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   "id": "88cfea73-04f1-41ca-b2e3-46e0bf4588ce",
   "metadata": {},
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   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "830fdf5c-0f18-49a7-b1ce-94b57187b8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2e20a5e-0809-409e-bc31-c939172167e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Create your prompts\n",
    "\n",
    "system_prompt = \"You are a Software Developer Assistant\"\n",
    "user_prompt = \"\"\"\n",
    "    You are a Software Engineer assistant. \\\n",
    "    When a user asks a technical question about any concept, explain the answer to the question \\\n",
    "    along with code examples or usage in a simple way \\\n",
    "    Always format the answer in markdown\n",
    "\"\"\"\n",
    "\n",
    "# Step 2: Make the messages list\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a Software Developer Assistant\"},\n",
    "    {\"role\": \"user\", \"content\": \"What is LLM?\"}\n",
    "]\n",
    "\n",
    "# Step 3: Call OpenAI\n",
    "\n",
    "response = openai.chat.completions.create(model=\"llama3.2\", messages=messages)\n",
    "\n",
    "\n",
    "# Step 4: print the result\n",
    "print(response.choices[0].message.content)\n"
   ]
  }
 ],
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