LLM_Engineering_OLD/week1/community-contributions/day5_challenge_exercise/day5_exercise.ipynb

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    "# 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!"
   ]
  },
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   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from IPython.display import Markdown, display, update_display\n",
    "from openai import OpenAI\n",
    "import ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e4c15b-7ae8-43e9-839d-7cc49345be5a",
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   "outputs": [],
   "source": [
    "!ollama pull llama3.2"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "# constants\n",
    "\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58f5f1e1-5296-4631-9698-8645d4621a0c",
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    "# set up environment\n",
    "\n",
    "# Get the openai key\n",
    "\n",
    "load_dotenv(override=True)\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if openai_api_key and openai_api_key.startswith('sk-proj-') and len(openai_api_key)>10:\n",
    "    print(\"API key looks good so far\")\n",
    "else:\n",
    "    print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
    "\n",
    "openai = OpenAI()\n",
    "# Get the ollama key using the llama model\n",
    "\n",
    "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f07b33-76b9-42fa-9962-21f2a5796126",
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   "source": [
    "system_prompt = \"You are a knowledgeable technical instructor who helps students understand \\\n",
    "complex concepts across a wide range of technical topics. Your expertise includes artificial]\\\n",
    "intelligence, machine learning, large language models (LLMs), and programming in languages \\\n",
    "such as Python, JavaScript, Java, and more. You also provide in-depth support for \\\n",
    "AI engineering questions and other advanced technical subjects.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "330abeb7-7db2-4f23-9d19-dd698058a400",
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   "source": [
    "# here is the question; type over this to ask something new\n",
    "\n",
    "question = \"\"\"\n",
    "Please explain what this code does and why:\n",
    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd11ad48-91ec-4cdf-9c57-99a0451e7a2f",
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   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "stream_GPT = openai.chat.completions.create(\n",
    "        model=MODEL_GPT,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": question}\n",
    "          ],\n",
    "        stream = True\n",
    "    )\n",
    "response_GPT = \"\"\n",
    "display_handle = display(Markdown(\"\"), display_id=True)\n",
    "for chunk in stream_GPT:\n",
    "    response_GPT += chunk.choices[0].delta.content or ''\n",
    "    response_GPT = response_GPT.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "    update_display(Markdown(response_GPT), display_id=display_handle.display_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd2527ae-0d75-4f15-a45f-92075e3059d6",
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   "source": [
    "# Get Llama 3.2 to answer\n",
    "\n",
    "response_llama = ollama_via_openai.chat.completions.create(\n",
    "        model=MODEL_LLAMA,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": question}\n",
    "          ],\n",
    "    )\n",
    "result = response_llama.choices[0].message.content\n",
    "\n",
    "display(Markdown(result))\n",
    "\n",
    "# import ollama\n",
    "\n",
    "# response = ollama.chat(model=MODEL_LLAMA, messages=[\n",
    "#             {\"role\": \"system\", \"content\": system_prompt},\n",
    "#             {\"role\": \"user\", \"content\": question}\n",
    "#           ])\n",
    "# print(response['message']['content'])"
   ]
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