Azure OpenAI Chat Completion 创建指南

To create an Azure OpenAI Chat Completion, you'll need to use the `azure openai.chatcompletion.create` method. This method allows you to create a new chat completion endpoint.

The `azure openai.chatcompletion.create` method requires a `ChatCompletion` object as input. The `ChatCompletion` object contains the parameters for the chat completion endpoint, such as the model ID and the maximum number of tokens.

You can specify the model ID to use for the chat completion endpoint. This can be either a custom model or a pre-trained model from OpenAI. The model ID is used to determine the language model that will be used for the chat completion.

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The API Reference for Azure OpenAI ChatCompletion.create is a crucial part of the process. It provides a detailed guide on how to use the API.

To create a chat completion, you need to make a POST request to the /chatcompletions endpoint. This endpoint is used to create a new chat completion.

The request body should contain the model ID, prompt, and max tokens. The model ID specifies the AI model to use for the chat completion. The prompt is the input text for the AI model to generate a response. The max tokens specify the maximum number of tokens in the response.

The API returns a JSON response with the chat completion ID and the generated text. The chat completion ID can be used to retrieve the completion details.

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You can use the chat completion API to enable your model for few-shot learning. This means you can provide the model with a series of messages between the user and the assistant, serving as examples for the model to learn from.

The new prompt format allows for a slight variation in few-shot learning methods. You can now add a series of messages between the user and the assistant in the prompt, as examples for the model to learn from.

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To get started, you can try different methods and see which one works best for your use case. Azure OpenAI provides a GitHub repository with more examples to explore.

You can also use Azure OpenAI to understand the details of the chat completion API. Additionally, you can quickly get started with the GPT-35-Turbo model by following the GPT-35-Turbo quickstart guide.

Here are the key differences between using GPT-35-Turbo and GPT-4 for few-shot learning:

Note that you can find more examples in the Azure OpenAI GitHub repository.

To deploy a model in Azure OpenAI, you need to enter the "部署" page in your Azure OpenAI workspace.

You can then choose the model you want to deploy and click "創建". In the example, a補全模型 and an對話模型 were deployed.

After deployment, you can use the API to invoke the model, or you can test it in the Playground.

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When you call the completion model, you're using the openai.Completion.create method, but since you're using Azure's API, you'll need to specify the model using the engine parameter.

The max_tokens parameter determines the maximum number of tokens that can be generated, and it's set to 50 in this example. This means the model will stop generating text when it reaches the 50th token, as indicated by the finish_reason value of length.

Temperature controls the randomness of the generated text, and a higher value can lead to unexpected results. For instance, if you increase the temperature, the model might generate text that's not what you intended.

You can control the model's behavior by setting the tool_choice parameter to none, auto, or a specific function. If you set it to none, the model won't call any tools and will only generate messages.

Here's a summary of the tool_choice options:

The Chat Completion Tool is a powerful feature that allows you to control which tools, if any, the model calls. This can be a game-changer for your chat completion needs.

You have three options: none, auto, and required. If you choose none, the model won't call any tools and will simply generate messages. If you choose auto, the model can choose between generating messages and calling tools. If you choose required, the model must call one or more tools.

The model can be forced to call a specific tool by specifying its name in the tool choice option. For example, you can use {"type": "function", "function": {"name": "my_function"}} to force the model to call a function named "my_function".

Here are the possible values for the tool choice option:

By using the Chat Completion Tool, you can customize the behavior of the model to suit your needs and preferences.