd0BW7v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download pdf file:\n",
        "!gdown \"1lWuq0COKnU9mCfMvTEq54DBLgAh3yYDx&confirm=t\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdjlG14ZBxsg",
        "outputId": "939e9ca3-63f2-495e-80e5-72c3a2704b5c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1lWuq0COKnU9mCfMvTEq54DBLgAh3yYDx&confirm=t\n",
            "To: /content/YOLOv10_Tutorials.pdf\n",
            "100% 16.6M/16.6M [00:00<00:00, 23.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Loader = PyPDFLoader\n",
        "FILE_PATH = '/content/YOLOv10_Tutorials.pdf'\n",
        "loader = Loader(FILE_PATH)\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "BQDkGFCaBzbx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Khởi tạo bộ tách văn bản (text splitter)"
      ],
      "metadata": {
        "id": "wPZqyTetBW-q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)"
      ],
      "metadata": {
        "id": "NKQHQlaEB3Z8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Với text_splitter, ta thực hiện tách file pdf:"
      ],
      "metadata": {
        "id": "EJo6aXKqBXBr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "docs = text_splitter.split_documents(documents)\n",
        "\n",
        "print(\"Number of sub-documents: \", len(docs))\n",
        "print(docs[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-taWJt5kB7h2",
        "outputId": "5a980d8f-0ecc-4b54-9eca-a6a9e4ffcfdb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of sub-documents:  33\n",
            "page_content='AI VIET NAM – AI COURSE 2024\n",
            "Tutorial: Phát hiện đối tượng trong ảnh với\n",
            "YOLOv10\n",
            "Dinh-Thang Duong, Nguyen-Thuan Duong, Minh-Duc Bui và\n",
            "Quang-Vinh Dinh\n",
            "Ngày 20 tháng 6 năm 2024\n",
            "I. Giới thiệu\n",
            "Object Detection (Tạm dịch: Phát hiện đối tượng) là một bài toán cổ điển thuộc lĩnh vực\n",
            "Computer Vision. Mục tiêu của bài toán này là tự động xác định vị trí của các đối tượng trong\n",
            "một tấm ảnh. Tính tới thời điểm hiện tại, đã có rất nhiều phương pháp được phát triển nhằm\n",
            "giải quyết hiệu quả bài toán này. Trong đó, các phương pháp thuộc họ YOLO (You Only Look\n",
            "Once) thu hút được sự chú ý rất lớn từ cộng đồng nghiên cứu bởi độ chính xác và tốc độ thực\n",
            "thi mà loại mô hình này mang lại.\n",
            "Hình 1: Logo của mô hình YOLO. Ảnh: link.\n",
            "Thời gian vừa qua, Ao Wang và các cộng sự tại Đại học Thanh Hoa (Tsinghua University)\n",
            "đã đề xuất mô hình YOLOv10 trong bài báo YOLOv10: Real-Time End-to-End Object\n",
            "Detection [10]. Với những cải tiến mới, mô hình đã đạt được hiệu suất vượt trội hơn so với các' metadata={'source': '/content/YOLOv10_Tutorials.pdf', 'page': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Khởi tạo instance vectorization"
      ],
      "metadata": {
        "id": "3KTPf898BXEm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534,
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        },
        "id": "r0YPgTjDBMma",
        "outputId": "34ec02ab-7910-4463-95c4-161fd9c69565"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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              "model_id": "ab5820666ce0421faf9f478ae33bbda1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20398d9acbea4b5d8dd3bca5e3c2e68f"
            }
          },
          "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,
              "version_minor": 0,
              "model_id": "c609d5e9162e4e19986c16ea3b09456f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb8797bdc1cb402682ea88fb1da31ce2"
            }
          },
          "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,
              "version_minor": 0,
              "model_id": "5a5e1bacfc204bada662868d65f28d21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2263630e9f0d42ddb4a1ef1898185a03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f64d5c4fa114ed7af2ddaeb9ee22e50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c64c21efedb47aebbed670d437ecfb9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "embedding = HuggingFaceEmbeddings()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Khởi tạo vector database"
      ],
      "metadata": {
        "id": "va4TAmAuCBRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vector_db = Chroma.from_documents(documents=docs, embedding=embedding)\n",
        "retriever = vector_db.as_retriever()"
      ],
      "metadata": {
        "id": "zlu8i2F2CEam"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ta có thể thử thực hiện truy vấn với một đoạn văn bản bất kì tại đây:"
      ],
      "metadata": {
        "id": "5IbaLxrhCBUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = retriever.invoke(\"What is YOLO?\")\n",
        "print(\"Number of relevant documents: \", len(result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBDw2MuUCI2G",
        "outputId": "5f677d36-5dfe-4b4e-dd84-e74dc4f656f5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of relevant documents:  4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. Khởi tạo mô hình ngôn ngữ lớn"
      ],
      "metadata": {
        "id": "pKRqszsiCBXQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Khai báo một số cài đặt cần thiết cho mô hình:"
      ],
      "metadata": {
