LLM_Engineering_OLD/week1/community-contributions/week1-EXERCISE-openai-ollama-tech-assistant.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": "4a456906-915a-4bfd-bb9d-57e505c5093f",
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   "source": [
    "# constants\n",
    "\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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   "source": [
    "# set up environment\n",
    "\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "# Check the key\n",
    "\n",
    "if not api_key:\n",
    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
    "elif not api_key.startswith(\"sk-proj-\"):\n",
    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
    "elif api_key.strip() != api_key:\n",
    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
    "else:\n",
    "    print(\"API key found and looks good so far!\")\n",
    "\n",
    "openai = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
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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": "1f879b7e-5ecc-4ec6-b269-78b6e2ed3480",
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   "source": [
    "# prompts\n",
    "\n",
    "system_prompt = \"You are a helpful tutor who answers technical questions about programming code(especially python code), software engineering, data science and LLMs\"\n",
    "user_prompt = \"Please give a detailed explanation to the following question: \" + question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ac74ae5-af61-4a5d-b991-554fa67cd3d1",
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   "source": [
    "messages = [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
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   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "stream = openai.chat.completions.create(\n",
    "        model=MODEL_GPT,\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "    \n",
    "response = \"\"\n",
    "display_handle = display(Markdown(\"\"), display_id=True)\n",
    "for chunk in stream:\n",
    "    response += chunk.choices[0].delta.content or ''\n",
    "    response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "    update_display(Markdown(response), display_id=display_handle.display_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bd10d96-ee72-4c86-acd8-4fa417c25960",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ollama pull llama3.2"
   ]
  },
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   "source": [
    "stream = ollama.chat(model=MODEL_LLAMA, messages=messages,  stream=True)\n",
    "\n",
    "response = \"\"\n",
    "display_handle = display(Markdown(\"\"), display_id=True)\n",
    "for chunk in stream:\n",
    "    response += chunk.get(\"message\", {}).get(\"content\", \"\")\n",
    "    response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "    update_display(Markdown(response), display_id=display_handle.display_id)"
   ]
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   "execution_count": null,
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