12_2_RAG_with_Pytorch__M.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fccb77cb-5122-4f5d-8cfe-da07f16237c7"
      },
      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network\" target=\"_blank\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
        "    </a>\n",
        "</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "be11ee3b-b5bc-4e09-a3a0-de98b826d70e"
      },
      "source": [
        "# RAG with PyTorch\n",
        "Estimated time needed: **60** minutes\n",
        "\n",
        "\n",
        "\n",
        "## Introduction\n",
        "As a machine learning engineer hired by a social media company, your task is determining whether songs shared on the platform are appropriate for children. Given the high costs associated with processing each song using large language models (LLMs) for content evaluation, an alternative method using retrieval-augmented generation (RAG) is proposed. RAG combines the benefits of a retriever model, which fetches relevant information (in this case, embeddings of pre-answered content appropriateness questions), and a generator model, which uses this information to predict the appropriateness of new content. This approach efficiently scales the evaluation process while ensuring that each song's content is scrutinized for child safety without the overhead of running a full LLM for each song.\n",
        "\n",
        "![A big yellow bird puppet and a purple dinosaur puppet singing next to a rapper and a metal band singer](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/MGy9JGkmyNnnTPqNlXk2uw/The%20scene%20is%20lively%20and%20colorful.jpg)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20f907f6-4f1e-42fc-b385-a8d75b61b8b3"
      },
      "source": [
        "# __Table of Contents__\n",
        "\n",
        "<ol>\n",
        "    <li><a href=\"#Objectives\">Objectives</a></li>\n",
        "    <li>\n",
        "        <a href=\"#Setup\">Setup</a>\n",
        "        <ol>\n",
        "            <li><a href=\"#Installing-required-libraries\">Installing required libraries</a></li>\n",
        "            <li><a href=\"#Importing-required-libraries\">Importing required libraries</a></li>\n",
        "            <li><a href=\"#Defining-helper-functions\">Defining helper functions</a></li>\n",
        "        </ol>\n",
        "    </li>\n",
        "    <li>\n",
        "        <a href=\"#Embeddings-using-BERT-and-PyTorch-Hub\">Embeddings using BERT and PyTorch Hub</a>\n",
        "        <ol>\n",
        "            <li><a href=\"#Loading-tokenizer-and-model\">Loading tokenizer and model</a>\n",
        "                <ol>\n",
        "                    <li><a href=\"#Tokenization\">Tokenization</a></li>\n",
        "                    <li><a href=\"#Text-decoding-and-verification\">Text decoding and verification</a></li>\n",
        "                    <li><a href=\"#Device-and-Convert-Tensor\">Device and Convert Tensor</a></li>\n",
        "                    <li><a href=\"#Loading-the-BERT-model\">Loading the BERT model</a></li>\n",
        "                    <li><a href=\"#Generating-aggregated-mean-embeddings-using-BERT-for-RAG\">Generating aggregated mean embeddings using BERT for RAG</a></li>\n",
        "                </ol>\n",
        "            </li>\n",
        "        </ol>\n",
        "    </li>\n",
        "    <li><a href=\"#Convert-questions-and-the-song-lyrics-into-embeddings\">Convert questions and the song lyrics into embeddings</a></li>\n",
        "    <li><a href=\"#Understanding-the-dot-product\">Understanding the dot product</a></li>\n",
        "    <li>\n",
        "        <a href=\"#Exercise\">Exercise</a>\n",
        "        <ol>\n",
        "            <li><a href=\"#Task-description\">Task description</a></li>\n",
        "        </ol>\n",
        "    </li>\n",
        "</ol>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab3d9094-8d82-4787-a0b0-dc5637c75ab3"
      },
      "source": [
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa51f03c-8ce6-4ba9-8211-49d9881add28"
      },
      "source": [
        "# Objectives\n",
        "\n",
        "After completing this lab, you will be able to:\n",
        "\n",
        "- **Understand embedding techniques**: Learn how to generate and use embeddings from pre-trained models for natural language processing tasks.\n",
        "- **Use PyTorch and TorchHub**: Explore and apply functions from PyTorch and TorchHub to load and manipulate advanced machine learning models.\n",
        "- **Apply t-SNE for visualization**: Use t-SNE to visualize high-dimensional data in lower-dimensional spaces, enhancing understanding of data distribution and clustering.\n",
        "- **Fine-tuning language models**: Gain practical experience in fine-tuning pretrained language models for specific tasks, enhancing model performance on targeted datasets.\n",
        "- **Develop practical NLP solutions**: Implement a system that effectively uses Retriever and Generator architectures to answer queries, demonstrating an end-to-end workflow from model tuning to deployment.\n",
        "- **Implement cosine similarity**: Replace dot product similarity measures with cosine similarity to improve relevance detection in response generation systems.\n",
        "- **Evaluate model performance**: Assess the impact of using cosine similarity over dot product in terms of retrieval accuracy and relevance in a QA system.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eeb495e-5d82-4d11-8d72-102ff2373c10"
      },
      "source": [
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dba5af5-7cca-4d0b-b5fb-7bc6e926f3fe"
      },
      "source": [
        "# Setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92baa8af-bca9-4e8a-8168-ab152096ae85"
      },
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37234423-7231-405b-9016-86dfa0b4aaf8"
      },
      "source": [
        "### Installing required libraries\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "399280cf-715a-47a2-9ddf-c6295ceff0b3"
      },
      "source": [
        "For this lab, you will be using the following libraries. These libraries are __not__ pre-installed in the Skills Network Labs environment. __You will need to run the following cell__ to install them:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0d11770-f285-4137-a177-1b1035d0b9a0",
        "outputId": "57fdbe41-4d19-4029-ab5f-ecbc63fc1259"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install numpy==1.24.1\n",
        "!pip install torch==2.2.0\n",
        "!pip install sacremoses==0.1.1\n",
        "!pip install torchtext==0.17\n",
        "!pip install transformers==4.40.2\n",
        "!pip install matplotlib==3.8.4\n",
        "!pip install sentencepiece==0.2.0\n",
        "!pip install scikit-learn==1.4.2"
      ],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b186ff6-366f-49b0-9171-96c901da9473"
      },
      "source": [
