ollama_RAG_exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ollama RAG exercises\n",
    "\n",
    "Just some code to learn more about RAG. Don't expect too much of it, it's just fun! \n",
    "\n",
    "### Contents\n",
    "1. Learn the theory of cosines similarity\n",
    "2. Create embeddings with ollama.\n",
    "3. Calculate cosine similarity between two sentences\n",
    "4. Convert a pdf to text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "metadata": {}
   },
   "source": [
    "## 1. Learn theory of cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "#Define two points\n",
    "\n",
    "# Choose values [1-10] (integers/floats) to properly plot it below\n",
    "# point 1\n",
    "p1x = 4\n",
    "p1y = 3\n",
    "\n",
    "#point 2\n",
    "p2x = 3\n",
    "p2y = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9838699100999074\n"
     ]
    }
   ],
   "source": [
    "#function to calculate cosine similarity\n",
    "def cosine_similarity(a, b):\n",
    "    dot_product = sum(x * y for x, y in zip(a, b))\n",
    "    magnitude_a = sum(x * x for x in a) ** 0.5\n",
    "    magnitude_b = sum(x * x for x in b) ** 0.5\n",
    "    cos_sim = dot_product / (magnitude_a * magnitude_b)\n",
    "    return cos_sim\n",
    "\n",
    "p1x, p1y = 1, 2  # Example values for the first vector\n",
    "p2x, p2y = 3, 4  # Example values for the second vector\n",
    "\n",
    "result = cosine_similarity((p1x, p1y), (p2x, p2y))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the points and create a vector from 0,0\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#get points and plot them\n",
    "points_x = p1x, p2x\n",
    "points_y = p1y, p2y\n",
    "plt.plot(points_x, points_y, 'o') \n",
    "\n",
    "#plot vectors \n",
    "plt.quiver([0, 0], [0, 0], [p1x, p2x], [p1y,p2y], angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "#plot axes and show it\n",
    "plt.title(f'Cosine similarity: {result}')\n",
    "plt.xlim(0, 10)\n",
    "plt.ylim(0, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1: \n",
    "1. play with the points and see what happens\n",
    "2. create two examples:\n",
    "    - Where cosine similarity is 1\n",
    "    - Where cosine similarity is 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create embeddings with ollama. Compare two models in speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                            \tID          \tSIZE  \tMODIFIED     \n",
      "llava:latest                    \t8dd30f6b0cb1\t4.7 GB\t12 days ago \t\n",
      "nomic-embed-text:latest         \t0a109f422b47\t274 MB\t3 weeks ago \t\n",
      "bramvanroy/fietje-2b-chat:Q3_K_M\t6dd6525c1e6c\t1.4 GB\t3 weeks ago \t\n",
      "mistral:latest                  \t61e88e884507\t4.1 GB\t3 weeks ago \t\n",
      "tinyllama:latest                \t2644915ede35\t637 MB\t4 months ago\t\n"
     ]
    }
   ],
   "source": [
    "#check if you have to nomic-embed-text\n",
    "!ollama list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 178 ms, sys: 80.2 ms, total: 258 ms\n",
      "Wall time: 35 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'embedding': [11.431639671325684,\n",
       "  3.544231653213501,\n",
       "  -1.106918454170227,\n",
       "  4.004763603210449,\n",
       "  5.365742206573486,\n",
       "  -2.406923294067383,\n",
       "  -3.4949405193328857,\n",
       "  7.587218761444092,\n",
       "  -1.8361834287643433,\n",
       "  -3.0675888061523438,\n",
       "  0.36008748412132263,\n",
       "  -1.7788317203521729,\n",
       "  0.9328023195266724,\n",
       "  6.9177565574646,\n",
       "  1.8435193300247192,\n",
       "  -5.257628917694092,\n",
       "  -6.054622173309326,\n",
       "  -3.472109079360962,\n",
       "  3.6425788402557373,\n",
       "  1.893931269645691,\n",
       "  2.5078177452087402,\n",
       "  -2.273056983947754,\n",
       "  -0.6021896004676819,\n",
