OpenAI的对话和图像API简单体验

前言
- [OpenAI API 对话和图像接口](#OpenAI API 对话和图像接口)
- - Python
  - JavaScript
- [Azure OpenAI API 对话和图像接口](#Azure OpenAI API 对话和图像接口)
- - Python
  - JavaScript
- 总结

前言

JS 和 Python 是比较受欢迎的两个调用 OpenAI 对话 API 的两个库, 这里简单记录这两个库对 OpenAI 的对话(Chat)和图像(Image)的使用.

使用上这里会包括原生的 OpenAI API 和 Azure OpenAI API 两个使用例子.

JavaScrip 在网络设置和后端平台开发上不如 Python 方便,但是我们的 AIGC_Playground 的项目是一个前后端的项目,未来能够在网页上有一个好的体验,并且尽量分离前后端,所以这个项目的请求优先用JavaScrip

下面的例子 node 版本: v16.20.2, Python 的版本是 3.10.12

OpenAI API 对话和图像接口

Python

首先 pip3 install openai

直接看代码吧代码比较简单

py 复制代码

import httpx
import asyncio
from openai import OpenAI

proxy_url = "http://xxxx:xxxx"
api_key = "xxxx"


def use_proxy():
    http_client = None
    if(not proxy_url):
        http_client = httpx.Client()
        return http_client

    http_client = httpx.Client(proxies={'http://': proxy_url, 'https://': proxy_url})
    return http_client



'''
# ===== 非流式的对话测试 =====
'''
async def no_stream_chat():
    http_client = use_proxy()
    client = OpenAI(api_key=api_key,http_client=http_client)

    # 请求
    results = client.chat.completions.create(model= "gpt-4o-mini", messages=[{"role": "user", "content": [{"type": "text", "text": "Hello?"}]}])
    print(results.choices[0].message.content)


'''
# ===== 流式的对话测试 =====
'''
async def stream_chat():
    http_client = use_proxy()
    client = OpenAI(api_key=api_key,http_client=http_client)

    # 请求
    results = client.chat.completions.create(model= "gpt-4o-mini", messages=[{"role": "user", "content": [{"type": "text", "text": "Hello?"}]}], stream=True)

    for chunk in results:
        choice = chunk.choices
        if choice == []:
            continue

        content = choice[0].delta.content
        print(content)

'''
# ===== 生成图像的函数 =====
'''
async def gen_dell3_pic():
    http_client = use_proxy()
    client = OpenAI(api_key=api_key,http_client=http_client)

    # 请求
    results = client.images.generate(model="dall-e-3", prompt="A cute cat")
    print(results.data[0].url)



if __name__ == "__main__":
    asyncio.run(no_stream_chat())
    asyncio.run(stream_chat())
    asyncio.run(gen_dell3_pic())

JavaScript

首先安装包 npm install openai https-proxy-agent --save

再配置 package.json 支持 ES6 如下:

json 复制代码

{
  "type": "module",
  "dependencies": {
    "https-proxy-agent": "^7.0.6",
    "openai": "^4.78.1"
  }
}

同样也直接看代码算了

js 复制代码

import { OpenAI } from "openai";

const proxyUrl = "http://xxxx:xxx";
const apiKey = "xxxxxxxxxxxxxxxxxxxxxxxxxxxx";

/** 配置网络设置 */
async function useProxy(client) {
  if (!proxyUrl) return;

  // 动态导入 https-proxy-agent 模块
  const { HttpsProxyAgent } = await import("https-proxy-agent");

  // 使用 HttpsProxyAgent
  const agent = new HttpsProxyAgent(proxyUrl);

  const originalFetchWithTimeout = client.fetchWithTimeout;

  client.fetchWithTimeout = async (url, init, ms, controller) => {
    const { signal, ...options } = init || {};
    if (signal) signal.addEventListener("abort", () => controller.abort());

    const timeout = setTimeout(() => controller.abort(), ms);

    const fetchOptions = {
      signal: controller.signal,
      ...options,
      agent: agent,
    };
    if (fetchOptions.method) {
      // Custom methods like 'patch' need to be uppercased
      fetchOptions.method = fetchOptions.method.toUpperCase();
    }

    try {
      return await originalFetchWithTimeout.call(
        client,
        url,
        fetchOptions,
        ms,
        controller
      );
    } finally {
      clearTimeout(timeout);
    }
  };
}

/** ===== 非流式的对话测试 ===== */
async function noStreamChat() {
  const client = new OpenAI({ apiKey, timeout: 5000 });
  await useProxy(client);

  // 请求
  const results = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: [{ type: "text", text: "Hello?" }] }],
  });

  for (const choice of results.choices) {
    console.log(choice.message);
  }
}

/** ===== 流式的对话测试 ===== */
async function streamChat() {
  const client = new OpenAI({ apiKey, timeout: 5000 });
  await useProxy(client);

  // 请求
  const results = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: [{ type: "text", text: "Hello?" }] }],
    stream: true,
  });

  for await (const chunk of results) {
    console.log(chunk.choices[0]?.delta?.content || "");
  }
}

/** ===== 图片请求 ===== */
async function genDell3Pic() {
  const client = new OpenAI({ apiKey, timeout: 60000 });
  await useProxy(client);

  // 请求
  const results = await client.images.generate({
    model: "dall-e-3",
    prompt: "cute cat",
  });
  console.log(results.data[0].url);
}

/** ===== 测试主函数 ===== */
async function main() {
  await noStreamChat();
  await streamChat();
  await genDell3Pic();
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

