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SageMakerEndpoint (SageMaker端点)

Amazon SageMaker 是一个可以构建、训练和部署机器学习（ML）模型的系统，适用于任何用例，具备完全托管的基础设施、工具和工作流程。

本笔记本介绍了如何使用在SageMaker端点上托管的LLM。

pip3 install langchain boto3

设置​

您需要设置SagemakerEndpoint调用的以下必需参数：

  • endpoint_name：部署的Sagemaker模型的端点名称。 在AWS区域内必须是唯一的。
  • credentials_profile_name：位于~/.aws/credentials或~/.aws/config文件中的配置文件的名称， 其中指定了访问密钥或角色信息。 如果未指定，将使用默认凭证配置文件，或者如果在EC2实例上， 将使用IMDS中的凭证。 参见：https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

示例​

from langchain.docstore.document import Document
example_doc_1 = """
Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.
Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.
Therefore, Peter stayed with her at the hospital for 3 days without leaving.
"""

docs = [
    Document(
        page_content=example_doc_1,
    )
]
from typing import Dict

from langchain import PromptTemplate, SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain.chains.question_answering import load_qa_chain
import json

query = """How long was Elizabeth hospitalized?
"""

prompt_template = """Use the following pieces of context to answer the question at the end.

{context}

Question: {question}
Answer:"""
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)


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

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

    def transform_output(self, output: bytes) -> str:
        response_json = json.loads(output.read().decode("utf-8"))
        return response_json[0]["generated_text"]


content_handler = ContentHandler()

chain = load_qa_chain(
    llm=SagemakerEndpoint(
        endpoint_name="endpoint-name",
        credentials_profile_name="credentials-profile-name",
        region_name="us-west-2",
        model_kwargs={"temperature": 1e-10},
        content_handler=content_handler,
    ),
    prompt=PROMPT,
)

chain({"input_documents": docs, "question": query}, return_only_outputs=True)