       "  3.2229857444763184,\n",
       "  -10.368236541748047,\n",
       "  -8.19961166381836,\n",
       "  -2.2583000659942627,\n",
       "  -13.680386543273926,\n",
       "  3.9005494117736816,\n",
       "  -1.7455397844314575,\n",
       "  -3.8200910091400146,\n",
       "  -2.9271435737609863,\n",
       "  3.115286350250244,\n",
       "  0.5505885481834412,\n",
       "  -1.3508365154266357,\n",
       "  -3.0127758979797363,\n",
       "  0.1425732523202896,\n",
       "  0.010346248745918274,\n",
       "  -0.5541189908981323,\n",
       "  8.441024780273438,\n",
       "  0.4676221013069153,\n",
       "  -2.2938406467437744,\n",
       "  -6.220666408538818,\n",
       "  -1.493468165397644,\n",
       "  -2.938239574432373,\n",
       "  -2.494166851043701,\n",
       "  -3.1885955333709717,\n",
       "  -3.464813232421875,\n",
       "  -2.074932813644409,\n",
       "  -1.5986841917037964,\n",
       "  -2.1823320388793945,\n",
       "  8.527981758117676,\n",
       "  -4.776736259460449,\n",
       "  -1.0232657194137573,\n",
       "  -2.554222583770752,\n",
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       "  0.7047680616378784,\n",
       "  ...]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "import ollama\n",
    "ollama.embeddings(model='mistral', prompt='The dog ran fast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.88 ms, sys: 3.33 ms, total: 6.21 ms\n",
      "Wall time: 1.67 s\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "import ollama\n",
    "ollama.embeddings(model='nomic-embed-text', prompt='The dog ran fast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculate cosine similarity between to sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (2.0.0)\n",
      "Requirement already satisfied: filelock in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from torch) (3.9.0)\n",
      "Requirement already satisfied: typing-extensions in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from torch) (4.11.0)\n",
      "Requirement already satisfied: sympy in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from torch) (1.11.1)\n",
      "Requirement already satisfied: networkx in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from torch) (2.8.4)\n",
      "Requirement already satisfied: jinja2 in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from torch) (3.1.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (from jinja2->torch) (2.1.1)\n",
      "Requirement already satisfied: mpmath>=0.19 in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages/mpmath-1.2.1-py3.10.egg (from sympy->torch) (1.2.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768\n",
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      "         4.9629e-01,  7.1099e-01,  5.9744e-01, -7.0047e-02,  2.7775e-02,\n",
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      "         1.8754e-01,  1.3257e-02,  2.7259e-01,  8.0583e-01, -1.3387e+00,\n",
      "         1.7289e+00,  7.7940e-01,  1.5260e-02,  1.0007e+00, -6.0251e-01,\n",
      "        -5.0219e-01,  2.9726e-01,  1.7004e-01, -1.6372e+00,  1.0240e+00,\n",
      "        -5.8322e-01, -8.3365e-01, -4.5333e-01,  3.3453e-01, -4.6071e-01,\n",
      "         5.9090e-01,  9.1230e-02, -3.6212e-01,  1.1751e-01,  4.3113e-01,\n",
      "         5.1299e-01, -2.4114e-01, -7.3479e-01,  1.6941e+00,  2.6313e-01,\n",
      "         4.5026e-01,  1.6113e+00, -2.2758e-02, -2.6792e-02, -7.1482e-01,\n",
      "         2.6402e-01, -5.8969e-01,  6.7241e-01,  3.8755e-01, -5.7846e-01,\n",
      "        -5.9721e-01,  1.0979e+00, -7.1029e-01,  1.4049e+00, -4.3013e-01,\n",
      "        -4.7861e-01,  8.2881e-01, -3.4616e-01, -1.8457e-01, -8.8247e-01,\n",
      "         1.0859e+00,  7.3756e-01,  1.6343e-01,  1.4509e+00,  2.7033e-01,\n",
      "         4.2976e-01, -1.5173e-01, -8.3415e-01, -4.6807e-01, -1.0415e-01,\n",
