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

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    "# LASER Language-Agnostic SEntence Representations Embeddings by Meta AI\n",
    "\n",
    ">[LASER](https://github.com/facebookresearch/LASER/) is a Python library developed by the Meta AI Research team and used for creating multilingual sentence embeddings for over 147 languages as of 2/25/2024 \n",
    ">- List of supported languages at https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200"
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   "source": [
    "## Dependencies\n",
    "\n",
    "To use LaserEmbed with LangChain, install the `laser_encoders` Python package."
   ]
  },
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   "outputs": [],
   "source": [
    "%pip install laser_encoders"
   ]
  },
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   "source": [
    "## Imports"
   ]
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   "source": [
    "from langchain_community.embeddings.laser import LaserEmbeddings"
   ]
  },
  {
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    "## Instantiating Laser\n",
    "   \n",
    "### Parameters\n",
    "- `lang: Optional[str]`\n",
    "    >If empty will default\n",
    "    to using a multilingual LASER encoder model (called \"laser2\").\n",
    "    You can find the list of supported languages and lang_codes [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)\n",
    "    and [here](https://github.com/facebookresearch/LASER/blob/main/laser_encoders/language_list.py)\n",
    "."
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   "source": [
    "# Ex Instantiationz\n",
    "embeddings = LaserEmbeddings(lang=\"eng_Latn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119fbaad-9442-4fff-8214-c5f597bc8e77",
   "metadata": {},
   "source": [
    "## Usage\n",
    "\n",
    "### Generating document embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62920051-cbd2-460d-ba24-0424c1ed395d",
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   "source": [
    "document_embeddings = embeddings.embed_documents(\n",
    "    [\"This is a sentence\", \"This is some other sentence\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd10d96-baee-468f-a532-b70b16b78d1f",
   "metadata": {},
   "source": [
    "### Generating query embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f793bb6-609a-4a4a-a5c7-8e8597228915",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_embeddings = embeddings.embed_query(\"This is a query\")"
   ]
  }
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