{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ZaYIfQdC8pO"
   },
   "source": [
    "# 🎯 Discovering Embeddings - Text as Vectors!\n",
    "\n",
    "## 🤔 What Are Embeddings?\n",
    "\n",
    "Imagine teaching a computer to understand language by converting words and sentences into **mathematical vectors**!\n",
    "\n",
    "**The Magic:** \n",
    "- 📝 \"The cat is sleeping\" → `[0.23, -0.45, 0.87, ...]` (384 numbers!)\n",
    "- 🧠 Similar meanings → Similar vectors\n",
    "- 🎯 Can measure semantic similarity mathematically\n",
    "\n",
    "---\n",
    "\n",
    "### 🌟 Why Embeddings Are Revolutionary:\n",
    "\n",
    "| Traditional Approach | Embeddings Approach |\n",
    "|---------------------|---------------------|\n",
    "| 🔤 Words as strings | 🔢 Words as vectors |\n",
    "| ❌ Can't compare meanings | ✅ Can measure similarity |\n",
    "| 🚫 No semantic understanding | 🧠 Captures meaning & context |\n",
    "| 📏 Fixed vocabulary | 🌐 Handles any text |\n",
    "\n",
    "---\n",
    "\n",
    "### 🎓 What You'll Learn:\n",
    "\n",
    "1. 🤖 **Load pre-trained models** - Use powerful sentence transformers\n",
    "2. 🔄 **Convert text to vectors** - See embeddings in action\n",
    "3. 📊 **Measure similarity** - Compare semantic meaning\n",
    "4. 🎨 **Visualize embeddings** - See language in 2D space\n",
    "5. 💡 **Real applications** - Discover practical use cases\n",
    "\n",
    "Let's dive in! 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BQs7EF55E98S"
   },
   "source": [
    "### 📦 The Model We'll Use:\n",
    "\n",
    "**`text-embedding-3-small`**\n",
    "- Supports multiple languages (English, French, Spanish, etc.)\n",
    "- Creates 1536-dimensional vectors\n",
    "- Optimized for semantic similarity tasks\n",
    "- Perfect balance of speed and accuracy\n",
    "\n",
    "\n",
    "- 🌍 **Multilingual:** Works in 50+ languages\n",
    "- ⚡ **Fast:** Encodes sentences in seconds\n",
    "- 🎯 **Accurate:** State-of-the-art semantic understanding\n",
    "\n",
    "Let's load it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R88Cd1WAp5Br"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Model loaded successfully!\n",
      "   Model can handle: Multiple languages\n",
      "   Embedding dimension: 1536\n",
      "   Ready to convert text to vectors! 🚀\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "\n",
    "# Load the pre-trained model\n",
    "load_dotenv()\n",
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    \"\"\n",
    ")\n",
    "\n",
    "# Initialize the embedding model\n",
    "\n",
    "embedding_model = OpenAIEmbeddings(\n",
    "    model=\"text-embedding-3-small\",\n",
    "    # With the `text-embedding-3` class\n",
    "    # of models, you can specify the size\n",
    "    # of the embeddings you want returned.\n",
    "    # dimensions=1024  # noqa: ERA001\n",
    ")\n",
    "\n",
    "\n",
    "print(\"✅ Model loaded successfully!\")\n",
    "print(\"   Model can handle: Multiple languages\")\n",
    "print(\"   Embedding dimension: 1536\")\n",
    "print(\"   Ready to convert text to vectors! 🚀\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.embeddings import Embeddings\n",
    "\n",
    "\n",
    "# 🔄 Creating Your First Embedding\n",
    "def embed(text: str) -> list[float]:\n",
    "    \"\"\"Convert a text string into a vector embedding using the pre-trained model.\"\"\"\n",
    "    return embedding_model.embed_query(text)\n",
    "\n",
    "\n",
    "\n",
    "class EmbeddingsTest(Embeddings):\n",
    "    \"\"\"Test class to implement the Embeddings interface using the OpenAIEmbeddings model.\"\"\"\n",
    "\n",
    "    def embed_query(self, text: str) -> list[float]:\n",
    "        \"\"\"Embed a single query string into a vector.\"\"\"\n",
    "        return embedding_model.embed_query(text)\n",
    "\n",
    "    def embed_documents(self, texts: list[str]) -> list[list[float]]:\n",
    "        \"\"\"Embed a list of documents into vectors.\"\"\"\n",
    "        return embedding_model.embed_documents(texts)\n",
    "\n",
    "\n",
    "e = EmbeddingsTest()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "langchain_core.embeddings.embeddings.Embeddings"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.embeddings import Embeddings\n",
    "\n",
    "Embeddings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "langchain_openai.embeddings.base.OpenAIEmbeddings"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OpenAIEmbeddings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 🔄 Creating Your First Embedding\n",
    "\n",
    "Let's convert a simple sentence into a vector!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "7qDnnbFn62Vk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📝 Input sentence: 'The weather is beautiful'\n",
      "\n",
      "🔢 Embedding (vector representation):\n",
      "   First 10 values: [0.010934686288237572, -0.027822494506835938, -0.011565890163183212, 0.010940873995423317, 0.026065023615956306, -0.01712915115058422, 0.0015609929105266929, -0.011058451607823372, -0.025891752913594246, -0.011293605901300907]\n",
      "   ...\n",
      "   Last 10 values: [0.026461074128746986, 0.011702032759785652, 0.0010303481249138713, -0.006079982500523329, 0.020446067675948143, 0.057130176573991776, -0.004919680301100016, -0.034307811409235, 0.0036139539442956448, 0.013304796069860458]\n",
      "\n",
      "📊 Each number captures different semantic aspects!\n"
     ]
    }
   ],
   "source": [
    "# Our input sentence\n",
    "input_str = \"The weather is beautiful\"\n",
    "\n",
    "# Convert to embedding (vector)\n",
    "embedding = embedding_model.embed_query(input_str)\n",
    "\n",
    "print(f\"📝 Input sentence: '{input_str}'\")\n",
    "print(\"\\n🔢 Embedding (vector representation):\")\n",
    "print(f\"   First 10 values: {embedding[:10]}\")\n",
    "print(\"   ...\")\n",
    "print(f\"   Last 10 values: {embedding[-10:]}\")\n",
    "print(\"\\n📊 Each number captures different semantic aspects!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "No35paV7687c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📏 Embedding dimension: 1536\n",
      "\n",
      "💡 This means our sentence is represented by 1536 numbers!\n",
      "   Each dimension captures different aspects of meaning:\n",
      "   • Some dimensions might encode 'weather' concepts\n",
      "   • Others might encode positive sentiment\n",
      "   • Together, they form a unique 'fingerprint' of the sentence\n"
     ]
    }
   ],
   "source": [
    "# Check the dimension\n",
    "dimension = len(embedding)\n",
    "\n",
    "print(f\"📏 Embedding dimension: {dimension}\")\n",
    "print(f\"\\n💡 This means our sentence is represented by {dimension} numbers!\")\n",
    "print(\"   Each dimension captures different aspects of meaning:\")\n",
    "print(\"   • Some dimensions might encode 'weather' concepts\")\n",
    "print(\"   • Others might encode positive sentiment\")\n",
    "print(\"   • Together, they form a unique 'fingerprint' of the sentence\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 📊 Measuring Similarity\n",
    "\n",
    "Now for the **magic** - let's compare multiple sentences and see which ones are semantically similar!\n",
    "\n",
    "## 🧪 The Experiment:\n",
    "\n",
    "We'll compare three sentences:\n",
    "1. \"The weather is lovely today\" 🌞\n",
    "2. \"It's so sunny outside!\" ☀️\n",
    "3. \"He drove to the stadium\" 🚗\n",
    "\n",
    "**Prediction:** Sentences 1 & 2 should be similar (both about weather), while 3 should be different.\n",
    "\n",
    "Let's test this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "QLgxvweMideU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Converting sentences to embeddings...\n",
      "📊 Computing similarity matrix...\n",
      "\n",
      "✅ Complete! Let's visualize the results...\n",
      "\n",
