langchain/docs/modules/models/text_embedding/examples/azureopenai.ipynb

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  {
   "cell_type": "markdown",
   "id": "c3852491",
   "metadata": {},
   "source": [
    "# AzureOpenAI\n",
    "\n",
    "Let's load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b40f827",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the environment variables needed for openai package to know to reach out to azure\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
    "os.environ[\"OPENAI_API_BASE\"] = \"https://<your-endpoint.openai.azure.com/\"\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"your AzureOpenAI key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb36d16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "\n",
    "embeddings = OpenAIEmbeddings(model=\"your-embeddings-deployment-name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228abcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60dd7fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83bc1a72",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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