        "### Importing required libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e26a057e-712a-42a7-9427-cca434ae99fa"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# You can also use this section to suppress warnings generated by your code:\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5a1ab168-3633-4d6c-ad35-1bdac9abaaca"
      },
      "source": [
        "### Defining helper functions\n",
        "\n",
        "The `tsne_plot` function applies t-SNE to reduce high-dimensional data to three dimensions and creates a 3D scatter plot of the results. It sets the `perplexity` to the number of data points minus one, plots each point in a unique color based on its index, and labels the axes for each t-SNE component. The visualization helps in exploring patterns in a 3D space.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b39096d-6be0-4955-a7eb-0f47672ff147"
      },
      "outputs": [],
      "source": [
        "def tsne_plot(data, plot):\n",
        "    # Apply t-SNE to reduce to 3D\n",
        "    tsne = TSNE(n_components=3, random_state=42, perplexity=min(50, data.shape[0] - 1))  # Using 50 or less based on data size\n",
        "    data_3d = tsne.fit_transform(data)\n",
        "\n",
        "    # Plotting\n",
        "    fig = plt.figure(figsize=(10, 7))\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "    # Assign colors for each point based on its index\n",
        "    colors = plt.cm.rainbow(np.linspace(0, 1, len(data_3d)))\n",
        "    for idx, point in zip(range(len(data_3d)), data_3d):\n",
        "        ax.scatter(point[0], point[1], point[2], color=colors[idx], label=f'{plot} {idx+1}')\n",
        "\n",
        "    # Adding labels and titles\n",
        "    ax.set_xlabel('TSNE Component 1')\n",
        "    ax.set_ylabel('TSNE Component 2')\n",
        "    ax.set_zlabel('TSNE Component 3')\n",
        "    plt.title('3D t-SNE Visualization of '+ plot +' Embeddings')\n",
        "    plt.legend(title=plot +' Index', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "    plt.show()"
      ],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8e3f53d3-28e5-478e-b73d-df7c3c79cc1c"
      },
      "source": [
        "# Embeddings using BERT and PyTorch Hub\n",
        "\n",
        "Use PyTorch and the Transformers library by Hugging Face to tokenize text, convert it to embeddings using BERT, and handle these embeddings with a model.\n",
        "\n",
        "## Loading tokenizer and model\n",
        "Let's begin by loading a tokenizer and later a model, both specifically bert-base-uncased. This is done using torch.hub.load, which is a convenient way to load pre-trained models and tokenizers directly from Hugging Face's model hub.\n"
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
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        "outputId": "7c83cb90-693b-4cc8-bab7-09c7fdcb2531"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/huggingface/pytorch-transformers/zipball/main\" to /root/.cache/torch/hub/main.zip\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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          "output_type": "display_data",
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              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cec2a3d74ba04d4788a8a3b7e27b8e98"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased')"
      ],
      "execution_count": 4
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77f7d8b9-95b2-4ebc-9c7a-62cc61551406"
      },
      "source": [
        "The input_text variable is set as a list of tuples.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21408fbe-2c6b-4bf1-b6d5-2471b91b5396"
      },
      "outputs": [],
      "source": [
        "# Input text to get embeddings for\n",
        "input_text = [(\"This is an example sentence for BERT embeddings.\", \"How do you like it \"),(\"There are other models\")]"
      ],
      "execution_count": 5
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7fc590e-bc2d-4c31-8a46-9fed6ca3299a"
      },
      "source": [
        "### Tokenization\n",
        "\n",
        "```batch_encode_plus``` method is used for tokenizing text. It automatically handles padding and truncation to ensure uniformity in input length, which is crucial for batch processing in models like BERT.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b71b8ab5-8c4f-4a41-bc86-8db8ebec8b62",
        "outputId": "1d9aa4fe-dc9f-4fd8-f9d1-7cd9b513ad4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[101, 2023, 2003, 2019, 2742, 6251, 2005, 14324, 7861, 8270, 4667, 2015, 1012, 102, 2129, 2079, 2017, 2066, 2009, 102], [101, 2045, 2024, 2060, 4275, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "input_ids = tokenizer.batch_encode_plus(input_text,add_special_tokens=True,padding=True,truncation=True)\n",
        "input_ids"
      ],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2a6173d-b66e-4dfd-97d7-068ced294ca0"
      },
      "source": [
        "### Text decoding and verification\n",
        "After tokenization, the script decodes the first tokenized input back to text to check the correctness and length of the tokenization process.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb85ef12-0e72-47de-966b-77bf36ffdfb7",
        "outputId": "11c94a68-6704-4fde-d06a-1924eae435c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] this is an example sentence for bert embeddings. [SEP] how do you like it [SEP]\n",
            "length 16\n"
          ]
        }
      ],
      "source": [
        "text=tokenizer.decode(input_ids['input_ids'][0])\n",
        "print(text)\n",
        "print(f\"length {len(text.split())}\")"
      ],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c154fc5a-ce63-4cd5-9da4-4a31eff462b5",
        "outputId": "65cc5a6a-8185-4f5a-8dc3-1fbed503513e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              " [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "input_ids['attention_mask']"
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74608dc5-a56d-4da4-a84f-0c180dafb110"
      },
      "source": [
        "\n",
        "When using the BERT tokenizer, the output includes key components that are essential for the model's processing:\n",
        "\n",
        "```input_ids```: A list of token IDs that represent each token in BERT's vocabulary.\n",
        "\n",
        "```token_type_ids```: Indicates which sentence each token belongs to, important for tasks involving sentence pairs.\n",
        "\n",
        "```attention_mask```: Identifies which tokens should be focused on, differentiating real content from padding.\n",
        "\n",
        "\n",
        "Special tokens:\n",
        "\n",
        "[CLS]: Placed at the start of every input for use in classification tasks.\n",
        "\n",
        "[SEP]: Separates sentences in dual-sentence tasks and marks the end of input sequences.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05a38cec-186d-4426-ae22-903930b22ce6"
      },
      "source": [
        "### Device and Convert Tensor\n",
