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 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "## Get embeddings\n",
    "\n",
    "The function `get_embedding` will give us an embedding for an input text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12288"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import openai\n",
    "\n",
    "embedding = openai.Embedding.create(input=\"Sample document text goes here\", engine=\"text-similarity-davinci-001\")['data'][0]['embedding']\n",
    "len(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1024\n"
     ]
    }
   ],
   "source": [
    "import openai\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
    "\n",
    "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))\n",
    "def get_embedding(text: str, engine=\"text-similarity-davinci-001\") -> List[float]:\n",
    "\n",
    "    # replace newlines, which can negatively affect performance.\n",
    "    text = text.replace(\"\\n\", \" \")\n",
    "\n",
    "    return openai.Embedding.create(input=[text], engine=engine)[\"data\"][0][\"embedding\"]\n",
    "\n",
    "embedding = get_embedding(\"Sample query text goes here\", engine=\"text-search-ada-query-001\")\n",
    "print(len(embedding))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1024\n"
     ]
    }
   ],
   "source": [
    "embedding = get_embedding(\"Sample document text goes here\", engine=\"text-search-ada-doc-001\")\n",
    "print(len(embedding))"
   ]
  }
 ],
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