      "📐 Similarity matrix shape: (3, 3)\n",
      "   (3 sentences x 3 sentences = 9 similarity scores)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Our test sentences\n",
    "inputs = [\n",
    "    \"The weather is lovely today.\",\n",
    "    \"It's so sunny outside!\",\n",
    "    \"He drove to the stadium.\",\n",
    "]\n",
    "\n",
    "print(\"🔄 Converting sentences to embeddings...\")\n",
    "embeddings = embedding_model.embed_documents(inputs)\n",
    "\n",
    "print(\"📊 Computing similarity matrix...\")\n",
    "embeddings = np.array(embeddings)\n",
    "similarities = np.dot(embeddings, embeddings.T)\n",
    "\n",
    "print(\"\\n✅ Complete! Let's visualize the results...\")\n",
    "print(f\"\\n📐 Similarity matrix shape: {similarities.shape}\")\n",
    "print(\"   (3 sentences x 3 sentences = 9 similarity scores)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎨 Creating a Visualization Function\n",
    "\n",
    "Let's make a beautiful heatmap to visualize similarities!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "C8PvTRf1i_i5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.5\n",
      "✅ Visualization function created!\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "print(np.__version__)\n",
    "\n",
    "def compute_and_display_similarities(inputs: list[str], title: str = \"Semantic Similarity Heatmap\") -> None:\n",
    "    \"\"\"Compute and display a beautiful heatmap of sentence similarities.\n",
    "\n",
    "    Args:\n",
    "        inputs: List of sentences to compare\n",
    "        title: Title for the plot\n",
    "\n",
    "    \"\"\"\n",
    "    # Compute embeddings and similarities\n",
    "    embeddings = embedding_model.embed_documents(inputs)\n",
    "    embeddings = np.array(embeddings)\n",
    "    similarities = np.dot(embeddings, embeddings.T)\n",
    "\n",
    "    # Create figure\n",
    "    plt.figure(figsize=(12, 10))\n",
    "\n",
    "    # Create heatmap\n",
    "    _ax = sns.heatmap(\n",
    "        similarities,\n",
    "        annot=True,  # Show values\n",
    "        fmt=\".3f\",  # 3 decimal places\n",
    "        xticklabels=inputs,\n",
    "        yticklabels=inputs,\n",
    "        cmap=\"YlGnBu\",  # Yellow-Green-Blue colormap\n",
    "        cbar_kws={\"label\": \"Similarity Score\"},\n",
    "        vmin=0,  # Minimum value\n",
    "        vmax=1,  # Maximum value\n",
    "        square=True,  # Square cells\n",
    "        linewidths=0.5,  # Grid lines\n",
    "        linecolor=\"gray\",\n",
    "    )\n",
    "\n",
    "    # Styling\n",
    "    plt.title(title, fontsize=16, fontweight=\"bold\", pad=20)\n",
    "    plt.xlabel(\"Sentences\", fontsize=12, fontweight=\"bold\")\n",
    "    plt.ylabel(\"Sentences\", fontsize=12, fontweight=\"bold\")\n",
    "    plt.xticks(rotation=45, ha=\"right\")\n",
    "    plt.yticks(rotation=0)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Print interpretation\n",
    "    print(\"\\n📖 How to read this heatmap:\")\n",
    "    print(\"   • 1.0 (dark blue) = Perfect similarity (same sentence)\")\n",
    "    print(\"   • 0.8-0.9 = Very similar meaning\")\n",
    "    print(\"   • 0.5-0.7 = Somewhat related\")\n",
    "    print(\"   • 0.0-0.4 = Different meanings\")\n",
    "    print(\"   • 0.0 (light yellow) = Completely different\")\n",
    "\n",
    "\n",
    "print(\"✅ Visualization function created!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "am4JPM-ojgng"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎨 Generating similarity heatmap...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tp/_ld5_pzs6nx6mv1pbjhq1l740000gn/T/ipykernel_24511/1064958240.py:45: UserWarning: Glyph 127775 (\\N{GLOWING STAR}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127775 (\\N{GLOWING STAR}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📖 How to read this heatmap:\n",
      "   • 1.0 (dark blue) = Perfect similarity (same sentence)\n",
      "   • 0.8-0.9 = Very similar meaning\n",
      "   • 0.5-0.7 = Somewhat related\n",
      "   • 0.0-0.4 = Different meanings\n",
      "   • 0.0 (light yellow) = Completely different\n",
      "\n",
      "🔍 Observations:\n",
      "   ✅ Sentences 1 & 2 have HIGH similarity (both about weather)\n",
      "   ❌ Sentence 3 has LOW similarity with others (different topic)\n",
      "   🎯 The model understands semantic meaning, not just word matching!\n"
     ]
    }
   ],
   "source": [
    "# Test with our sentences\n",
    "inputs = [\n",
    "    \"The weather is lovely today\",\n",
    "    \"It's so sunny outside!\",\n",
    "    \"He drove to the stadium.\",\n",
    "]\n",
    "\n",
    "print(\"🎨 Generating similarity heatmap...\\n\")\n",
    "compute_and_display_similarities(inputs, \"🌟 Semantic Similarity Between Sentences\")\n",
    "\n",
    "print(\"\\n🔍 Observations:\")\n",
    "print(\"   ✅ Sentences 1 & 2 have HIGH similarity (both about weather)\")\n",
    "print(\"   ❌ Sentence 3 has LOW similarity with others (different topic)\")\n",
    "print(\"   🎯 The model understands semantic meaning, not just word matching!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🌍 Multilingual Magic!\n",
    "\n",
    "Our model works across languages! Let's test with French and English:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌍 Testing multilingual understanding...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tp/_ld5_pzs6nx6mv1pbjhq1l740000gn/T/ipykernel_24511/1064958240.py:45: UserWarning: Glyph 127760 (\\N{GLOBE WITH MERIDIANS}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127760 (\\N{GLOBE WITH MERIDIANS}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📖 How to read this heatmap:\n",
      "   • 1.0 (dark blue) = Perfect similarity (same sentence)\n",
      "   • 0.8-0.9 = Very similar meaning\n",
      "   • 0.5-0.7 = Somewhat related\n",
      "   • 0.0-0.4 = Different meanings\n",
      "   • 0.0 (light yellow) = Completely different\n",
      "\n",
      "🤯 Amazing! The model recognizes that:\n",
      "   • French 'Il fait beau' ≈ English 'sunny outside'\n",
      "   • Despite different languages, it captures the SAME meaning!\n",
      "   • This is the power of multilingual embeddings! 🚀\n"
     ]
    }
   ],
   "source": [
    "# Mix French and English!\n",
    "multilingual_inputs = [\n",
    "    \"Il fait beau aujourd'hui\",  # French: \"The weather is nice today\"\n",
    "    \"It's so sunny outside!\",  # English\n",
    "    \"He drove to the stadium.\",  # English (different topic)\n",
    "]\n",
    "\n",
    "print(\"🌍 Testing multilingual understanding...\\n\")\n",
    "compute_and_display_similarities(\n",
    "    multilingual_inputs, \"🌐 Cross-Language Semantic Similarity\",\n",
    ")\n",
    "\n",
    "print(\"\\n🤯 Amazing! The model recognizes that:\")\n",
    "print(\"   • French 'Il fait beau' ≈ English 'sunny outside'\")\n",
    "print(\"   • Despite different languages, it captures the SAME meaning!\")\n",
    "print(\"   • This is the power of multilingual embeddings! 🚀\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PpUEegq2Hd4s"
   },
   "source": [
    "---\n",
    "\n",
    "# 🧪 Hands-On Exercise: Understanding Similarity Metrics\n",
    "\n",
    "## 🎯 Goal\n",
    "\n",
    "Explore how **cosine similarity** and **Euclidean distance** behave with embeddings.\n",
    "\n",
    "### 📐 Two Ways to Compare Vectors:\n",
    "\n",
    "| Metric | Formula | What it measures |\n",
    "|--------|---------|------------------|\n",
    "| **Cosine Similarity** | $\\frac{u \\cdot v}{\\|u\\| \\|v\\|}$ | Angle between vectors (0-1) |\n",
    "| **Euclidean Distance** | $\\|u - v\\|$ | Straight-line distance |\n",
    "\n",
    "---\n",
    "\n",
    "### 🤔 Why Does This Matter?\n",
    "\n",
    "**Cosine similarity** is preferred for text because:\n",
    "- 🎯 Focuses on **direction** (meaning) not magnitude (length)\n",
    "- 📏 Normalized to 0-1 range (easy to interpret)\n",