      "         1.2499e+00, -7.2818e-01,  2.9466e-01, -6.6822e-01, -5.1232e-01,\n",
      "        -1.2298e+00,  9.6810e-01,  1.1182e+00, -5.5709e-01, -1.0031e-01,\n",
      "         6.3505e-01, -5.6864e-01,  5.2311e-01, -2.9775e-01,  1.4004e+00,\n",
      "         8.1554e-01, -1.0205e+00,  4.4879e-01, -1.0452e+00, -6.1481e-01,\n",
      "         4.0870e-01, -7.3406e-01,  1.7588e-01,  5.5186e-04,  3.0423e-01,\n",
      "        -1.1342e+00, -1.3192e-01,  4.8707e-02, -3.1964e-01, -4.3701e-02,\n",
      "         7.5232e-02,  6.9573e-01,  4.1214e-01,  6.8030e-01,  4.0948e-01,\n",
      "         4.9492e-01, -5.1702e-01,  2.2307e-01, -8.7377e-01,  5.4579e-01,\n",
      "         9.7385e-01,  9.8361e-01, -2.9993e-01,  7.4628e-01,  1.6068e-01,\n",
      "        -6.2333e-01, -4.1413e-01,  4.4689e-01, -2.5635e-01, -4.0550e-01,\n",
      "        -1.8337e-01, -2.1892e+00, -1.1597e+00, -1.0131e+00,  8.3174e-01,\n",
      "        -3.4356e-01, -1.0970e+00, -1.7899e-01, -1.8507e-01,  3.6220e-01,\n",
      "         4.5938e-01,  5.0039e-01, -5.6911e-01, -7.8044e-01, -6.2315e-03,\n",
      "         2.5499e-01, -1.3253e+00, -1.2400e+00,  1.8913e-01,  1.0471e-01,\n",
      "        -7.2848e-01,  7.7375e-01, -6.7843e-01, -1.3424e+00,  3.3546e-01,\n",
      "        -6.5707e-01, -7.6870e-01,  3.6293e-01, -3.2118e-01,  1.5505e-02,\n",
      "         1.4216e+00,  1.4401e-01, -5.2856e-01,  5.1749e-01, -5.6374e-01,\n",
      "         1.3200e+00,  3.8203e-01, -3.3204e-01, -5.6805e-01,  3.0787e-01,\n",
      "         1.3907e+00,  1.2469e+00,  3.5377e-01,  5.4416e-01, -9.5557e-01,\n",
      "         3.3188e-01,  4.8953e-01, -3.0183e-01,  2.2048e-01,  3.1654e-01,\n",
      "        -2.9014e-03,  7.5897e-01,  2.8866e-01,  2.2954e-01, -1.8977e+00,\n",
      "        -5.7592e-02,  8.9168e-01,  4.3507e-01,  1.5791e-01, -1.7707e-01,\n",
      "         2.7296e-01, -9.1247e-01,  1.5130e-01,  1.8733e-01,  1.2184e+00,\n",
      "         1.1737e+00, -1.3147e+00, -3.2503e-01, -1.6060e-01,  1.1512e+00,\n",
      "         1.5491e+00,  1.0821e+00, -1.6050e+00, -3.6445e-02,  7.8619e-01,\n",
      "         3.5293e-03, -1.2683e-01,  6.4668e-01,  8.8912e-01,  1.4213e+00,\n",
      "        -1.3257e+00, -5.0304e-01,  1.6857e-02, -2.6903e-01,  1.3699e+00,\n",
      "         1.4705e+00,  3.9220e-01, -1.7819e-01,  5.9006e-01, -2.1631e-01,\n",
      "        -4.9260e-01,  2.0136e-01, -2.6928e-01,  5.1893e-01,  1.1307e+00,\n",
      "        -8.6279e-01,  6.1432e-01,  1.1035e+00, -2.6754e-01, -4.2307e-01,\n",
      "        -2.5804e-01, -2.1511e-01,  1.6994e-01,  9.2932e-01,  1.6032e+00,\n",
      "         4.2960e-01, -1.0628e+00, -1.9711e+00, -1.4875e+00,  2.0990e-01,\n",
      "         5.4538e-01,  3.4266e-01,  2.1108e-01,  7.3013e-01, -5.6359e-01,\n",
      "         7.9467e-01, -3.1867e-01,  6.0522e-01,  2.5604e-01,  7.1347e-01,\n",
      "        -8.7361e-01, -1.0987e+00,  1.1098e+00, -5.8698e-01, -1.0673e+00,\n",
      "         3.6738e-01,  7.0508e-01, -2.1999e-02,  3.1755e-01,  7.7943e-01,\n",
      "         4.5200e-01, -2.0505e-01, -1.7038e+00, -8.1653e-01,  9.2433e-01,\n",
      "        -1.2747e+00, -2.7000e-01,  1.1448e+00, -6.1683e-02,  8.3156e-01,\n",
      "        -1.6605e+00,  2.8323e-01,  2.2800e-01, -1.3082e+00,  6.1563e-01,\n",
      "         8.2309e-01, -8.7286e-01, -5.1371e-01, -1.4849e+00, -1.5808e+00,\n",
      "         3.6504e-01,  1.7285e-01, -1.0162e+00, -1.0995e-01,  2.8420e-01,\n",
      "         6.3931e-01, -7.8427e-01, -8.2099e-01, -4.6961e-02, -7.2568e-01,\n",
      "        -4.9696e-01, -1.7981e-01,  1.0014e+00,  4.7688e-01,  3.1984e-01,\n",
      "        -1.9984e-02, -5.9795e-01,  1.7529e-01, -7.7270e-01, -1.0235e+00,\n",
      "         8.0361e-01, -1.6630e+00, -1.9581e-01, -1.2022e+00, -9.2604e-02,\n",
      "         3.3859e-01, -1.2274e+00, -2.6983e-01, -8.4342e-01,  7.0254e-01,\n",
      "        -1.4451e+00, -1.0936e+00, -4.2266e-01, -1.0842e+00, -7.0246e-01,\n",
