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": "aeb35e48",
   "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": "fab91471",
   "metadata": {},
   "outputs": [],
   "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": "9d2784cb",
   "metadata": {},
   "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": "939c0c10",
   "metadata": {},
   "outputs": [],
   "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": "50838a98",
   "metadata": {},
   "source": [
    "### Prompt the model and get a completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04602f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital of France is Paris.\n"
     ]
    }
   ],
   "source": [
    "response = get_completion(\"What is the capital of France?\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742e6698",
   "metadata": {},
   "source": [
    "### Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "325732da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polilol\n"
     ]
    }
   ],
   "source": [
    "response = get_completion(\"Take the letters in lollipop and reverse them\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8f41ee",
   "metadata": {},
   "source": [
    "\"lollipop\" in reverse should be \"popillol\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bdd5e9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-o-p-i-l-l-o-l\n"
     ]
    }
   ],
   "source": [
    "response = get_completion(\"Take the letters in l-o-l-l-i-p-o-p and reverse them\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0212c63b",
   "metadata": {},
   "source": [
    "### Helper function (chat format)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843df752",
   "metadata": {},
   "source": [
    "Here's the helper function we'll use in this course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e60d20f",
   "metadata": {},
   "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": 8,
   "id": "bbe35788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once there was a carrot so bright,\n",
      "Grew tall and orange in the sunlight,\n",
      "With a grin so wide and a skip in its root,\n",
      "It danced and spun with a happy hoot!\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",
    "# System message specifying the overall tone\n",
    "response = get_completion_from_messages(messages, temperature=1)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de580ca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once upon a time, there was a happy carrot named Carl who lived in a cozy garden and had a cheerful disposition every day, spreading joy to the other vegetables around him.\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": "51af3067",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once was a carrot named Larry, who sprouted up so tall and hairy, with a smile that stretched from ear to ear, he simply filled the field with cheer.\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": "e496552b",
   "metadata": {},
   "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": "2bc3e440",
   "metadata": {},
   "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": "0083bffa",
   "metadata": {},
   "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": "3333ca11",
   "metadata": {},
   "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": "78580185",
   "metadata": {},
   "source": [
    "#### Notes on using the OpenAI API outside of this classroom\n",
    "To install the OpenAI Python library:\n",
    "\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": "0c3f1ee4",
   "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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