    "- 🎨 More robust to text length variations\n",
    "\n",
    "Let's see this in action!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqWKiIJ8GOC7"
   },
   "source": [
    "## 🔧 Implementing Similarity Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ibFv2FWOGgU4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Similarity functions defined!\n",
      "\n",
      "📊 Available metrics:\n",
      "   1. cosine_similarity(u, v) → 0 to 1 (higher = more similar)\n",
      "   2. euclidean_distance(u, v) → 0 to ∞ (lower = more similar)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def cosine_similarity(u: list[float] | np.ndarray, v: list[float] | np.ndarray) -> float:\n",
    "    \"\"\"Calculate cosine similarity between two vectors.\n",
    "\n",
    "    Returns a value between 0 and 1:\n",
    "    - 1.0 = vectors point in same direction (very similar)\n",
    "    - 0.0 = vectors are perpendicular (unrelated)\n",
    "    \"\"\"\n",
    "    u, v = np.array(u), np.array(v)\n",
    "    # Normalize vectors to unit length\n",
    "    u_norm = u / np.linalg.norm(u)\n",
    "    v_norm = v / np.linalg.norm(v)\n",
    "    # Dot product of normalized vectors\n",
    "    return float(u_norm @ v_norm)\n",
    "\n",
    "\n",
    "def euclidean_distance(u: list[float] | np.ndarray, v: list[float] | np.ndarray) -> float:\n",
    "    \"\"\"Calculate Euclidean distance between two vectors.\n",
    "\n",
    "    Returns the straight-line distance:\n",
    "    - 0.0 = identical vectors\n",
    "    - Larger values = more different\n",
    "    \"\"\"\n",
    "    u, v = np.array(u), np.array(v)\n",
    "    return float(np.linalg.norm(u - v))\n",
    "\n",
    "\n",
    "print(\"✅ Similarity functions defined!\")\n",
    "print(\"\\n📊 Available metrics:\")\n",
    "print(\"   1. cosine_similarity(u, v) → 0 to 1 (higher = more similar)\")\n",
    "print(\"   2. euclidean_distance(u, v) → 0 to ∞ (lower = more similar)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🐱 Test Sentences: The Cat Family\n",
    "\n",
    "We'll compare three sentences of increasing length, all about cats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "44YoBMZMh7Ro"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🐱 Our test sentences:\n",
      "\n",
      "1️⃣  SHORT: 'the cat is a feline'\n",
      "    Length: 19 characters\n",
      "\n",
      "2️⃣  MEDIUM: 'the cat is a feline, the cat is a feline'\n",
      "    Length: 40 characters\n",
      "\n",
      "3️⃣  LONG: 'the cat is a feline, the cat is a feline, the cat is a feline, the cat is a feline'\n",
      "    Length: 82 characters\n",
      "\n",
      "💡 All three sentences are about cats, but with different lengths!\n"
     ]
    }
   ],
   "source": [
    "# Three sentences about cats, with increasing detail\n",
    "phrase_chat_1 = \"the cat is a feline\"\n",
    "phrase_chat_2 = \"the cat is a feline, the cat is a feline\"\n",
    "phrase_chat_3 = (\n",
    "    \"the cat is a feline, the cat is a feline, the cat is a feline, the cat is a feline\"\n",
    ")\n",
    "\n",
    "print(\"🐱 Our test sentences:\\n\")\n",
    "print(f\"1️⃣  SHORT: '{phrase_chat_1}'\")\n",
    "print(f\"    Length: {len(phrase_chat_1)} characters\\n\")\n",
    "\n",
    "print(f\"2️⃣  MEDIUM: '{phrase_chat_2}'\")\n",
    "print(f\"    Length: {len(phrase_chat_2)} characters\\n\")\n",
    "\n",
    "print(f\"3️⃣  LONG: '{phrase_chat_3}'\")\n",
    "print(f\"    Length: {len(phrase_chat_3)} characters\\n\")\n",
    "\n",
    "print(\"💡 All three sentences are about cats, but with different lengths!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "UZWhMtD8uWdK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Converting sentences to embeddings...\n",
      "\n",
      "✅ Embeddings created!\n",
      "   Each is a 1536-dimensional vector\n"
     ]
    }
   ],
   "source": [
    "# Generate embeddings\n",
    "print(\"🔄 Converting sentences to embeddings...\\n\")\n",
    "\n",
    "phrase_chat_1_emb = embedding_model.embed_query(phrase_chat_1)\n",
    "phrase_chat_2_emb = embedding_model.embed_query(phrase_chat_2)\n",
    "phrase_chat_3_emb = embedding_model.embed_query(phrase_chat_3)\n",
    "\n",
    "print(\"✅ Embeddings created!\")\n",
    "print(f\"   Each is a {len(phrase_chat_1_emb)}-dimensional vector\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 📏 Task 1: Calculate Euclidean Distance\n",
    "\n",
    "Now let's compare using **Euclidean distance**.\n",
    "\n",
    "**What to expect:** Lower values mean more similar!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📏 Euclidean Distance Results:\n",
      "\n",
      "============================================================\n",
      "📐 Short vs Long:    0.5506\n",
      "📐 Short vs Medium:  0.4474\n",
      "📐 Medium vs Long:   0.3820\n",
      "============================================================\n",
      "\n",
      "🔍 Observations:\n",
      "   • Distances INCREASE with text length difference\n",
      "   • Short vs Long has LARGEST distance: 0.5506\n",
      "   • Medium vs Long has SMALLEST distance: 0.3820\n",
      "\n",
      "⚠️  Problem: Euclidean distance is affected by vector magnitude!\n",
      "   Longer texts → larger vectors → larger distances\n",
      "   Even if the MEANING is similar!\n"
     ]
    }
   ],
   "source": [
    "# Calculate all pairwise Euclidean distances\n",
    "print(\"📏 Euclidean Distance Results:\\n\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "\n",
    "# Sentence 1 vs Sentence 3\n",
    "dist_1_3 = euclidean_distance(phrase_chat_1_emb, phrase_chat_3_emb)\n",
    "print(f\"📐 Short vs Long:    {dist_1_3:.4f}\")\n",
    "\n",
    "dist_1_2 = euclidean_distance(phrase_chat_1_emb, phrase_chat_2_emb)\n",
    "print(f\"📐 Short vs Medium:  {dist_1_2:.4f}\")\n",
    "\n",
    "# Sentence 2 vs Sentence 3\n",
    "dist_2_3 = euclidean_distance(phrase_chat_2_emb, phrase_chat_3_emb)\n",
    "print(f\"📐 Medium vs Long:   {dist_2_3:.4f}\")\n",
    "\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Analysis\n",
    "print(\"\\n🔍 Observations:\")\n",
    "print(\"   • Distances INCREASE with text length difference\")\n",
    "print(f\"   • Short vs Long has LARGEST distance: {dist_1_3:.4f}\")\n",
    "print(f\"   • Medium vs Long has SMALLEST distance: {dist_2_3:.4f}\")\n",
    "print(\"\\n⚠️  Problem: Euclidean distance is affected by vector magnitude!\")\n",
    "print(\"   Longer texts → larger vectors → larger distances\")\n",
    "print(\"   Even if the MEANING is similar!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O179uX3OGi_9"
   },
   "source": [
    "---\n",
    "\n",
    "## 📊 Task 2: Calculate Cosine Similarity\n",
    "\n",
    "Compare each pair of sentences using **cosine similarity**.\n",
    "\n",
    "**What to expect:** Values close to 1 mean very similar meanings!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "-hyq4pfMGtS8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Cosine Similarity Results:\n",
      "\n",
      "============================================================\n",
      "🐱 Short vs Medium:  0.8999\n",
      "🐱 Short vs Long:    0.8484\n",
      "🐱 Medium vs Long:   0.9270\n",
      "============================================================\n",
      "\n",
      "🔍 Observations:\n",
      "   • Average similarity: 0.8918\n",
      "   • All values are HIGH (close to 1.0)\n",
      "   • The model recognizes they're all about CATS! 🐱\n",
      "   • Text length doesn't drastically affect similarity\n",
      "\n",
      "💡 Cosine similarity focuses on MEANING, not LENGTH!\n"
     ]
    }
   ],
   "source": [
    "# Calculate all pairwise cosine similarities\n",
    "print(\"📊 Cosine Similarity Results:\\n\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Sentence 1 vs Sentence 2\n",
    "sim_1_2 = cosine_similarity(phrase_chat_1_emb, phrase_chat_2_emb)\n",
    "print(f\"🐱 Short vs Medium:  {sim_1_2:.4f}\")\n",