      "         1.0005e+00, -2.5271e-01,  1.3391e+00,  6.5424e-01,  4.9784e-01,\n",
      "         2.5030e-01,  5.2506e-01,  2.0013e-01,  3.7415e-01,  1.7099e-01,\n",
      "         2.7218e-01, -1.4271e+00, -1.3695e+00, -2.0658e-01, -8.3333e-02,\n",
      "         3.5507e-01, -1.6230e-01,  9.9951e-01, -6.7959e-01, -4.0861e-01,\n",
      "         5.7530e-01, -8.1386e-01, -2.8471e-01,  1.1949e+00,  4.3304e-01,\n",
      "         3.4497e-02, -5.7265e-01, -1.4710e+00,  1.8408e+00, -2.3974e-01,\n",
      "        -6.8301e-01,  2.9440e-01,  4.8936e-01, -3.6722e-01, -2.0428e-01,\n",
      "         2.5906e-01, -2.9889e-01, -1.8803e+00,  7.3069e-02, -2.9138e-01,\n",
      "        -2.6273e-01, -2.1925e-02,  6.4811e-01, -9.0378e-01, -1.1024e+00,\n",
      "         2.3623e-01, -9.2100e-05, -3.5606e-01, -2.4001e-01,  2.0289e-01,\n",
      "         4.3678e-01,  2.0660e-01, -2.0078e-01, -7.4486e-01,  1.3146e-01,\n",
      "        -1.7764e-01,  1.4118e+00, -1.1597e-01, -1.0221e+00,  3.2686e-02,\n",
      "         3.5999e-01, -5.1951e-01,  8.4682e-01, -5.7118e-02,  8.2974e-01,\n",
      "        -3.7130e-01, -9.8690e-01, -1.4465e+00,  5.2144e-01,  1.1340e+00,\n",
      "        -8.7606e-01, -1.3694e-01, -1.9409e+00, -4.6437e-01, -7.1464e-01,\n",
      "         5.6243e-01, -1.4238e-01,  6.4657e-01,  9.4148e-02,  1.9106e+00,\n",
      "         1.7328e+00,  7.3207e-01,  1.9418e-01,  1.2972e+00,  6.2479e-01,\n",
      "        -1.6851e-01,  8.9916e-01,  6.8623e-01,  8.9949e-01,  3.4342e-02,\n",
      "         2.0741e+00,  1.9983e+00,  1.1110e+00, -2.1191e-01,  2.0006e-01,\n",
      "        -1.1766e+00,  1.1685e+00, -7.9794e-01, -1.0760e+00, -2.6250e-01,\n",
      "         2.9439e-01,  4.8977e-02,  3.7159e-01,  5.8354e-01,  7.6623e-01,\n",
      "        -4.1525e-01,  1.2691e-01, -7.7349e-01, -1.7951e+00, -3.2843e-01,\n",
      "         8.5712e-01,  6.7413e-01, -1.0326e-01, -2.9083e-01,  2.7281e-01,\n",
      "         7.8557e-01,  1.0979e+00,  9.5849e-02,  6.1663e-01, -1.1634e-01,\n",
      "         1.3476e-01, -1.0316e-01,  3.6882e-01, -1.0270e+00,  7.5310e-01,\n",
      "        -9.9192e-01,  1.2557e+00, -1.2426e+00,  4.7942e-01, -4.0357e-02,\n",
      "        -2.1379e-01, -1.0353e+00,  1.5156e-01,  2.8309e-01, -5.3139e-01,\n",
      "         5.8704e-01, -7.8372e-01,  1.9464e-01, -5.7947e-01,  1.4091e+00,\n",
      "        -1.3865e-01,  9.8165e-01,  3.3237e-01,  1.0386e+00, -1.4421e+00,\n",
      "        -9.8216e-02,  3.0834e-01, -3.2048e-01, -1.9949e-01,  2.9379e-01,\n",
      "         3.4779e-01,  2.4849e-02, -7.2681e-01,  1.0476e+00,  1.5000e+00,\n",
      "         4.7448e-01, -1.6519e-01, -2.5077e-01, -6.5528e-02,  6.7645e-01,\n",
      "         1.3279e+00, -6.4188e-01, -1.3117e+00, -1.3150e+00, -7.6347e-01,\n",
      "        -5.1740e-01,  2.4498e-01, -5.1707e-01,  8.0976e-01, -7.5405e-01,\n",
      "        -9.8660e-02,  5.2152e-01, -1.3080e+00,  3.1069e-01, -2.3895e-01,\n",
      "        -1.1903e-01, -9.6332e-01, -4.7011e-01,  7.9666e-02, -3.0178e-01,\n",
      "        -9.1727e-01, -7.1133e-02,  5.3268e-01, -9.4022e-02,  2.9925e-01,\n",
      "         3.4658e-01, -8.9282e-01,  1.1735e+00, -3.2238e-02,  6.3813e-02,\n",
      "        -2.3950e-01, -4.4658e-01, -9.1633e-01,  5.8801e-01,  6.2397e-01,\n",
      "         2.2290e-01, -2.7817e-01,  7.3628e-01,  1.8008e+00, -2.7838e-02,\n",
      "         4.7719e-01, -5.1060e-01,  7.4375e-01,  4.3490e-01,  1.7567e-01,\n",
      "        -1.9920e+00, -1.5402e+00,  7.1853e-02])\n"
     ]
    }
   ],
   "source": [
    "import ollama\n",
    "import torch\n",
    "embeddings_dict = ollama.embeddings(model='nomic-embed-text', prompt='The dog ran fast')\n",
    "#embeddings_0 = embeddings_dict['embedding']\n",
    "embeddings_0 = torch.tensor(embeddings_dict['embedding'])\n",
    "print(len(embeddings_0))\n",
    "print(embeddings_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768\n",
