SageMaker
让我们加载SageMaker Endpoints Embeddings 类。如果您在 SageMaker 上托管了自己的 Hugging Face 模型，则可以使用此类。
有关如何执行此操作的说明，请参阅此处。
注意：为了处理批处理请求，您需要调整自定义inference.py 脚本中predict_fn() 函数中的返回值。
从
return {"vectors": sentence_embeddings[0].tolist()}
更改为
return {"vectors": sentence_embeddings.tolist()}.
!pip3 install langchain boto3
import json
from typing import Dict, List
from langchain_community.embeddings import SagemakerEndpointEmbeddings
from langchain_community.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:
"""
Transforms the input into bytes that can be consumed by SageMaker endpoint.
Args:
inputs: List of input strings.
model_kwargs: Additional keyword arguments to be passed to the endpoint.
Returns:
The transformed bytes input.
"""
# Example: inference.py expects a JSON string with a "inputs" key:
input_str = json.dumps({"inputs": inputs, **model_kwargs})
return input_str.encode("utf-8")
def transform_output(self, output: bytes) -> List[List[float]]:
"""
Transforms the bytes output from the endpoint into a list of embeddings.
Args:
output: The bytes output from SageMaker endpoint.
Returns:
The transformed output - list of embeddings
Note:
The length of the outer list is the number of input strings.
The length of the inner lists is the embedding dimension.
"""
# Example: inference.py returns a JSON string with the list of
# embeddings in a "vectors" key:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["vectors"]
content_handler = ContentHandler()
embeddings = SagemakerEndpointEmbeddings(
# 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,
)
# client = boto3.client(
# "sagemaker-runtime",
# region_name="us-west-2"
# )
# embeddings = SagemakerEndpointEmbeddings(
# endpoint_name="huggingface-pytorch-inference-2023-03-21-16-14-03-834",
# client=client
# content_handler=content_handler,
# )
query_result = embeddings.embed_query("foo")
doc_results = embeddings.embed_documents(["foo"])
doc_results