        "Here, you are going to convert the token IDs and attention masks into PyTorch tensors and transfers them to a computing device (DEVICE) for input into BERT. This device should be defined elsewhere in the script (typically as \"cuda\" for GPU or \"cpu\").\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75fde995-9aa6-4c04-b57d-59c6d4fa5715",
        "outputId": "6e919a03-e4fe-4302-fb30-139ddcce6c2f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "DEVICE"
      ],
      "execution_count": 9
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38109243-8549-48c5-9b50-0cf8ccceba8f"
      },
      "outputs": [],
      "source": [
        "input_ids_tensors = torch.tensor(input_ids['input_ids']).to(DEVICE)\n",
        "mask_tensors = torch.tensor(input_ids['attention_mask']).to(DEVICE)"
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4b563803-bcb9-42ca-8ba5-96920b9794fe"
      },
      "source": [
        "### Loading the BERT model\n",
        "Now, load the BERT model and move it to the same device as the input tensors. The model then processes these inputs to generate embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        },
        "id": "d58bfcf6-e9c5-431b-8b45-2a96028ff6b8",
        "outputId": "b61a1304-1e87-43c1-f3e6-a8ad8e38728e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/huggingface_pytorch-transformers_main\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f9570b76603438baf94e1b2779119fd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "bert_model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased')"
      ],
      "execution_count": 11
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74294572-5d3c-46af-b3ab-a5382fa73b09",
        "outputId": "f69c3184-e273-4c19-c880-fc1c318e295f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 768)\n",
              "    (token_type_embeddings): Embedding(2, 768)\n",
              "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0-11): 12 x BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "bert_model.to(DEVICE)"
      ],
      "execution_count": 12
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adb5a2de-2fd2-4a94-b565-723e321aedb6"
      },
      "outputs": [],
      "source": [
        "word_embding=bert_model(input_ids_tensors,mask_tensors)"
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32d1f054-4f4a-4da2-bb30-4382a6198d19"
      },
      "source": [
        "### Generating aggregated mean embeddings using BERT for RAG\n",
        "Here, you'll compute aggregated mean embeddings for input sequences using the BERT model you just loaded. It processes each pair of token IDs and attention masks from the input data, extracts word embeddings for non-padded tokens, and calculates their mean. The result is a list of mean embeddings for each sequence, which is then concatenated into a single tensor. This process allows for the generation of simplified yet informative representations of the input sequences, useful for tasks like clustering, similarity search, or as input to downstream models. Each document must be under 512 tokens.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7dd3b9e-d7b1-44af-a265-19c0d1c9a228",
        "outputId": "b775f047-4a62-4525-c581-7f217d997647"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00,  6.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "token_ids_tensor shape: torch.Size([1, 20]) torch.Size([1, 20])\n",
            "Word embeddings shape: torch.Size([20, 768])\n",
            "Number of zero padding embeddings: 0\n",
            "valid_embeddings_mask: tensor([True, True, True, True, True, True, True, True, True, True, True, True,\n",
            "        True, True, True, True, True, True, True, True])\n",
            "Word embeddings after zero padding embeddings removed: torch.Size([20, 768])\n",
            "Mean embedding shape: torch.Size([768])\n",
            "token_ids_tensor shape: torch.Size([1, 20]) torch.Size([1, 20])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2it [00:00,  6.84it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word embeddings shape: torch.Size([20, 768])\n",
            "Number of zero padding embeddings: 14\n",
            "valid_embeddings_mask: tensor([ True,  True,  True,  True,  True,  True, False, False, False, False,\n",
            "        False, False, False, False, False, False, False, False, False, False])\n",
            "Word embeddings after zero padding embeddings removed: torch.Size([6, 768])\n",
            "Mean embedding shape: torch.Size([768])\n",
            "All mean embeddings shape: torch.Size([2, 768])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Initialize a list to store the mean embeddings for each input sequence\n",
        "aggregated_mean_embeddings = []\n",
        "\n",
        "# Loop over each pair of input_ids and attention_masks\n",
        "for token_ids, attention_mask in tqdm(zip(input_ids['input_ids'], input_ids['attention_mask'])):\n",
        "    # Convert list of token ids and attention mask to tensors\n",
        "    token_ids_tensor = torch.tensor([token_ids]).to(DEVICE)\n",
        "    attention_mask_tensor = torch.tensor([attention_mask]).to(DEVICE)\n",
        "    print(\"token_ids_tensor shape:\",token_ids_tensor.shape, attention_mask_tensor.shape)  # Print the shapes of the input tensors\n",
        "    with torch.no_grad():  # Disable gradient calculations for faster execution\n",
        "        # Retrieve the batch of word embeddings from the BERT model\n",
        "        embeddings = bert_model(token_ids_tensor, attention_mask=attention_mask_tensor)[0].squeeze(0)\n",
        "        print(\"Word embeddings shape:\", embeddings.shape)\n",
        "\n",
        "        # Count and print the number of zero-padding embeddings\n",
        "        num_zero_paddings = (attention_mask_tensor == 0).sum().item()\n",
        "        print(\"Number of zero padding embeddings:\", num_zero_paddings)\n",
        "\n",
        "        # Create a mask for positions that are not zero-padded\n",
        "        valid_embeddings_mask = attention_mask_tensor[0] != 0\n",
        "        print(\"valid_embeddings_mask:\",valid_embeddings_mask)\n",
        "\n",
        "        # Filter out the embeddings corresponding to zero-padded positions\n",
        "        filtered_embeddings = embeddings[valid_embeddings_mask, :]\n",
        "        print(\"Word embeddings after zero padding embeddings removed:\", filtered_embeddings.shape)\n",
        "\n",
        "        # Compute the mean of the filtered embeddings\n",
        "        mean_embedding = filtered_embeddings.mean(axis=0)\n",
        "        print(\"Mean embedding shape:\", mean_embedding.shape)\n",
        "\n",
        "        # Append the mean embedding to the list, adding a batch dimension\n",
        "        aggregated_mean_embeddings.append(mean_embedding.unsqueeze(0))\n",
        "\n",
        "# Concatenate all mean embeddings to form a single tensor\n",
        "aggregated_mean_embeddings = torch.cat(aggregated_mean_embeddings)\n",
        "print('All mean embeddings shape:', aggregated_mean_embeddings.shape)"
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77636f19-ea82-4379-8806-326837a4ea2f"
      },
      "source": [
        "The above code is converted to the function ```aggregate_embeddings``` that takes token indices and their corresponding attention masks, and uses a BERT model to convert these tokens into word embeddings. It then filters out the embeddings for zero-padded tokens and computes the mean embedding for each sequence. This helps in reducing the dimensionality of the data while retaining the most important information from the embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd167e71-90c7-487c-91d2-21d1b4322b47"
      },
      "outputs": [],
      "source": [
        "def aggregate_embeddings(input_ids, attention_masks, bert_model=bert_model):\n",
        "    \"\"\"\n",
        "    Converts token indices and masks to word embeddings, filters out zero-padded embeddings,\n",
        "    and aggregates them by computing the mean embedding for each input sequence.\n",
        "\n",
        "    \"\"\"\n",
        "    mean_embeddings = []\n",
        "    # Process each sequence in the batch\n",
        "    print('number of inputs',len(input_ids))\n",
        "    for input_id, mask in tqdm(zip(input_ids, attention_masks)):\n",
        "        input_ids_tensor = torch.tensor([input_id]).to(DEVICE)\n",
        "        mask_tensor = torch.tensor([mask]).to(DEVICE)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            # Obtain the word embeddings from the BERT model\n",
        "            word_embeddings = bert_model(input_ids_tensor, attention_mask=mask_tensor)[0].squeeze(0)\n",
        "\n",
        "            # Filter out the embeddings at positions where the mask is zero\n",
        "            valid_embeddings_mask=mask_tensor[0] != 0\n",
        "            valid_embeddings = word_embeddings[valid_embeddings_mask,:]\n",
        "            # Compute the mean of the filtered embeddings\n",
        "            mean_embedding = valid_embeddings.mean(dim=0)\n",
        "            mean_embeddings.append(mean_embedding.unsqueeze(0))\n",
        "\n",
        "    # Concatenate the mean embeddings from all sequences in the batch\n",
        "    aggregated_mean_embeddings = torch.cat(mean_embeddings)\n",
        "    return aggregated_mean_embeddings"
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "695fee4a-d899-42e6-a38b-fc73e5f01a6a"
      },
      "source": [
        "Let's further simplify the process with the function ```text_to_emb``` which is designed to convert a list of text strings into their corresponding embeddings using a pre-defined tokenizer and calling ```aggregate_embeddings```.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e397573-1efd-499b-bbfd-6c4a37bad0dd"
      },
      "outputs": [],
      "source": [
        "def text_to_emb(list_of_text,max_input=512):\n",
        "    data_token_index  = tokenizer.batch_encode_plus(list_of_text, add_special_tokens=True,padding=True,truncation=True,max_length=max_input)\n",
        "    question_embeddings=aggregate_embeddings(data_token_index['input_ids'], data_token_index['attention_mask'])\n",
        "    return question_embeddings"
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "590c53cf-9126-4ef9-a1c8-1b76c3930235"
      },
      "source": [
        "# Convert questions and the song lyrics into embeddings\n",
        "\n",
        "Let's first convert both the predefined appropriateness questions and the song lyrics into \"RAG embeddings\". Then, you measure the similarity between these embeddings to determine appropriateness:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "813832da-7b7c-4670-b127-9749dfe44406"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "def process_song(song):\n",
        "    # Remove line breaks from the song\n",
        "    song_new = re.sub(r'[\\n]', ' ', song)\n",
        "\n",
        "    # Remove single quotes from the song\n",
        "    song_new = [song_new.replace(\"\\'\", \"\")]\n",
        "\n",
        "    return song_new"
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "804e579f-fc2f-4656-beaf-ae5e877e3c2f"
      },
      "source": [
        "Here you can find more such sample questions.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ae90bee-28eb-49dd-b14a-53ca4e5c5a30"
      },
      "outputs": [],
      "source": [
        "song_questions = [\n",
        "    \"Does this song contain any violent themes, such as references to guns, killing, or physical aggression? Example: Does the song describe or promote physical violence, like fighting or shootings?\",\n",
        "    \"Are there any explicit lyrics or bad words used in this song that might be considered offensive or inappropriate? Example: Does the song use language commonly recognized as profanity or derogatory terms?\",\n",
        "    \"Is the overall content of this song suitable for children, considering its themes, language, and messages? Example: Are there elements in the song that could be deemed too mature or unsuitable for young listeners?\",\n",
        "    \"Does this song explicitly mention weapons, such as guns, knives, or other similar items? Example: Are specific types of weapons described or glorified in the lyrics?\",\n",
        "    \"Are the messages conveyed in this song positive and uplifting for children? Example: Does the song promote values like kindness, friendship, and positivity?\",\n",
        "    \"Does this song include any sexual content, references to sexual behavior, or suggestive language? Example: Are there lyrics that explicitly or implicitly discuss sexual themes or experiences?\",\n",
        "    \"Does this song offer any educational value, such as teaching the alphabet, basic math, or other learning content? Example: Are there educational segments in the song that could help children learn fundamental skills like the ABCs or counting?\",\n",
        "    \"Does this song promote emotional resilience and social skills among children? Example: Does the song include themes of overcoming challenges or building friendships?\"\n",
        "]"
      ],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbc00cbf-be6a-4b30-9477-0b86c7a350b7"
      },
      "source": [
        "Let's convert the questions to embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ddf1ac6-060b-45f3-975c-c3a503c0bb9a",
        "outputId": "74f2f144-c53b-42cd-e7aa-84b3cc7e4593"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "8it [00:01,  4.42it/s]\n"
          ]
        }
      ],
      "source": [
        "embeddings_questions=street=text_to_emb(song_questions)"
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ea69c58e-b5ec-49cd-99c3-07f9eb0529a1"
      },
      "source": [