      "tensor([ 4.2110e-01,  6.6635e-01, -5.0505e+00, -1.9031e-01,  1.1473e+00,\n",
      "         2.0951e-01,  5.1880e-01,  4.4031e-01, -1.4749e+00,  2.0616e-01,\n",
      "        -5.9510e-01,  2.2953e+00,  4.6167e-01, -4.2906e-01,  8.7373e-01,\n",
      "        -1.3725e+00, -9.1714e-01, -7.1614e-01, -3.0354e-01, -1.6448e-01,\n",
      "         7.4766e-03, -9.7808e-01, -4.0316e-01,  9.2544e-01,  1.7428e+00,\n",
      "         7.3068e-01,  4.2925e-02,  8.6875e-01, -2.6145e-01,  1.6886e+00,\n",
      "        -8.8135e-02, -1.0700e+00,  1.2055e-01, -4.9429e-01, -6.6349e-01,\n",
      "        -7.2826e-01,  5.0264e-01,  9.4046e-01,  6.5916e-01, -5.1168e-01,\n",
      "         5.6753e-02,  1.5958e-01, -8.9299e-01, -7.3276e-01,  1.0355e+00,\n",
      "        -1.4667e+00, -1.5041e-01,  5.4064e-01,  2.2586e-01, -1.1992e+00,\n",
      "        -1.3947e-02, -2.2479e-02,  1.5459e-01, -1.6186e+00,  6.6384e-02,\n",
      "         1.1639e+00,  1.6904e+00, -1.2976e+00, -4.1743e-01, -3.9385e-02,\n",
      "         1.7161e-01,  9.3485e-01, -1.1881e-01,  6.4385e-01,  4.0669e-01,\n",
      "        -2.2116e+00, -1.1474e+00,  1.1536e+00,  5.0095e-01, -9.2384e-01,\n",
      "         1.5217e-01, -1.5448e-01,  1.3520e+00,  7.7251e-01, -1.0204e+00,\n",
      "         1.6498e-01, -2.3906e-02, -6.6536e-01,  1.4578e-01, -1.3806e+00,\n",
      "         5.8075e-02, -1.2707e+00,  1.2064e+00,  5.8944e-01,  5.9517e-01,\n",
      "         2.0164e-01, -7.6932e-02,  7.0637e-01, -1.4081e+00,  4.2545e-01,\n",
      "         7.6929e-01, -1.8052e-02,  1.5121e-01,  1.2175e+00, -6.2353e-02,\n",
      "        -2.0478e-01, -2.1199e+00,  3.5289e-01, -6.3771e-01,  1.3416e-02,\n",
      "        -8.5170e-01, -2.3994e-01, -3.9381e-01,  8.9154e-01,  1.1459e+00,\n",
      "         1.0899e+00,  9.8022e-02,  7.2475e-01, -1.6240e-01, -4.1283e-01,\n",
      "        -2.3875e-02,  2.9639e-01, -5.9662e-01, -1.2695e+00,  4.6903e-01,\n",
      "        -2.9257e-01,  6.6595e-01, -1.5551e+00,  6.2504e-01,  6.7456e-01,\n",
      "        -4.8894e-02, -1.6266e+00,  1.0682e-01,  8.8346e-01,  1.0880e+00,\n",
      "         5.6048e-01, -1.2278e+00,  1.6718e+00,  1.2134e+00, -1.1071e+00,\n",
      "        -9.4455e-01,  4.7952e-01, -7.2466e-01,  1.0117e+00, -2.0018e-01,\n",
      "         8.5413e-02, -4.7112e-01, -1.2626e+00, -4.1466e-01,  7.8321e-01,\n",
      "         1.4181e+00,  6.6851e-01,  6.5924e-01, -9.9593e-01,  8.7428e-01,\n",
      "        -6.3550e-01,  1.1459e+00, -7.6631e-01, -1.2033e+00,  1.2650e+00,\n",
      "         5.7751e-01,  8.4312e-01,  3.1847e-01,  9.3339e-01,  1.7928e-01,\n",
      "        -9.9691e-01, -1.0268e+00, -1.8007e-01,  6.6739e-01, -7.8827e-01,\n",
      "        -2.1429e-01,  1.3943e+00, -5.1460e-01,  8.7423e-02, -6.0910e-01,\n",
      "        -1.4748e+00,  1.1737e+00,  1.7639e+00, -8.8427e-02,  7.8396e-01,\n",
      "        -1.9345e+00, -1.1954e+00,  5.2771e-01,  6.4152e-01,  1.8812e-01,\n",
      "        -4.7127e-01,  2.3413e-01, -3.9525e-01, -3.3026e-01, -1.1251e-01,\n",
      "         2.8065e-01, -5.9394e-01,  9.8637e-01, -4.8999e-01, -1.2161e+00,\n",
      "         1.2049e+00, -1.3035e-01, -1.1236e-01, -1.9357e-01, -9.5142e-01,\n",
      "        -6.8157e-01,  7.1574e-01, -1.6197e+00,  8.8767e-02, -3.8111e-01,\n",
      "        -2.4283e-01,  1.6876e+00,  5.2371e-01, -1.8849e-01, -5.7808e-01,\n",
      "        -9.9074e-01,  3.0291e-02, -1.7721e-01, -1.9784e-03, -1.4990e+00,\n",
      "         5.2358e-01,  4.8901e-01,  1.7607e-01, -2.1677e-01,  5.7277e-01,\n",
      "         1.2081e+00, -4.2073e-01,  1.8659e-01,  3.0131e-01,  1.2719e+00,\n",
      "        -1.0529e+00, -1.3613e-01, -1.6672e+00, -2.6668e-01,  5.6423e-01,\n",
      "         7.9826e-01,  7.0241e-01, -7.3237e-02,  1.3462e-01, -4.0043e-01,\n",
      "         1.1967e+00, -3.3165e-01, -1.1849e+00, -1.7890e-01, -6.3581e-01,\n",
      "        -4.7286e-01, -7.6413e-01,  9.1803e-01, -6.5821e-02, -7.9172e-01,\n",
