Advanced_RAG_Using_Llama_Index.ipynb

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  "cells": [
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6Z1zrxSG8lQ",
        "outputId": "363665d6-5d08-445b-d3cf-3ba67a8c7512"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m28.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.9 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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mCollecting pypdf\n",
            "  Downloading pypdf-3.17.3-py3-none-any.whl (277 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m277.9/277.9 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-3.17.3\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement doc2txt (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for doc2txt\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m47.1 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",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.9.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "!pip install llama-index -qU\n",
        "!pip install -q openai\n",
        "!pip install pypdf\n",
        "!pip install doc2txt\n",
        "!pip install -qU llama-cpp-python\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QD0WJen5PLiE",
        "outputId": "07271eea-7785-40af-9f8c-93c85ab4956e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.25.0-py3-none-any.whl (265 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.25.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## import required dependencies"
      ],
      "metadata": {
        "id": "rFlOqNQnMr0S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "from getpass import getpass\n",
        "#\n",
        "import logging\n",
        "import sys\n",
        "from pprint import pprint\n",
        "#\n",
        "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
        "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
        "#\n"
      ],
      "metadata": {
        "id": "GMQjaDGOMqgU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import(VectorStoreIndex,\n",
        "                        SimpleDirectoryReader,\n",
        "                        load_index_from_storage,\n",
        "                        StorageContext,\n",
        "                        ServiceContext,\n",
        "                        Document)\n",
        "\n",
        "from llama_index.llms import OpenAI,HuggingFaceLLM\n",
        "from llama_index.prompts import PromptTemplate\n",
        "from llama_index.text_splitter import SentenceSplitter\n",
        "from llama_index.embeddings import OpenAIEmbedding,HuggingFaceEmbedding\n",
        "from llama_index.schema import MetadataMode\n",
        "from llama_index.postprocessor import MetadataReplacementPostProcessor"
      ],
      "metadata": {
        "id": "Z3oOEiBENONC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## setup the openai api key"
      ],
      "metadata": {
        "id": "YqlgBAWzSVtF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
        "os.environ[\"HUGGINGFACE_API_KEY\"] = userdata.get(\"HUGGINGFACE_API_KEY\")"
      ],
      "metadata": {
        "id": "XFPj8BPCVngj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up the llm"
      ],
      "metadata": {
        "id": "2gt_ULxFSTJw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms import HuggingFaceLLM\n",
        "from llama_index.prompts import PromptTemplate\n",
        "llm = HuggingFaceLLM(\n",
        "    model_name=\"HuggingFaceH4/zephyr-7b-beta\",\n",
        "    tokenizer_name=\"HuggingFaceH4/zephyr-7b-beta\",\n",
        "    #query_wrapper_prompt=PromptTemplate(\"<|system|>Please check if the following pieces of context has any mention of the keywords provided in the question.If not ten say that you do not know the answer.Please do not make up your own answer.</s>\\n<|user|>\\nQuestion:{query_str}</s>\\n<|assistant|>\\n\"),\n",
        "    # query_wrapper_prompt=PromptTemplate(template),\n",
        "    context_window=4096,\n",
        "    max_new_tokens=512,\n",
        "    model_kwargs={'trust_remote_code':True},\n",
        "    generate_kwargs={\"temperature\": 0.0},\n",
        "    device_map=\"auto\",)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845,
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)"
      ],
      "metadata": {
        "id": "xZrytbZLuq8W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up the embedding Model"
      ],
      "metadata": {
        "id": "FU45GtbSSrMi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings import resolve_embed_model\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "#embed_model = resolve_embed_model(\"local:BAAI/bge-large-en-v1.5\")\n",
        "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-large-en-v1.5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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            "56651eacb45049d485db5c2e34a818e8",
            "0217a877cf2c449eb32f91f1898cfe49",
            "f28d7a97976f44a8bda3d0b1b0ee0c0e",
            "93b5347693d84d9a90156bb85a9dafdb",
            "cb9f59ee456e49fe9eec10a95432d56f",
            "073cdfba99cd463287516d3d38951349",
            "1549e4eaf63540f7b1197e1532dde6d8",
            "504517717f254336ab48bad8116ce196",
            "d1f7c713c2d7467fbd43337746f9e54b",
            "8ba9dacbf72741ab9fac4a4a9842ff58",
            "ac02305986384894abc4ff995d6d326f",
            "6a4bf67790f24fcc9be6dc0f707bd751",
            "dd957547ac874bc696e811cd4d3bf5b0",
            "e43832244f3f4e5fb73296a245ecbb33",
            "67a59dfe0e4e4ef78914046194a3dd3e",
            "72ebb07704544be0b349710ad69783f7",
            "94e5129fd9a542eaba8fc2109ba24b29",
            "00428198a96341d98e4ae37428740f1b",
            "7c944a332d33469daabd5c4ddcf4c940",
            "258f7c46ff084d06a0da378c971f640b",
            "dade205b4e4b4044af9adf6829a65218",
            "6cca6516a41f42809bc9ffecfa27ae7b"
          ]
        },
        "id": "RLB-TGdlStx8",
        "outputId": "306e759f-add4-4e91-9fdb-02988d92fadb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/779 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbf39ed86d86408f87afd0236db79c3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28420310e04f4152bec1b8d4705b05b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68820c4417c843ee9b63738ec6045bc9"
            }
          },
          "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": "da857bb42bed424dbc3f4e17ae62bd73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56651eacb45049d485db5c2e34a818e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a4bf67790f24fcc9be6dc0f707bd751"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embed_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vevcai5R4y7r",
        "outputId": "7c652cab-1bba-4432-af8f-07da6fd3908e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HuggingFaceEmbedding(model_name='BAAI/bge-large-en-v1.5', embed_batch_size=10, callback_manager=<llama_index.callbacks.base.CallbackManager object at 0x7d0ffcb23b50>, tokenizer_name='BAAI/bge-large-en-v1.5', max_length=512, pooling=<Pooling.CLS: 'cls'>, normalize=True, query_instruction=None, text_instruction=None, cache_folder=None)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "GqSAdSJJvH6I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fecth the data nd save it locally"
      ],
      "metadata": {
        "id": "P6gljPY6SytD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p 'Data'"
      ],
      "metadata": {
        "id": "HTK6KZn9vjPq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!wget 'https://www.gutenberg.org/cache/epub/72306/pg72306.txt' -O 'Data/teahistory.txt'\n",
        "!wget 'https://www.gutenberg.org/cache/epub/11367/pg11367.txt' -O 'Data/chinahistory.txt'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hv66jnk2Suad",
        "outputId": "5c3a32e3-960d-454f-aa9c-709dc620e9e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-18 14:53:57--  https://www.gutenberg.org/cache/epub/72306/pg72306.txt\n",
            "Resolving www.gutenberg.org (www.gutenberg.org)... 152.19.134.47, 2610:28:3090:3000:0:bad:cafe:47\n",
            "Connecting to www.gutenberg.org (www.gutenberg.org)|152.19.134.47|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 493827 (482K) [text/plain]\n",
            "Saving to: ‘Data/teahistory.txt’\n",
            "\n",
            "Data/teahistory.txt 100%[===================>] 482.25K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-12-18 14:53:57 (4.87 MB/s) - ‘Data/teahistory.txt’ saved [493827/493827]\n",
            "\n",
            "--2023-12-18 14:53:58--  https://www.gutenberg.org/cache/epub/11367/pg11367.txt\n",
            "Resolving www.gutenberg.org (www.gutenberg.org)... 152.19.134.47, 2610:28:3090:3000:0:bad:cafe:47\n",
            "Connecting to www.gutenberg.org (www.gutenberg.org)|152.19.134.47|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 977274 (954K) [text/plain]\n",
            "Saving to: ‘Data/chinahistory.txt’\n",
            "\n",
            "Data/chinahistory.t 100%[===================>] 954.37K  5.48MB/s    in 0.2s    \n",
            "\n",
            "2023-12-18 14:53:58 (5.48 MB/s) - ‘Data/chinahistory.txt’ saved [977274/977274]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the data into Document Object"
      ],
      "metadata": {
        "id": "pk8b8z8KV3kt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader('./Data/').load_data()\n",
        "print(len(documents))\n",
        "pprint(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzAeNTVWTvVF",
        "outputId": "b1f1a4e7-663f-4263-996f-be9cb63bc7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n",
            "[Document(id_='4538122a-90cd-4af5-b6e9-84b8399da135', embedding=None, metadata={'page_label': '1', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='a819d446ca212183fcf6284e3cc010747bf04a25aa39040bff8877fe5e35734d', text='Face Recognition System Using Python\\n\\xa0This article was published as a part of the Data Science Blogathon.\\nIntroduction\\nFace recognition is different from face detection. In face detection, we had only detected the location of\\nhuman faces, and we recognized the identity of faces in the face recognition task.\\nIn this article, we are going to build a face recognition system using python with the help of face\\nrecognition library .\\nThere are many algorithms available in the market for face recognition. This broad computer vision\\nchallenge is detecting faces from videos and pictures. Many applications can be built on top of recognition\\nsystems. Many big companies are adopting recognition systems for their security and authentication\\npurposes.\\nUse Cases of Recognition Systems\\nFace recognition systems are widely used in the modern era, and many new innovative systems are built on\\ntop of recognition systems.\\nThere are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\nTraditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards. They were\\ndesigned to recognize faces using old conventional algorithms.\\nOpenCV provides some traditional facial Recognition Algorithms.\\nEigenfaces\\nScale Invariant Feature Transform (SIFT)\\nFisher faces\\nLocal Binary Patterns Histograms (LBPH)\\nCOMPUTER VISION\\nIMAGE ANALYSIS\\nINTERMEDIATE\\nPYTHON', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='8698f987-7c3a-4044-b901-5a263d82d149', embedding=None, metadata={'page_label': '2', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='6767023ca9f71cb353321901ae1295153c1e8e19ce422d21bb835bda5ec8b516', text='These methods differ in the way they extract image information and match input and output images.\\nLBPH algorithm is a simple yet very efficient method still in use but it’s slow compared to modern days\\nalgorithms.\\nDeep Learning For Face Recognition\\nThere are various deep learning-based facial recognition algorithms available.\\nDeepFace\\nDeepID series of systems,\\nFaceNet\\nVGGFace\\nGenerally, face recognizers that are based on landmarks take face images and try to find essential feature\\npoints such as eyebrows, corners of the mouth, eyes, nose, lips, etc. There are more than 60 points.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='c13848e0-5c2a-4f10-98ba-fe575b022a3d', embedding=None, metadata={'page_label': '3', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='08e311a09e5272312d306bc2f3fdcc6ab72910b0efcf22a24add9724b926e9d7', text='Source: Medium.com\\nSteps Involved in Face Recognition\\n1. Face Detection: Locate the face, note the coordinates of each face locate,d and draw a bounding box\\naround every faces.\\n2. Face Alignments. Normalize the faces in order to attain fast training.\\n3. Feature Extraction. Local feature extraction from facial pictures for training, this step is performed\\ndifferently by different algorithms.\\n4. Face Recognition. Match the input face with one or more known faces in our dataset.\\nSource: wp.com\\nThis article focuses on implementing face recognition using the library face_recognition, built on deep\\nlearning techniques and promises accuracy greater than 96% using a single training image.\\nImplementation\\nImplementing a face recognition system using python. Implementing a Deep learning-based face\\nrecognition system using the face_recognition library.\\n1. Setting face recognition libraries:\\nIn order to install the face recognition library, we need to first install the dlib.\\ndlib : It is a modern C++ toolkit that contains ML-related algorithms and tools.\\n# installing dlib pip install dlib\\nface recognition The actual face recognition library can be installed after dlib.\\n# installing face recognition pip install face recognition\\nOpencv for some image pre-processing', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='449a3d3e-0017-4580-baec-67c25c971d94', embedding=None, metadata={'page_label': '4', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='cec5883731a60abdfd731e768fea95bea49bf6fe2a1d9a655535ac0fb4e79154', text=\"# installing opencv pip install opencv N o t e: Sometimes installing dlib throws error in that case install install the C++ development toolkit\\nusing vs_code community .\\nImporting Libraries\\nimport cv2 import numpy as np import face_recognition\\n2. Loading Image:\\nWe are done with installing and importing the libraries. It’s time to load some sample images to the\\nface_recognition library.\\nThe library face_recognitionsupports only the BGR format of images. While printing the output image\\nwe should convert it into RGB using OpenCV.\\nFace_recognition Loads images only in BGR format.