        "id": "5W6RjmOsCBaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nf4_config = BitsAndBytesConfig(load_in_4bit=True,\n",
        "                                bnb_4bit_quant_type=\"nf4\",\n",
        "                                bnb_4bit_use_double_quant=True,\n",
        "                                bnb_4bit_compute_dtype=torch.bfloat16)"
      ],
      "metadata": {
        "id": "A-ZGXVmECQXR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Khởi tạo mô hình và tokenizer"
      ],
      "metadata": {
        "id": "fdQqjYeNCBlw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"lmsys/vicuna-7b-v1.5\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(MODEL_NAME,\n",
        "                                            quantization_config=nf4_config,\n",
        "                                            low_cpu_mem_usage=True)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502,
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        "id": "jCg9_44tCUu8",
        "outputId": "ce5f2e4a-dbeb-47c2-d776-68b36ae91ced"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0e33ea81cf7466883159ebb2e4b22a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42eefe896908448b9c0b727571d54734"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c9c7a10a2ff4d4992701c94b92c0a90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f79186ea6ea40cfbd827e149919a904"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fea30fc69074783a1c96521a0c4ea80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc6541e2210745eeabb5336322a479a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- 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:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/749 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e2e4415c2294138a0cba2f4cac2c34a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe6f5d31f0104aba8d6184d9be920809"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8defef8bde574499bda5b10fbb2f5752"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tích hợp tokenizer và model thành một pipeline để tiện sử dụng"
      ],
      "metadata": {
        "id": "JR1CAkh5CXV7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_pipeline = pipeline(\"text-generation\",\n",
        "                        model=model,\n",
        "                        tokenizer=tokenizer,\n",
        "                        max_new_tokens=512,\n",
        "                        pad_token_id=tokenizer.eos_token_id,\n",
        "                        device_map=\"auto\")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=model_pipeline)"
      ],
      "metadata": {
        "id": "50TO_sfECbZc"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.4. Chạy chương trình:"
      ],
      "metadata": {
        "id": "HRdc33kICXYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = hub.pull(\"rlm/rag-prompt\")\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "rag_chain = (\n",
        "            {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "            | prompt\n",
        "            | llm\n",
        "            | StrOutputParser()\n",
        ")\n",
        "\n",
        "USER_QUESTION = \"YOLOv10 là gì?\"\n",
        "output = rag_chain.invoke(USER_QUESTION)\n",
        "answer = output.split('Answer:')[1].strip()\n",
        "print(answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd8FfwHpCf91",
        "outputId": "81790dd2-146e-4be1-9205-07406d0e23e1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- 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:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "YOLOv10 là một phiên bản của YOLO (You Only Look Once) - một hệ thống dự đoán hình ảnh được huấn luyện sẵn trên bộ dữ liệu COCO. Phiên bản này được tạo bằng cách tải về trọng số (file.pt) từ GitHub và khởi tạo mô hình bằng cách sử dụng thư viện ultralytics.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Xây dựng giao diện chat"
      ],
      "metadata": {
        "id": "junkUFgZCXb0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1. Tải các gói thư viện"
      ],
      "metadata": {
        "id": "GlqKb3euCXel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers==4.41.2\n",
        "!pip install -q bitsandbytes==0.43.1\n",
        "!pip install -q accelerate==0.31.0\n",
        "!pip install -q langchain==0.2.5\n",
        "!pip install -q langchainhub==0.1.20\n",
        "!pip install -q langchain-chroma==0.1.1\n",
        "!pip install -q langchain-community==0.2.5\n",
        "!pip install -q langchain-openai==0.1.9\n",
        "!pip install -q langchain_huggingface==0.0.3\n",
        "!pip install -q chainlit==1.1.304\n",
        "!pip install -q python-dotenv==1.0.1\n",
        "!pip install -q pypdf==4.2.0\n",
        "!npm install -g localtunnel\n",
        "!pip install -q numpy==1.24.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8n9SIM-CtU0",
        "outputId": "52ed817c-ed84-4505-8344-3364f5b56f7c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.8/91.8 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.7/48.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m50.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.2/76.2 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.3/60.3 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m65.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for literalai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for syncer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js\n",
