Azure ML

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中探索要部署的模型类型，该目录提供了来自不同提供商的基础模型和通用模型。

此笔记本介绍如何使用托管在Azure ML 在线端点上的 LLM。

##Installing the langchain packages needed to use the integration
%pip install -qU langchain-community

from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint

API 参考：AzureMLOnlineEndpoint

设置

您必须在 Azure ML 上部署模型或部署到 Azure AI studio 并获取以下参数

endpoint_url：端点提供的 REST 端点 URL。
endpoint_api_type：在将模型部署到**专用端点**（托管的托管基础设施）时，使用endpoint_type='dedicated'。在使用**按需付费**产品（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）使用端点的模型的部署名称。

内容格式化程序

content_formatter参数是用于转换 AzureML 端点的请求和响应以匹配所需模式的处理程序类。由于模型目录中存在各种模型，每个模型可能以不同的方式处理数据，因此提供了ContentFormatterBase类以允许用户根据自己的喜好转换数据。提供了以下内容格式化程序

GPT2ContentFormatter：格式化 GPT2 的请求和响应数据
DollyContentFormatter：格式化 Dolly-v2 的请求和响应数据
HFContentFormatter：格式化文本生成 Hugging Face 模型的请求和响应数据
CustomOpenAIContentFormatter：格式化遵循 OpenAI API 兼容方案的模型（如 LLaMa2）的请求和响应数据。

注意：OSSContentFormatter 即将弃用，并由GPT2ContentFormatter替换。逻辑相同，但GPT2ContentFormatter 是更合适的名称。您仍然可以继续使用OSSContentFormatter，因为更改是向后兼容的。

示例

示例：使用实时端点的 LlaMa 2 补全

from langchain_community.llms.azureml_endpoint import (
    AzureMLEndpointApiType,
    CustomOpenAIContentFormatter,
)
from langchain_core.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

API 参考：AzureMLEndpointApiType | CustomOpenAIContentFormatter | HumanMessage

模型参数也可以在调用期间指示

response = llm.invoke("Write me a song about sparkling water:", temperature=0.5)
response

示例：使用按需付费部署（模型即服务）的聊天补全

from langchain_community.llms.azureml_endpoint import (
    AzureMLEndpointApiType,
    CustomOpenAIContentFormatter,
)
from langchain_core.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

API 参考：AzureMLEndpointApiType | CustomOpenAIContentFormatter | HumanMessage

示例：自定义内容格式化程序

以下是一个使用来自 Hugging Face 的摘要模型的示例。

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)

API 参考：AzureMLOnlineEndpoint | ContentFormatterBase

示例：使用 LLMChain 的 Dolly

from langchain.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"}))

API 参考：LLMChain | DollyContentFormatter | PromptTemplate

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)

API 参考：load_llm

LLM 概念指南
LLM 操作指南

Azure ML

设置

内容格式化程序

示例

示例：使用实时端点的 LlaMa 2 补全

示例：使用按需付费部署（模型即服务）的聊天补全

示例：自定义内容格式化程序

示例：使用 LLMChain 的 Dolly

序列化 LLM

此页面是否有帮助？

您还可以留下详细的反馈在 GitHub 上.

设置​

内容格式化程序​

示例​

示例：使用实时端点的 LlaMa 2 补全​

示例：使用按需付费部署（模型即服务）的聊天补全​

示例：自定义内容格式化程序​

示例：使用 LLMChain 的 Dolly​

序列化 LLM​

相关​

此页面是否有帮助？

您还可以留下详细的反馈 在 GitHub 上.

设置

内容格式化程序

示例

示例：使用实时端点的 LlaMa 2 补全

示例：使用按需付费部署（模型即服务）的聊天补全

示例：自定义内容格式化程序

示例：使用 LLMChain 的 Dolly

序列化 LLM

相关

您还可以留下详细的反馈在 GitHub 上.