Azure OpenAI API 对话和图像接口

Python

装依赖 pip3 install openai 看代码

py 复制代码

import httpx
import asyncio
from openai import AzureOpenAI

proxy_url = ""
azure_endpoint = "xxxxxxxxxxxxxxxx"
api_key = "xxxxxxxxxxxxxxxx"
chat_deployment = "xxxxxx"
image_deployment = "xxxxxxx"

def use_proxy():
    http_client = None
    if(not proxy_url):
        http_client = httpx.Client()
        return http_client

    http_client = httpx.Client(proxies={'http://': proxy_url, 'https://': proxy_url})
    return http_client



'''
# ===== 非流式的对话测试 =====
'''
async def no_stream_chat():
    deployment = chat_deployment
    api_version = "2024-05-01-preview"
    http_client = use_proxy()
    client = AzureOpenAI(azure_endpoint=azure_endpoint, api_key=api_key, api_version=api_version, http_client=http_client)

    # 请求
    results = client.chat.completions.create(model=deployment, messages=[{"role": "user", "content": [{"type": "text", "text": "Hello?"}]}])
    print(results.choices[0].message.content)


'''
# ===== 流式的对话测试 =====
'''
async def stream_chat():
    deployment = chat_deployment
    api_version = "2024-05-01-preview"
    http_client = use_proxy()
    client = AzureOpenAI(azure_endpoint=azure_endpoint, api_key=api_key, api_version=api_version, http_client=http_client)

    # 请求
    results = client.chat.completions.create(model=deployment, messages=[{"role": "user", "content": [{"type": "text", "text": "Hello?"}]}], stream=True)

    for chunk in results:
        choice = chunk.choices
        if choice == []:
            continue

        content = choice[0].delta.content
        print(content)

'''
# ===== 生成图像的函数 =====
'''
async def gen_dell3_pic():
    deployment = image_deployment
    api_version = "2024-05-01-preview"
    http_client = use_proxy()
    client = AzureOpenAI(azure_endpoint=azure_endpoint, api_key=api_key, api_version=api_version, http_client=http_client)

    # 请求
    results = client.images.generate(model=deployment, prompt="cute cat")
    print(results.data[0].url)



if __name__ == "__main__":
    asyncio.run(no_stream_chat())
    asyncio.run(stream_chat())
    asyncio.run(gen_dell3_pic())

JavaScript

安装包 npm install openai https-proxy-agent --save, 再配置 package.json 如下:

json 复制代码

{
  "type": "module",
  "dependencies": {
    "https-proxy-agent": "^7.0.6",
    "openai": "^4.78.1"
  }
}

直接看代码吧:

js 复制代码

import { AzureOpenAI } from "openai";

const proxyUrl = "";
const endpoint = "xxxxxxxxx";
const apiKey = "xxxxxxxxx";
const chatDeployment = "xxx";
const dellDelpoyment = "xxxxxx";

/** 配置网络设置 */
async function useProxy(client) {
  if (!proxyUrl) return;

  // 动态导入 https-proxy-agent 模块
  const { HttpsProxyAgent } = await import("https-proxy-agent");

  // 使用 HttpsProxyAgent
  const agent = new HttpsProxyAgent(proxyUrl);

  const originalFetchWithTimeout = client.fetchWithTimeout;

  client.fetchWithTimeout = async (url, init, ms, controller) => {
    const { signal, ...options } = init || {};
    if (signal) signal.addEventListener("abort", () => controller.abort());

    const timeout = setTimeout(() => controller.abort(), ms);

    const fetchOptions = {
      signal: controller.signal,
      ...options,
      agent: agent,
    };
    if (fetchOptions.method) {
      // Custom methods like 'patch' need to be uppercased
      fetchOptions.method = fetchOptions.method.toUpperCase();
    }

    try {
      return await originalFetchWithTimeout.call(
        client,
        url,
        fetchOptions,
        ms,
        controller
      );
    } finally {
      clearTimeout(timeout);
    }
  };
}

/** ===== 非流式的对话测试 ===== */
async function noStreamChat() {
  const deployment = chatDeployment;
  const apiVersion = "2024-05-01-preview";
  const client = new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment });
  useProxy(client);

  // 请求
  const results = await client.chat.completions.create({
    messages: [{ role: "user", content: [{ type: "text", text: "Hello?" }] }],
  });

  for (const choice of results.choices) {
    console.log(choice.message);
  }
}

/** ===== 流式的对话测试 ===== */
async function streamChat() {
  const apiVersion = "2024-05-01-preview";
  const deployment = chatDeployment;
  const client = new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment });
  useProxy(client);

  // 请求
  const results = await client.chat.completions.create({
    messages: [{ role: "user", content: [{ type: "text", text: "Hello?" }] }],
    stream: true,
  });

  for await (const chunk of results) {
    console.log(chunk.choices[0]?.delta?.content || "");
  }
}

/** ===== 图片请求 ===== */
async function genDell3Pic() {
  // The prompt to generate images from
  const deployment = dellDelpoyment;
  const apiVersion = "2024-04-01-preview";
  const client = new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment });
  useProxy(client);

  // 请求
  const results = await client.images.generate({ prompt: "cute cat" });
  console.log("image.url :", results.data[0].url);
}

/** ===== 测试主函数 ===== */
async function main() {
  await noStreamChat();
  await streamChat();
  await genDell3Pic();
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

总结

对于 JavaScript 的代码在配置网络时候需要对原来的 fetch 的参数进行复写, 虽然 openai 的 npm 的包时候提供了传入自定义的 fetch 的值, 但是我测试发现这个传入对返回的 response 要做一些处理, 暂时这样操作.
Python 的库直接用 httpx 直接定义网络设置, 还是比较方便的.
后续再介绍其他的接口.