See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.htmlExample‚Äãfrom langchain.docstore.document import Documentexample_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 Dictfrom langchain.prompts import PromptTemplatefrom langchain.llms import SagemakerEndpointfrom langchain.llms.sagemaker_endpoint import LLMContentHandlerfrom langchain.chains.question_answering import load_qa_chainimport jsonquery = """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)PreviousRunhouseNextStochasticAISet
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. ->: See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.htmlExample‚Äãfrom langchain.docstore.document import Documentexample_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 Dictfrom langchain.prompts import PromptTemplatefrom langchain.llms import SagemakerEndpointfrom langchain.llms.sagemaker_endpoint import LLMContentHandlerfrom langchain.chains.question_answering import load_qa_chainimport jsonquery = """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)PreviousRunhouseNextStochasticAISet