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

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Description: added comments to address the relationship between
input/output transformations and the customised inference.py script.
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Corvus Lee 2023-08-29 19:38:52 +01:00 committed by GitHub
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1 changed files with 22 additions and 2 deletions
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24
docs/extras/integrations/text_embedding/sagemaker-endpoint.ipynb
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@ -48,10 +48,31 @@
" accepts = \"application/json\"\n", " accepts = \"application/json\"\n",
"\n", "\n",
" def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:\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", " return input_str.encode(\"utf-8\")\n",
"\n", "\n",
" def transform_output(self, output: bytes) -> List[List[float]]:\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", " response_json = json.loads(output.read().decode(\"utf-8\"))\n",
" return response_json[\"vectors\"]\n", " return response_json[\"vectors\"]\n",
"\n", "\n",
@ -60,7 +81,6 @@
"\n", "\n",
"\n", "\n",
"embeddings = SagemakerEndpointEmbeddings(\n", "embeddings = SagemakerEndpointEmbeddings(\n",
" # endpoint_name=\"endpoint-name\",\n",
" # credentials_profile_name=\"credentials-profile-name\",\n", " # credentials_profile_name=\"credentials-profile-name\",\n",
" endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n", " endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n",
" region_name=\"us-east-1\",\n", " region_name=\"us-east-1\",\n",