      "         6.0560e-01,  1.1330e+00,  1.4147e+00, -3.3769e-01,  3.3918e-01,\n",
      "         5.2467e-01,  7.8108e-01, -1.0245e+00, -1.0247e+00, -2.4197e-01,\n",
      "        -2.9640e-01, -1.4600e-01, -3.8037e-01,  5.9480e-02,  1.7117e+00,\n",
      "         2.4494e-01,  5.8312e-01,  3.3350e-01,  1.3783e-01,  1.8727e+00,\n",
      "        -1.3069e+00, -8.4772e-01, -1.1862e-01,  9.2846e-01, -1.3747e+00,\n",
      "         9.2867e-02, -5.7584e-01,  1.4165e-01, -1.5083e+00, -1.0793e+00,\n",
      "         4.5566e-01,  2.5295e-01, -1.2466e-01,  5.2528e-01, -1.0223e+00,\n",
      "         2.1282e+00,  6.2825e-01,  4.6545e-01,  1.4062e+00, -5.7433e-01,\n",
      "        -7.3647e-01,  3.2242e-01,  5.2841e-01, -1.1400e+00,  1.0031e+00,\n",
      "        -2.8326e-01, -9.3748e-01, -7.9931e-02,  6.1399e-01, -4.7505e-01,\n",
      "         5.1958e-01, -8.1896e-01,  2.9041e-02,  4.8513e-01,  5.4225e-01,\n",
      "         1.7448e-01, -3.8285e-01, -3.3195e-01,  1.0417e+00,  9.1890e-01,\n",
      "        -5.7424e-02,  1.8927e+00,  8.0919e-02, -1.7582e-01, -4.2895e-01,\n",
      "         4.1756e-01, -3.7173e-01,  7.8976e-01,  1.2536e+00, -5.1765e-01,\n",
      "         4.0839e-02,  1.2402e+00, -6.7798e-01,  1.5929e+00, -3.4833e-01,\n",
      "        -6.6867e-01,  1.0257e+00,  3.0743e-01, -6.4791e-01, -1.7353e+00,\n",
      "         1.0315e+00,  1.3468e+00,  4.4264e-01,  1.6137e+00,  4.0311e-01,\n",
      "         5.3102e-01, -1.1011e-01, -6.5081e-01, -7.0073e-01, -2.8339e-01,\n",
      "         1.3526e+00, -8.9855e-01,  2.6437e-01, -3.4273e-01, -1.5185e+00,\n",
      "        -1.7914e+00,  7.8471e-01,  4.1753e-01, -3.6381e-01,  6.2430e-01,\n",
      "         1.1523e+00, -2.1111e-01,  2.6896e-01, -4.3360e-01,  1.3474e+00,\n",
      "         1.0279e+00, -1.0667e+00,  4.7196e-01, -1.3860e+00, -6.9255e-01,\n",
      "         7.9183e-01, -9.3660e-01, -1.7579e-01, -1.1528e-01,  4.6181e-01,\n",
      "        -1.2031e+00,  1.8605e-01, -1.0640e-01, -3.4241e-01, -4.1578e-01,\n",
      "         3.6165e-01,  5.3893e-01, -1.5936e-01,  6.9213e-01,  9.5401e-01,\n",
      "         4.2971e-01, -5.2492e-01, -1.4702e-01, -1.1885e+00,  6.9112e-01,\n",
      "         8.7451e-01,  8.8563e-01, -4.9808e-01,  3.8202e-01,  5.7432e-01,\n",
      "        -3.9749e-01, -4.6724e-01,  9.1679e-01, -5.7637e-01,  4.0558e-01,\n",
      "        -8.0606e-01, -1.9283e+00, -7.5445e-01, -7.2032e-01,  1.1355e+00,\n",
      "        -8.5845e-01, -7.4136e-01, -3.3410e-01,  5.1065e-01,  4.8593e-01,\n",
      "         7.8195e-01,  6.2094e-01, -3.9376e-01, -1.1112e+00, -1.7925e-01,\n",
      "        -1.7460e-01, -1.3796e+00, -1.0337e+00,  8.1358e-03,  3.6535e-01,\n",
      "        -7.9858e-01,  1.4474e+00, -4.3687e-02, -1.3199e+00, -4.5393e-01,\n",
      "        -1.2804e+00, -1.3143e+00,  3.2882e-01, -3.9545e-01, -2.5259e-02,\n",
      "         1.5816e+00, -2.1922e-01, -1.4465e+00,  1.0792e+00, -3.3162e-01,\n",
      "         1.2781e+00,  4.9929e-01, -3.8789e-01, -7.3077e-01, -4.4949e-01,\n",
      "         1.3366e+00,  3.9627e-01,  8.9039e-02,  5.9262e-01, -7.7383e-01,\n",
      "         1.4022e-01,  7.9235e-01,  1.2768e-01, -3.1847e-01,  2.3861e-01,\n",
      "         1.7712e-01,  1.6861e+00, -2.8196e-01,  8.9334e-01, -1.5930e+00,\n",
      "        -5.4673e-01,  1.1597e+00,  3.3128e-01,  5.6051e-01, -2.2893e-01,\n",
      "         1.1079e-01, -1.4461e-01,  4.6492e-01, -2.6314e-01,  1.2572e+00,\n",
      "         1.0853e+00, -8.9941e-01, -8.5739e-01, -1.0573e-02,  7.5041e-01,\n",
      "         3.3779e-01,  1.9551e+00, -1.6117e+00, -9.3746e-01,  5.6555e-01,\n",
      "         3.3650e-02,  9.2455e-02,  7.7278e-01,  1.2557e+00,  7.6042e-01,\n",
      "        -1.2078e+00, -1.0426e+00, -6.0864e-01, -7.4216e-01,  1.2088e+00,\n",
      "         9.9061e-01, -2.8926e-01, -3.8176e-01,  7.0128e-01, -3.6139e-02,\n",
      "        -8.4117e-01,  6.1197e-01,  1.8032e-02,  6.2804e-02,  1.5271e+00,\n",
