迁移到 OpenAI JavaScript API 库 4.x

import { AzureOpenAI } from "openai";
const deployment = "Your Azure OpenAI deployment";
const apiVersion = "2024-04-01-preview";
const options = { azureADTokenProvider, deployment, apiVersion }
const client = new AzureOpenAI(options);

Azure OpenAI 资源的终结点可以通过设置 endpoint 选项指定，但也可以通过环境变量 AZURE_OPENAI_ENDPOINT 由客户端加载。 这是设置终结点的推荐方法，因为它允许客户端在不同环境中使用，而无需更改代码，同时还能防止终结点在代码中公开。

需要指定 API 版本，这是确保现有代码在预览 API 版本之间不会中断所必需的。 请参阅 API 版本控制文档，了解有关 Azure OpenAI API 版本的更多信息。 此外，不需要 deployment 属性，但建议进行设置。 设置 deployment 后，它将用作所有需要它的操作的默认部署。 如果客户端不是用 deployment 选项创建的，则应使用部署名称设置 options 对象中的 model 属性。 不过，audio.transcriptions.create 等音频操作要求在创建客户端时将 deployment 选项设置为部署名称。

import { OpenAIClient } from "@azure/openai";
const endpoint = "Your Azure OpenAI resource endpoint";
const client = new OpenAIClient(endpoint, credential);

如果未设置，API 版本默认为客户端发布前的最后一个已知版本。 客户端也不会锁定到一个单一的模型部署，这意味着部署名称必须传递给每个需要它的方法。

const result = await client.chat.completions.create({ messages, model: '', max_tokens: 100 });

const result = await client.getChatCompletions(deploymentName, messages, { maxTokens: 100 });

const stream = await client.chat.completions.create({ model: '', messages, max_tokens: 100, stream: true });

const stream = await client.streamChatCompletions(deploymentName, messages, { maxTokens: 100 });

import "@azure/openai/types";

const azureSearchEndpoint = "Your Azure Search resource endpoint";
const azureSearchIndexName = "Your Azure Search index name";
const result = await client.chat.completions.create({ model: '', messages, data_sources: [{
      type: "azure_search",
      parameters: {
        endpoint: azureSearchEndpoint,
        index_name: azureSearchIndexName,
        authentication: {
          type: "system_assigned_managed_identity",
        }
      }
    }]
});

const azureSearchEndpoint = "Your Azure Search resource endpoint";
const azureSearchIndexName = "Your Azure Search index name";
const result = await client.getChatCompletions(deploymentName, messages, { azureExtensionOptions: { 
    data_sources: [{
      type: "azure_search",
      endpoint: azureSearchEndpoint,
      indexName: azureSearchIndexName,
      authentication: {
        type: "system_assigned_managed_identity",
      }
    }]
  } 
});

import { createReadStream } from "fs";

const result = await client.audio.transcriptions.create({
  model: '',
  file: createReadStream(audioFilePath),
});

import { readFile } from "fs/promises";

const audioFilePath = "path/to/audio/file";
const audio = await readFile(audioFilePath);
const result = await client.getAudioTranscription(deploymentName, audio);

import { createReadStream } from "fs";

const result = await client.audio.translations.create({
  model: '',
  file: createReadStream(audioFilePath),
});

import { readFile } from "fs/promises";

const audioFilePath = "path/to/audio/file";
const audio = await readFile(audioFilePath);
const result = await client.getAudioTranslation(deploymentName, audio);

const options = ...;
const assistantResponse = await assistantsClient.beta.assistants.create(
  options
);

const options = {
  model: azureOpenAIDeployment,
  name: "Math Tutor",
  instructions:
    "You are a personal math tutor. Write and run JavaScript code to answer math questions.",
  tools: [{ type: "code_interpreter" }],
};
const assistantResponse = await assistantsClient.createAssistant(options);

const assistantThread = await assistantsClient.beta.threads.create();

const assistantThread = await assistantsClient.createThread();

const threadResponse = await assistantsClient.beta.threads.messages.create(
  assistantThread.id,
  {
    role,
    content: message,
  }
);

const threadResponse = await assistantsClient.createMessage(
  assistantThread.id,
  role,
  message
);

使用 createAndPoll 方法可以迁移和简化该代码，该方法会创建一个运行并轮询它，直到它处于终止状态。

const runResponse = await assistantsClient.beta.threads.runs.createAndPoll(
  assistantThread.id,
  {
    assistant_id: assistantResponse.id,
  },
  { pollIntervalMs: 500 }
);

let runResponse = await assistantsClient.createRun(assistantThread.id, {
  assistantId: assistantResponse.id,
});

do {
  await new Promise((r) => setTimeout(r, 500));
  runResponse = await assistantsClient.getRun(
    assistantThread.id,
    runResponse.id
  );
} while (
  runResponse.status === "queued" ||
  runResponse.status === "in_progress"

可以使用 for await 循环遍历页面。

for await (const runMessageDatum of runMessages) {
  for (const item of runMessageDatum.content) {
    ...
  }
}

如果没有分页功能，必须使用 response 对象的 data 属性逐页手动访问结果。 例如，可以按以下方式访问第一页：

for (const runMessageDatum of runMessages.data) {
  for (const item of runMessageDatum.content) {
    ...
  }
}

const embeddings = await client.embeddings.create({ input, model: '' });

const embeddings = await client.getEmbeddings(deploymentName, input);

  const results = await client.images.generate({ prompt, model: '', n, size });

const results = await client.getImages(deploymentName, prompt, { n, size });

import "@azure/openai/types";

const result = await client.chat.completions.create({ model: '', messages });
for (const choice of results.choices) {
  const filterResults = choice.content_filter_results;
  if (!filterResults) {
    console.log("No content filter is found");
    return;
  }
  if (filterResults.error) {
    console.log(
      `Content filter ran into the error ${filterResults.error.code}: ${filterResults.error.message}`);
  }
  const { hate, sexual, self_harm, violence } = filterResults;
  ...
}

const results = await client.getChatCompletions(deploymentName, messages);
for (const choice of results.choices) {
  if (!choice.contentFilterResults) {
    console.log("No content filter is found");
    return;
  }
  if (choice.contentFilterResults.error) {
    console.log(
      `Content filter ran into the error ${choice.contentFilterResults.error.code}: ${choice.contentFilterResults.error.message}`);
  }
  const { hate, sexual, selfHarm, violence } = choice.contentFilterResults;
  ...
}