langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/together.ipynb

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   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: Together AI\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49f1e0d",
   "metadata": {},
   "source": [
    "# TogetherEmbeddings\n",
    "\n",
    "This notebook covers how to get started with open source embedding models hosted in the Together AI API.\n",
    "\n",
    "## Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c3bef91",
   "metadata": {},
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   "source": [
    "# install package\n",
    "%pip install --upgrade --quiet  langchain-together"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4f3e15",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Make sure to set the following environment variables:\n",
    "\n",
    "- `TOGETHER_API_KEY`\n",
    "\n",
    "## Usage\n",
    "\n",
    "First, select a supported model from [this list](https://docs.together.ai/docs/embedding-models). In the following example, we will use `togethercomputer/m2-bert-80M-8k-retrieval`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62e0dbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_together.embeddings import TogetherEmbeddings\n",
    "\n",
    "embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12fcfb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings.embed_query(\"My query to look up\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2e6104",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings.embed_documents(\n",
    "    [\"This is a content of the document\", \"This is another document\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46739f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# async embed query\n",
    "await embeddings.aembed_query(\"My query to look up\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e48632ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# async embed documents\n",
    "await embeddings.aembed_documents(\n",
    "    [\"This is a content of the document\", \"This is another document\"]\n",
    ")"
   ]
  }
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