        "Let's visualize the question embeddings through a t-SNE plot.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "35bc7ad4-0d91-4ae6-b478-609c3bcecafd",
        "outputId": "00e35b6d-1665-46eb-d828-a78a09b77d7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tsne_plot(embeddings_questions, \"Question\")"
      ],
      "execution_count": 20
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09be920d-d893-493f-8f06-35e1ba9021d2"
      },
      "source": [
        "Let's also develop a set of responses when a song has similar attributes.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3da8b001-2db8-4a95-b10c-4b5029442a4e"
      },
      "outputs": [],
      "source": [
        "yes_responses = [\n",
        "    \"Yes, this song contains violent themes, including references to guns, killing, or physical aggression, and is not suitable for children.\",\n",
        "    \"Yes, this song includes explicit lyrics or bad words that might be considered offensive or inappropriate for young audiences.\",\n",
        "    \"No, the overall content of this song is not suitable for children as it includes themes, language, and messages that are too mature or unsuitable for young listeners.\",\n",
        "    \"Yes, this song explicitly mentions weapons, such as guns and knives, which could be disturbing or inappropriate for children’s entertainment.\",\n",
        "    \"Yes, the messages conveyed in this song are positive and uplifting, promoting values like kindness, friendship, and positivity, beneficial for children.\",\n",
        "    \"Yes, this song includes sexual content and references to sexual behavior or suggestive language, which are inappropriate for a child-friendly environment.\",\n",
        "    \"Yes, this song offers significant educational value, including segments that teach the alphabet, basic math, and other learning content, making it both fun and educational for children.\",\n",
        "    \"Yes, this song promotes emotional resilience and social skills, incorporating themes about overcoming challenges and building friendships, which are essential for children's development.\"\n",
        "]"
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18b2d804-1a55-4d11-9002-a431a46dc63b"
      },
      "source": [
        "Convert the responses to their embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d2df857-4149-4216-981f-fa8a2cfe3f24",
        "outputId": "9e7ebc35-5fe7-4b4a-b685-98e5febd5234"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "8it [00:01,  5.46it/s]\n"
          ]
        }
      ],
      "source": [
        "embeddings_responses = text_to_emb(yes_responses)"
      ],
      "execution_count": 22
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6957f7e5-fcc2-47c6-9f8d-aac563c4e696"
      },
      "source": [
        "Visualize the responses embeddings through a t-SNE plot.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "f0b0c66a-7fa1-49a5-b1c5-849a4ac99f8e",
        "outputId": "270ceab0-ddf9-4bd8-ffe7-02db3ef08b62"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tsne_plot(embeddings_responses, \"Response\")"
      ],
      "execution_count": 23
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0a4c871-1cac-4d10-915c-afa6151d9df7"
      },
      "source": [
        "Consider the song [Bullet in the Head](https://en.wikipedia.org/wiki/Bullet_in_the_Head_(song)) by the activist group 'Rage Against the Machine'. The band portrays the government's use of media as a tool for population control, drawing stark comparisons between ordinary residents and Alcatraz. This song is clearly geared towards adults and is not appropriate for children due to the explicit language and violent rhetoric, which could be confusing and unsuitable for younger audiences.\n",
        "\n",
        "<span style=\"color:red\">Note: To browse the lyrics you can go to [Bullet in the Head](https://www.google.com/search?q=bullet+in+your+head+lyrics&rlz=1C5GCCM_en&oq=bulet+in+you+head+l&gs_lcrp=EgZjaHJvbWUqCQgBEAAYDRiABDIGCAAQRRg5MgkIARAAGA0YgAQyCQgCEC4YDRiABDIJCAMQABgNGIAEMggIBBAAGBYYHjIICAUQABgWGB4yCAgGEAAYFhgeMggIBxAAGBYYHjIICAgQABgWGB4yCAgJEAAYFhge0gEINjM1NGowajeoAgCwAgA&sourceid=chrome&ie=UTF-8) and then paste the content of the song in below cell and process it as other songs. Also, note that the song lyrics is extremely abusive</span>\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82a54ad1-814f-478e-92c4-297536adf0fe"
      },
      "outputs": [],
      "source": [
        "song_rage = \"\"\"\n",
        "\"\"\""
      ],
      "execution_count": 24
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1168cdc-0318-4acf-9d36-5fc49a230306"
      },
      "source": [
        "After some minor preprocessing of the text, you can generate the RAG embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e5e06e1-41c5-4e64-a3c7-fc065ed6c021",
        "outputId": "65b0499a-4239-420d-8faa-c01c43fe9470"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00, 11.18it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "song_rage=process_song(song_rage)\n",
        "embeddings_rage=text_to_emb(song_rage)\n",
        "embeddings_rage.shape"
      ],
      "execution_count": 25
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ff72aa95-df6d-4177-bf2d-70c2895010e8"
      },
      "source": [
        "Now consider the song [Can You Tell Me How to Get to Sesame Street?](https://en.wikipedia.org/wiki/Can_You_Tell_Me_How_to_Get_to_Sesame_Street%3F) — the introductory theme song of a popular children's show. Similar to previous methods, the text is processed to generate RAG embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adaaf194-0894-4a4f-9a52-bf4ea7c5d518"
      },
      "outputs": [],
      "source": [
        "sesame_street = \"\"\"\n",
        "Sunny day\n",
        "Sweepin' the clouds away\n",
        "On my way to where the air is sweet\n",
        "Can you tell me how to get\n",
        "How to get to Sesame Street?\n",
        "\n",
        "Come and play\n",
        "Everything's A-okay\n",
        "Friendly neighbors there\n",
        "That's where we meet\n",
        "Can you tell me how to get\n",
        "How to get to Sesame Street?\n",
        "\n",
        "It's a magic carpet ride\n",
        "Every door will open wide\n",
        "To happy people like you\n",
        "Happy people like\n",
        "What a beautiful\n",
        "\n",
        "Sunny day\n",
        "Sweepin' the clouds away\n",
        "On my way to where the air is sweet\n",
        "Can you tell me how to get\n",
        "How to get to Sesame Street?\n",
        "How to get to Sesame Street?\n",
        "How to get to Sesame Street?\n",
        "How to get to Sesame Street?\n",
        "How to get to Sesame Street?\n",
        "\"\"\""
      ],
      "execution_count": 26
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c23fb9b8-0f3e-4545-9aea-b3fcab27cb2a",
        "outputId": "10e818e8-5f3e-41e9-b873-ae55aa8f141e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00,  1.73it/s]\n"
          ]
        }
      ],
      "source": [