            "+ localtunnel@2.0.2\n",
            "updated 1 package in 0.83s\n",
            "\n",
            "\u001b[33m\u001b[39m\n",
            "\u001b[33m   ╭────────────────────────────────────────────────────────────────╮\u001b[39m\n",
            "   \u001b[33m│\u001b[39m                                                                \u001b[33m│\u001b[39m\n",
            "   \u001b[33m│\u001b[39m      New \u001b[31mmajor\u001b[39m version of npm available! \u001b[31m6.14.8\u001b[39m → \u001b[32m10.8.1\u001b[39m       \u001b[33m│\u001b[39m\n",
            "   \u001b[33m│\u001b[39m   \u001b[33mChangelog:\u001b[39m \u001b[36mhttps://github.com/npm/cli/releases/tag/v10.8.1\u001b[39m   \u001b[33m│\u001b[39m\n",
            "   \u001b[33m│\u001b[39m               Run \u001b[32mnpm install -g npm\u001b[39m to update!                \u001b[33m│\u001b[39m\n",
            "   \u001b[33m│\u001b[39m                                                                \u001b[33m│\u001b[39m\n",
            "\u001b[33m   ╰────────────────────────────────────────────────────────────────╯\u001b[39m\n",
            "\u001b[33m\u001b[39m\n",
            "\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",
            "chainlit 1.1.304 requires numpy<2.0,>=1.26; python_version >= \"3.9\", but you have numpy 1.24.4 which is incompatible.\n",
            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.24.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2. Import các gói thư viện cần thiết"
      ],
      "metadata": {
        "id": "1xAY5LtkCX_t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import chainlit as cl\n",
        "import torch\n",
        "\n",
        "from chainlit.types import AskFileResponse\n",
        "\n",
        "from transformers import BitsAndBytesConfig\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "from langchain_huggingface.llms import HuggingFacePipeline\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain_community.chat_message_histories import ChatMessageHistory\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_chroma import Chroma\n",
        "from langchain_community.document_loaders import PyPDFLoader, TextLoader\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain import hub"
      ],
      "metadata": {
        "id": "aOuebUuWC00B"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3. Cài đặt lại các hàm và instance ở file trước"
      ],
      "metadata": {
        "id": "bbZrGEhHC3zI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
        "embedding = HuggingFaceEmbeddings()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7vn4OzYDAv9",
        "outputId": "193351ca-2960-4f0c-8174-00328b72b7d4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.4. Xây dựng hàm xử lý file input đầu vào"
      ],
      "metadata": {
        "id": "fYlY7EhKC313"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def process_file(file: AskFileResponse):\n",
        "    if file.type == \"text/plain\":\n",
        "        Loader = TextLoader\n",
        "    elif file.type == \"application/pdf\":\n",
        "        Loader = PyPDFLoader\n",
        "\n",
        "    loader = Loader(file.path)\n",
        "    documents = loader.load()\n",
        "    docs = text_splitter.split_documents(documents)\n",
        "    for i, doc in enumerate(docs):\n",
        "        doc.metadata[\"source\"] = f\"source_{i}\"\n",
        "    return docs"
      ],
      "metadata": {
        "id": "-ZuwYoW2DbnH"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.5. Xây dựng hàm khởi tạo Chroma database"
      ],
      "metadata": {
        "id": "ZWehQACnC34v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_vector_db(file: AskFileResponse):\n",
        "    docs = process_file(file)\n",
        "    cl.user_session.set(\"docs\", docs)\n",
        "    vector_db = Chroma.from_documents(documents=docs, embedding=embedding)\n",
        "    return vector_db"
      ],
      "metadata": {
        "id": "I3RMGzX7DgDY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.6. Khởi tạo mô hình ngôn ngữ lớn"
      ],
      "metadata": {
        "id": "7RqE84LLC37l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_huggingface_llm(model_name: str = \"lmsys/vicuna-7b-v1.5\", max_new_token: int = 512):\n",
        "    nf4_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_use_double_quant=True,\n",
        "        bnb_4bit_compute_dtype=torch.bfloat16\n",
        "    )\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        quantization_config=nf4_config,\n",
        "        low_cpu_mem_usage=True\n",
        "    )\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "    model_pipeline = pipeline(\n",
        "        \"text-generation\",\n",
        "        model=model,\n",
        "        tokenizer=tokenizer,\n",
        "        max_new_tokens=max_new_token,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        device_map=\"auto\"\n",
        "    )\n",
        "\n",
        "    llm = HuggingFacePipeline(pipeline=model_pipeline)\n",
        "\n",
        "    return llm\n",
        "\n",
        "LLM = get_huggingface_llm()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "c8f2e53ece31479abaf51743f68eee62",
            "a732f454850c462d9f039c3287e985c6",
            "d83511db27d8405883744a0afcc28492",
            "52a22563b02741ba90c71c8d0ac7b4a1",
            "f5b325fb116e4d6c99f6852e3f1a6c72",
