LangChain

SageMaker

Amazon SageMaker (opens in a new tab) 是一个系统，可以使用完全托管的基础设施、工具和工作流程构建、训练和部署任何用例的机器学习（ML)模型。

本教程将介绍如何使用托管在 SageMaker endpoint 上的LLM。

!pip3 install langchain boto3

设置#

您必须设置 SagemakerEndpoint 调用的以下必需参数：

endpoint_name：已部署的Sagemaker模型的端点名称。必须在AWS区域内是唯一的。
credentials_profile_name：位于~/.aws/credentials或~/.aws/config文件中的配置文件名称，其中指定了访问密钥或角色信息。如果未指定，将使用默认凭据文件配置文件或，如果在EC2实例上，则使用来自IMDS的凭据。参见：https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html (opens in a new tab)

Example#

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 ContentHandlerBase
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(ContentHandlerBase):
    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)

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