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与部署在Amazon SageMaker端点中的LLM通过LlamaIndex进行交互

Amazon SageMaker 端点是一种完全托管的资源,可用于部署机器学习模型(特别是大语言模型),以便对新数据进行预测。

本笔记本演示了如何使用 SageMakerLLM 与LLM端点进行交互,解锁额外的llamaIndex功能。 因此,我们假设LLM已部署在SageMaker端点上。

如果您在 Colab 上打开这个笔记本,您可能需要安装 LlamaIndex 🦙。

%pip install llama-index-llms-sagemaker-endpoint
! pip install llama-index

您必须指定端点名称以进行交互。

ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"

需要提供凭据以连接到端点。您可以:

  • 通过指定 profile_name 参数使用 AWS 配置文件,如果未指定,将使用默认凭据配置文件。
  • 将凭据作为参数传递(aws_access_key_idaws_secret_access_keyaws_session_tokenregion_name)。

更多详情请查看此链接

AWS 配置文件名称

from llama_index.llms.sagemaker_endpoint import SageMakerLLM
AWS_ACCESS_KEY_ID = "<-YOUR-AWS-ACCESS-KEY-ID->"
AWS_SECRET_ACCESS_KEY = "<-YOUR-AWS-SECRET-ACCESS-KEY->"
AWS_SESSION_TOKEN = "<-YOUR-AWS-SESSION-TOKEN->"
REGION_NAME = "<-YOUR-ENDPOINT-REGION-NAME->"
llm = SageMakerLLM(
endpoint_name=ENDPOINT_NAME,
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
aws_session_token=AWS_SESSION_TOKEN,
region_name=REGION_NAME,
)

使用凭据

from llama_index.llms.sagemaker_endpoint import SageMakerLLM
ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"
PROFILE_NAME = "<-YOUR-PROFILE-NAME->"
llm = SageMakerLLM(
endpoint_name=ENDPOINT_NAME, profile_name=PROFILE_NAME
) # Omit the profile name to use the default profile
resp = llm.complete(
"Paul Graham is ", formatted=True
) # formatted=True to avoid adding system prompt
print(resp)
66 years old (birthdate: September 4, 1951). He is a British-American computer scientist, programmer, and entrepreneur who is known for his work in the fields of artificial intelligence, machine learning, and computer vision. He is a professor emeritus at Stanford University and a researcher at the Stanford Artificial Intelligence Lab (SAIL).
Graham has made significant contributions to the field of computer science, including the development of the concept of "n-grams," which are sequences of n items that occur together in a dataset. He has also worked on the development of machine learning algorithms and has written extensively on the topic of machine learning.
Graham has received numerous awards for his work, including the Association for Computing Machinery (ACM) A.M. Turing Award, the IEEE Neural Networks Pioneer Award, and the IJCAI Award
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)
print(resp)
assistant: Arrrr, shiver me timbers! *adjusts eye patch* Me name be Cap'n Blackbeak, the most feared and infamous pirate on the seven seas! *winks*
*ahem* But enough about me, matey. What be bringin' ye to these fair waters? Are ye here to plunder some booty, or just to share a pint o' grog with a salty old sea dog like meself? *chuckles*
resp = llm.stream_complete("Paul Graham is ", formatted=True)
for r in resp:
print(r.delta)
64 today. He’s a computer sci
ist, entrepreneur, and writer, best known for his work in the fields of artificial intelligence, machine learning, and computer graphics.
Graham was born in 1956 in Boston, Massachusetts. He earned his Bachelor’s degree in Computer Science from Harvard University in 1978 and his PhD in Computer Science from the University of California, Berkeley in 1982.
Graham’s early work focused on the development of the first computer graphics systems that could generate photorealistic images. In the 1980s, he became interested in the field of artificial intelligence and machine learning, and he co-founded a number of companies to explore these areas, including Viaweb, which was one of the first commercial web hosting services.
Graham is also a prolific writer and has published a number of influential essays on topics such as the nature
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
ARRGH! *adjusts eye patch* Me hearty? *winks* Me name be Captain Blackbeak, the most feared and infamous pirate to ever sail the seven seas! *chuckles* Or, at least, that's what me matey mates tell me. *winks*
So, what be bringin' ye to these waters, matey? Are ye here to plunder some booty or just to hear me tales of the high seas? *grins* Either way, I be ready to share me treasure with ye! *winks* Just don't be tellin' any landlubbers about me hidden caches o' gold, or ye might be walkin' the plank, savvy? *winks*

SageMakerLLM 是一个用于与部署在 Amazon SageMaker 中的不同语言模型(LLM)进行交互的抽象层。所有默认参数均与 Llama 2 模型兼容。因此,如果您使用不同的模型,很可能需要设置以下参数:

  • messages_to_prompt:一个可调用对象,接收一个ChatMessage对象列表,以及未在消息中指定的系统提示。它应返回一个包含终端LLM兼容格式消息的字符串。

  • completion_to_prompt: 一个可调用对象,接收带有系统提示的完成字符串,并返回端点LLM兼容格式的字符串。

  • content_handler: 一个继承自 llama_index.llms.sagemaker_llm_endpoint_utils.BaseIOHandler 并实现以下方法的类:serialize_inputdeserialize_outputdeserialize_streaming_outputremove_prefix