            "912721ea0c3f4217a4b6d9575a6ef003",
            "93cb4c8c59d74fa2aa574509a2b03615",
            "ee743653388e4a5cb810f54efe76a155",
            "7623a88aed0c48058d413318dc6a5c43",
            "49c534963c344516bb0f5f4adc3ef1f6",
            "242bc908dce248839c816f0032189ef6"
          ]
        },
        "id": "dd89YRi1DxuF",
        "outputId": "823b638c-6321-4a7a-fe8a-171ee4292287"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8f2e53ece31479abaf51743f68eee62"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- 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:520: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.7. Khởi tạo welcome message"
      ],
      "metadata": {
        "id": "9cPARphbEDn6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welcome_message = \"\"\"Welcome to the PDF QA! To get started:\n",
        "                    1. Upload a PDF or text file\n",
        "                    2. Ask a question about the file\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "qwHlPJgEEFe5"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.8. Khởi tạo hàm on_chat_start"
      ],
      "metadata": {
        "id": "yMEPXeRIEDrE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@cl.on_chat_start\n",
        "async def on_chat_start():\n",
        "    files = None\n",
        "    while files is None:\n",
        "        files = await cl.AskFileMessage(\n",
        "            content=welcome_message,\n",
        "            accept=[\"text/plain\", \"application/pdf\"],\n",
        "            max_size_mb=20,\n",
        "            timeout=180\n",
        "        ).send()\n",
        "    file = files[0]\n",
        "\n",
        "    msg = cl.Message(content=f\"Processing '{file.name}'...\",\n",
        "        disable_feedback=True)\n",
        "\n",
        "    await msg.send()\n",
        "\n",
        "    vector_db = await cl.make_async(get_vector_db)(file)\n",
        "\n",
        "    message_history = ChatMessageHistory()\n",
        "    memory = ConversationBufferMemory(\n",
        "        memory_key=\"chat_history\",\n",
        "        output_key=\"answer\",\n",
        "        chat_memory=message_history,\n",
        "        return_messages=True\n",
        "    )\n",
        "    retriever = vector_db.as_retriever(search_type=\"mmr\",\n",
        "                                    search_kwargs={'k': 3})\n",
        "\n",
        "    chain = ConversationalRetrievalChain.from_llm(\n",
        "        llm=LLM,\n",
        "        chain_type=\"stuff\",\n",
        "        retriever=retriever,\n",
        "        memory=memory,\n",
        "        return_source_documents=True\n",
        "    )\n",
        "\n",
        "    msg.content = f\"'{file.name}' processed. You can now ask questions!\"\n",
        "    await msg.update()\n",
        "\n",
        "    cl.user_session.set(\"chain\", chain)"
      ],
      "metadata": {
        "id": "POp_ypkoEJZ8"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.9. Khởi tạo hàm on_message"
      ],
      "metadata": {
        "id": "yAlaR2oREDup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@cl.on_message\n",
        "async def on_message(message: cl.Message):\n",
        "    chain = cl.user_session.get(\"chain\")\n",
        "    cb = cl.AsyncLangchainCallbackHandler()\n",
        "    res = await chain.ainvoke(message.content, callbacks=[cb])\n",
        "    answer = res[\"answer\"]\n",
        "    source_documents = res[\"source_documents\"]\n",
        "    text_elements = []\n",
        "\n",
        "    if source_documents:\n",
        "        for source_idx, source_doc in enumerate(source_documents):\n",
        "            source_name = f\"source_{source_idx}\"\n",
        "            text_elements.append(\n",
        "                cl.Text(content=source_doc.page_content, name=source_name)\n",
        "            )\n",
        "        source_names = [text_el.name for text_el in text_elements]\n",
        "\n",
        "    if source_names:\n",
        "        answer += f\"\\nSources: {', '.join(source_names)}\"\n",
        "    else:\n",
        "        answer += \"\\nNo sources found\"\n",
        "\n",
        "    await cl.Message(content=answer, elements=text_elements).send()"
      ],
      "metadata": {
        "id": "VWq4BuFKEWGX"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.10. Chạy chainlit app"
      ],
      "metadata": {
        "id": "ppam0IwqED3B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!chainlit run app.py --host 0.0.0.0 --port 8000 &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "RmD7Dn0vfvxx"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.11. Expose local host thành public host bằng local tunnel:"
      ],
      "metadata": {
        "id": "eA6HHnLTf3ZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "print(\"Password/Enpoint IP for localtunnel is:\", urllib.request.urlopen(\n",
        "    'https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n",
        "!lt --port 8000 --subdomain aivn-simple-rag"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxpJ2MrMfwbo",
        "outputId": "b1b39d99-1f2b-44e3-8611-c67236d48a7b"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Password/Enpoint IP for localtunnel is: 34.82.194.133\n",
            "your url is: https://aivn-simple-rag.loca.lt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BfaPPXU9f_7l"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}