Docs: enrich SageMaker endpoint embeddings with docstrings and examples (#9924)

Description: added comments to address the relationship between
input/output transformations and the customised inference.py script.

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

@@ -48,10 +48,31 @@
     "    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",
+    "        \"\"\"\n",
+    "        Transforms the input into bytes that can be consumed by SageMaker endpoint.\n",
+    "        Args:\n",
+    "            inputs: List of input strings.\n",
+    "            model_kwargs: Additional keyword arguments to be passed to the endpoint.\n",
+    "        Returns:\n",
+    "            The transformed bytes input.\n",
+    "        \"\"\"\n",
+    "        # Example: inference.py expects a JSON string with a \"inputs\" key:\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",
+    "        \"\"\"\n",
+    "        Transforms the bytes output from the endpoint into a list of embeddings.\n",
+    "        Args:\n",
+    "            output: The bytes output from SageMaker endpoint.\n",
+    "        Returns:\n",
+    "            The transformed output - list of embeddings\n",
+    "        Note:\n",
+    "            The length of the outer list is the number of input strings.\n",
+    "            The length of the inner lists is the embedding dimension.\n",
+    "        \"\"\"\n",
+    "        # Example: inference.py returns a JSON string with the list of\n",
+    "        # embeddings in a \"vectors\" key:\n",
     "        response_json = json.loads(output.read().decode(\"utf-8\"))\n",
     "        return response_json[\"vectors\"]\n",
     "\n",
@@ -60,7 +81,6 @@
     "\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",