        "song_sesame_street= process_song(sesame_street)\n",
        "embeddings_sesame_street=text_to_emb(song_sesame_street)"
      ],
      "execution_count": 27
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1e71cb4-7c6c-4a70-b92a-1a900f04fcf7"
      },
      "source": [
        "Finally, you have the song [Straight Outta Compton](https://en.wikipedia.org/wiki/Straight_Outta_Compton) — a track known for its intense themes, which nearly led to it being banned in the U.S. As with the previous examples, process the lyrics to calculate the word embeddings for a detailed analysis of its content.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58db57a8-77eb-40e2-bdb3-af7f750d391c"
      },
      "source": [
        "<span style=\"color:red\">Note: To browse the lyrics you can go to [Straight Outta Compton Lyrics](https://www.google.com/search?q=straight+outta+compton+song+lyrics&sca_esv=4f737f0b4de0cae7&sca_upv=1&rlz=1C5GCEM_enUS1092CA1092&sxsrf=ADLYWIIPASIwexxpdt-VqsvSF759A-iolw%3A1724787162733&ei=2inOZsW0LNSp5NoPq_XR6Qw&ved=0ahUKEwjFiZfx9JWIAxXUFFkFHat6NM0Q4dUDCA8&uact=5&oq=straight+outta+compton+song+lyrics&gs_lp=Egxnd3Mtd2l6LXNlcnAiInN0cmFpZ2h0IG91dHRhIGNvbXB0b24gc29uZyBseXJpY3MyCxAAGIAEGJECGIoFMgYQABgWGB4yBhAAGBYYHjIGEAAYFhgeMgsQABiABBiGAxiKBTILEAAYgAQYhgMYigUyCxAAGIAEGIYDGIoFMggQABiABBiiBDIIEAAYgAQYogQyCBAAGKIEGIkFSOQjUPEEWKghcAF4AZABAJgBqQGgAeAKqgEDNi42uAEDyAEA-AEBmAINoAKEC8ICChAAGLADGNYEGEfCAg0QABiABBiwAxhDGIoFwgIOEAAYsAMY5AIY1gTYAQHCAhMQLhiABBiwAxhDGMgDGIoF2AECwgIFEAAYgATCAg0QLhiABBixAxhDGIoFwgIKEAAYgAQYQxiKBcICChAuGIAEGEMYigXCAgUQLhiABMICHRAuGIAEGJcFGNwEGN4EGOAEGPQDGPEDGPUD2AEDwgIIEAAYFhgKGB6YAwCIBgGQBhK6BgYIARABGAm6BgYIAhABGAi6BgYIAxABGBSSBwM1LjigB7GKAQ&sclient=gws-wiz-serp) and then paste the content of the song in below cell and process it as other songs. Also, note that the song lyrics is extremely abusive</span>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd458f0c-30dc-4e61-9dd7-7a39d751aea7"
      },
      "outputs": [],
      "source": [
        "straight_outta_compton_lyrics = \"\"\"\n",
        "\"\"\""
      ],
      "execution_count": 28
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0b3687c4-64c6-4d8c-8c5b-9b20b9d8d62a",
        "outputId": "702618a0-4dc7-4ab9-906d-d96d9e7c6b08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00, 12.51it/s]\n"
          ]
        }
      ],
      "source": [
        "straight_outta_compton_lyrics= process_song(straight_outta_compton_lyrics)\n",
        "embeddings_compton=text_to_emb(straight_outta_compton_lyrics)"
      ],
      "execution_count": 29
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14b1470f-2c97-401e-b4e8-aefe16efa2cf"
      },
      "source": [
        "Let's consider one more song.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "155113a2-6334-4f96-88f6-355e2bc5859a"
      },
      "outputs": [],
      "source": [
        "my_shoe_lyrics=\"\"\"Barney is a dinosaur from our imagination\n",
        "And when he's tall\n",
        "He's what we call a dinosaur sensation\n",
        "Barney's friends are big and small\n",
        "They come from lots of places\n",
        "After school they meet to play\n",
        "And sing with happy faces\n",
        "Barney shows us lots of things\n",
        "Like how to play pretend\n",
        "ABC's, and 123's\n",
        "And how to be a friend\n",
        "Barney comes to play with us\n",
        "Whenever we may need him\n",
        "Barney can be your friend too\n",
        "If you just make-believe him!\"\"\""
      ],
      "execution_count": 30
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cde4bd7-8bc5-492b-9011-aeb4b1babac0",
        "outputId": "b20c2d13-fe69-4524-e0b1-e8161b15c558"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00,  2.41it/s]\n"
          ]
        }
      ],
      "source": [
        "my_shoe_lyrics= process_song(my_shoe_lyrics)\n",
        "embeddings_my_shoe=text_to_emb(my_shoe_lyrics)"
      ],
      "execution_count": 31
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "329024e6-85e9-48e0-9a63-d901cd1e3793"
      },
      "source": [
        "Let's create a single array from the embeddings of the songs you have till now and plot it through the tsne_plot\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dd6ade8-e437-4541-8e14-6dc9202045f4",
        "outputId": "ba39ff4a-5702-4f34-aa1b-b1b2f5fc87a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00, 13.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00,  1.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00, 11.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of inputs 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:00,  2.41it/s]\n"
          ]
        }
      ],
      "source": [
        "songs = [song_rage, song_sesame_street, straight_outta_compton_lyrics, my_shoe_lyrics]\n",
        "embeddings = [text_to_emb(song) for song in songs]\n",
        "all_embeddings = np.vstack(embeddings)"
      ],
      "execution_count": 32
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "866b2688-95b1-47da-8bf1-2522bff55579",
        "outputId": "605901ce-fbda-411a-d526-3df57b34b0ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tsne_plot(all_embeddings, \"Song\")"
      ],
      "execution_count": 33
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b558fcfa-49f8-4416-b5fa-0a68c043abf9"
      },
      "source": [
        "# Understanding the dot product\n",
        "\n",
        "The dot product of two vectors, $\\mathbf{a}$ and $\\mathbf{b}$, each with components $a_i$ and $b_i$, is a fundamental operation in linear algebra calculated as follows:\n",
        "\n",
        "$\\mathbf{a} \\cdot \\mathbf{b} = \\sum_{i=1}^{n} a_i b_i$\n",
        "\n",
        "This calculation results in a single scalar value. Geometrically, the dot product quantifies how much one vector projects onto another or how close they are to eachother.\n",
        "\n",
        "Now, consider the matrix $A$, where each row is the RAG embeddings. In the context of matrix multiplication involving a dot product, if $A$ is a matrix and $\\mathbf{b}$ is a vector, then $A\\mathbf{b}$ computes the dot product of $\\mathbf{b}$ with each row of $A$. In this scenario, each vector in $A$ is treated as a row vector. Here, $A$ represents `embeddings_questions`, while `embeddings_rage` is the RAG vector $\\mathbf{b}$. The output is a column tensor that measures how closely each RAG vector for the questions aligns with the song.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be2e6201-d9a7-4ee3-b7d1-d0496823e00b"
      },
      "outputs": [],
      "source": [
        "dot_product = embeddings_questions@embeddings_rage.T"
      ],
      "execution_count": 34
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d57edb02-37ea-4229-a0f0-f9738efd68c4"
      },
      "source": [
        "Reshape the tensor for processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22390f32-c113-43cb-bffa-4353c056a3b9"
      },
      "outputs": [],
      "source": [
        "dot_product = dot_product.reshape(-1)"
      ],
      "execution_count": 35
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3464fc6-aece-4f22-8b90-e21d011623fc"
      },
      "source": [
        "You can find the questions that are closest to the song by sorting them and converting the result to a list.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ee9e7d6-1c75-4d32-8e75-aef7fd0ee21a"
      },
      "outputs": [],
      "source": [
        "sorted_indices = torch.argsort(dot_product,descending=True)\n",
        "sorted_indices=sorted_indices.tolist()"
      ],
      "execution_count": 36
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0c42470-5aef-4ce2-b44d-b0b323a20991"
      },
      "source": [
        "The nearest vectors corresponding to the best response can be printed in the following manner. For simplicity, use the topmost response using ``yes_responses``.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49828a80-44b0-4c6e-b656-073cfd7a363a",
        "outputId": "61943acc-0871-4c81-f439-b8eb493cd29d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, this song includes sexual content and references to sexual behavior or suggestive language, which are inappropriate for a child-friendly environment.\n",
            "Yes, this song contains violent themes, including references to guns, killing, or physical aggression, and is not suitable for children.\n",
            "Yes, this song explicitly mentions weapons, such as guns and knives, which could be disturbing or inappropriate for children’s entertainment.\n"
          ]
        }
      ],
      "source": [
        "for indices in  sorted_indices[0:3]:\n",
        "    print(yes_responses[indices])"
      ],
      "execution_count": 37
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e0a3ee7-e9a6-4ed6-981e-f214457330c7"
      },
      "source": [
        "Now, convert the above to a function where you only input the embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1921ddcb-bcd4-4425-bb4e-4b61b49da7d9"
      },
      "outputs": [],
      "source": [
        "def RAG_QA(embeddings_questions, embeddings, n_responses=3):\n",
        "    # Calculate the dot product between the question embeddings and the provided embeddings (transpose of the second matrix for proper alignment).\n",
        "    dot_product = embeddings_questions @ embeddings.T\n",
        "\n",
        "    # Reshape the dot product results to a 1D tensor for easier processing.\n",
        "    dot_product = dot_product.reshape(-1)\n",
        "\n",
        "    # Sort the indices of the dot product results in descending order (setting descending to False should be True for typical similarity tasks).\n",
        "    sorted_indices = torch.argsort(dot_product, descending=True)\n",
        "\n",
        "    # Convert sorted indices to a list for easier iteration.\n",
        "    sorted_indices = sorted_indices.tolist()\n",
        "\n",
        "    # Print the top 'n_responses' responses from the sorted list, which correspond to the highest dot product values.\n",
        "    for index in sorted_indices[:n_responses]:\n",
        "        print(yes_responses[index])"
      ],
      "execution_count": 38
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7065bd7-9eb5-4a51-8149-480604d68ca1"
      },
      "source": [
        "Now, use the ```RAG_QA``` for the songs defined above to see the top three responses by RAG.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efb6d686-a0ae-4045-8fb7-933041c452ae",
        "outputId": "a48ad201-fa4a-46ee-e070-8509db836548"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, the messages conveyed in this song are positive and uplifting, promoting values like kindness, friendship, and positivity, beneficial for children.\n",
            "Yes, this song offers significant educational value, including segments that teach the alphabet, basic math, and other learning content, making it both fun and educational for children.\n",
            "Yes, this song includes explicit lyrics or bad words that might be considered offensive or inappropriate for young audiences.\n"
          ]
        }
      ],
      "source": [
        "RAG_QA(embeddings_questions, embeddings_sesame_street)"
      ],
      "execution_count": 39
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fa5c1b0b-7417-4235-934c-8ceea1b44508",
        "outputId": "a8623a8b-d912-49a3-86ee-83e5a32e9ed2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, this song includes sexual content and references to sexual behavior or suggestive language, which are inappropriate for a child-friendly environment.\n",
            "Yes, this song contains violent themes, including references to guns, killing, or physical aggression, and is not suitable for children.\n",
            "Yes, this song explicitly mentions weapons, such as guns and knives, which could be disturbing or inappropriate for children’s entertainment.\n"
          ]
        }
      ],
      "source": [
        "RAG_QA(embeddings_questions, embeddings_compton)"
      ],
      "execution_count": 40
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e1ec730-fd3d-4bdc-af77-a4691bc25b1c",
        "outputId": "4c655fe1-a112-42c7-977e-bb9944def026"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, this song offers significant educational value, including segments that teach the alphabet, basic math, and other learning content, making it both fun and educational for children.\n",
            "Yes, this song includes explicit lyrics or bad words that might be considered offensive or inappropriate for young audiences.\n",
            "Yes, the messages conveyed in this song are positive and uplifting, promoting values like kindness, friendship, and positivity, beneficial for children.\n"
          ]
        }
      ],
      "source": [
        "RAG_QA(embeddings_questions, embeddings_my_shoe)"
      ],
      "execution_count": 41
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "db5b59bf-3a62-4441-b1d5-59175e929369"
      },
      "source": [
        "## Exercise\n",
        "\n",
        "In many machine learning and natural language processing tasks, measuring the similarity between vectors is crucial. While the dot product is a common measure, it considers both the magnitude and direction of the vectors. In contrast, **cosine similarity** measures the cosine of the angle between two vectors, providing a similarity value that purely reflects their orientation in space regardless of their magnitude. This makes cosine similarity particularly useful in text processing where only the directionality of the vectors (i.e., the orientation of the words in the vector space) matters, not their length.\n",
        "\n",
        "Your task is to modify the `RAG_QA()` function, which currently uses the dot product to find the most relevant responses to a given query. You will replace the dot product with cosine similarity to improve the function's ability to determine response relevance based solely on the direction of the vectors.\n",
