notebooks/openai_vectors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI Embeddings\n",
    "\n",
    "(C) 2024 by [Damir Cavar](http://damir.cavar.me/)\n",
    "\n",
    "**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-for-ipython).\n",
    "\n",
    "\n",
    "Pulling embeddings for words and phrases from OpenAI's GPT.\n",
    "\n",
    "    Model\t~ Pages per dollar\tPerformance on MTEB eval\tMax input\n",
    "    text-embedding-3-small\t62,500\t62.3%\t8191\n",
    "    text-embedding-3-large\t9,615\t64.6%\t8191\n",
    "    text-embedding-ada-002\t12,500\t61.0%\t8191\n",
    "\n",
    "For this code to work you will need the `openai` Python module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U openai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the file `secret.py` in the same folder as this notebook create the variable `openai_apikey` as a string variable and set it to your API key from OpenAI. You might have to create the file `secret.py`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt, retry_if_not_exception_type\n",
    "from secret import openai_apikey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use either  `text-embedding-3-small` or `text-embedding-3-large` for the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMBEDDING_MODEL = \"text-embedding-3-large\"\n",
    "EMBEDDING_CTX_LENGTH = 8191\n",
    "EMBEDDING_ENCODING = 'cl100k_base'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a client for the OpenAI API endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = openai.OpenAI(api_key=openai_apikey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function will fetch the vectors for the words in the text_or_tokens string. It will wait and repeat, if the endpoint is busy or a delay is imposed by it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(openai.BadRequestError))\n",
    "def get_embedding(text_or_tokens, client, model=EMBEDDING_MODEL):\n",
    "\ttext_or_tokens = text_or_tokens.replace(\"\\n\", \" \")\n",
    "\treturn client.embeddings.create(input=text_or_tokens, model=model).data[0].embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this word list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordlist = list(set(\"\"\"\n",
    "cat dog fish bird\n",
    "car truck bike bus\n",
    "\"\"\".split()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loop over the word list and request the embedding vector from the OpenAI API endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for word in wordlist:\n",
    "    try:\n",
    "        embeddings = get_embedding(word, client)\n",
    "        print(word, len(embeddings), embeddings)\n",
    "    except openai.BadRequestError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(C) 2024 by [Damir Cavar](http://damir.cavar.com/)"
   ]
  }
 ],
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