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  {
   "cell_type": "markdown",
   "id": "1f83f273",
   "metadata": {},
   "source": [
    "# SageMaker Endpoint Embeddings\n",
    "\n",
    "Let's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
    "\n",
    "For instructions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). **Note**: In order to handle batched requests, you will need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:\n",
    "\n",
    "Change from\n",
    "\n",
    "`return {\"vectors\": sentence_embeddings[0].tolist()}`\n",
    "\n",
    "to:\n",
    "\n",
    "`return {\"vectors\": sentence_embeddings.tolist()}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88d366bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install langchain boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e9b926a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List\n",
    "from langchain.embeddings import SagemakerEndpointEmbeddings\n",
    "from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler\n",
    "import json\n",
    "\n",
    "\n",
    "class ContentHandler(EmbeddingsContentHandler):\n",
    "    content_type = \"application/json\"\n",
    "    accepts = \"application/json\"\n",
    "\n",
    "    def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:\n",
    "        input_str = json.dumps({\"inputs\": inputs, **model_kwargs})\n",
    "        return input_str.encode(\"utf-8\")\n",
    "\n",
    "    def transform_output(self, output: bytes) -> List[List[float]]:\n",
    "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
    "        return response_json[\"vectors\"]\n",
    "\n",
    "\n",
    "content_handler = ContentHandler()\n",
    "\n",
    "\n",
    "embeddings = SagemakerEndpointEmbeddings(\n",
    "    # endpoint_name=\"endpoint-name\",\n",
    "    # credentials_profile_name=\"credentials-profile-name\",\n",
    "    endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n",
    "    region_name=\"us-east-1\",\n",
    "    content_handler=content_handler,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe9797b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76f1b752",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_results = embeddings.embed_documents([\"foo\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff99b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaad49f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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