\\nimport cv2 import numpy as np import face_recognition img_bgr =\\nface_recognition.load_image_file('student_images/modi.jpg') img_rgb = cv2.cvtColor(img_bgr,cv2.COLOR_BGR2RGB)\\ncv2.imshow('bgr', img_bgr) cv2.imshow('rgb', img_rgb) cv2.waitKey\\nOutput → BGR vs RGB\\n3. Detecting and Locating Faces:\\xa0\\nThe library face_recognitioncan quickly locate faces on its own, we don’t need to use haar_cascade\\nand other techniques.\\nimg_modi=face_recognition.load_image_file('student_images/modi.jpg') img_modi_rgb =\\ncv2.cvtColor(img_modi,cv2.COLOR_BGR2RGB) #--------- Detecting Face ------- face =\\nface_recognition.face_locations(img_modi_rgb)[0] copy = img_modi_rgb.copy() # ------ Drawing bounding boxes\\naround Faces------------------------ cv2.rectangle(copy, (face[3], face[0]),(face[1], face[2]), (255,0,255),\\n2) cv2.imshow('copy', copy) cv2.imshow('MODI',img_modi_rgb) cv2.waitKey(0)\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='a4a86487-d314-49a1-80e0-b40e9b1a299f', embedding=None, metadata={'page_label': '5', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='7e2bd77a15a694d1caa24ebbf1a2324863dae06dcd58490640358cc61967f7d6', text=\"4. Sample Image Recognition:\\nThe library face_recognition is based on deep learning, it supports single-shot learning which means it\\nneeds a single picture to train itself to detect a person.\\nimg_modi = face_recognition.load_image_file('student_images/modi.jpg') img_modi =\\ncv2.cvtColor(img_modi,cv2.COLOR_BGR2RGB)\\n#------to find the face location face = face_recognition.face_locations(img_modi)[0]\\n#--Converting image into encodings train_encode = face_recognition.face_encodings(img_modi)[0]\\n#----- lets test an image test = face_recognition.load_image_file('student_images/modi2.jpg') test =\\ncv2.cvtColor(test, cv2.COLOR_BGR2RGB) test_encode = face_recognition.face_encodings(test)[0]\\nprint(face_recognition.compare_faces([train_encode],test_encode)) cv2.rectangle(img_modi, (face[3], face[0]),\\n(face[1], face[2]), (255,0,255), 1) cv2.imshow('img_modi', img_modi) cv2.waitKey(0)\\nThe above code took two pictures of the prime minister, and it returnedTrue\\xa0because both photos were of\\nthe same person.\\nface_recognition.face_encodings(imgelon)[0] →Returns encoding of passed Image.\\nface_recognition.compare_faces([train_encode],test_encode) → Takes a list of trained\\nencodings and a test encoding of the unknown Image. It returns True\\xa0if both test encoding has a\\nmatch in train encoding; otherwise, it returns.\\xa0False.\\nUnderstand the Working of Face Recognition\\n1. We pass the person’s picture to the model and their name.\\n2. The model takes every picture, converts them into some numerical encoding, and stores them in a list\\nand all the labels(names of persons) in another list.\\n3. In the Prediction Phase when we pass a picture of an unknown person recognition model converts the\\nunfamiliar person’s Image into encoding.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='eaabc246-1f4a-4231-a280-76a8a8fe5786', embedding=None, metadata={'page_label': '6', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='3f80c40bbeb2a72a106ca7120458ad63cf79e68bd33dbfa59ed8431d31675b43', text='4. After converting an unknown person’s Image into encoding, it tries to find the most similar encoding\\nbased on the distance parameter. The store encoding with the least distance from the encoding of an\\nunknown person will be the closest match.\\n5. After getting the closest match encoding, we take the index of that encoding from that list and use\\nindexing. We find the detected person’s name.\\nChallenges in Recognition Systems\\nThese are significant challenges faced by recognition systems and need to be resolved.\\nPose: Recognition systems are susceptible to the human pose. Facial recognition systems will not be\\nable to predict if the person’s face is not visible.\\nIllumination: Illumination changes the face contours drastically. Pictures for face recognition should\\nbe taken in proper lighting conditions.\\nFacial Expressions: Different facial expressions can result in different predictions of the same person’s\\nImage.\\nLow Resolution: Low-resolution pictures contain less information, hence not good for face recognition\\ntraining.\\nConclusion\\nThis article discussed how to implement a face recognition system using python with a single-shot image\\ntraining technique. You can further use GUI like python Tkinter to design a GUI-based attendance system.\\nWe saw various challenges that affect a recognition system and how to solve them.\\nIn the next article, we will create a face recognition attendance system using the same concepts which we\\nhave discussed today.\\nThe media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.\\nArticle Url - https://www.analyticsvidhya.com/blog/2022/04/face-recognition-system-using-python/\\nAbhishek Jaiswal', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='3d60cb97-d362-4828-8a18-f9f824d391d4', embedding=None, metadata={'page_label': '1', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='0edbce908d3993571991cbf621199243147d8c1e2fb54f1f9bb85eae0de07b45', text='Language Detection Using Natural Language Processing\\nIntroduction\\nEvery Machine Learning enthusiast has a dream of building/working on a cool project, isn’t it? Mere\\nunderstandings of the theory aren’t enough, you need to work on projects, try to deploy them, and learn\\nfrom them. Moreover, working on specific domains like NLP gives you wide opportunities and problem\\nstatements to explore. Through this article, I wish to introduce you to an amazing project, the Language\\nDetection model using Natural Language Processing. This will take you through a real-world example of\\nML(application to say). So, let’s not wait anymore.\\n\\xa0\\nAbout the dataset\\nWe are using the Language Detection dataset, which contains text details for 17 different languages.\\nLanguages are:\\n* English\\n* Portuguese\\n* French\\n* Greek\\n* Dutch\\n* Spanish\\n* Japanese\\n* Russian\\n* Danish\\n* Italian\\n* Turkish\\n* Swedish\\n* Arabic\\n* Malayalam\\n* Hindi\\n* Tamil\\nADVANCED\\nCLASSIFICATION\\nNLP\\nPROJECT\\nPYTHON\\nSTRUCTURED DATA\\nSUPERVISED\\nTECHNIQUE\\nTEXT', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='00fb5c60-998f-43ed-ac97-7b33d00cc423', embedding=None, metadata={'page_label': '2', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='3f3cc4db8ff52079b58d07a061ab08353a8eb7829b57cba972aef0d82b6f3ac2', text='* Telugu\\nUsing the text we have to create a model which will be able to predict the given language. This is a solution\\nfor many artificial intelligence applications and computational linguists. These kinds of prediction systems\\nare widely used in electronic devices such as mobiles, laptops, etc for machine translation, and also on\\nrobots. It helps in tracking and identifying multilingual documents too. The domain of NLP is still a lively\\narea of researchers.\\n\\xa0\\nImplementation\\nImporting libraries and dataset\\nSo let’s get started. First of all, we will import all the required libraries.\\nimport pandas as pd import numpy as np import re import seaborn as sns import matplotlib.pyplot as plt import\\nwarnings warnings.simplefilter(\"ignore\")\\nNow let’s import the language detection dataset\\ndata = pd.read_csv(\"Language Detection.csv\") data.head(10)\\nAs I told you earlier this dataset contains text details for 17 different languages. So let’s count the value\\ncount for each language.\\ndata[\"Language\"].value_counts()\\nOutput :\\nEnglish 1385 French 1014 Spanish 819 Portugeese 739 Italian 698 Russian 692 Sweedish 676 Malayalam 594 Dutch\\n546 Arabic 536 Turkish 474 German 470 Tamil 469 Danish 428 Kannada 369 Greek 365 Hindi 63 Name: Language,\\ndtype: int64', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='e933eafa-6dd6-4500-a3a4-8e79b7509c0b', embedding=None, metadata={'page_label': '3', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='e3ee52b1a21a8c4ee3dd53c25ac46caf8db6f124d5e117377524c50f3d4715b4', text='Separating Independent and Dependent features\\nNow we can separate the dependent and independent variables, here text data is the independent variable\\nand the language name is the dependent variable.\\nX = data[\"Text\"] y = data[\"Language\"]\\nLabel Encoding\\nOur output variable, the name of languages is a categorical variable. For training the model we should have\\nto convert it into a numerical form, so we are performing label encoding on that output variable. For this\\nprocess, we are importing LabelEncoder from sklearn.\\nfrom sklearn.preprocessing import LabelEncoder le = LabelEncoder() y = le.fit_transform(y)\\nText Preprocessing\\nThis is a dataset created using scraping the Wikipedia, so it contains many unwanted symbols, numbers\\nwhich will affect the quality of our model. So we should perform text preprocessing techniques.\\n# creating a list for appending the preprocessed text data_list = [] # iterating through all the text for\\ntext in X: # removing the symbols and numbers text = re.sub(r\\'[!@#$(),n\"%^*?:;~`0-9]\\', \\' \\', text) text =\\nre.sub(r\\'[[]]\\', \\' \\', text) # converting the text to lower case text = text.lower() # appending to data_list\\ndata_list.append(text)\\nBag of Words\\nAs we all know that, not only the output feature but also the input feature should be of the numerical form.\\nSo we are converting text into numerical form by creating a Bag of Words model using CountVectorizer.\\nfrom sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer() X =\\ncv.fit_transform(data_list).toarray() X.shape # (10337, 39419)\\nTrain Test Splitting\\nWe preprocessed our input and output variable. The next step is to create the training set, for training the\\nmodel and test set, for evaluating the test set. For this process, we are using a train test split.\\nfrom sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(X,\\ny, test_size = 0.20)\\nModel Training and Prediction', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='0a5181ae-92d7-4138-9ded-1b62578d2b1a', embedding=None, metadata={'page_label': '4', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='a152a4bf34991a7403e4506758a9dfd7c713cc3126da46291644714885f0d1e2', text='And we almost there, the model creation part. We are using the naive_bayes algorithm for our model\\ncreation. Later we are training the model using the training set.\\nfrom sklearn.naive_bayes import MultinomialNB model = MultinomialNB() model.fit(x_train, y_train)\\nSo we’ve trained our model using the training set. Now let’s predict the output for the test set.\\ny_pred = model.predict(x_test)\\nModel Evaluation\\nNow we can evaluate our model\\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report ac =\\naccuracy_score(y_test, y_pred) cm = confusion_matrix(y_test, y_pred) print(\"Accuracy is :\",ac) # Accuracy is\\n: 0.9772727272727273\\nThe accuracy of the model is 0.97 which is very good and our model is performing well. Now let’s plot the\\nconfusion matrix using the seaborn heatmap.\\nplt.figure(figsize=(15,10)) sns.heatmap(cm, annot = True) plt.show()\\nThe graph will look like this:\\nWhen looking into each language, almost all the predictions are right. And yes !! you are almost there !!\\nPredicting with some more data', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='a4b1f114-415f-439d-b3f4-379c8d71fc43', embedding=None, metadata={'page_label': '5', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='f76b1e514907769b0c6b977778cdeb701807c1e74065734e04c9ba3b0fe7b4a7', text='Now let’s test the model prediction using text in different languages.\\ndef predict(text): x = cv.transform([text]).toarray() # converting text to bag of words model (Vector) lang =\\nmodel.predict(x) # predicting the language lang = le.inverse_transform(lang) # finding the language\\ncorresponding the the predicted value print(\"The langauge is in\",lang[0]) # printing the language\\nAs you can see, the predictions done by the model are very accurate. You can test using different other\\nlanguages.\\n\\xa0\\nFull Code\\nimport pandas as pd import numpy as np import re import seaborn as sns import matplotlib.pyplot as plt import\\nwarnings warnings.simplefilter(\"ignore\") # Loading the dataset data = pd.read_csv(\"Language Detection.csv\") #\\nvalue count for each language data[\"Language\"].value_counts() # separating the independent and dependant', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
            " Document(id_='be02eeb2-39d2-475d-8c40-af7879a7b5d3', embedding=None, metadata={'page_label': '6', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, hash='15b9bf9c7bb643421cd407c943bf4f53e9903d33d8c04a89a093860337b421e4', text='features X = data[\"Text\"] y = data[\"Language\"] # converting categorical variables to numerical from\\nsklearn.preprocessing import LabelEncoder le = LabelEncoder() y = le.fit_transform(y) # creating a list for\\nappending the preprocessed text data_list = [] # iterating through all the text for text in X: # removing the\\nsymbols and numbers text = re.sub(r\\'[!@#$(),n\"%^*?:;~`0-9]\\', \\' \\', text) text = re.sub(r\\'[[]]\\', \\' \\', text) #\\nconverting the text to lower case text = text.lower() # appending to data_list data_list.append(text) #\\ncreating bag of words using countvectorizer from sklearn.feature_extraction.text import CountVectorizer cv =\\nCountVectorizer() X = cv.fit_transform(data_list).toarray() #train test splitting from\\nsklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(X, y,\\ntest_size = 0.20) #model creation and prediction from sklearn.naive_bayes import MultinomialNB model =\\nMultinomialNB() model.fit(x_train, y_train) # prediction y_pred = model.predict(x_test) # model evaluation\\nfrom sklearn.metrics import accuracy_score, confusion_matrix ac = accuracy_score(y_test, y_pred) cm =\\nconfusion_matrix(y_test, y_pred) # visualising the confusion matrix plt.figure(figsize=(15,10))\\nsns.heatmap(cm, annot = True) plt.show() # function for predicting language def predict(text): x =\\ncv.transform([text]).toarray() lang = model.predict(x) lang = le.inverse_transform(lang) print(\"The langauge\\nis in\",lang[0]) # English prediction(\"Analytics Vidhya provides a community based knowledge portal for\\nAnalytics and Data Science professionals\") # French prediction(\"Analytics Vidhya fournit un portail de\\nconnaissances basé sur la communauté pour les professionnels de l\\'analyse et de la science des données\") #\\nArabic prediction(\"ﺗﻮﻓﺮ Analytics Vidhya اﻟﺒﻴﺎﻧﺎت  وﻋﻠﻮم  اﻟﺘﺤﻠﻴﻼت  ﻟﻤﺤﺘﺮﻓﻲ  اﻟﻤﺠﺘﻤﻊ  ﻋﻠﻰ  ﻗﺎﺋﻤﺔ  ﻣﻌﺮﻓﻴﺔ  ﺑﻮاﺑﺔ Spanish\\nprediction(\"Analytics Vidhya proporciona un portal de conocimiento basado en la comunidad para profesionales\\nde Analytics y Data Science.\") # Malayalam prediction(\" \\x00 \\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 , \\x00\\x00 \\x00\\x00 \\x00\\x00  \\x00 \\x00 \\x00 \\x00\\x00  \\x00\\x00 \\x00\\x00 \\x00 \\x00 \\x00 \\x00\\x00 \\x00 \\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00\\n\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00  \\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00  \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00  \\x00\\x00 \\x00 \\x00\\x00 \\x00 \\x00  \\x00 \\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00  \\x00\\x00 \\x00\\x00 \\x00  \\x00 \\x00 \\x00\\x00 \\x00\\x00 \\x00\\x00 \") # Russian prediction(\"Analytics Vidhya -\\nэто портал знаний на базе сообщества для профессионалов в области аналитики и данных.\")\\nConclusion\\nThat was an interesting project, right? I hope you might have got an intuition on how such projects are\\nworked out. This would have definitely given you a diagram of basic NLP programs. You need to analyze\\nthe data and preprocess it accordingly. A bag of words model becomes a way of representing your text\\ndata. Text extraction and vectorization are important steps for good predictions in NLP. Naive Bayes always\\nproves to be a better model in such text classification problems, hence more accurate results we get.\\nYou can also find the complete end to end project for the above language detection model in my Github\\nThank you for showing interest in the project, hope you follow up with more amazing projects and\\nfamiliarise yourself with real-life problem statements. Feel free to connect with me on LinkedIn.\\nThe media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.\\nArticle Url - https://www.analyticsvidhya.com/blog/2021/03/language-detection-using-natural-language-\\nprocessing/\\nBasilB2S', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documents[0].get_content()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "m1B_n_BmWn2h",
        "outputId": "9467e46b-498e-444f-fe55-2f818298b8f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Face Recognition System Using Python\\n\\xa0This article was published as a part of the Data Science Blogathon.\\nIntroduction\\nFace recognition is different from face detection. In face detection, we had only detected the location of\\nhuman faces, and we recognized the identity of faces in the face recognition task.\\nIn this article, we are going to build a face recognition system using python with the help of face\\nrecognition library .\\nThere are many algorithms available in the market for face recognition. This broad computer vision\\nchallenge is detecting faces from videos and pictures. Many applications can be built on top of recognition\\nsystems. Many big companies are adopting recognition systems for their security and authentication\\npurposes.\\nUse Cases of Recognition Systems\\nFace recognition systems are widely used in the modern era, and many new innovative systems are built on\\ntop of recognition systems.\\nThere are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\nTraditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards. They were\\ndesigned to recognize faces using old conventional algorithms.\\nOpenCV provides some traditional facial Recognition Algorithms.\\nEigenfaces\\nScale Invariant Feature Transform (SIFT)\\nFisher faces\\nLocal Binary Patterns Histograms (LBPH)\\nCOMPUTER VISION\\nIMAGE ANALYSIS\\nINTERMEDIATE\\nPYTHON'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documents[0].metadata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXuqKBWGWuQJ",
        "outputId": "008d86bb-a3f3-4c9d-ca72-bc42af028607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'file_path': 'Data/chinahistory.txt',\n",
              " 'file_name': 'chinahistory.txt',\n",
              " 'file_type': 'text/plain',\n",
              " 'file_size': 977274,\n",
              " 'creation_date': '2023-12-18',\n",
              " 'last_modified_date': '2023-12-05',\n",
              " 'last_accessed_date': '2023-12-18'}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Node parsing and Indexing (Base and Sentence Window Method)"
      ],
      "metadata": {
        "id": "WqF0I5GJW7xK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create senetence window node parser with default settings\n",
        "from llama_index.node_parser import SentenceWindowNodeParser,SimpleNodeParser\n",
        "sentence_node_parser = SentenceWindowNodeParser.from_defaults(\n",
        "    window_size=3,\n",
        "    window_metadata_key=\"window\",\n",
        "    original_text_metadata_key=\"original_text\")\n",
        "#base_node_parser = SentenceSplitter(llm=llm)\n",
        "base_node_parser = SimpleNodeParser()"
      ],
      "metadata": {
        "id": "yeVvkxsmW2f6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nodes = sentence_node_parser.get_nodes_from_documents(documents)\n",
        "base_nodes = base_node_parser.get_nodes_from_documents(documents)"
      ],
      "metadata": {
        "id": "6OURS0UFX9AE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"SENTENCE NODES :\\n {nodes[10]}\")\n",
        "print(f\"BASE NODES :\\n {base_nodes[10]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wctPu6uMcMkX",
        "outputId": "0a593250-f782-486e-9161-309ea72ab425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SENTENCE NODES :\n",
            " Node ID: 8418b939-dc08-42a6-8ee1-821e46f7a2a1\n",
            "Text: Traditional Face Recognition Algorithm Traditional face\n",
            "recognition algorithms don’t meet modern-day’s facial recognition\n",
            "standards.\n",
            "BASE NODES :\n",
            " Node ID: 7a94495b-2f49-4cc4-8fd4-87f5fb0f645e\n",
            "Text: Now let’s test the model prediction using text in different\n",
            "languages. def predict(text): x = cv.transform([text]).toarray() #\n",
            "converting text to bag of words model (Vector) lang = model.predict(x)\n",
            "# predicting the language lang = le.inverse_transform(lang) # finding\n",
            "the language corresponding the the predicted value print(\"The langauge\n",
            "is in\",l...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dict(nodes[10]) # since no indexing performed so embeding is None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJI3SQeIcal2",
        "outputId": "c5859420-f78b-4ed9-dfc5-1c9ca3e57dbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id_': '8418b939-dc08-42a6-8ee1-821e46f7a2a1',\n",
              " 'embedding': None,\n",
              " 'metadata': {'window': 'Many big companies are adopting recognition systems for their security and authentication\\npurposes.\\n Use Cases of Recognition Systems\\nFace recognition systems are widely used in the modern era, and many new innovative systems are built on\\ntop of recognition systems.\\n There are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\n Traditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards.  They were\\ndesigned to recognize faces using old conventional algorithms.\\n OpenCV provides some traditional facial Recognition Algorithms.\\n',\n",
              "  'original_text': 'Traditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards. ',\n",
              "  'page_label': '1',\n",
              "  'file_name': 'face-recognition-system-using-python.pdf',\n",
              "  'file_path': 'Data/face-recognition-system-using-python.pdf',\n",
              "  'file_type': 'application/pdf',\n",
              "  'file_size': 465666,\n",
              "  'creation_date': '2023-12-21',\n",
              "  'last_modified_date': '2023-12-21',\n",
              "  'last_accessed_date': '2023-12-21'},\n",
              " 'excluded_embed_metadata_keys': ['file_name',\n",
              "  'file_type',\n",
              "  'file_size',\n",
              "  'creation_date',\n",
              "  'last_modified_date',\n",
              "  'last_accessed_date',\n",
              "  'window',\n",
              "  'original_text'],\n",
              " 'excluded_llm_metadata_keys': ['file_name',\n",
              "  'file_type',\n",
              "  'file_size',\n",
              "  'creation_date',\n",
              "  'last_modified_date',\n",
              "  'last_accessed_date',\n",
              "  'window',\n",
              "  'original_text'],\n",
              " 'relationships': {<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='4538122a-90cd-4af5-b6e9-84b8399da135', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '1', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, hash='a819d446ca212183fcf6284e3cc010747bf04a25aa39040bff8877fe5e35734d'),\n",
              "  <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='942980f2-6328-45a8-94ce-2afe8fba03ba', node_type=<ObjectType.TEXT: '1'>, metadata={'window': 'Many applications can be built on top of recognition\\nsystems.  Many big companies are adopting recognition systems for their security and authentication\\npurposes.\\n Use Cases of Recognition Systems\\nFace recognition systems are widely used in the modern era, and many new innovative systems are built on\\ntop of recognition systems.\\n There are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\n Traditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards.  They were\\ndesigned to recognize faces using old conventional algorithms.\\n', 'original_text': 'There are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\n', 'page_label': '1', 'file_name': 'face-recognition-system-using-python.pdf', 'file_path': 'Data/face-recognition-system-using-python.pdf', 'file_type': 'application/pdf', 'file_size': 465666, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, hash='c7993150674462ffbf83c9cfbb786980c1f1d5ea27b2313954b4901f55448f59'),\n",
              "  <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='18f77456-c93d-478d-a162-e4d01ad8135b', node_type=<ObjectType.TEXT: '1'>, metadata={'window': 'Use Cases of Recognition Systems\\nFace recognition systems are widely used in the modern era, and many new innovative systems are built on\\ntop of recognition systems.\\n There are a few used cases :\\nFinding Missing Person\\nIdentifying accounts on social media\\nRecognizing Drivers in Cars\\nSchool Attendance System\\nSeveral methods and algorithms implement facial recognition systems depending on the performance and\\naccuracy.\\n Traditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards.  They were\\ndesigned to recognize faces using old conventional algorithms.\\n OpenCV provides some traditional facial Recognition Algorithms.\\n Eigenfaces\\nScale Invariant Feature Transform (SIFT)\\nFisher faces\\nLocal Binary Patterns Histograms (LBPH)\\nCOMPUTER VISION\\nIMAGE ANALYSIS\\nINTERMEDIATE\\nPYTHON', 'original_text': 'They were\\ndesigned to recognize faces using old conventional algorithms.\\n'}, hash='4c57a3fcceaebf806622383637926ea4e27e153542a4d2ce7a4a82d3df8a72de')},\n",
              " 'hash': '5f83425f868962e1066c20252056676e299fc51a8af0dbab4bb3c8bcc9130e2f',\n",
              " 'text': 'Traditional Face Recognition Algorithm\\nTraditional face recognition algorithms don’t meet modern-day’s facial recognition standards. ',\n",
              " 'start_char_idx': 1166,\n",
              " 'end_char_idx': 1299,\n",
              " 'text_template': '{metadata_str}\\n\\n{content}',\n",
              " 'metadata_template': '{key}: {value}',\n",
              " 'metadata_seperator': '\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* the sentence nodes captures the relationshsips as well"
      ],
      "metadata": {
        "id": "S2UiiqJCdWlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict(base_nodes[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUVs_SHEc69D",
        "outputId": "68bf4b88-04b8-4015-b940-cf29a2d91930"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id_': '7a94495b-2f49-4cc4-8fd4-87f5fb0f645e',\n",
              " 'embedding': None,\n",
              " 'metadata': {'page_label': '5',\n",
              "  'file_name': 'language-detection-using-natural-language-processing.pdf',\n",
              "  'file_path': 'Data/language-detection-using-natural-language-processing.pdf',\n",
              "  'file_type': 'application/pdf',\n",
              "  'file_size': 195783,\n",
              "  'creation_date': '2023-12-21',\n",
              "  'last_modified_date': '2023-12-21',\n",
              "  'last_accessed_date': '2023-12-21'},\n",
              " 'excluded_embed_metadata_keys': ['file_name',\n",
              "  'file_type',\n",
              "  'file_size',\n",
              "  'creation_date',\n",
              "  'last_modified_date',\n",
              "  'last_accessed_date'],\n",
              " 'excluded_llm_metadata_keys': ['file_name',\n",
              "  'file_type',\n",
              "  'file_size',\n",
              "  'creation_date',\n",
              "  'last_modified_date',\n",
              "  'last_accessed_date'],\n",
              " 'relationships': {<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='a4b1f114-415f-439d-b3f4-379c8d71fc43', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '5', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, hash='f76b1e514907769b0c6b977778cdeb701807c1e74065734e04c9ba3b0fe7b4a7'),\n",
              "  <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='10fbf0bb-5c61-4aad-b46f-6eb774620df1', node_type=<ObjectType.TEXT: '1'>, metadata={'page_label': '4', 'file_name': 'language-detection-using-natural-language-processing.pdf', 'file_path': 'Data/language-detection-using-natural-language-processing.pdf', 'file_type': 'application/pdf', 'file_size': 195783, 'creation_date': '2023-12-21', 'last_modified_date': '2023-12-21', 'last_accessed_date': '2023-12-21'}, hash='bfe1b6d4a868679c202ab1cbdfeb8142ab7b58aa2cdeb4ab2e39a544c44054fc'),\n",
              "  <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='0fada0d4-4fc5-4624-ba2d-625d01907fe4', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='e9ab5e4b4d35818ac4f01203c6c756732e8bd302136467c7aab00f698f2c4584')},\n",
              " 'hash': 'cc3f4c2fb51725dbd2e8386960c5451c056454e3408568321840debe993c6207',\n",
              " 'text': 'Now let’s test the model prediction using text in different languages.\\ndef predict(text): x = cv.transform([text]).toarray() # converting text to bag of words model (Vector) lang =\\nmodel.predict(x) # predicting the language lang = le.inverse_transform(lang) # finding the language\\ncorresponding the the predicted value print(\"The langauge is in\",lang[0]) # printing the language\\nAs you can see, the predictions done by the model are very accurate. You can test using different other\\nlanguages.\\n\\xa0\\nFull Code\\nimport pandas as pd import numpy as np import re import seaborn as sns import matplotlib.pyplot as plt import\\nwarnings warnings.simplefilter(\"ignore\") # Loading the dataset data = pd.read_csv(\"Language Detection.csv\") #\\nvalue count for each language data[\"Language\"].value_counts() # separating the independent and dependant',\n",
              " 'start_char_idx': 0,\n",
              " 'end_char_idx': 830,\n",
              " 'text_template': '{metadata_str}\\n\\n{content}',\n",
              " 'metadata_template': '{key}: {value}',\n",
              " 'metadata_seperator': '\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup the ServiceContext"
      ],
      "metadata": {
        "id": "VsNm7470eGJ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### If using openai"
      ],
      "metadata": {
        "id": "_sse6JuQyNOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm = OpenAI(temperature=0.1,model=\"gpt-3.5-turbo\")"
      ],
      "metadata": {
        "id": "4B3tBcUSj8sL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ctx_sentence = ServiceContext.from_defaults(llm=llm,\n",
        "                                            embed_model=OpenAIEmbedding(embed_batch_size=50),\n",
        "                                            node_parser=nodes)"
      ],
      "metadata": {
        "id": "9rzWbrbC5FGC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "ctx_base = ServiceContext.from_defaults(llm=llm,\n",
        "                                            embed_model=OpenAIEmbedding(embed_batch_size=50),\n",
        "                                            node_parser=base_nodes)"
      ],
      "metadata": {
        "id": "KvrU8FZ0dw1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### When using Local LLMS"
      ],
      "metadata": {
        "id": "nG0KLboZyTjh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ctx_sentence = ServiceContext.from_defaults(\n",
        "    llm=llm,\n",
        "    embed_model=embed_model,\n",
        "    node_parser=nodes)\n",
        "#\n",
        "#\n",
        "ctx_base = ServiceContext.from_defaults(\n",
        "    llm=llm,\n",
        "    embed_model=embed_model,\n",
        "    node_parser=base_nodes)"
      ],
      "metadata": {
        "id": "YDiGIvoQy9Gy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_index = VectorStoreIndex(\n",
        "    nodes,\n",
        "    service_context=ctx_sentence)\n",
        "base_index = VectorStoreIndex(\n",
        "    base_nodes,\n",
        "    service_context=ctx_base)"
      ],
      "metadata": {
        "id": "qrpixKVCe_DD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Query Engine"
      ],
      "metadata": {
        "id": "ZUJ1NE8d1thj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.indices.postprocessor import MetadataReplacementPostProcessor\n",
        "sentence_query_engine = sentence_index.as_query_engine(\n",
        "    similarity_top_k=5,\n",
        "    verbose=True,\n",
        "    node_postprocessor=[\n",
        "        MetadataReplacementPostProcessor(\"window\")\n",
        "        ],\n",
        "    )"
      ],
      "metadata": {
        "id": "Hr854qPA1yuI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_query_engine = base_index.as_query_engine(\n",
        "    similarity_top_k=5,\n",
        "    verbose=True,\n",
        "    node_postprocessor=[\n",
        "        MetadataReplacementPostProcessor(\"window\")\n",
        "        ],\n",
        "    )"
      ],
      "metadata": {
        "id": "KNScwpnv24Ok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Query for SentenceWindow Parser Query engine"
      ],
      "metadata": {
        "id": "myJpCqy62hUG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query =\"Sample code to detect faces in an image using python.\"\n",
        "response = sentence_query_engine.query(query)"
      ],
      "metadata": {
        "id": "F2hER4-q2MIv",
        "outputId": "8f8ddbb8-261a-4936-ae35-c26fbda5f0f9",
        "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/utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display,Markdown\n",
        "display(Markdown(f\"<b>{response}</b>\"))"
      ],
      "metadata": {
        "id": "eJv9utW22kqD",
        "outputId": "f460e235-a6ba-445c-9b15-141ea1752616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\nHere's a sample code to detect faces in an image using Python and the OpenCV library:\n\n```python\nimport cv2\nimport numpy as np\n\n# Load the pre-trained face detection model\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n# Load the image\nimg = cv2.imread('image.jpg')\n\n# Convert the image to grayscale\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# Detect faces in the grayscale image\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\n# Draw a rectangle around each face\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\n# Display the image with the detected faces\ncv2.imshow('Face Detection', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nIn this code, we first load the pre-trained face detection model using the `CascadeClassifier` function from OpenCV. We then load the image, convert it to grayscale, and pass it to the `detectMultiScale` function of the face detection model to detect faces. We then draw a rectangle around each face using the `rectangle` function of OpenCV. Finally, we display the image with the detected faces using the `imshow` function of OpenCV.\n\nNote: Make sure to replace 'haarcascade_frontalface_default.xml' with the path to your pre-trained face detection model.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Query for Base Node Parser Query engine"
      ],
      "metadata": {
        "id": "U8liuknG4L9u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = base_query_engine.query(query)\n",
        "#\n",
        "display(Markdown(f\"<b>{response}</b>\"))"
      ],
      "metadata": {
        "id": "oQbniBiG4RwC",
        "outputId": "42c09a2e-b1b9-49b7-9a1a-6982b15dfed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\n```python\nimport cv2\nimport numpy as np\n\nimg = cv2.imread('image.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nThis code uses the Haar Cascade algorithm to detect faces in an image. The `haarcascade_frontalface_default.xml` file contains the trained classifier for face detection. The `detectMultiScale()` function is used to detect multiple faces in the image with a certain scale factor and minimum neighbor size. The detected faces are then drawn as rectangles on the original image using the `rectangle()` function. The `imshow()` function is used to display the image, and the `waitKey()` function is used to wait for a key press before closing the window. The `destroyAllWindows()` function is used to destroy all the windows created during the execution of the program.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save and Reload the VectorStore"
      ],
      "metadata": {
        "id": "FoDemQoq86yR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mount drive"
      ],
      "metadata": {
        "id": "_dKiPfa_tkRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lq5m77yCtl4N",
        "outputId": "1f1828ab-dd02-48ee-f9e4-adcdd0edf8b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save to Persistent Storage"
