PaiEasChatEndpoint

Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system.

Setup Eas Service

One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information. Try to set environment variables to init eas service url and token:

export EAS_SERVICE_URL=XXX
export EAS_SERVICE_TOKEN=XXX

or run as follow codes:

import os

from langchain.chat_models import PaiEasChatEndpoint
from langchain_core.language_models.chat_models import HumanMessage

os.environ["EAS_SERVICE_URL"] = "Your_EAS_Service_URL"
os.environ["EAS_SERVICE_TOKEN"] = "Your_EAS_Service_Token"
chat = PaiEasChatEndpoint(
    eas_service_url=os.environ["EAS_SERVICE_URL"],
    eas_service_token=os.environ["EAS_SERVICE_TOKEN"],
)

Run Chat Model

You can use the default settings to call eas service as follows:

output = chat([HumanMessage(content="write a funny joke")])
print("output:", output)

Or, call eas service with new inference params:

kwargs = {"temperature": 0.8, "top_p": 0.8, "top_k": 5}
output = chat([HumanMessage(content="write a funny joke")], **kwargs)
print("output:", output)

Or, run a stream call to get a stream response:

outputs = chat.stream([HumanMessage(content="hi")], streaming=True)
for output in outputs:
    print("stream output:", output)