    "\n",
    "# Sentence 1 vs Sentence 3\n",
    "sim_1_3 = cosine_similarity(phrase_chat_1_emb, phrase_chat_3_emb)\n",
    "print(f\"🐱 Short vs Long:    {sim_1_3:.4f}\")\n",
    "\n",
    "# Sentence 2 vs Sentence 3\n",
    "sim_2_3 = cosine_similarity(phrase_chat_2_emb, phrase_chat_3_emb)\n",
    "print(f\"🐱 Medium vs Long:   {sim_2_3:.4f}\")\n",
    "\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Analysis\n",
    "print(\"\\n🔍 Observations:\")\n",
    "avg_similarity = (sim_1_2 + sim_1_3 + sim_2_3) / 3\n",
    "print(f\"   • Average similarity: {avg_similarity:.4f}\")\n",
    "print(\"   • All values are HIGH (close to 1.0)\")\n",
    "print(\"   • The model recognizes they're all about CATS! 🐱\")\n",
    "print(\"   • Text length doesn't drastically affect similarity\")\n",
    "print(\"\\n💡 Cosine similarity focuses on MEANING, not LENGTH!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E-xtDp93HNDX"
   },
   "source": [
    "---\n",
    "\n",
    "## 🤔 Task 3: Why Cosine Similarity?\n",
    "\n",
    "### 💡 Key Insight:\n",
    "\n",
    "Let's compare the two metrics side by side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Metric Comparison:\n",
      "\n",
      "     Comparison Cosine Similarity Euclidean Distance\n",
      "Short vs Medium            0.8999             0.4474\n",
      "  Short vs Long            0.8484             0.5506\n",
      " Medium vs Long            0.9270             0.3820\n",
      "\n",
      "======================================================================\n",
      "🎯 CONCLUSION: Why Cosine Similarity Wins for Text\n",
      "======================================================================\n",
      "\n",
      "✅ Cosine Similarity Advantages:\n",
      "   1. 📐 Length-invariant: Not affected by text length\n",
      "   2. 🎯 Semantic focus: Measures meaning, not magnitude\n",
      "   3. 📊 Normalized: Always between 0 and 1 (easy to interpret)\n",
      "   4. 🔄 Consistent: Similar meanings = similar scores\n",
      "\n",
      "❌ Euclidean Distance Disadvantages:\n",
      "   1. 📏 Length-sensitive: Longer texts → larger distances\n",
      "   2. 🎭 Misleading: Can be large even for similar meanings\n",
      "   3. ⚖️ Not normalized: Hard to interpret absolute values\n",
      "   4. 📉 Inconsistent: Same meaning, different distance based on length\n",
      "\n",
      "🌟 This is why NLP systems use COSINE SIMILARITY! 🌟\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create comparison table\n",
    "comparison_data = {\n",
    "    \"Comparison\": [\"Short vs Medium\", \"Short vs Long\", \"Medium vs Long\"],\n",
    "    \"Cosine Similarity\": [f\"{sim_1_2:.4f}\", f\"{sim_1_3:.4f}\", f\"{sim_2_3:.4f}\"],\n",
    "    \"Euclidean Distance\": [f\"{dist_1_2:.4f}\", f\"{dist_1_3:.4f}\", f\"{dist_2_3:.4f}\"],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(comparison_data)\n",
    "print(\"📊 Metric Comparison:\\n\")\n",
    "print(df.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\" * 70)\n",
    "print(\"🎯 CONCLUSION: Why Cosine Similarity Wins for Text\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "print(\"\\n✅ Cosine Similarity Advantages:\")\n",
    "print(\"   1. 📐 Length-invariant: Not affected by text length\")\n",
    "print(\"   2. 🎯 Semantic focus: Measures meaning, not magnitude\")\n",
    "print(\"   3. 📊 Normalized: Always between 0 and 1 (easy to interpret)\")\n",
    "print(\"   4. 🔄 Consistent: Similar meanings = similar scores\")\n",
    "\n",
    "print(\"\\n❌ Euclidean Distance Disadvantages:\")\n",
    "print(\"   1. 📏 Length-sensitive: Longer texts → larger distances\")\n",
    "print(\"   2. 🎭 Misleading: Can be large even for similar meanings\")\n",
    "print(\"   3. ⚖️ Not normalized: Hard to interpret absolute values\")\n",
    "print(\"   4. 📉 Inconsistent: Same meaning, different distance based on length\")\n",
    "\n",
    "print(\"\\n🌟 This is why NLP systems use COSINE SIMILARITY! 🌟\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Aznk0AOrIRFQ"
   },
   "source": [
    "---\n",
    "\n",
    "# 🎨 Visualizing Embeddings in 2D\n",
    "\n",
    "## 🤯 The Challenge\n",
    "\n",
    "Our embeddings have **384 dimensions** - impossible to visualize directly!\n",
    "\n",
    "**Solution:** Use **UMAP** (Uniform Manifold Approximation and Projection)\n",
    "- 🎯 Reduces 384D → 2D while preserving relationships\n",
    "- 🎨 Keeps similar items close together\n",
    "- 👀 Makes patterns visible to human eyes\n",
    "\n",
    "---\n",
    "\n",
    "## 🧪 The Experiment\n",
    "\n",
    "We'll embed words from **two different domains**:\n",
    "- 🐾 **Animals:** Cat, Dog, Elephant, Tiger, Lion\n",
    "- 💻 **Technology:** Phone, Antenna, Modem, Router, Cable\n",
    "\n",
    "**Hypothesis:** Words from the same domain should cluster together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "xCrpwv63_m9K"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📋 Test Words:\n",
      "\n",
      "🐾 Animals:\n",
      "   Cat, Dog, Elephant, Tiger, Lion\n",
      "\n",
      "💻 Technology:\n",
      "   Phone, Antenna, Modem, Router, Cable\n",
      "\n",
      "🎯 Goal: See if embeddings naturally separate these domains!\n"
     ]
    }
   ],
   "source": [
    "# Our test words\n",
    "elements = [\n",
    "    # Animals\n",
    "    \"Cat\",\n",
    "    \"Dog\",\n",
    "    \"Elephant\",\n",
    "    \"Tiger\",\n",
    "    \"Lion\",\n",
    "    # Technology\n",
    "    \"Phone\",\n",
    "    \"Antenna\",\n",
    "    \"Modem\",\n",
    "    \"Router\",\n",
    "    \"Cable\",\n",
    "]\n",
    "\n",
    "# Categories for coloring\n",
    "categories = [\n",
    "    \"Animals\",\n",
    "    \"Animals\",\n",
    "    \"Animals\",\n",
    "    \"Animals\",\n",
    "    \"Animals\",\n",
    "    \"Technology\",\n",
    "    \"Technology\",\n",
    "    \"Technology\",\n",
    "    \"Technology\",\n",
    "    \"Technology\",\n",
    "]\n",
    "\n",
    "print(\"📋 Test Words:\")\n",
    "print(\"\\n🐾 Animals:\")\n",
    "print(\"   \" + \", \".join(elements[:5]))\n",
    "print(\"\\n💻 Technology:\")\n",
    "print(\"   \" + \", \".join(elements[5:]))\n",
    "print(\"\\n🎯 Goal: See if embeddings naturally separate these domains!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "sT6m2Wkv2gZx"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Step 1: Creating 1536D embeddings...\n",
      "   ✅ Created 10 embeddings of 1536 dimensions each\n",
      "\n",
      "📉 Step 2: Reducing 1536D → 2D with UMAP...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ✅ Reduced to 2D coordinates\n",
      "\n",
      "🎨 Step 3: Creating visualization...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tp/_ld5_pzs6nx6mv1pbjhq1l740000gn/T/ipykernel_24511/980470006.py:68: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🔍 Analysis:\n",
      "   🐾 RED cluster = Animals (Cat, Dog, Elephant, Tiger, Lion)\n",
      "   💻 TEAL cluster = Technology (Phone, Modem, Router, etc.)\n",
      "\n",
      "✨ Magic Observation:\n",
      "   • The model AUTOMATICALLY groups related concepts together!\n",
      "   • No human told it that 'Cat' and 'Dog' are related\n",
      "   • It learned these relationships from massive text data\n",
      "   • This is the power of embeddings! 🚀\n",
      "\n",
      "💡 Why This Matters:\n",
      "   1. Search: Find similar items even with different words\n",
      "   2. Clustering: Automatically group related content\n",
      "   3. Recommendations: Suggest similar items\n",
      "   4. Classification: Categorize new content\n"
     ]
    }
   ],
   "source": [
    "import umap\n",
    "\n",
    "rng = np.random.default_rng(42)\n",
    "# Set random seed for reproducibilit\n",
    "\n",
    "print(\"🔄 Step 1: Creating 1536D embeddings...\")\n",
    "embeddings_1536d = embedding_model.embed_documents(elements)\n",
    "print(\n",
