langchain/docs/modules/models/text_embedding/examples/sagemaker-endpoint.ipynb

SageMaker Endpoint Embeddings

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.

For instructions on how to do this, please see here. 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:

Change from

return {"vectors": sentence_embeddings[0].tolist()}

to:

return {"vectors": sentence_embeddings.tolist()}.

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!pip3 install langchain boto3

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from typing import Dict, List
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
import json


class ContentHandler(ContentHandlerBase):
    content_type = "application/json"
    accepts = "application/json"

    def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:
        input_str = json.dumps({"inputs": inputs, **model_kwargs})
        return input_str.encode('utf-8')

    def transform_output(self, output: bytes) -> List[List[float]]:
        response_json = json.loads(output.read().decode("utf-8"))
        return response_json["vectors"]

content_handler = ContentHandler()


embeddings = SagemakerEndpointEmbeddings(
    # endpoint_name="endpoint-name", 
    # credentials_profile_name="credentials-profile-name", 
    endpoint_name="huggingface-pytorch-inference-2023-03-21-16-14-03-834", 
    region_name="us-east-1", 
    content_handler=content_handler
)

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query_result = embeddings.embed_query("foo")

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doc_results = embeddings.embed_documents(["foo"])

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doc_results

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