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Merge branch 'master' into docs-memory-type-menu

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Leonid Ganeline 2023-08-29 11:46:29 -07:00 committed by GitHub
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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",
"\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",

15
libs/experimental/README.md
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@ -1,3 +1,16 @@
# 🦜️🧪 LangChain Experimental
This repository holds more experimental LangChain code.
This package holds experimental LangChain code, intended for research and experimental
uses.
> [!WARNING]
> Portions of the code in this package may be dangerous if not properly deployed
> in a sandboxed environment. Please be wary of deploying experimental code
> to production unless you've taken appropriate precautions and
> have already discussed it with your security team.
Some of the code here may be marked with security notices. However,
given the exploratory and experimental nature of the code in this package,
the lack of a security notice on a piece of code does not mean that
the code in question does not require additional security considerations
in order to be safe to use.