    "    f\"   ✅ Created {len(embeddings_1536d)} embeddings of {len(embeddings_1536d[0])} dimensions each\",\n",
    ")\n",
    "\n",
    "print(\"\\n📉 Step 2: Reducing 1536D → 2D with UMAP...\")\n",
    "reducer = umap.UMAP(\n",
    "    n_neighbors=3,  # Local neighborhood size\n",
    "    min_dist=0.1,  # Minimum distance between points\n",
    "    metric=\"cosine\",  # Use cosine similarity\n",
    "    random_state=42,\n",
    ")\n",
    "embeddings_2d = reducer.fit_transform(embeddings_1536d)\n",
    "print(\"   ✅ Reduced to 2D coordinates\")\n",
    "\n",
    "print(\"\\n🎨 Step 3: Creating visualization...\\n\")\n",
    "\n",
    "# Create beautiful plot\n",
    "plt.figure(figsize=(14, 10))\n",
    "\n",
    "# Scatter plot with categories\n",
    "scatter = sns.scatterplot(\n",
    "    x=embeddings_2d[:, 0],\n",
    "    y=embeddings_2d[:, 1],\n",
    "    hue=categories,\n",
    "    palette=[\"#FF6B6B\", \"#4ECDC4\"],  # Red for animals, teal for tech\n",
    "    s=300,\n",
    "    alpha=0.7,\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "# Add labels for each word\n",
    "for i, word in enumerate(elements):\n",
    "    plt.annotate(\n",
    "        word,\n",
    "        (embeddings_2d[i, 0], embeddings_2d[i, 1]),\n",
    "        xytext=(10, 10),\n",
    "        textcoords=\"offset points\",\n",
    "        fontsize=12,\n",
    "        fontweight=\"bold\",\n",
    "        bbox={\n",
    "            \"boxstyle\": \"round,pad=0.5\", \"facecolor\": \"white\", \"alpha\": 0.7, \"edgecolor\": \"gray\",\n",
    "        },\n",
    "        arrowprops={\n",
    "            \"arrowstyle\": \"->\", \"connectionstyle\": \"arc3,rad=0\", \"color\": \"gray\", \"lw\": 1.5,\n",
    "        },\n",
    "    )\n",
    "\n",
    "# Styling\n",
    "plt.title(\n",
    "    \"🎯 Semantic Space: Animals vs Technology\\n(1536D embeddings projected to 2D)\",\n",
    "    fontsize=16,\n",
    "    fontweight=\"bold\",\n",
    "    pad=20,\n",
    ")\n",
    "plt.xlabel(\"Dimension 1\", fontsize=13, fontweight=\"bold\")\n",
    "plt.ylabel(\"Dimension 2\", fontsize=13, fontweight=\"bold\")\n",
    "plt.legend(title=\"Category\", fontsize=12, title_fontsize=13, loc=\"best\")\n",
    "plt.grid(visible=True, alpha=0.3, linestyle=\"--\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Analysis\n",
    "print(\"\\n🔍 Analysis:\")\n",
    "print(\"   🐾 RED cluster = Animals (Cat, Dog, Elephant, Tiger, Lion)\")\n",
    "print(\"   💻 TEAL cluster = Technology (Phone, Modem, Router, etc.)\")\n",
    "print(\"\\n✨ Magic Observation:\")\n",
    "print(\"   • The model AUTOMATICALLY groups related concepts together!\")\n",
    "print(\"   • No human told it that 'Cat' and 'Dog' are related\")\n",
    "print(\"   • It learned these relationships from massive text data\")\n",
    "print(\"   • This is the power of embeddings! 🚀\")\n",
    "\n",
    "print(\"\\n💡 Why This Matters:\")\n",
    "print(\"   1. Search: Find similar items even with different words\")\n",
    "print(\"   2. Clustering: Automatically group related content\")\n",
    "print(\"   3. Recommendations: Suggest similar items\")\n",
    "print(\"   4. Classification: Categorize new content\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ua8DR4AbIpOq"
   },
   "source": [
    "---\n",
    "\n",
    "# 💡 Real-World Applications\n",
    "\n",
    "## 🚀 Where Are Embeddings Used?\n",
    "\n",
    "Embeddings power many modern AI applications:\n",
    "\n",
    "### 1. 🔍 Semantic Search\n",
    "**Use Case:** Search by meaning, not just keywords\n",
    "- Query: \"how to fix a broken faucet\"\n",
    "- Finds: \"plumbing repair for leaky tap\" ✅\n",
    "- Traditional search would miss this!\n",
    "\n",
    "**Example:** Google Search, Elasticsearch with vector search\n",
    "\n",
    "---\n",
    "\n",
    "### 2. 🤖 Chatbots & Virtual Assistants\n",
    "**Use Case:** Understanding user intent\n",
    "- \"I'm hungry\" ≈ \"I'd like something to eat\" ≈ \"Feed me\"\n",
    "- All trigger the same response\n",
    "\n",
    "**Example:** Customer service bots, Siri, Alexa\n",
    "\n",
    "---\n",
    "\n",
    "### 3. 📚 Recommendation Systems\n",
    "**Use Case:** Find similar content\n",
    "- Watched \"The Matrix\"? Try \"Inception\" (similar themes)\n",
    "- Read article about AI? Here are related papers\n",
    "\n",
    "**Example:** Netflix, YouTube, Amazon recommendations\n",
    "\n",
    "---\n",
    "\n",
    "### 4. 📝 Document Clustering\n",
    "**Use Case:** Automatically organize documents\n",
    "- Group news articles by topic\n",
    "- Organize customer support tickets\n",
    "- Cluster research papers by subject\n",
    "\n",
    "**Example:** Google News topic grouping\n",
    "\n",
    "---\n",
    "\n",
    "### 5. 🔐 Duplicate Detection\n",
    "**Use Case:** Find similar or duplicate content\n",
    "- Detect plagiarism\n",
    "- Remove duplicate customer queries\n",
    "- Find copyright violations\n",
    "\n",
    "**Example:** Turnitin, Stack Overflow duplicate detection\n",
    "\n",
    "---\n",
    "\n",
    "### 6. 🌐 Translation & Cross-Lingual Search\n",
    "**Use Case:** Search across languages\n",
    "- Query in English, find French documents\n",
    "- Multilingual customer support\n",
    "\n",
    "**Example:** Wikipedia cross-language search\n",
    "\n",
    "---\n",
    "\n",
    "### 7. 📊 Text Classification\n",
    "**Use Case:** Automatically categorize text\n",
    "- Email spam detection\n",
    "- Sentiment analysis (positive/negative reviews)\n",
    "- Topic classification (sports, politics, tech)\n",
    "\n",
    "**Example:** Gmail spam filter, Twitter sentiment analysis\n",
    "\n",
    "---\n",
    "\n",
    "### 8. 🎯 RAG (Retrieval-Augmented Generation)\n",
    "**Use Case:** Power AI assistants with relevant context\n",
    "- Embed company documents\n",
    "- Find relevant passages for LLM context\n",
    "- Ground AI responses in real data\n",
    "\n",
    "**Example:** ChatGPT with custom knowledge bases\n",
    "\n",
    "---\n",
    "\n",
    "### 9. 🔗 Entity Linking\n",
    "**Use Case:** Connect mentions to real entities\n",
    "- \"Apple\" → Apple Inc. (company) or apple (fruit)?\n",
    "- Disambiguate based on context\n",
    "\n",
    "**Example:** Wikipedia entity linking\n",
    "\n",
    "---\n",
    "\n",
    "### 10. 🎨 Creative Applications\n",
    "**Use Case:** AI art & content generation\n",
    "- Text-to-image (DALL-E, Midjourney)\n",
    "- Music generation based on mood\n",
    "- Story continuation\n",
    "\n",
    "**Example:** DALL-E converts text embeddings to image space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 🎉 Congratulations!\n",
    "\n",
    "You've completed the embeddings tutorial! Here's what you've mastered:\n",
    "\n",
    "## 📚 Key Concepts:\n",
    "\n",
    "✅ **Embeddings** - Converting text to semantic vectors  \n",
    "✅ **Sentence Transformers** - Pre-trained models for embeddings  \n",
    "✅ **Similarity Metrics** - Cosine similarity vs Euclidean distance  \n",
    "✅ **Dimensionality Reduction** - Visualizing high-D data with UMAP  \n",
    "✅ **Semantic Clustering** - Finding natural groups in text  \n",
    "✅ **Real Applications** - Where embeddings power modern AI  \n",
    "\n",
    "---\n",
    "\n",
    "## 🎯 Key Takeaways:\n",
    "\n",
    "1. **Embeddings = Semantic Fingerprints**\n",
    "   - Each text gets a unique vector representation\n",
    "   - Similar meanings → Similar vectors\n",
    "\n",
    "2. **Cosine Similarity FTW**\n",
    "   - Best for comparing text embeddings\n",
    "   - Length-invariant, normalized, semantic-focused\n",
    "\n",
    "3. **Pre-trained Models Are Powerful**\n",
    "   - No need to train from scratch\n",
    "   - Multilingual capabilities out of the box\n",
    "   - Ready for production use\n",
    "\n",
    "4. **Dimensionality Reduction Makes Patterns Visible**\n",
