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   "source": [
    "# End of week 1 exercise\n",
    "\n",
    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1070317-3ed9-4659-abe3-828943230e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import os\n",
    "from openai import OpenAI\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up environment\n",
    "system_prompt = \"\"\"\n",
    "You are a technical expert of AI and LLMs.\n",
    "\"\"\"\n",
    "\n",
    "user_prompt_prefix = \"\"\"\n",
    "Provide deep explanations of the provided text.\n",
    "\"\"\"\n",
    "\n",
    "user_prompt = \"\"\"\n",
    "Explain the provided text.\n",
    "\"\"\"\n",
    "client = OpenAI()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is the question; type over this to ask something new\n",
    "\n",
    "question = \"\"\"\n",
    "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "def messages_for(question):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
    "    ]\n",
    "\n",
    "def run_model_streaming(model_name, question):\n",
    "    stream = client.chat.completions.create(\n",
    "        model=model_name,\n",
    "        messages=messages_for(question),\n",
    "        stream=True\n",
    "    )\n",
    "    for chunk in stream:\n",
    "        content = chunk.choices[0].delta.content\n",
    "        if content:\n",
    "            print(content, end=\"\", flush=True)\n",
    "\n",
    "run_model_streaming(MODEL_GPT, question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "# imports\n",
    "import os\n",
    "from openai import OpenAI\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# set up environment\n",
    "client = OpenAI(\n",
    "    base_url=os.getenv(\"OPENAI_BASE_URL\", \"http://localhost:11434/v1\"),\n",
    "    api_key=os.getenv(\"OPENAI_API_KEY\", \"ollama\")\n",
    ")\n",
    "\n",
    "system_prompt = \"\"\"\n",
    "You are a technical expert of AI and LLMs.\n",
    "\"\"\"\n",
    "\n",
    "user_prompt_prefix = \"\"\"\n",
    "Provide deep explanations of the provided text.\n",
    "\"\"\"\n",
    "\n",
    "# question\n",
    "question = \"\"\"\n",
    "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
    "\"\"\"\n",
    "\n",
    "# message\n",
    "def messages_for(question):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
    "    ]\n",
    "\n",
    "# response\n",
    "def run_model(model_name, question):\n",
    "    response = client.chat.completions.create(\n",
    "        model=model_name,\n",
    "        messages=messages_for(question)\n",
    "    )\n",
    "    return response.choices[0].message.content\n",
    "\n",
    "# run and print result\n",
    "print(run_model(MODEL_LLAMA, question))\n"
   ]
  }
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