      "        -7.8655e-01,  9.7209e-01,  1.0244e+00, -1.7334e-01,  3.5270e-01,\n",
      "        -3.4805e-01, -4.9608e-02,  4.7891e-02,  8.0750e-01,  1.6529e+00,\n",
      "         7.7481e-01, -5.2209e-01, -1.4429e+00, -1.5707e+00,  6.0376e-01,\n",
      "         6.3976e-01,  7.4440e-01, -7.8592e-01,  6.3428e-01, -2.1310e-01,\n",
      "         3.6978e-01, -1.1911e+00, -7.5264e-02,  8.5656e-01,  9.0953e-01,\n",
      "        -1.3640e+00, -8.7072e-01,  1.3040e+00, -1.1361e+00, -1.7230e+00,\n",
      "         1.0031e+00,  1.6979e+00, -2.6261e-01,  4.6404e-01, -3.5621e-02,\n",
      "         1.3775e+00, -1.7005e-01, -2.4896e+00, -4.1002e-01,  6.0559e-01,\n",
      "        -1.3279e+00, -1.2335e-01,  8.2084e-01, -4.9227e-01,  1.1973e+00,\n",
      "        -1.2805e+00,  1.0519e+00,  7.1615e-02, -1.3229e+00,  7.3347e-01,\n",
      "         6.9202e-01, -7.2699e-01, -2.1920e-01, -2.5287e+00, -1.2424e+00,\n",
      "         1.9082e-01,  2.1629e-01, -7.2682e-01,  5.9501e-01, -6.5112e-01,\n",
      "         9.4988e-01, -3.3404e-01, -1.3145e+00, -1.0770e-01, -1.5098e+00,\n",
      "        -1.4773e+00, -2.5902e-01,  1.0651e+00,  6.9876e-01,  3.7602e-01,\n",
      "        -3.7576e-01,  2.9344e-01,  7.9897e-01, -8.4917e-01, -9.4685e-01,\n",
      "         1.0509e+00, -1.0622e+00, -1.3226e-02, -8.2294e-01, -1.1368e+00,\n",
      "        -3.5304e-02, -1.4483e+00, -2.4820e-01, -7.4727e-01,  1.0878e-01,\n",
      "        -1.0141e+00, -9.1956e-01, -7.8730e-01, -7.5376e-01, -9.4361e-01,\n",
      "         8.6054e-01, -3.2350e-01,  1.3294e+00,  7.7265e-01,  4.2641e-01,\n",
      "         7.2080e-01,  3.6830e-01,  6.1005e-01,  7.3073e-01,  2.3972e-01,\n",
      "         2.0841e-01, -1.6755e+00, -1.4753e+00,  7.7184e-02, -2.5520e-01,\n",
      "         7.7630e-01,  1.9768e-01,  3.9148e-01, -1.2694e+00, -5.7681e-01,\n",
      "         5.3152e-01, -6.8036e-01, -5.3306e-01,  1.2326e+00,  6.1157e-01,\n",
      "         6.1909e-01, -4.4724e-01, -2.1998e+00,  1.9898e+00, -3.4353e-01,\n",
      "        -9.4479e-01,  3.1337e-02,  3.9538e-01,  6.2703e-01, -9.8101e-01,\n",
      "         3.5700e-01, -2.6217e-01, -1.7812e+00, -8.4093e-03,  1.4581e-01,\n",
      "         2.0776e-01,  4.4111e-02,  1.0799e+00, -8.7729e-01, -5.9653e-01,\n",
      "         7.7491e-02,  7.4153e-02,  2.4757e-01, -9.3665e-02,  1.6921e-01,\n",
      "         1.3324e+00,  2.7547e-01, -2.7349e-01, -5.5993e-01,  4.7719e-01,\n",
      "        -6.8526e-01,  1.5399e+00,  1.3792e-01, -1.1268e+00,  3.0249e-01,\n",
      "         1.0382e+00, -1.2165e+00,  2.8784e-01, -6.6340e-01,  6.8017e-01,\n",
      "        -7.5553e-01, -4.0702e-01, -1.3396e+00,  4.2548e-02,  1.5906e+00,\n",
      "        -9.6156e-01, -8.6762e-02, -1.4620e+00, -5.0771e-01, -4.9369e-02,\n",
      "         8.0169e-01, -5.2597e-02,  1.1256e+00,  5.5626e-01,  1.6390e+00,\n",
      "         2.2014e+00,  4.6989e-01, -2.4832e-01,  1.2165e+00,  7.2312e-01,\n",
      "        -4.8542e-01,  8.5801e-01,  7.9264e-01,  1.0339e+00, -2.0153e-01,\n",
      "         2.0849e+00,  2.0548e+00,  6.8752e-01, -6.4258e-03, -1.1698e-01,\n",
      "        -9.5586e-01,  6.3713e-01, -1.1395e+00, -7.5491e-01,  4.9258e-02,\n",
      "        -8.4111e-01,  6.3519e-01,  8.9631e-01, -9.9591e-03,  1.6442e+00,\n",
      "        -3.1420e-01,  6.3293e-01, -1.1051e+00, -1.7147e+00, -4.6664e-01,\n",
      "         5.4147e-01,  8.4355e-01,  1.5580e-01, -1.4035e-01,  5.0838e-01,\n",
      "         7.8122e-01,  1.4241e+00,  2.7496e-01,  2.3718e-01, -4.8364e-01,\n",
      "         6.9937e-02, -3.9958e-01,  2.3084e-01, -1.1305e+00,  6.8808e-01,\n",
      "        -7.0752e-01,  1.1729e+00, -1.4602e+00,  8.4472e-01, -3.9754e-01,\n",
      "        -5.6444e-01, -1.1804e+00, -4.8685e-01, -1.7948e-01, -9.4431e-01,\n",
      "         5.4315e-01, -5.9293e-01,  2.5268e-01, -3.5975e-01,  1.6406e+00,\n",
