langchain/docs/extras/modules/data_connection/text_embedding/integrations/openai.ipynb

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  {
   "cell_type": "markdown",
   "id": "278b6c63",
   "metadata": {},
   "source": [
    "# OpenAI\n",
    "\n",
    "Let's load the OpenAI Embedding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0be1af71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c66e5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01370375",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb6142c",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0356c3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb61bbeb",
   "metadata": {},
   "source": [
    "Let's load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see [here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b072cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a56b70f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14aefb64",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c39ed33",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3221db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through\n",
    "os.environ[\"OPENAI_PROXY\"] = \"http://proxy.yourcompany.com:8080\""
   ]
  }
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