{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Embedding Similarity Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai, numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_distance(a, b):\n",
    "    \"\"\"Calculate the cosine distance between two numpy arrays.\n",
    "    \n",
    "    Parameters:\n",
    "    a (numpy array): First input array.\n",
    "    b (numpy array): Second input array.\n",
    "    \n",
    "    Returns:\n",
    "    float: Cosine distance between a and b.\n",
    "    \"\"\"\n",
    "    # Calculate dot product and magnitudes of the input arrays\n",
    "    dot   = np.dot(a, b)\n",
    "    a_mag = np.linalg.norm(a)\n",
    "    b_mag = np.linalg.norm(b)\n",
    "    \n",
    "    if np.isclose(a_mag, 0, rtol=1e-9, atol=1e-12):\n",
    "        print(f\"a_mag is very small: {a_mag}\")\n",
    "    if np.isclose(b_mag, 0, rtol=1e-9, atol=1e-12):\n",
    "        print(f\"b_mag is very small: {b_mag}\")\n",
    "    \n",
    "    # Calculate and return the cosine distance\n",
    "    return 1.0 - (dot / (a_mag * b_mag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def semantically_similar(string1, string2):\n",
    "    response = openai.Embedding.create(\n",
    "                    input=[string1, string2],\n",
    "                    engine=\"text-similarity-davinci-001\"\n",
    "                )\n",
    "    embedding_a = response['data'][0]['embedding']\n",
    "    embedding_b = response['data'][1]['embedding']\n",
    "    similarity_score = cosine_distance(embedding_a, embedding_b)\n",
    "    print(f\"similarity: {similarity_score}\")\n",
    "\n",
    "    return similarity_score < 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity: 0.22501948669661986\n",
      "similarity: 0.2318907843871436\n",
      "similarity: 0.12933868208210475\n",
      "similarity: 0.10699853725782704\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semantically_similar(\"fight a war\", \"water supply\"),\n",
    "semantically_similar(\"fight a war\", \"solar energy\"),\n",
    "semantically_similar(\"fight a war\", \"defend a country\"),\n",
    "semantically_similar(\"fight a war\", \"win a battle\"),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity: 0.2496415604648079\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semantically_similar(\"the sky is blue\", \"I like to eat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity: 0.10193029028713485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semantically_similar(\"the cat meows\", \"the feline animal says\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity: 0.19759407795526762\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semantically_similar(\"what is the best way to win a war?\", \"strategizing a war\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity: 0.1949772795717004\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semantically_similar(\"what is the best way to win a war?\", \"fight a war\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chain",
   "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": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}