OpenAI Chat Completion API Application and Usage

OpenAI ChatGPT is a very powerful AI conversational system. Simply by entering a prompt, it can generate fluent and natural responses in just a few seconds. ChatGPT stands out in the industry with its outstanding language understanding and generation capabilities. Today, ChatGPT has already been widely used across various industries and fields, and its influence is becoming increasingly significant. Whether for daily conversations, creative writing, professional consultation, or code programming, ChatGPT can provide amazing intelligent assistance, greatly improving human work efficiency and creativity.

This document mainly introduces the usage process of OpenAI Chat Completion API operations. With it, we can easily use the conversational functionality of the official OpenAI ChatGPT.

Compatible model requests accepted by the platform are billed based on the model's input, output, and cache usage. The prompt_tokens of Chat Completions include cached tokens, and the reported cached portion is charged at the corresponding cache price; switching interface formats will not result in fixed-count billing. Fees are subject to the current model prices in the console and the response usage.cost.

¶ GPT-6.1 Terra

Use model: "gpt-6.1-terra" to select this model. It supports text and image input as well as streaming output; function tools and structured output should be called according to the parameter format of this interface. Reasoning levels support low, medium, high, xhigh, and max, and it is recommended to start with reasoning_effort: "low"; none or minimal are not supported. Long-context pricing applies when the input exceeds 272,000 tokens. Actual fees are subject to the current prices in the console and the usage in the response.

¶ GPT-6.1 Sol

Use model: "gpt-6.1-sol" to select this model. It supports streaming output, function calling, structured output, and image input. During the first week of availability, it is only available to verified ACE T1+ holders (at least 100,000 ACE) or authorized users; the specific availability time is subject to the access prompt in the console.

Reasoning levels support low, medium, high, xhigh, and max; none or minimal are not currently supported. It is recommended to start with reasoning_effort: "low". When the input exceeds 272,000 tokens, long-context pricing applies to the entire request, and fees are subject to the current prices in the console.

¶ Application Process

To use the OpenAI Chat Completion API, first go to the WorldAi Console to obtain your API Token and keep it for later use.

If you have not yet logged in or registered, you will be automatically redirected to the login page to register and log in, and you will automatically return to the current page after completion.

One API Token can call all platform services; there is no need to apply separately for each service. Your first application includes free credits for a free trial; when your credits are insufficient, you can recharge your general balance in the console.

📘 Complete documentation: OpenAI Chat Completion API →

¶ Basic Usage

Next, you can fill in the corresponding content in the interface, as shown in the figure:

When using this interface for the first time, we need to fill in at least three items. One is authorization, which can be selected directly from the drop-down list. Another parameter is model; model is the model category on the official OpenAI ChatGPT website that we choose to use. Here, we mainly have 20 models, and details can be found in the models we provide. The final parameter is messages; messages is the array of prompt words we enter. It is an array, indicating that multiple prompt words can be uploaded at the same time. Each prompt word contains role and content, where role indicates the role of the questioner. We provide three identities: user, assistant, and system. The other field, content, is the specific content of our question.

At the same time, you can notice that corresponding call code is generated on the right. You can copy the code and run it directly, or click the “Try” button directly for testing.

Common optional parameters:

max_tokens: Limits the maximum number of tokens in a single reply.
temperature: Generation randomness, between 0 and 2; the larger the value, the more divergent it is.
n: How many candidate replies to generate at once.
response_format: Return format settings.

After calling it, we find that the returned result is as follows:

{
  "id": "chatcmpl-Cmd6uwSxN75F4PAdQSFEO8f2QPs4E",
  "object": "chat.completion",
  "created": 1765706120,
  "model": "gpt-5.5",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! What can I help you with today?",
        "refusal": null,
        "annotations": []
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 7,
    "completion_tokens": 13,
    "total_tokens": 20,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "service_tier": "default",
  "system_fingerprint": null
}

The returned result contains multiple fields, described as follows:

id, the ID generated for this conversation task, used to uniquely identify this conversation task.
model , the selected official OpenAI ChatGPT model.
choices, the response information provided by ChatGPT for the prompt.
usage : statistical information about tokens for this Q&A session.

Among them, choices contains ChatGPT's response information. The choices inside it is ChatGPT, as shown in the figure.

As you can see, the content field in choices contains the specific content of the ChatGPT reply.

¶ Streaming Response

This interface also supports streaming responses, which is very useful for web integration and can enable a webpage to achieve a word-by-word display effect.