    "   - UMAP/t-SNE reveal structure in high-D data\n",
    "   - Great for exploration and understanding\n",
    "\n",
    "5. **Embeddings Power Modern AI**\n",
    "   - From search to recommendations\n",
    "   - From chatbots to content generation\n",
    "   - The foundation of most NLP systems\n",
    "\n",
    "---\n",
    "\n",
    "## 🚀 Next Steps:\n",
    "\n",
    "1. **🧪 Experiment**\n",
    "   - Try different sentences and languages\n",
    "   - Test various domains (sports, science, art)\n",
    "   - Compare different models from Sentence Transformers\n",
    "\n",
    "2. **🔨 Build Something**\n",
    "   - Create a semantic search engine\n",
    "   - Build a recommendation system\n",
    "   - Make a duplicate detector\n",
    "\n",
    "---\n",
    "\n",
    "<div style=\"text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 30px; border-radius: 15px; margin: 30px 0;\">\n",
    "    <h2 style=\"margin: 0; font-size: 2em;\">🎓 You Now Understand Embeddings!</h2>\n",
    "    <p style=\"margin: 15px 0 0 0; font-size: 1.2em;\">The foundation of modern NLP is in your hands! 🚀</p>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "### 🌟 Remember:\n",
    "\n",
    "> *\"Embeddings turn words into geometry, making language computable and comparable.\"*\n",
    "\n",
    "Now go build something amazing! 💪✨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📚 Exercise Set 1: Basic Embedding Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most similar pair: 'I love programming in Python' and 'Coding with Python is my favorite hobby'\n",
      "Cosine similarity score: 0.6924\n"
     ]
    }
   ],
   "source": [
    "sentences = [\n",
    "    \"I love programming in Python\",\n",
    "    \"Coding with Python is my favorite hobby\",\n",
    "    \"The sunset was beautiful yesterday\",\n",
    "    \"I enjoy playing basketball\",\n",
    "    \"Python is a great programming language\",\n",
    "]\n",
    "\n",
    "# Your code here:\n",
    "# 1. Generate embeddings for all sentences\n",
    "embeddings = embedding_model.embed_documents(sentences)\n",
    "# 2. Calculate cosine similarity between all pairs\n",
    "similarities = np.dot(embeddings, np.array(embeddings).T)\n",
    "np.fill_diagonal(similarities, -1)  # Ignore self-similarity\n",
    "max_sim_indices = np.unravel_index(np.argmax(similarities), similarities.shape)\n",
    "most_similar_pair = (sentences[max_sim_indices[0]], sentences[max_sim_indices[1]])\n",
    "similarity_score = similarities[max_sim_indices]\n",
    "# 3. Print the most similar pair and their similarity score\n",
    "print(f\"Most similar pair: '{most_similar_pair[0]}' and '{most_similar_pair[1]}'\")\n",
    "print(f\"Cosine similarity score: {similarity_score:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.507 | Doc: Neural networks mimic the human brain\n",
      "Score: 0.376 | Doc: Deep learning is a subset of machine learning\n",
      "Score: 0.342 | Doc: Machine learning uses statistical techniques\n",
      "Most similar pair: 'I love programming in Python' and 'Coding with Python is my favorite hobby'\n",
      "Cosine similarity score: 0.6924\n"
     ]
    }
   ],
   "source": [
    "documents = [\n",
    "    \"Python is a high-level programming language\",\n",
    "    \"Machine learning uses statistical techniques\",\n",
    "    \"Dogs are loyal pets and companions\",\n",
    "    \"Neural networks mimic the human brain\",\n",
    "    \"Cats are independent animals\",\n",
    "    \"Deep learning is a subset of machine learning\",\n",
    "]\n",
    "\n",
    "def semantic_search(query: str, documents: list, top_k: int = 3) -> list[tuple[str, int | float]]:\n",
    "    \"\"\"Find the most relevant documents for a query.\n",
    "\n",
    "    Args:\n",
    "        query: Search query string\n",
    "        documents: List of documents to search\n",
    "        top_k: Number of top results to return\n",
    "\n",
    "    Returns:\n",
    "        List of (document, similarity_score) tuples\n",
    "\n",
    "    \"\"\"\n",
    "    # Your code here:\n",
    "    # 1. Embed the query\n",
    "    # 2. Embed all documents\n",
    "    # 3. Calculate similarities\n",
    "    # 4. Return top_k most similar documents\n",
    "    query_emb = embedding_model.embed_query(query)\n",
    "    doc_embs = embedding_model.embed_documents(documents)\n",
    "    similarities = np.dot(doc_embs, query_emb)\n",
    "    top_indices = np.argsort(similarities)[-top_k:][::-1]\n",
    "    return [(documents[i], similarities[i]) for i in top_indices]\n",
    "\n",
    "# Test your function\n",
    "query = \"What is AI and neural networks?\"\n",
    "results = semantic_search(query, documents, top_k=3)\n",
    "for doc, score in results:\n",
    "    print(f\"Score: {score:.3f} | Doc: {doc}\")\n",
    "\n",
    "embeddings = embedding_model.embed_documents(sentences)\n",
    "similarities = np.dot(embeddings, np.array(embeddings).T)\n",
    "np.fill_diagonal(similarities, -1)  # Ignore self-similarity\n",
    "max_sim_indices = np.unravel_index(np.argmax(similarities), similarities.shape)\n",
    "most_similar_pair = (sentences[max_sim_indices[0]], sentences[max_sim_indices[1]])\n",
    "similarity_score = similarities[max_sim_indices]\n",
    "\n",
    "print(f\"Most similar pair: '{most_similar_pair[0]}' and '{most_similar_pair[1]}'\")\n",
    "print(f\"Cosine similarity score: {similarity_score:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "English: 'Good morning' | Best French Match: 'Bonjour' | Similarity: 0.4983\n",
      "English: 'Thank you' | Best French Match: 'Merci' | Similarity: 0.5890\n",
      "English: 'How are you?' | Best French Match: 'Comment allez-vous?' | Similarity: 0.5474\n",
      "English: 'Goodbye' | Best French Match: 'Au revoir' | Similarity: 0.7500\n"
     ]
    }
   ],
   "source": [
    "english_phrases = [\n",
    "    \"Good morning\",\n",
    "    \"Thank you\",\n",
    "    \"How are you?\",\n",
    "    \"Goodbye\",\n",
    "]\n",
    "\n",
    "french_phrases = [\n",
    "    \"Au revoir\",\n",
    "    \"Bonjour\",\n",
    "    \"Merci\",\n",
    "    \"Comment allez-vous?\",\n",
    "]\n",
    "\n",
    "# Your code here:\n",
    "# 1. Create embeddings for both lists\n",
    "# 2. For each English phrase, find its best French match\n",
    "# 3. Print the matches with similarity scores\n",
    "embeddings_en = embedding_model.embed_documents(english_phrases)\n",
    "embeddings_fr = embedding_model.embed_documents(french_phrases)\n",
    "similarities = np.dot(embeddings_en, np.array(embeddings_fr).T)\n",
    "for i, eng_phrase in enumerate(english_phrases):\n",
    "    best_match_idx = np.argmax(similarities[i])\n",
    "    best_match_fr = french_phrases[best_match_idx]\n",
    "    similarity_score = similarities[i][best_match_idx]\n",
    "    print(f\"English: '{eng_phrase}' | Best French Match: '{best_match_fr}' | Similarity: {similarity_score:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📊 Exercise Set 2: Advanced Similarity Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False: 'The cat is on the mat' ↔ 'A feline sits on the rug'\n",
      "False: 'I enjoy pizza' ↔ 'Pizza is my favorite food'\n",
      "False: 'The sky is blue' ↔ 'Elephants are large animals'\n"
     ]
    }
   ],
   "source": [
    "def is_paraphrase(sentence1: str, sentence2: str, threshold: float = 0.85) -> bool:\n",
    "    \"\"\"Determine if two sentences are paraphrases of each other.\n",
    "\n",
    "    Args:\n",
    "        sentence1: First sentence\n",
    "        sentence2: Second sentence\n",
    "        threshold: Minimum similarity to consider paraphrases\n",
    "\n",
    "    Returns:\n",
    "        True if paraphrases, False otherwise\n",
    "\n",
    "    \"\"\"\n",
    "    emb1 = embedding_model.embed_query(sentence1)\n",
    "    emb2 = embedding_model.embed_query(sentence2)\n",
    "    similarity = cosine_similarity(emb1, emb2)\n",
