L1_student.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# L1 Language Models, the Chat Format and Tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c797991-8486-4d79-8c1d-5dc0c1289c2f",
   "metadata": {},
   "source": [
    "## Setup\n",
    "#### Load the API key and relevant Python libaries.\n",
    "In this course, we've provided some code that loads the OpenAI API key for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19cd4e96",
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   "source": [
    "import os\n",
    "import openai\n",
    "import tiktoken\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "openai.api_key  = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ba0938-7ca5-46c4-a9d1-b55708d4dc7c",
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   "source": [
    "#### helper function\n",
    "This may look familiar if you took the earlier course \"ChatGPT Prompt Engineering for Developers\" Course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ed96988",
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   "source": [
    "def get_completion(prompt, model=\"gpt-3.5-turbo\"):\n",
    "    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=0,\n",
    "    )\n",
    "    return response.choices[0].message[\"content\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe10a390-2461-447d-bf8b-8498db404c44",
   "metadata": {},
   "source": [
    "## Prompt the model and get a completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1cc57b2",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "response = get_completion(\"What is the capital of France?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76774108",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital of France is Paris.\n"
     ]
    }
   ],
   "source": [
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83d4e38-3e3c-4c5a-a949-040a27f29d63",
   "metadata": {},
   "source": [
    "## Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc2d9e40",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppilolol\n"
     ]
    }
   ],
   "source": [
    "response = get_completion(\"Take the letters in lollipop \\\n",
    "and reverse them\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2b14d0-749d-4a79-9812-7b00ace9ae6f",
   "metadata": {},
   "source": [
    "\"lollipop\" in reverse should be \"popillol\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37cab84f",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "response = get_completion(\"\"\"Take the letters in \\\n",
    "l-o-l-l-i-p-o-p and reverse them\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1577c561",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p-o-p-i-l-l-o-l'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b88940-d3ab-4c00-b5c0-31531deaacbd",
   "metadata": {},
   "source": [
    "## Helper function (chat format)\n",
    "Here's the helper function we'll use in this course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f89efad",
   "metadata": {
    "height": 200
   },
   "outputs": [],
   "source": [
    "def get_completion_from_messages(messages, \n",
    "                                 model=\"gpt-3.5-turbo\", \n",
    "                                 temperature=0, \n",
    "                                 max_tokens=500):\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=temperature, # this is the degree of randomness of the model's output\n",
    "        max_tokens=max_tokens, # the maximum number of tokens the model can ouptut \n",
    "    )\n",
    "    return response.choices[0].message[\"content\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b28c3424",
   "metadata": {
    "height": 183
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once there was a little carrot,\n",
      "That grew up tall and true,\n",
      "It smiled and danced so happily,\n",
      "Because it knew it was yummy too!\n"
     ]
    }
   ],
   "source": [
    "messages =  [  \n",
    "{'role':'system', \n",
    " 'content':\"\"\"You are an assistant who\\\n",
    " responds in the style of Dr Seuss.\"\"\"},    \n",
    "{'role':'user', \n",
    " 'content':\"\"\"write me a very short poem\\\n",
    " about a happy carrot\"\"\"},  \n",
    "] \n",
    "response = get_completion_from_messages(messages, temperature=1)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56c6978d",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once upon a time, there was a happy carrot named Carlin who loved to soak up the sun and drink lots of water in the vegetable garden, until one day he was plucked from the earth and turned into a delicious carrot cake that brought joy to everyone who tasted it.\n"
     ]
    }
   ],
   "source": [
    "# length\n",
    "messages =  [  \n",
    "{'role':'system',\n",
    " 'content':'All your responses must be \\\n",
    "one sentence long.'},    \n",
    "{'role':'user',\n",
    " 'content':'write me a story about a happy carrot'},  \n",
    "] \n",
    "response = get_completion_from_messages(messages, temperature =1)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14fd6331",
   "metadata": {
    "height": 217
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once upon a time in a garden so bright, a carrot named Charlie was growing upright.\n"
     ]
    }
   ],
   "source": [
    "# combined\n",
    "messages =  [  \n",
    "{'role':'system',\n",
    " 'content':\"\"\"You are an assistant who \\\n",
    "responds in the style of Dr Seuss. \\\n",
    "All your responses must be one sentence long.\"\"\"},    \n",
    "{'role':'user',\n",
    " 'content':\"\"\"write me a story about a happy carrot\"\"\"},\n",
    "] \n",
    "response = get_completion_from_messages(messages, \n",
    "                                        temperature =1)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "89a70c79",
   "metadata": {
    "height": 370
   },
   "outputs": [],
   "source": [
    "def get_completion_and_token_count(messages, \n",
    "                                   model=\"gpt-3.5-turbo\", \n",
    "                                   temperature=0, \n",
    "                                   max_tokens=500):\n",
    "    \n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=temperature, \n",
    "        max_tokens=max_tokens,\n",
    "    )\n",
    "    \n",
    "    content = response.choices[0].message[\"content\"]\n",
    "    \n",
    "    token_dict = {\n",
    "'prompt_tokens':response['usage']['prompt_tokens'],\n",
    "'completion_tokens':response['usage']['completion_tokens'],\n",
    "'total_tokens':response['usage']['total_tokens'],\n",
    "    }\n",
    "\n",
    "    return content, token_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a64cf3c6",
   "metadata": {
    "height": 166
   },
   "outputs": [],
   "source": [
    "messages = [\n",
    "{'role':'system', \n",
    " 'content':\"\"\"You are an assistant who responds\\\n",
    " in the style of Dr Seuss.\"\"\"},    \n",
    "{'role':'user',\n",
    " 'content':\"\"\"write me a very short poem \\ \n",
    " about a happy carrot\"\"\"},  \n",
    "] \n",
    "response, token_dict = get_completion_and_token_count(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfd8fbd4",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oh, the happy carrot, so bright and so bold,\n",
      "With a smile on its face, and a story untold.\n",
      "It grew in the garden, with sun and with rain,\n",
      "And now it's so happy, it can't help but exclaim!\n"
     ]
    }
   ],
   "source": [
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "352ad320",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'prompt_tokens': 39, 'completion_tokens': 52, 'total_tokens': 91}\n"
     ]
    }
   ],
   "source": [
    "print(token_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65372cdd-d869-4768-947a-0173e7f96335",
   "metadata": {},
   "source": [
    "#### Notes on using the OpenAI API outside of this classroom\n",
    "\n",
    "To install the OpenAI Python library:\n",
    "```\n",
    "!pip install openai\n",
    "```\n",
    "\n",
    "The library needs to be configured with your account's secret key, which is available on the [website](https://platform.openai.com/account/api-keys). \n",
    "\n",
    "You can either set it as the `OPENAI_API_KEY` environment variable before using the library:\n",
    " ```\n",
    " !export OPENAI_API_KEY='sk-...'\n",
    " ```\n",
    "\n",
    "Or, set `openai.api_key` to its value:\n",
    "\n",
    "```\n",
    "import openai\n",
    "openai.api_key = \"sk-...\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f889c1-f2e4-40a5-bd27-164facb54402",
   "metadata": {},
   "source": [
    "#### A note about the backslash\n",
    "- In the course, we are using a backslash `\\` to make the text fit on the screen without inserting newline '\\n' characters.\n",
    "- GPT-3 isn't really affected whether you insert newline characters or not.  But when working with LLMs in general, you may consider whether newline characters in your prompt may affect the model's performance."
   ]
  },
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