      ],
      "metadata": {
        "id": "jyYWiWFzfdGl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_index.storage_context.persist(persist_dir=\"/content/drive/MyDrive/s_index\")\n",
        "base_index.storage_context.persist(persist_dir=\"/content/drive/MyDrive/b_index\")"
      ],
      "metadata": {
        "id": "YwjEfsunfTDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uKi4FaCe_TSA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Retrieve from Storage"
      ],
      "metadata": {
        "id": "Z0GEJxbuhgq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# rebuild storage\n",
        "SC_retrieved_sentence = StorageContext.from_defaults(persist_dir=\"/content/drive/MyDrive/s_index/\")\n",
        "SC_retrieved_base = StorageContext.from_defaults(persist_dir=\"/content/drive/MyDrive/b_index/\")"
      ],
      "metadata": {
        "id": "5ct3kFQihkYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Index"
      ],
      "metadata": {
        "id": "IUKJNlhuiH--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retrieved_sentence_index = load_index_from_storage(\n",
        "    SC_retrieved_sentence,\n",
        "    service_context=ctx_sentence)\n",
        "retrieved_base_index = load_index_from_storage(\n",
        "    SC_retrieved_base,\n",
        "    service_context=ctx_base)"
      ],
      "metadata": {
        "id": "wTKAurOeiJ93"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Query Engine"
      ],
      "metadata": {
        "id": "Oc_BsmBzib45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.postprocessor import MetadataReplacementPostProcessor\n",
        "#\n",
        "sentence_query_engine = retrieved_sentence_index.as_query_engine(similarity_top_k=5,\n",
        "                                                                 verbose=True,\n",
        "                                                                 node_postprocessor=[MetadataReplacementPostProcessor(\"window\")],)\n",
        "base_query_engine = retrieved_base_index.as_query_engine(similarity_top_k=5,\n",
        "                                                                 verbose=True,\n",
        "                                                                 )"
      ],
      "metadata": {
        "id": "kszHGpekieUl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference"
      ],
      "metadata": {
        "id": "dGNAi_Q4vZTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "base_response = base_query_engine.query(query)\n",
        "#\n",
        "display(Markdown(f\"<b>{response}</b>\"))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "SfajBQRuvbGc",
        "outputId": "d96cf44b-594a-4472-9be0-7f50fe9c742c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\n```python\nimport cv2\nimport numpy as np\n\nimg = cv2.imread('image.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nThis code uses the Haar Cascade algorithm to detect faces in an image. The `haarcascade_frontalface_default.xml` file contains the trained classifier for face detection. The `detectMultiScale()` function is used to detect multiple faces in the image with a certain scale factor and minimum neighbor size. The detected faces are then drawn as rectangles on the original image using the `rectangle()` function. The `imshow()` function is used to display the image, and the `waitKey()` function is used to wait for a key press before closing the window. The `destroyAllWindows()` function is used to destroy all the windows created during the execution of the program.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{base_response}</b>\"))"
      ],
      "metadata": {
        "id": "AQNfIrJ5ITXZ",
        "outputId": "c2e54165-2c57-4d02-91b4-c92617254c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\n```python\nimport cv2\nimport numpy as np\n\nimg = cv2.imread('image.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nThis code uses the Haar Cascade algorithm to detect faces in an image. The `haarcascade_frontalface_default.xml` file contains the trained classifier for face detection. The `detectMultiScale()` function is used to detect multiple faces in the image with a certain scale factor and minimum neighbor size. The detected faces are then drawn as rectangles on the original image using the `rectangle()` function. The `imshow()` function is used to display the image, and the `waitKey()` function is used to wait for a key press before closing the window. The `destroyAllWindows()` function is used to destroy all the windows created during the execution of the program.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_response = sentence_query_engine.query(query)\n",
        "display(Markdown(f\"<b>{response}</b>\"))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "KU8DzLNdvxqJ",
        "outputId": "b8eab37e-9404-4f3a-bfd1-6297e53623d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\n```python\nimport cv2\nimport numpy as np\n\nimg = cv2.imread('image.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nThis code uses the Haar Cascade algorithm to detect faces in an image. The `haarcascade_frontalface_default.xml` file contains the trained classifier for face detection. The `detectMultiScale()` function is used to detect multiple faces in the image with a certain scale factor and minimum neighbor size. The detected faces are then drawn as rectangles on the original image using the `rectangle()` function. The `imshow()` function is used to display the image, and the `waitKey()` function is used to wait for a key press before closing the window. The `destroyAllWindows()` function is used to destroy all the windows created during the execution of the program.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{sentence_response}</b>\"))"
      ],
      "metadata": {
        "id": "HTGcVCBr-h1h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "2d38c628-16cc-4000-99d1-f1452fa57ec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>\n\nHere's a sample code to detect faces in an image using Python and the OpenCV library:\n\n```python\nimport cv2\nimport numpy as np\n\n# Load the pre-trained face detection model\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n# Load the image\nimg = cv2.imread('image.jpg')\n\n# Convert the image to grayscale\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# Detect faces in the grayscale image\nfaces = face_cascade.detectMultiScale(gray, 1.2, 5)\n\n# Draw a rectangle around each face\nfor (x, y, w, h) in faces:\n    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\n# Display the image with the detected faces\ncv2.imshow('Face Detection', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n```\n\nIn this code, we first load the pre-trained face detection model using the `CascadeClassifier` function from OpenCV. We then load the image, convert it to grayscale, and pass it to the `detectMultiScale` function of the face detection model to detect faces. We then draw a rectangle around each face using the `rectangle` function of OpenCV. Finally, we display the image with the detected faces using the `imshow` function of OpenCV.\n\nNote: Make sure to replace 'haarcascade_frontalface_default.xml' with the path to your pre-trained face detection model.</b>"
          },
          "metadata": {}
        }
      ]
    }
  ]
}