    "    return similarity >= threshold\n",
    "\n",
    "# Test cases\n",
    "test_pairs = [\n",
    "    (\"The cat is on the mat\", \"A feline sits on the rug\"),\n",
    "    (\"I enjoy pizza\", \"Pizza is my favorite food\"),\n",
    "    (\"The sky is blue\", \"Elephants are large animals\"),\n",
    "]\n",
    "\n",
    "for sent1, sent2 in test_pairs:\n",
    "    result = is_paraphrase(sent1, sent2)\n",
    "    print(f\"{result}: '{sent1}' ↔ '{sent2}'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 1 outlier: The car needs gasoline\n",
      "Group 2 outlier: Bananas are yellow fruits\n"
     ]
    }
   ],
   "source": [
    "groups = [\n",
    "    [\n",
    "        \"I love reading books\",\n",
    "        \"Books are my passion\",\n",
    "        \"Reading is enjoyable\",\n",
    "        \"The car needs gasoline\",  # Outlier\n",
    "        \"I enjoy a good novel\",\n",
    "    ],\n",
    "    [\n",
    "        \"Python is a programming language\",\n",
    "        \"Java is used for software development\",\n",
    "        \"JavaScript runs in browsers\",\n",
    "        \"Bananas are yellow fruits\",  # Outlier\n",
    "        \"C++ is a compiled language\",\n",
    "    ],\n",
    "]\n",
    "\n",
    "def find_outlier(sentences: list) -> str:\n",
    "    \"\"\"Find the sentence that is least similar to others in the group.\n",
    "\n",
    "    Args:\n",
    "        sentences: List of sentences\n",
    "\n",
    "    Returns:\n",
    "        The outlier sentence\n",
    "\n",
    "    \"\"\"\n",
    "    embeddings = embedding_model.embed_documents(sentences)\n",
    "    similarities = np.dot(embeddings, np.array(embeddings).T)\n",
    "    np.fill_diagonal(similarities, 0)  # Ignore self-similarity\n",
    "    avg_similarities = similarities.sum(axis=1) / (len(sentences) - 1)\n",
    "    outlier_index = np.argmin(avg_similarities)\n",
    "    return sentences[outlier_index]\n",
    "\n",
    "for i, group in enumerate(groups, 1):\n",
    "    outlier = find_outlier(group)\n",
    "    print(f\"Group {i} outlier: {outlier}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 1 outlier: The car needs gasoline\n",
      "Group 2 outlier: Bananas are yellow fruits\n"
     ]
    }
   ],
   "source": [
    "groups = [\n",
    "    [\n",
    "        \"I love reading books\",\n",
    "        \"Books are my passion\",\n",
    "        \"Reading is enjoyable\",\n",
    "        \"The car needs gasoline\",  # Outlier\n",
    "        \"I enjoy a good novel\",\n",
    "    ],\n",
    "    [\n",
    "        \"Python is a programming language\",\n",
    "        \"Java is used for software development\",\n",
    "        \"JavaScript runs in browsers\",\n",
    "        \"Bananas are yellow fruits\",  # Outlier\n",
    "        \"C++ is a compiled language\",\n",
    "    ],\n",
    "]\n",
    "\n",
    "def find_outlier(sentences: list) -> str:\n",
    "    \"\"\"Find the sentence that is least similar to others in the group.\n",
    "\n",
    "    Args:\n",
    "        sentences: List of sentences\n",
    "\n",
    "    Returns:\n",
    "        The outlier sentence\n",
    "\n",
    "    \"\"\"\n",
    "    embeddings = embedding_model.embed_documents(sentences)\n",
    "    similarities = np.dot(embeddings, np.array(embeddings).T)\n",
    "    np.fill_diagonal(similarities, 0)  # Ignore self-similarity\n",
    "    avg_similarities = similarities.sum(axis=1) / (len(sentences) - 1)\n",
    "    outlier_index = np.argmin(avg_similarities)\n",
    "    return sentences[outlier_index]\n",
    "\n",
    "for i, group in enumerate(groups, 1):\n",
    "    outlier = find_outlier(group)\n",
    "    print(f\"Group {i} outlier: {outlier}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎨 Exercise Set 3: Visualization Challenges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(\n",
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/umap/umap_.py:2462: UserWarning: n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "domains = {\n",
    "    \"Sports\": [\"Football\", \"Basketball\", \"Tennis\", \"Swimming\", \"Running\"],\n",
    "    \"Food\": [\"Pizza\", \"Sushi\", \"Burger\", \"Pasta\", \"Salad\"],\n",
    "    \"Music\": [\"Guitar\", \"Piano\", \"Drums\", \"Violin\", \"Saxophone\"],\n",
    "}\n",
    "\n",
    "# Your code here:\n",
    "# 1. Flatten the words and track their domains\n",
    "all_words = []\n",
    "domain_labels = []\n",
    "for domain, words in domains.items():\n",
    "    all_words.extend(words)\n",
    "    domain_labels.extend([domain] * len(words))\n",
    "\n",
    "# 2. Generate embeddings\n",
    "embeddings = embedding_model.embed_documents(all_words)\n",
    "\n",
    "# 3. Reduce to 2D with UMAP\n",
    "umap_model = umap.UMAP(n_components=2, random_state=42)\n",
    "reduced_embeddings = umap_model.fit_transform(embeddings)\n",
    "\n",
    "# 4. Create a scatter plot with different colors per domain\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "for domain in domains:\n",
    "    indices = [i for i, label in enumerate(domain_labels) if label == domain]\n",
    "    x_coords = [reduced_embeddings[i][0] for i in indices]\n",
    "    y_coords = [reduced_embeddings[i][1] for i in indices]\n",
    "    plt.scatter(x_coords, y_coords, label=domain, alpha=0.7)\n",
    "plt.title(\"UMAP Projection of Word Embeddings by Domain\")\n",
    "# 5. Add labels to each point\n",
    "for i, word in enumerate(all_words):\n",
    "    plt.annotate(word, (reduced_embeddings[i][0], reduced_embeddings[i][1]), fontsize=8, alpha=0.7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(\n",
      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/umap/umap_.py:2462: UserWarning: n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_word = \"Summer\"\n",
    "end_word = \"Winter\"\n",
    "related_words = [\n",
    "    \"Summer\", \"Warm\", \"Sunny\", \"Hot\", \"Beach\",\n",
    "    \"Autumn\", \"Cool\", \"Falling leaves\", \"Harvest\",\n",
    "    \"Winter\", \"Cold\", \"Snow\", \"Ice\", \"Freezing\",\n",
    "]\n",
    "\n",
    "# Your code here:\n",
    "# 1. Generate embeddings for all words\n",
    "embeddings = embedding_model.embed_documents(related_words)\n",
    "\n",
    "# 2. Reduce to 2D\n",
    "umap_model = umap.UMAP(n_components=2, random_state=42)\n",
    "reduced_embeddings = umap_model.fit_transform(embeddings)\n",
    "# 3. Create a path visualization connecting summer → autumn → winter\n",
    "plt.figure(figsize=(10, 8))\n",
    "for i, word in enumerate(related_words):\n",
    "    plt.scatter(reduced_embeddings[i][0], reduced_embeddings[i][1], alpha=0.7)\n",
    "    plt.annotate(word, (reduced_embeddings[i][0], reduced_embeddings[i][1]), fontsize=9)\n",
    "plt.title(\"Semantic Journey: Summer → Autumn → Winter\")\n",
    "# 4. Use arrows to show the semantic journey\n",
    "for i in range(len(related_words) - 1):\n",
    "    plt.arrow(\n",
    "        reduced_embeddings[i][0],\n",
    "        reduced_embeddings[i][1],\n",
    "        reduced_embeddings[i + 1][0] - reduced_embeddings[i][0],\n",
    "        reduced_embeddings[i + 1][1] - reduced_embeddings[i][1],\n",
    "        head_width=0.05,\n",
    "        head_length=0.1,\n",
    "        fc=\"gray\",\n",
    "        ec=\"gray\",\n",
    "        alpha=0.5,\n",
    "    )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔨 Exercise Set 4: Real-World Applications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Q: I forgot my password, what should I do?\n",
      "No matching FAQ found\n",
      "\n",
      "Q: How much does delivery cost?\n",
      "No matching FAQ found\n",
      "\n",
      "Q: Can I return items I purchased?\n",
      "No matching FAQ found\n",
      "\n",
      "Q: What is the meaning of life?\n",
      "No matching FAQ found\n"
     ]
    }
   ],
   "source": [
    "faqs = {\n",