If you want to return responses in a stream, you can change the stream parameter in the request header to true.

The modification is shown in the figure, but the call code needs corresponding changes to support streaming responses.

After changing stream to true, the API will return the corresponding JSON data line by line. At the code level, we need to make corresponding changes to obtain the line-by-line results.

Python sample call code:

import requests

url = "https://api.worldai.chat/openai/chat/completions"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "gpt-4",
    "messages": [{"role":"user","content":"hello"}],
    "stream": True
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

The output is as follows:

data: {"choices": [{"delta": {"role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": "Hi", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " there", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": "!", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " How", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " can", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " I", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " assist", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " you", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": " today", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"content": "?", "role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"role": "assistant"}, "index": 0}], "created": 1721007348, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: {"choices": [{"delta": {"role": "assistant"}, "finish_reason": "stop", "index": 0}], "created": 1721007349, "id": "chatcmpl-YzczYjVhNjhjMzMwNDQ5MDkyNGYzOGZjZGE1ZGQ5OGU", "model": "gpt-4", "object": "chat.completion.chunk", "recipient": "all"}

data: [DONE]

As you can see, there are many data entries in the response, and the choices within data are the latest response content, consistent with the content introduced above. choices are the newly added response content, and you can integrate them into your system based on the results. At the same time, the end of the streaming response is determined based on the content of data; if the content is [DONE], it indicates that the streaming response has completely ended. The returned data result contains multiple fields, introduced as follows:

id, the ID generated for this conversation task, used to uniquely identify this conversation task.
model , the selected model from the official OpenAI ChatGPT website.
choices, the response information provided by ChatGPT for the prompt.

JavaScript is also supported, for example, the Node.js streaming call code is as follows:

const options = {
  method: "post",
  headers: {
    accept: "application/json",
    authorization: "Bearer {token}",
    "content-type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-4",
    messages: [{ role: "user", content: "hello" }],
    stream: true,
  }),
};

fetch("https://api.worldai.chat/openai/chat/completions", options)
  .then((response) => response.json())
  .then((response) => console.log(response))
  .catch((err) => console.error(err));

Java sample code:

JSONObject jsonObject = new JSONObject();
jsonObject.put("model", "gpt-4");
jsonObject.put("messages", [{"role":"user","content":"hello"}]);
jsonObject.put("stream", true);
MediaType mediaType = "application/json; charset=utf-8".toMediaType();
RequestBody body = jsonObject.toString().toRequestBody(mediaType);
Request request = new Request.Builder()
  .url("https://api.worldai.chat/openai/chat/completions")
  .post(body)
  .addHeader("accept", "application/json")
  .addHeader("authorization", "Bearer {token}")
  .addHeader("content-type", "application/json")
  .build();

OkHttpClient client = new OkHttpClient();
Response response = client.newCall(request).execute();
System.out.print(response.body!!.string())

Other languages can be rewritten separately on your own; the principle is the same.

¶ Multi-turn Conversation

If you want to integrate multi-turn conversation functionality, you need to upload multiple prompts to the messages field. A specific example of multiple prompts is shown in the figure below:

Python sample invocation code:

import requests

url = "https://api.worldai.chat/openai/chat/completions"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "gpt-4",
    "messages": [{"role":"user","content":"Hello"},{"role":"assistant","content":"Hi! How can I assist you today?"},{"role":"user","content":"What I say just now?"}]
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

By uploading multiple prompts, multi-turn conversations can be easily implemented, and the following response can be obtained:

{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "You said, \"Hello.\""
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1721323012,
  "id": "chatcmpl-NWZmOTA5MDlkZjBjNDRjNGEwMzRjYzA5NmM1MzQwMWY",
  "model": "gpt-4",
  "object": "chat.completion.chunk",
  "recipient": "all",
  "usage": {
    "prompt_tokens": 31,
    "completion_tokens": 6,
    "total_tokens": 37
  }
}

It can be seen that the information contained in choices is consistent with the basic usage content. This contains the specific content of ChatGPT's replies to multiple conversations, so corresponding questions can be answered based on multiple conversation contents.

¶ Integrating OpenAI-Python

The OpenAI Chat Completion API is compatible with the official OpenAI interface and can be integrated directly using the official SDK OpenAI-Python. This article will briefly introduce how to use it.

First, you need to set up a local Python environment. You can search Google for this process.
Download and install a development environment, such as the VSCode editor.
Configure the OpenAI environment variables.

