Skip to main content
Azure ML 是一个用于构建、训练和部署机器学习模型的平台。用户可以在模型目录(Model Catalog)中探索可部署的模型类型,该目录提供了来自不同提供商的基础模型和通用模型。 本笔记本将介绍如何在 Azure ML Online Endpoint 上托管的 LLM 的使用方法。
##安装用于此集成所需的 langchain 包
pip install -qU langchain-community
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint

设置

您必须在 Azure ML 上部署模型部署到 Azure AI Foundry(原 Azure AI Studio),并获取以下参数:
  • endpoint_url:端点提供的 REST 端点 URL。
  • endpoint_api_type:将模型部署到专用端点(托管基础架构)时,使用 endpoint_type='dedicated'。使用按量付费服务(模型即服务)部署模型时,使用 endpoint_type='serverless'
  • endpoint_api_key:端点提供的 API 密钥。
  • deployment_name:(可选)使用该端点的模型的部署名称。

内容格式化器

content_formatter 参数是一个处理程序类,用于转换 AzureML 端点的请求和响应以匹配所需的模式。由于模型目录中包含多种模型,且每种模型的数据处理方式可能各不相同,因此提供了一个 ContentFormatterBase 类,允许用户按需转换数据。提供以下内容格式化器:
  • GPT2ContentFormatter:为 GPT2 格式化请求和响应数据
  • DollyContentFormatter:为 Dolly-v2 格式化请求和响应数据
  • HFContentFormatter:为文本生成的 Hugging Face 模型格式化请求和响应数据
  • CustomOpenAIContentFormatter:为遵循 OpenAI API 兼容方案的模型(如 LLaMa2)格式化请求和响应数据。
注意:OSSContentFormatter 正在被弃用,并将由 GPT2ContentFormatter 取代。逻辑相同,但 GPT2ContentFormatter 是更合适的名称。由于更改向后兼容,您仍可以继续使用的 OSSContentFormatter

示例

示例:使用实时端点进行 LlaMa 2 补全

from langchain_community.llms.azureml_endpoint import (
    AzureMLEndpointApiType,
    CustomOpenAIContentFormatter,
)
from langchain.messages import HumanMessage

llm = AzureMLOnlineEndpoint(
    endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
    endpoint_api_type=AzureMLEndpointApiType.dedicated,
    endpoint_api_key="my-api-key",
    content_formatter=CustomOpenAIContentFormatter(),
    model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
)
response = llm.invoke("Write me a song about sparkling water:")
response
也可以在调用期间指定模型参数:
response = llm.invoke("Write me a song about sparkling water:", temperature=0.5)
response

示例:使用按量付费部署(模型即服务)进行聊天补全

from langchain_community.llms.azureml_endpoint import (
    AzureMLEndpointApiType,
    CustomOpenAIContentFormatter,
)
from langchain.messages import HumanMessage

llm = AzureMLOnlineEndpoint(
    endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions",
    endpoint_api_type=AzureMLEndpointApiType.serverless,
    endpoint_api_key="my-api-key",
    content_formatter=CustomOpenAIContentFormatter(),
    model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
)
response = llm.invoke("Write me a song about sparkling water:")
response

示例:自定义内容格式化器

以下是使用 Hugging Face 摘要模型的示例。
import json
import os
from typing import Dict

from langchain_community.llms.azureml_endpoint import (
    AzureMLOnlineEndpoint,
    ContentFormatterBase,
)


class CustomFormatter(ContentFormatterBase):
    content_type = "application/json"
    accepts = "application/json"

