langchain/docs/extras/integrations/llms/azure_openai_example.ipynb

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    "# Azure OpenAI\n",
    "\n",
    "This notebook goes over how to use Langchain with [Azure OpenAI](https://aka.ms/azure-openai).\n",
    "\n",
    "The Azure OpenAI API is compatible with OpenAI's API.  The `openai` Python package makes it easy to use both OpenAI and Azure OpenAI.  You can call Azure OpenAI the same way you call OpenAI with the exceptions noted below.\n",
    "\n",
    "## API configuration\n",
    "You can configure the `openai` package to use Azure OpenAI using environment variables.  The following is for `bash`:\n",
    "\n",
    "```bash\n",
    "# Set this to `azure`\n",
    "export OPENAI_API_TYPE=azure\n",
    "# The API version you want to use: set this to `2023-05-15` for the released version.\n",
    "export OPENAI_API_VERSION=2023-05-15\n",
    "# The base URL for your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.\n",
    "export OPENAI_API_BASE=https://your-resource-name.openai.azure.com\n",
    "# The API key for your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.\n",
    "export OPENAI_API_KEY=<your Azure OpenAI API key>\n",
    "```\n",
    "\n",
    "Alternatively, you can configure the API right within your running Python environment:\n",
    "\n",
    "```python\n",
    "import os\n",
    "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
    "...\n",
    "```\n",
    "\n",
    "## Deployments\n",
    "With Azure OpenAI, you set up your own deployments of the common GPT-3 and Codex models.  When calling the API, you need to specify the deployment you want to use.\n",
    "\n",
    "_**Note**: These docs are for the Azure text completion models. Models like GPT-4 are chat models. They have a slightly different interface, and can be accessed via the `AzureChatOpenAI` class. For docs on Azure chat see [Azure Chat OpenAI documentation](/docs/integrations/chat/azure_chat_openai)._\n",
    "\n",
    "Let's say your deployment name is `text-davinci-002-prod`.  In the `openai` Python API, you can specify this deployment with the `engine` parameter.  For example:\n",
    "\n",
    "```python\n",
    "import openai\n",
    "\n",
    "response = openai.Completion.create(\n",
    "    engine=\"text-davinci-002-prod\",\n",
    "    prompt=\"This is a test\",\n",
    "    max_tokens=5\n",
    ")\n",
    "```\n"
   ]
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   "execution_count": null,
   "id": "89fdb593-5a42-4098-87b7-1496fa511b1c",
   "metadata": {
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   "outputs": [],
   "source": [
    "!pip install openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faacfa54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
    "os.environ[\"OPENAI_API_VERSION\"] = \"2023-05-15\"\n",
    "os.environ[\"OPENAI_API_BASE\"] = \"...\"\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fad2a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Azure OpenAI\n",
    "from langchain.llms import AzureOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c80213a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of Azure OpenAI\n",
    "# Replace the deployment name with your own\n",
    "llm = AzureOpenAI(\n",
    "    deployment_name=\"td2\",\n",
    "    model_name=\"text-davinci-002\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "592dc404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the LLM\n",
    "llm(\"Tell me a joke\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfebea1",
   "metadata": {},
   "source": [
    "We can also print the LLM and see its custom print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c33fa19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mAzureOpenAI\u001b[0m\n",
      "Params: {'deployment_name': 'text-davinci-002', 'model_name': 'text-davinci-002', 'temperature': 0.7, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n"
     ]
    }
   ],
   "source": [
    "print(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a8b5917",
   "metadata": {},
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