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

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   "source": [
    "# Embaas\n",
    "\n",
    "[embaas](https://embaas.io) is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a [variety of pre-trained models](https://embaas.io/docs/models/embeddings).\n",
    "\n",
    "In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.\n",
    "\n",
    "### Prerequisites\n",
    "Create your free embaas account at [https://embaas.io/register](https://embaas.io/register) and generate an [API key](https://embaas.io/dashboard/api-keys)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Set API key\n",
    "embaas_api_key = \"YOUR_API_KEY\"\n",
    "# or set environment variable\n",
    "os.environ[\"EMBAAS_API_KEY\"] = \"YOUR_API_KEY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import EmbaasEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = EmbaasEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "# Create embeddings for a single document\n",
    "doc_text = \"This is a test document.\"\n",
    "doc_text_embedding = embeddings.embed_query(doc_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print created embedding\n",
    "print(doc_text_embedding)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "# Create embeddings for multiple documents\n",
    "doc_texts = [\"This is a test document.\", \"This is another test document.\"]\n",
    "doc_texts_embeddings = embeddings.embed_documents(doc_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print created embeddings\n",
    "for i, doc_text_embedding in enumerate(doc_texts_embeddings):\n",
    "    print(f\"Embedding for document {i + 1}: {doc_text_embedding}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
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   },
   "outputs": [],
   "source": [
    "# Using a different model and/or custom instruction\n",
    "embeddings = EmbaasEmbeddings(\n",
    "    model=\"instructor-large\",\n",
    "    instruction=\"Represent the Wikipedia document for retrieval\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more detailed information about the embaas Embeddings API, please refer to [the official embaas API documentation](https://embaas.io/api-reference)."
   ]
  }
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