LLM_Engineering_OLD/week1/community-contributions/week1 EXERCISE - TechTutor.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",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "from IPython.display import Markdown, display, update_display\n",
    "from openai import OpenAI"
   ]
  },
  {
   "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",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "\n",
    "# set up clients\n",
    "openai = OpenAI()\n",
    "ollama = OpenAI(base_url=\"http://localhost:11434/v1\" , api_key=\"ollama\")\n",
    "\n",
    "# set up system prompt\n",
    "system_prompt = \"You are a coding tutor. If the user asks you a question, answer it to the point. If you are asked to create a code snippet, generate the code in Python and then explain it shortly.\""
   ]
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   "cell_type": "code",
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    "def send_request(user_prompt, model=MODEL_LLAMA, stream=False):\n",
    "    message = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": user_prompt}]\n",
    "    if model.startswith(\"gpt\"):\n",
    "        model_client = openai\n",
    "    else:\n",
    "        model_client = ollama\n",
    "\n",
    "    \n",
    "    response = model_client.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=message,\n",
    "        stream=stream\n",
    "    )\n",
    "\n",
    "    if stream:\n",
    "        streaming = \"\"\n",
    "        display_handle = display(Markdown(\"\"), display_id=True)\n",
    "        for chunk in response:\n",
    "            streaming += chunk.choices[0].delta.content or ''\n",
    "            streaming = streaming.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
    "            update_display(Markdown(streaming), display_id=display_handle.display_id)\n",
    "\n",
    "    else:\n",
    "        return display(Markdown(response.choices[0].message.content))\n",
    "\n"
   ]
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     "text": [
      " How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\n"
     ]
    }
   ],
   "source": [
    "# here is the question; type over this to ask something new\n",
    "question = input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2bc093fa-b2ff-47e9-8ea8-e41499385116",
   "metadata": {},
   "outputs": [],
   "source": [
    "# question = \"\"\"How can I display python code properly while streaming the answer from openai? Create a code snippet for this. The streaming should happen in the code canvas.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
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   "outputs": [],
   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "send_request(model=MODEL_GPT, user_prompt=question, stream=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
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     "data": {
      "text/markdown": [
       "To display Python code properly with OpenAI's chat interface, you'll need to use the `code` formatting in the response format provided by the API endpoint. \n",
       "\n",
       "Here's an example of how you can modify the API request URL to include the formatted code:\n",
       "\n",
       "```python\n",
       "import requests\n",
       "import json\n",
       "\n",
       "query = {\n",
       "    \"text\": \"{\\n}  # Python code here\\n}\"\n",
       "\n",
       "headers = {\n",
       "    'Content-Type': 'application/json'\n",
       "}\n",
       "\n",
       "response = requests.post('https://api.openai.com/v1/answers', data=json.dumps(query), headers=headers)\n",
       "\n",
       "answer = response.json()\n",
       "```\n",
       "\n",
       "However, the most convenient way to display the code is by using the `code` directive directly in your chat prompt. OpenAI will automatically format and highlight your code."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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   ],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "send_request(user_prompt=question)"
   ]
  }
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