diff --git a/docs/modules/indexes/examples/embeddings.ipynb b/docs/modules/indexes/examples/embeddings.ipynb
index 209856797a..0ceb437cc7 100644
--- a/docs/modules/indexes/examples/embeddings.ipynb
+++ b/docs/modules/indexes/examples/embeddings.ipynb
@@ -77,7 +77,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "bb61bbeb",
    "metadata": {},
@@ -136,7 +135,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c3852491",
    "metadata": {},
@@ -212,7 +210,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ca9e2b3a",
    "metadata": {},
@@ -655,14 +652,15 @@
    ]
   },
   {
-   "attachments": {},
    "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 learn more [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker)"
+    "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 instrucstions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker)"
    ]
   },
   {
@@ -675,572 +673,6 @@
     "!pip3 install langchain boto3"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c5855922",
-   "metadata": {},
-   "source": [
-    "## _### TEMPORARY: Showing how to deploy a SageMaker Endpoint from a Hugging Face model ###_ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e0ddd9b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sagemaker --quiet\n",
-    "\n",
-    "import os \n",
-    "os.environ[\"AWS_DEFAULT_REGION\"] = \"us-east-1\"\n",
-    "import boto3\n",
-    "from sagemaker import Session\n",
-    "# get sagemaker execution role to deploy\n",
-    "iam = boto3.client('iam')\n",
-    "role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']\n",
-    "sess = Session()\n",
-    "# create code/ dir\n",
-    "os.makedirs(\"model/code\", exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "86ce76c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing model/code/inference.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile model/code/inference.py\n",
-    "\n",
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "\n",
-    "# Helper: Mean Pooling - Take attention mask into account for correct averaging\n",
-    "def mean_pooling(model_output, attention_mask):\n",
-    "    token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n",
-    "    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n",
-    "    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n",
-    "\n",
-    "\n",
-    "def model_fn(model_dir):\n",
-    "  # Load model from HuggingFace Hub\n",
-    "  tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-    "  model = AutoModel.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-    "  return model, tokenizer\n",
-    "\n",
-    "def predict_fn(data, model_and_tokenizer):\n",
-    "    # destruct model and tokenizer\n",
-    "    model, tokenizer = model_and_tokenizer\n",
-    "\n",
-    "    # Tokenize sentences\n",
-    "    sentences = data.pop(\"inputs\", data)\n",
-    "    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n",
-    "\n",
-    "    # Compute token embeddings\n",
-    "    with torch.no_grad():\n",
-    "        model_output = model(**encoded_input)\n",
-    "\n",
-    "    # Perform pooling\n",
-    "    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n",
-    "\n",
-    "    # Normalize embeddings\n",
-    "    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)\n",
-    "\n",
-    "    # return dictonary, which will be json serializable\n",
-    "    return {\"embeddings\": sentence_embeddings[0].tolist()}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "24b809d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "code/\n",
-      "code/inference.py\n",
-      "----!"
-     ]
-    }
-   ],
-   "source": [
-    "from sagemaker.s3 import S3Uploader\n",
-    "from sagemaker.huggingface.model import HuggingFaceModel\n",
-    "\n",
-    "# create model.tar.gz and upload to s3 \n",
-    "parent_dir=os.getcwd()\n",
-    "# change to model dir\n",
-    "os.chdir(\"model\")\n",
-    "# use pigz for faster and parallel compression\n",
-    "!tar zcvf model.tar.gz *\n",
-    "# change back to parent dir\n",
-    "os.chdir(parent_dir)\n",
-    "\n",
-    "\n",
-    "# upload model.tar.gz to s3\n",
-    "s3_model_uri = S3Uploader.upload(local_path=\"model/model.tar.gz\", desired_s3_uri=f\"s3://{sess.default_bucket()}/embeddings\")\n",
-    "\n",
-    "# create Hugging Face Model Class\n",
-    "huggingface_model = HuggingFaceModel(\n",
-    "   model_data=s3_model_uri,      # path to your model and script\n",
-    "   role=role,                    # iam role with permissions to create an Endpoint\n",
-    "   transformers_version=\"4.26\",  # transformers version used\n",
-    "   pytorch_version=\"1.13\",       # pytorch version used\n",
-    "   py_version='py39',            # python version used\n",
-    ")\n",
-    "\n",
-    "# deploy the endpoint endpoint\n",
-    "predictor = huggingface_model.deploy(1,\"ml.m5.2xlarge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "324213fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'huggingface-pytorch-inference-2023-03-21-16-14-03-834'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictor.endpoint_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3dff3efa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  -0.015338928438723087]}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictor.predict({\"inputs\": \"This is a test document.\"})"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "3764f159",
-   "metadata": {},
-   "source": [
-    "## _### END TEMPORARY: Showing how to deploy a SageMaker Endpoint from a Hugging Face model ###_ "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -1280,404 +712,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "836e3ea5",
+   "execution_count": null,
+   "id": "fe9797b8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       " 0.0810166597366333,\n",
-       " 0.06980433315038681,\n",
-       " -0.04784897342324257,\n",
-       " 0.01753094792366028,\n",
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-       " 0.0874418243765831,\n",
-       " 0.09520682692527771,\n",
-       " 0.10083616524934769]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "query_result = embeddings.embed_query(\"foo\")"
    ]
@@ -1694,404 +732,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "221f2f0e",
+   "execution_count": null,
+   "id": "fff99b21",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  0.042190469801425934,\n",
-       "  0.08844445645809174,\n",
-       "  0.0810166597366333,\n",
-       "  0.06980433315038681,\n",
-       "  -0.04784897342324257,\n",
-       "  0.01753094792366028,\n",
-       "  -0.10126522183418274,\n",
-       "  -0.016526369377970695,\n",
-       "  0.11310216039419174,\n",
-       "  0.0874418243765831,\n",
-       "  0.09520682692527771,\n",
-       "  0.10083616524934769]]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "doc_results"
    ]
@@ -2107,7 +751,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2121,7 +765,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {