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Azure Machine Learning 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，该目录提供来自不同提供商的基础模型和通用模型。 通常，您需要部署模型才能使用其预测（推理）。在 Azure Machine Learning 中，在线端点用于通过实时服务部署这些模型。它们基于 端点 和 部署 的概念，允许您将生产工作负载的接口与提供服务的实现解耦。
本笔记本介绍如何使用托管在 Azure Machine Learning 端点 上的聊天模型。 
from langchain_community.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint

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设置

您必须在 Azure ML 上部署模型或部署到 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（托管的托管基础设施）时，使用 endpoint_type='dedicated'。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。

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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。由于模型目录中有各种各样的模型，每种模型处理数据的方式可能各不相同，因此提供了 ContentFormatterBase 类，允许用户按照自己的需求转换数据。提供了以下内容格式化器：
  • CustomOpenAIChatContentFormatter：为遵循 OpenAI API 规范的模型（如 LLaMa2-chat）格式化请求和响应数据。
注意：langchain.chat_models.azureml_endpoint.LlamaChatContentFormatter 正在被弃用，替换为 langchain.chat_models.azureml_endpoint.CustomOpenAIChatContentFormatter。 您可以从 langchain_community.llms.azureml_endpoint.ContentFormatterBase 类派生，实现特定于您的模型的自定义内容格式化器。

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示例

以下部分包含有关如何使用此类的示例：

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示例：使用实时端点进行聊天补全

from langchain_community.chat_models.azureml_endpoint import (
    AzureMLEndpointApiType,
    CustomOpenAIChatContentFormatter,
)
from langchain.messages import HumanMessage

chat = AzureMLChatOnlineEndpoint(
    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=CustomOpenAIChatContentFormatter(),
)
response = chat.invoke(
    [HumanMessage(content="Will the Collatz conjecture ever be solved?")]
)
response

AIMessage(content='  The Collatz Conjecture is one of the most famous unsolved problems in mathematics, and it has been the subject of much study and research for many years. While it is impossible to predict with certainty whether the conjecture will ever be solved, there are several reasons why it is considered a challenging and important problem:\n\n1. Simple yet elusive: The Collatz Conjecture is a deceptively simple statement that has proven to be extraordinarily difficult to prove or disprove. Despite its simplicity, the conjecture has eluded some of the brightest minds in mathematics, and it remains one of the most famous open problems in the field.\n2. Wide-ranging implications: The Collatz Conjecture has far-reaching implications for many areas of mathematics, including number theory, algebra, and analysis. A solution to the conjecture could have significant impacts on these fields and potentially lead to new insights and discoveries.\n3. Computational evidence: While the conjecture remains unproven, extensive computational evidence supports its validity. In fact, no counterexample to the conjecture has been found for any starting value up to 2^64 (a number', additional_kwargs={}, example=False)

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示例：使用按需付费部署（模型即服务）进行聊天补全

chat = AzureMLChatOnlineEndpoint(
    endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions",
    endpoint_api_type=AzureMLEndpointApiType.serverless,
    endpoint_api_key="my-api-key",
    content_formatter=CustomOpenAIChatContentFormatter,
)
response = chat.invoke(
    [HumanMessage(content="Will the Collatz conjecture ever be solved?")]
)
response
如果需要向模型传递额外参数，请使用 model_kwargs 参数： 
chat = AzureMLChatOnlineEndpoint(
    endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions",
    endpoint_api_type=AzureMLEndpointApiType.serverless,
    endpoint_api_key="my-api-key",
    content_formatter=CustomOpenAIChatContentFormatter,
    model_kwargs={"temperature": 0.8},
)
参数也可以在调用时传递： 
response = chat.invoke(
    [HumanMessage(content="Will the Collatz conjecture ever be solved?")],
    max_tokens=512,
)
response
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