In the project folder, create and save a file named .env
.env file content:

OPENAI_API_KEY="sk-xxx"
OPENAI_BASE_URL="https://api.worldai.chat/openai"  # 再次提醒：如果你使用官网OpenAI的key，不要用这个地址。

Replace sk-xxx with your own key. OPENAI_BASE_URL is the proxy interface for accessing OpenAI.

Install the packages required by the project

pip install openai

The command in Mac OS is:

pip3 install openai

Create a sample source code file

Assume we create a sample code file index.py, with the specific content as follows:

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

response = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "hello",
        }
    ],
    model="gpt-4",
)

print(response.text)

¶ Internet-Connected Models

The gpt-3.5-browsing and gpt-4-browsing models are different from other models. They can perform internet searches based on prompts and appropriately adjust the internet search results before returning them to you. This article will demonstrate the internet-connected functionality through a specific example. Next, you can fill in the corresponding content on the OpenAI Chat Completion API interface, as shown in the figure:

At the same time, you can notice that there is corresponding generated invocation code on the right. You can copy the code and run it directly, or directly click the "Try" button for testing.

After invocation, we find that the returned result is as follows:

{
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "For the latest news in China today, you can check major news websites such as:\n\n- [BBC News China](https://www.bbc.com/news/world/asia/china)\n- [CNN China News](https://edition.cnn.com/china)\n- [Reuters China](https://www.reuters.com/news/archive/china-news)\n\nThese sources will have up-to-date information on current events in China."
      },
      "finish_reason": "stop"
    }
  ],
  "created": 1721009347,
  "id": "chatcmpl-YzA0M2RjZDVkYThlNDkxNTkzOThmZWQ4OGMzNzdhNzA",
  "model": "gpt-4-browsing",
  "object": "chat.completion.chunk",
  "recipient": "all",
  "usage": {
    "prompt_tokens": 325,
    "completion_tokens": 82,
    "total_tokens": 407
  }
}

It can be seen that the response information in choices is obtained after internet-connected queries, and related links are also provided. The response information in choices needs to be rendered using markdown syntax in order to obtain the best experience. Finally, this also demonstrates the powerful advantages of our model's internet-connected functionality.

¶ Vision Model

gpt-4o is a multimodal large language model developed by OpenAI. It adds visual understanding capabilities on the basis of GPT-4. This model can process both text and image inputs simultaneously, achieving cross-modal understanding and generation.

The text processing of the gpt-4o model is consistent with the basic usage content above. The following will briefly introduce how to use the model's image processing capabilities.

The image processing capability of the gpt-4o model is mainly enabled by adding a type field to the original content content. Through this field, it can be determined whether the uploaded content is text or an image, thereby using the image processing capability of the gpt-4o model. The following mainly describes two ways to invoke this feature using Curl and Python.

Curl script method

curl -X POST 'https://api.worldai.chat/openai/chat/completions' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What'\''s in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            }
          }
        ]
      }
    ]
  }'

Python script method

import requests

url = "https://api.worldai.chat/openai/chat/completions"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text", "text": "What's in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                    }
                },
            ],
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

Then you can get the following result. The field information in the result is consistent with the above, specifically as follows:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4-vision-preview",
  "system_fingerprint": "fp_44709d6fcb",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\n\nThis image shows a wooden boardwalk extending through a lush green marshland."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21
  }
}

It can be seen that the response content is based on the image. Therefore, through the above two methods, you can easily use the text and image processing capabilities of the gpt-4-vision model.

In addition to gpt-4o, there is also a lower-cost model called gpt-4o-mini. gpt-4o-mini is the latest generation of large language model developed by OpenAI. It not only responds quickly, but is also cheaper and supports multimodality. For the use of the vision feature, refer to the usage of the gpt-4o model above.

¶ GPT-4o Image Generation Model

¶ Generating Images Based on a Reference Image

Below is an example of generating an image with a custom style based on an image. First, let us look at the image we input, as shown below:

It can be seen that the reference image is an image of a real person. We can make it change to a different style, such as turning it into an anime-style image. The specific request example is:

{
  "model": "gpt-4o-image",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "生成动漫风格的图片，并且带上个帽子"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.acedata.cloud/qzx2z1.png"
          }
        }
      ]
    }
  ],
  "stream": false
}

Example result:

{
  "id": "chatcmpl-89DPQxbLuyRNzH5YLCPYM5WElV3dm",
  "object": "chat.completion",
  "created": 1781020664,
  "model": "gpt-4o-image",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\n\n> 🎨 生成中...\n\n![https://platform.cdn.acedata.cloud/20260609/0f7b6cf1b14843b1bab8e261fe5765b3.png](https://platform.cdn.acedata.cloud/20260609/0f7b6cf1b14843b1bab8e261fe5765b3.png)\n\n[点击下载](https://platform.cdn.acedata.cloud/download/20260609/0f7b6cf1b14843b1bab8e261fe5765b3.png)"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 122,
    "total_tokens": 222,
    "prompt_tokens_details": {
      "text_tokens": 93,
      "cached_tokens_details": {}
    },
    "completion_tokens_details": {}
  }
}

Among them, message.content in choices is the complete generated conversation result, and the image is included in it in Markdown format (the image link is a temporary address, so please download and save it promptly). It can be seen that the generated image is indeed in anime style, specifically as shown below:

¶ Text-to-Image Generation Only

We can use a prompt to generate an image and return it to us as a conversational result. Below, we use 创建一张未来城市日落的图片 as an example. The specific example is as follows:

{
  "model": "gpt-4o-image",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "创建一张未来城市日落的图片"
        }
      ]
    }
  ],
  "stream": false
}

Example result:

{
  "id": "chatcmpl-89DqkpQoPGkQqJ6kPKMKWejjLXVxQ",
  "object": "chat.completion",
  "created": 1781020587,
  "model": "gpt-4o-image",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\n\n> 🎨 生成中...\n\n![https://platform.cdn.acedata.cloud/20260609/ed2cca68732540fc99162ddc10ddc153.png](https://platform.cdn.acedata.cloud/20260609/ed2cca68732540fc99162ddc10ddc153.png)\n\n[点击下载](https://platform.cdn.acedata.cloud/download/20260609/ed2cca68732540fc99162ddc10ddc153.png)"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 17,
    "completion_tokens": 104,
    "total_tokens": 121,
    "prompt_tokens_details": {
      "text_tokens": 10,
      "cached_tokens_details": {}
    },
    "completion_tokens_details": {}
  }
}

It can be seen that the result matches the prompt, specifically as shown below:

¶ Generating One Image from Multiple Images

We can also use multiple reference images to generate one image. For example, by using an image of a handsome man and an image of coffee, we can use these two images to generate an image of a handsome man drinking coffee. Below are the specific reference images:

Below, we use 生成男生举着咖啡，并且马上要喝的样子 as an example. The specific example is as follows:

{
  "model": "gpt-4o-image",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Generate an image of a boy holding coffee and about to drink it"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.acedata.cloud/pqquv3.jpg"
          }
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.acedata.cloud/h8j2i0.jpg"
          }
        }
      ]
    }
  ],
  "stream": false
}

Sample result:

{
  "id": "chatcmpl-89DnHbbzOIQvU1VzJrNjzMU8BRUgG",
  "object": "chat.completion",
  "created": 1781021018,
  "model": "gpt-4o-image",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\n\n> 🎨 Generating...\n\n![https://platform.cdn.acedata.cloud/20260610/f1d9ddee3c304230a9f92929f04b95be.png](https://platform.cdn.acedata.cloud/20260610/f1d9ddee3c304230a9f92929f04b95be.png)\n\n[Click to download](https://platform.cdn.acedata.cloud/download/20260610/f1d9ddee3c304230a9f92929f04b95be.png)"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 193,
    "completion_tokens": 116,
    "total_tokens": 309,
    "prompt_tokens_details": {
      "text_tokens": 186,
      "cached_tokens_details": {}
    },
    "completion_tokens_details": {}
  }
}

As you can see, the generated result is indeed created by combining the two images. Below is the specific result:

¶ Error Handling

When calling the API, if an error is encountered, the API will return the corresponding error code and information. For example:

400 token_mismatched: Bad request, possibly due to missing or invalid parameters.
400 api_not_implemented: Bad request, possibly due to missing or invalid parameters.
401 invalid_token: Unauthorized, invalid or missing authorization token.
429 too_many_requests: Too many requests, you have exceeded the rate limit.
500 api_error: Internal server error, something went wrong on the server.

¶ Error Response Example

{
  "success": false,
  "error": {
    "code": "api_error",
    "message": "fetch failed"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

¶ Conclusion

Through this document, you have learned how to easily implement the conversation functionality of the official OpenAI ChatGPT using the OpenAI Chat Completion API. We hope this document can help you better integrate and use this API. If you have any questions, please feel free to contact our technical support team.