      "         4.8757e-01,  9.2822e-01,  7.2838e-01,  1.3547e+00, -8.7312e-01,\n",
      "        -2.7791e-01,  3.8480e-01, -5.5117e-03,  6.9830e-02,  3.6161e-01,\n",
      "         2.5194e-01,  2.6525e-01, -1.2432e+00,  9.5893e-01,  1.0030e+00,\n",
      "        -1.9534e-01, -1.3182e-01, -4.8906e-01,  1.6288e-01,  5.1909e-01,\n",
      "         1.6915e+00, -5.6143e-01, -1.0214e-01, -7.9113e-01, -9.9406e-01,\n",
      "        -9.5259e-01,  7.0283e-01, -3.7326e-01,  1.2124e+00,  1.8257e-01,\n",
      "        -1.1745e-01,  5.4176e-01, -9.8896e-01,  7.1424e-01, -1.8954e-01,\n",
      "        -5.3401e-02, -6.9627e-01, -5.1727e-01,  2.9214e-01, -7.0190e-01,\n",
      "        -5.4399e-01, -3.9338e-01,  5.4205e-01, -2.1031e-01, -4.3742e-01,\n",
      "         1.0239e+00, -9.7949e-01,  8.4645e-01, -2.0468e-01,  5.0942e-01,\n",
      "        -3.2701e-01, -1.9194e-01, -1.1297e+00, -1.8069e-01,  3.4035e-01,\n",
      "         5.8532e-01, -3.3923e-02,  1.2242e+00,  7.7478e-01, -5.8048e-01,\n",
      "         4.1765e-02, -5.1123e-01,  3.4226e-01,  1.0630e+00,  3.6712e-01,\n",
      "        -1.8251e+00, -1.5668e+00,  1.7883e-01])\n",
      "CPU times: user 10 ms, sys: 2.27 ms, total: 12.3 ms\n",
      "Wall time: 46.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import ollama\n",
    "embeddings_dict = ollama.embeddings(model='nomic-embed-text', prompt='The cat ran fast')\n",
    "embeddings_1 = torch.tensor(embeddings_dict['embedding'])\n",
    "print(len(embeddings_1))\n",
    "print(embeddings_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosines similarity two sentences:  0.9390218257904053\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "#calculate the cosine similarity\n",
    "cos = torch.nn.CosineSimilarity(dim=0)\n",
    "\n",
    "similarity = cos(embeddings_0,embeddings_1).item()\n",
    "similarity = (similarity+1)/2\n",
    "\n",
    "print(\"Cosines similarity two sentences: \", similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Convert a .pdf to .txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: PyPDF2 in /Users/michielbontenbal/anaconda3/lib/python3.10/site-packages (3.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install PyPDF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "from PyPDF2 import PdfReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pulp-fiction-1994 (1).pdf'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wget\n",
    "document = wget.download('https://assets.scriptslug.com/live/pdf/scripts/pulp-fiction-1994.pdf')\n",
    "document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PyPDF2._reader.PdfReader at 0x7fed072813c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the .pdf file to read \n",
    "reader = PdfReader('./pulp-fiction-1994.pdf')\n",
    "reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PULP FICTION\n",
      "by\n",
      "Quentin Tarantino & Roger AvaryPULP [pulp] n.\n",
      "1. A soft, moist, shapeless\n",
      "mass or matter.\n",
      "2. A magazine or book containing lurid\n",
      "subject matter and being characteristically\n",
      "printed on \n",
      "--------------------------------------------------\n",
      "The text is 152213 characters\n"
     ]
    }
   ],
   "source": [
    "# read data from the file and put them into a variable called raw_text\n",
    "raw_text = ''\n",
    "for i, page in enumerate(reader.pages):\n",
    "    text = page.extract_text()\n",
    "    if text:\n",
    "        raw_text += text\n",
    "print(raw_text[:200])\n",
    "print(50 * '-')\n",
    "print(f'The text is {len(raw_text)} characters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "#save raw_text to a .txt file\n",
    "file = open(\"pulp_fiction.txt\", \"w\") #w for write\n",
    "file.write(raw_text)\n",
    "file.close()"
   ]
  }
 ],
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    "name": "ipython",
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