        "\n",
        "### Task description\n",
        "1. **Modify the function**: Replace the dot product calculation in the `RAG_QA()` function with cosine similarity. Remember, cosine similarity is defined as:\n",
        "\n",
        "   $\n",
        "   \\text{Cosine Similarity} = \\frac{\\mathbf{A} \\cdot \\mathbf{B}}{\\|\\mathbf{A}\\| \\|\\mathbf{B}\\|}\n",
        "   $\n",
        "\n",
        "   where:\n",
        "   - $\\mathbf{A} \\cdot \\mathbf{B}$ is the dot product of vectors $\\mathbf{A}$ and $\\mathbf{B}$,\n",
        "   - $\\|\\mathbf{A}\\|$ and $\\|\\mathbf{B}\\|$ are the norms (or magnitudes) of vectors $\\mathbf{A}$ and $\\mathbf{B}$ respectively.\n",
        "\n",
        "2. **Calculate norms**: Compute the norms of the question embeddings and the response embeddings.\n",
        "\n",
        "3. **Compute cosine similarity**: Adjust the computation of similarity by dividing the dot product by the product of the norms of the question and response embeddings.\n",
        "\n",
        "4. **Sort and select responses**: After computing the cosine similarities, sort the responses based on these values and select the top responses as done currently with the dot product.\n"
      ]
    },
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          "text": [
            "Yes, this song offers significant educational value, including segments that teach the alphabet, basic math, and other learning content, making it both fun and educational for children.\n",
            "Yes, the messages conveyed in this song are positive and uplifting, promoting values like kindness, friendship, and positivity, beneficial for children.\n",
            "No, the overall content of this song is not suitable for children as it includes themes, language, and messages that are too mature or unsuitable for young listeners.\n"
          ]
        }
      ],
      "source": [
        "# Write your code here\n",
        "\n",
        "def RAG_QA_cosine(embeddings, n_responses=3):\n",
        "    # Calculate the magnitudes (norms) of the question and response embeddings\n",
        "    question_norms = torch.norm(embeddings_questions, dim=1, keepdim=True)\n",
        "    response_norms = torch.norm(embeddings, dim=1, keepdim=True)\n",
        "\n",
        "    # Calculate the dot product between the question embeddings and the provided embeddings (transpose of the second matrix for proper alignment)\n",
        "    dot_product = torch.mm(embeddings_questions, embeddings.T)\n",
        "\n",
        "    # Calculate cosine similarity by dividing the dot product by the product of the magnitudes\n",
        "    cosine_similarity = dot_product / (question_norms * response_norms.T)\n",
        "\n",
        "    # Flatten the cosine similarity tensor to a 1D tensor for easier processing\n",
        "    cosine_similarity = cosine_similarity.reshape(-1)\n",
        "\n",
        "    # Sort the indices of the cosine similarity results in descending order to get the indices with the highest similarity\n",
        "    sorted_indices = torch.argsort(cosine_similarity, descending=True)\n",
        "\n",
        "    # Convert sorted indices to a list for easier iteration\n",
        "    sorted_indices = sorted_indices.tolist()\n",
        "\n",
        "    # Print the top 'n_responses' responses from the sorted list, which correspond to the highest cosine similarity values\n",
        "    for index in sorted_indices[:n_responses]:\n",
        "        print(yes_responses[index])  # Ensure 'responses' is defined and accessible in your scope\n",
        "\n",
        "RAG_QA_cosine(embeddings_my_shoe, n_responses=3)"
      ],
      "execution_count": 42
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a3641a9-83b6-4d99-80d6-956025e4acf8"
      },
      "source": [
        "<details>\n",
        "    <summary>Click here for Solution</summary>\n",
        "\n",
        "```python\n",
        "def RAG_QA_cosine(embeddings, n_responses=3):\n",
        "    # Calculate the magnitudes (norms) of the question and response embeddings\n",
        "    question_norms = torch.norm(embeddings_questions, dim=1, keepdim=True)\n",
        "    response_norms = torch.norm(embeddings, dim=1, keepdim=True)\n",
        "    \n",
        "    # Calculate the dot product between the question embeddings and the provided embeddings (transpose of the second matrix for proper alignment)\n",
        "    dot_product = torch.mm(embeddings_questions, embeddings.T)\n",
        "    \n",
        "    # Calculate cosine similarity by dividing the dot product by the product of the magnitudes\n",
        "    cosine_similarity = dot_product / (question_norms * response_norms.T)\n",
        "    \n",
        "    # Flatten the cosine similarity tensor to a 1D tensor for easier processing\n",
        "    cosine_similarity = cosine_similarity.reshape(-1)\n",
        "    \n",
        "    # Sort the indices of the cosine similarity results in descending order to get the indices with the highest similarity\n",
        "    sorted_indices = torch.argsort(cosine_similarity, descending=True)\n",
        "    \n",
        "    # Convert sorted indices to a list for easier iteration\n",
        "    sorted_indices = sorted_indices.tolist()\n",
        "    \n",
        "    # Print the top 'n_responses' responses from the sorted list, which correspond to the highest cosine similarity values\n",
        "    for index in sorted_indices[:n_responses]:\n",
        "        print(yes_responses[index])  # Ensure 'responses' is defined and accessible in your scope\n",
        "\n",
        "RAG_QA_cosine(embeddings_my_shoe, n_responses=3)\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bc028930-7b2e-41e8-8f29-00218c143577"
      },
      "source": [
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14475bf6-f900-4536-9106-0a2da3663498"
      },
      "source": [
        "## Congratulations! You have completed the lab\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89579407-b446-4193-a4bb-350fbf5c76af"
      },
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3c927971-95d9-44ab-b1d0-0140cae3d896"
      },
      "source": [
        "[Joseph Santarcangelo](https://author.skills.network/instructors/joseph_santarcangelo) has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his Ph.D.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42ae5b7d-8646-49cb-b9b9-a6bb4f0fe272"
      },
      "source": [
        "[Ashutosh Sagar](https://www.linkedin.com/in/ashutoshsagar/) is completing his MS in CS from Dalhousie University. He has previous experience working with Natural Language Processing and as a Data Scientist.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "354ab0f3-9993-4b5a-a466-37ad5e68b7c7"
      },
      "source": [
        "© Copyright IBM Corporation. All rights reserved.\n"
      ]
    }
  ],
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