SageMakerEndpoint
Amazon SageMaker 是一个系统，可以为任何用例构建、训练和部署机器学习（ML）模型，提供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托管在SageMaker endpoint上的LLM。
!pip3 install langchain boto3
设置
您需要设置以下SagemakerEndpoint调用所需的参数：
endpoint_name: 部署的Sagemaker模型中的端点名称。在AWS区域内必须是唯一的。credentials_profile_name: 在 ~/.aws/credentials 或 ~/.aws/config 文件中配置文件的名称，该文件包含访问密钥或角色信息。 如果未指定，将使用默认的凭证配置文件，如果在 EC2 实例上，则使用来自 IMDS 的凭证。 参见: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
示例
from langchain_core.documents import Document
API Reference: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,
)
]
使用外部 boto3 会话初始化的示例
跨账户场景
import json
from typing import Dict
import boto3
from langchain.chains.question_answering import load_qa_chain
from langchain_community.llms import SagemakerEndpoint
from langchain_community.llms.sagemaker_endpoint import LLMContentHandler
from langchain_core.prompts import PromptTemplate
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"]
)
roleARN = "arn:aws:iam::123456789:role/cross-account-role"
sts_client = boto3.client("sts")
response = sts_client.assume_role(
RoleArn=roleARN, RoleSessionName="CrossAccountSession"
)
client = boto3.client(
"sagemaker-runtime",
region_name="us-west-2",
aws_access_key_id=response["Credentials"]["AccessKeyId"],
aws_secret_access_key=response["Credentials"]["SecretAccessKey"],
aws_session_token=response["Credentials"]["SessionToken"],
)
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({"inputs": prompt, "parameters": 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",
client=client,
model_kwargs={"temperature": 1e-10},
content_handler=content_handler,
),
prompt=PROMPT,
)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
import json
from typing import Dict
from langchain.chains.question_answering import load_qa_chain
from langchain_community.llms import SagemakerEndpoint
from langchain_community.llms.sagemaker_endpoint import LLMContentHandler
from langchain_core.prompts import PromptTemplate
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({"inputs": prompt, "parameters": 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)
相关
- LLM 概念指南
- LLM how-to guides