    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps(
            {
                "inputs": [prompt],
                "parameters": model_kwargs,
                "options": {"use_cache": False, "wait_for_model": True},
            }
        )
        return str.encode(input_str)

    def format_response_payload(self, output: bytes) -> str:
        response_json = json.loads(output)
        return response_json[0]["summary_text"]


content_formatter = CustomFormatter()

llm = AzureMLOnlineEndpoint(
    endpoint_api_type="dedicated",
    endpoint_api_key=os.getenv("BART_ENDPOINT_API_KEY"),
    endpoint_url=os.getenv("BART_ENDPOINT_URL"),
    model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
    content_formatter=content_formatter,
)
large_text = """On January 7, 2020, Blockberry Creative announced that HaSeul would not participate in the promotion for Loona's
next album because of mental health concerns. She was said to be diagnosed with "intermittent anxiety symptoms" and would be
taking time to focus on her health.[39] On February 5, 2020, Loona released their second EP titled [#] (read as hash), along
with the title track "So What".[40] Although HaSeul did not appear in the title track, her vocals are featured on three other
songs on the album, including "365". Once peaked at number 1 on the daily Gaon Retail Album Chart,[41] the EP then debuted at
number 2 on the weekly Gaon Album Chart. On March 12, 2020, Loona won their first music show trophy with "So What" on Mnet's
M Countdown.[42]

On October 19, 2020, Loona released their third EP titled [12:00] (read as midnight),[43] accompanied by its first single
"Why Not?". HaSeul was again not involved in the album, out of her own decision to focus on the recovery of her health.[44]
The EP then became their first album to enter the Billboard 200, debuting at number 112.[45] On November 18, Loona released
the music video for "Star", another song on [12:00].[46] Peaking at number 40, "Star" is Loona's first entry on the Billboard
Mainstream Top 40, making them the second K-pop girl group to enter the chart.[47]

On June 1, 2021, Loona announced that they would be having a comeback on June 28, with their fourth EP, [&] (read as and).
[48] The following day, on June 2, a teaser was posted to Loona's official social media accounts showing twelve sets of eyes,
confirming the return of member HaSeul who had been on hiatus since early 2020.[49] On June 12, group members YeoJin, Kim Lip,
Choerry, and Go Won released the song "Yum-Yum" as a collaboration with Cocomong.[50] On September 8, they released another
collaboration song named "Yummy-Yummy".[51] On June 27, 2021, Loona announced at the end of their special clip that they are
making their Japanese debut on September 15 under Universal Music Japan sublabel EMI Records.[52] On August 27, it was announced
that Loona will release the double A-side single, "Hula Hoop / Star Seed" on September 15, with a physical CD release on October
20.[53] In December, Chuu filed an injunction to suspend her exclusive contract with Blockberry Creative.[54][55]
"""
summarized_text = llm.invoke(large_text)
print(summarized_text)

示例:Dolly 配合 LLMChain

from langchain_classic.chains import LLMChain
from langchain_community.llms.azureml_endpoint import DollyContentFormatter
from langchain_core.prompts import PromptTemplate

formatter_template = "Write a {word_count} word essay about {topic}."

prompt = PromptTemplate(
    input_variables=["word_count", "topic"], template=formatter_template
)

content_formatter = DollyContentFormatter()

llm = AzureMLOnlineEndpoint(
    endpoint_api_key=os.getenv("DOLLY_ENDPOINT_API_KEY"),
    endpoint_url=os.getenv("DOLLY_ENDPOINT_URL"),
    model_kwargs={"temperature": 0.8, "max_tokens": 300},
    content_formatter=content_formatter,
)

chain = LLMChain(llm=llm, prompt=prompt)
print(chain.invoke({"word_count": 100, "topic": "how to make friends"}))

序列化 LLM

您还可以保存和加载 LLM 配置
from langchain_community.llms.loading import load_llm

save_llm = AzureMLOnlineEndpoint(
    deployment_name="databricks-dolly-v2-12b-4",
    model_kwargs={
        "temperature": 0.2,
        "max_tokens": 150,
        "top_p": 0.8,
        "frequency_penalty": 0.32,
        "presence_penalty": 72e-3,
    },
)
save_llm.save("azureml.json")
loaded_llm = load_llm("azureml.json")

print(loaded_llm)