    "    \"How do I reset my password?\": \"Go to Settings > Security > Reset Password\",\n",
    "    \"What are your shipping costs?\": \"Shipping is free for orders over $50\",\n",
    "    \"How can I track my order?\": \"Use the tracking number sent to your email\",\n",
    "    \"Do you accept returns?\": \"Yes, within 30 days with original receipt\",\n",
    "    \"What payment methods do you accept?\": \"We accept credit cards, PayPal, and bank transfers\",\n",
    "}\n",
    "\n",
    "def match_faq(user_question: str, faqs: dict, threshold: float = 0.7) -> tuple[str, str, float] | None:\n",
    "    \"\"\"Find the best matching FAQ for a user question.\n",
    "\n",
    "    Args:\n",
    "        user_question: Question from user\n",
    "        faqs: Dictionary of FAQ questions and answers\n",
    "        threshold: Minimum similarity to return a match\n",
    "\n",
    "    Returns:\n",
    "        Tuple of (matching_question, answer, similarity) or None\n",
    "\n",
    "    \"\"\"\n",
    "    # Generate embedding for user question\n",
    "    user_emb = embedding_model.embed_query(user_question)\n",
    "\n",
    "    # Find best matching FAQ\n",
    "    best_match = None\n",
    "    best_similarity = -1\n",
    "\n",
    "    for faq_question, answer in faqs.items():\n",
    "        faq_emb = embedding_model.embed_query(faq_question)\n",
    "        similarity = cosine_similarity(user_emb, faq_emb)\n",
    "\n",
    "        if similarity > best_similarity:\n",
    "            best_similarity = similarity\n",
    "            best_match = (faq_question, answer, similarity)\n",
    "\n",
    "    # Return match only if above threshold\n",
    "    if best_similarity >= threshold:\n",
    "        return best_match\n",
    "    return None\n",
    "\n",
    "# Test queries\n",
    "test_queries = [\n",
    "    \"I forgot my password, what should I do?\",\n",
    "    \"How much does delivery cost?\",\n",
    "    \"Can I return items I purchased?\",\n",
    "    \"What is the meaning of life?\",  # Should not match\n",
    "]\n",
    "\n",
    "for query in test_queries:\n",
    "    result = match_faq(query, faqs)\n",
    "    if result:\n",
    "        print(f\"\\nQ: {query}\")\n",
    "        print(f\"Match: {result[0]} (similarity: {result[2]:.2f})\")\n",
    "        print(f\"A: {result[1]}\")\n",
    "    else:\n",
    "        print(f\"\\nQ: {query}\")\n",
    "        print(\"No matching FAQ found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate groups found:\n",
      "\n",
      "Group 1:\n",
      "  [0] The quick brown fox jumps over the lazy dog\n",
      "  [5] The quick brown fox jumps over the lazy dog\n"
     ]
    }
   ],
   "source": [
    "documents = [\n",
    "    \"The quick brown fox jumps over the lazy dog\",\n",
    "    \"A fast brown fox leaps over a sleepy dog\",  # Near-duplicate of #1\n",
    "    \"Python is a popular programming language\",\n",
    "    \"The weather is nice today\",\n",
    "    \"Python is widely used for programming\",  # Near-duplicate of #3\n",
    "    \"The quick brown fox jumps over the lazy dog\",  # Exact duplicate of #1\n",
    "]\n",
    "\n",
    "def find_duplicates(documents: list, similarity_threshold: float = 0.85) -> list[list[int]]:\n",
    "    \"\"\"Find groups of near-duplicate documents.\n",
    "\n",
    "    Args:\n",
    "        documents: List of text documents\n",
    "        similarity_threshold: Minimum similarity to consider duplicates\n",
    "\n",
    "    Returns:\n",
    "        List of duplicate groups, each group is a list of indices\n",
    "\n",
    "    \"\"\"\n",
    "    # Your code here:\n",
    "    # 1. Generate embeddings\n",
    "    embeddings = embedding_model.embed_documents(documents)\n",
    "    # 2. Compute pairwise similarities\n",
    "    n = len(embeddings)\n",
    "    similarities = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            similarities[i, j] = cosine_similarity(embeddings[i], embeddings[j])\n",
    "    # 3. Group documents with similarity > threshold\n",
    "    visited = set()\n",
    "    duplicate_groups = []\n",
    "    for i in range(n):\n",
    "        if i in visited:\n",
    "            continue\n",
    "        group = [i]\n",
    "        visited.add(i)\n",
    "        for j in range(i + 1, n):\n",
    "            if j not in visited and similarities[i, j] > similarity_threshold:\n",
    "                group.append(j)\n",
    "                visited.add(j)\n",
    "        if len(group) > 1:\n",
    "            duplicate_groups.append(group)\n",
    "    # 4. Return groups of duplicate indices\n",
    "    return duplicate_groups\n",
    "\n",
    "duplicate_groups = find_duplicates(documents)\n",
    "print(\"Duplicate groups found:\")\n",
    "for i, group in enumerate(duplicate_groups, 1):\n",
    "    print(f\"\\nGroup {i}:\")\n",
    "    for idx in group:\n",
    "        print(f\"  [{idx}] {documents[idx]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User has read:\n",
      "  • Introduction to Machine Learning with Python\n",
      "  • Understanding Neural Networks and Deep Learning\n",
      "\n",
      "Recommended articles:\n",
      "  • Building AI Applications with TensorFlow\n",
      "  • Advanced Python Programming Techniques\n",
      "  • How to Train Your Dog: A Complete Guide\n"
     ]
    }
   ],
   "source": [
    "articles = [\n",
    "    \"Introduction to Machine Learning with Python\",\n",
    "    \"10 Best Pasta Recipes for Beginners\",\n",
    "    \"Understanding Neural Networks and Deep Learning\",\n",
    "    \"How to Train Your Dog: A Complete Guide\",\n",
    "    \"Advanced Python Programming Techniques\",\n",
    "    \"The Ultimate Guide to Italian Cooking\",\n",
    "    \"Cat Behavior: Understanding Your Feline Friend\",\n",
    "    \"Building AI Applications with TensorFlow\",\n",
    "]\n",
    "\n",
    "def recommend_articles(user_history: list, all_articles: list, n_recommendations: int = 3) -> list[int]:\n",
    "    \"\"\"Recommend articles based on reading history.\n",
    "\n",
    "    Args:\n",
    "        user_history: List of article indices the user has read\n",
    "        all_articles: List of all available articles\n",
    "        n_recommendations: Number of recommendations to return\n",
    "\n",
    "    Returns:\n",
    "        List of recommended article indices (excluding already read)\n",
    "\n",
    "    \"\"\"\n",
    "    # Your code here:\n",
    "    # 1. Get embeddings for articles in user history\n",
    "    user_embeddings = embedding_model.embed_documents([all_articles[i] for i in user_history])\n",
    "    # 2. Compute \"user profile\" (average embedding of read articles)\n",
    "    user_profile = np.mean(user_embeddings, axis=0)\n",
    "    # 3. Find articles most similar to user profile\n",
    "    all_embeddings = embedding_model.embed_documents(all_articles)\n",
    "    similarities = [cosine_similarity(user_profile, emb) for emb in all_embeddings]\n",
    "    # 4. Exclude already-read articles\n",
    "    recommendations = [(i, similarities[i]) for i in range(len(all_articles)) if i not in user_history]\n",
    "    # 5. Return top N recommendations\n",
    "    return [idx for idx, _ in sorted(recommendations, key=lambda x: x[1], reverse=True)[:n_recommendations]]\n",
    "\n",
    "# Test: User has read articles about ML/AI\n",
    "user_read = [0, 2]  # Machine Learning and Neural Networks articles\n",
    "recommendations = recommend_articles(user_read, articles, n_recommendations=3)\n",
    "\n",
    "print(\"User has read:\")\n",
    "for idx in user_read:\n",
    "    print(f\"  • {articles[idx]}\")\n",
    "\n",
    "print(\"\\nRecommended articles:\")\n",
    "for idx in recommendations:\n",
    "    print(f\"  • {articles[idx]}\")\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "studies (3.13.9)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}