add everyting for docs

docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb

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+{
+ "cells": [
+  {
+   "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}