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Ray Serve

Ray Serve 是一个可扩展的模型服务库，用于构建在线推理API。Serve特别适合系统组合，使您能够构建一个由多个链和业务逻辑组成的复杂推理服务，全部使用Python代码。

本笔记本的目标​

本笔记本展示了一个简单的示例，说明如何将OpenAI链部署到生产环境中。您可以扩展它以部署您自己的自托管模型，在其中可以轻松定义在生产环境中高效运行模型所需的硬件资源（GPU和CPU）数量。了解更多关于Ray Serve中可用选项（包括自动扩展）的信息，请参阅文档。

设置 Ray Serve​

使用pip install ray[serve]安装ray。

通用框架​

部署服务的一般框架如下：

# 0: Import ray serve and request from starlette
from ray import serve
from starlette.requests import Request


# 1: Define a Ray Serve deployment.
@serve.deployment
class LLMServe:
    def __init__(self) -> None:
        # All the initialization code goes here
        pass

    async def __call__(self, request: Request) -> str:
        # You can parse the request here
        # and return a response
        return "Hello World"


# 2: Bind the model to deployment
deployment = LLMServe.bind()

# 3: Run the deployment
serve.api.run(deployment)
# Shutdown the deployment
serve.api.shutdown()

使用自定义提示部署和OpenAI链的示例​

从这里获取一个OpenAI API密钥。通过运行以下代码，您将被要求提供您的API密钥。

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
API Reference:LLMChain | PromptTemplate | OpenAI
from getpass import getpass

OPENAI_API_KEY = getpass()
@serve.deployment
class DeployLLM:
    def __init__(self):
        # We initialize the LLM, template and the chain here
        llm = OpenAI(openai_api_key=OPENAI_API_KEY)
        template = "Question: {question}\n\nAnswer: Let's think step by step."
        prompt = PromptTemplate.from_template(template)
        self.chain = LLMChain(llm=llm, prompt=prompt)

    def _run_chain(self, text: str):
        return self.chain(text)

    async def __call__(self, request: Request):
        # 1. Parse the request
        text = request.query_params["text"]
        # 2. Run the chain
        resp = self._run_chain(text)
        # 3. Return the response
        return resp["text"]

现在我们可以绑定部署。

# Bind the model to deployment
deployment = DeployLLM.bind()

我们可以在想要运行部署时分配端口号和主机。

# Example port number
PORT_NUMBER = 8282
# Run the deployment
serve.api.run(deployment, port=PORT_NUMBER)

现在服务已部署在端口 localhost:8282 上，我们可以发送一个 post 请求来获取结果。

import requests

text = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
response = requests.post(f"http://localhost:{PORT_NUMBER}/?text={text}")
print(response.content.decode())

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