API Manual

Platform Features

OpenAI Compatible Mode

APIYI uses OpenAI-compatible format, allowing you to call 200+ mainstream large models with a unified interface: Supported Model Providers:

🤖 OpenAI: gpt-4o, gpt-5-chat-latest, gpt-3.5-turbo, etc.
🧠 Anthropic: claude-sonnet-4-20250514, claude-opus-4-1-20250805, etc.
💎 Google: gemini-2.5-pro, gemini-2.5-flash, etc.
🚀 xAI: grok-4-0709, grok-3, etc.
🔍 DeepSeek: deepSeek-r1, deepSeek-v3, etc.
🌟 Alibaba: Qwen series models
💬 Moonshot: Kimi models, etc.

Feature Support Scope

✅ Supported Features:

💬 Chat Completions: Chat Completions interface
🖼️ Image Generation: gpt-image-1, flux-kontext-pro, flux-kontext-max, etc.
🔊 Audio Processing: Whisper transcription
📊 Embeddings: Text vectorization
⚡ Function Calling: Function Calling
📡 Streaming Output: Real-time response
🔧 OpenAI Parameters: temperature, top_p, max_tokens, etc.
🆕 Responses Endpoint: OpenAI latest features

❌ Unsupported Features:

🔧 Fine-tuning interface
📁 Files management interface
🏢 Organization management interface
💳 Billing management interface

Easy Model Switching

Core Advantage: One Codebase, Multiple Models After getting your code working with OpenAI format, you only need to change the model name to switch to other models:

# Using GPT-4o
response = client.chat.completions.create(
    model="gpt-4o",  # OpenAI model
    messages=[...]
)

# Switch to Claude, everything else stays the same!
response = client.chat.completions.create(
    model="claude-3.5-sonnet",  # Only change model name
    messages=[...]
)

# Switch to Gemini
response = client.chat.completions.create(
    model="gemini-1.5-pro",  # Only change model name
    messages=[...]
)

This design allows you to easily compare different models’ performance, or flexibly switch models based on cost and performance needs, without rewriting code!

Quick Start

Get API Key

Visit APIYI Console
Login to your account
Click “New” in the token management page to create an API Key
Copy the generated API Key for API calls

View Request Examples

In the token management page, you can quickly get code examples in various programming languages: Steps:

Go to Token Management Page
Find the row with your API Key
Click the 🔧wrench icon (tool icon) in the “Actions” column
Select “Request Examples” in the popup menu
View complete code examples including the following languages:

Supported Programming Languages:

cURL - Command line testing
Python (SDK) - Using official OpenAI library
Python (requests) - Using requests library
Node.js - JavaScript/TypeScript
Java - Java application development
C# - .NET application development
Go - Go language development
PHP - Web development
Ruby - Ruby application development
And more languages…

Code Example Features:

✅ Complete & Runnable: Copy and paste to use
✅ Parameter Descriptions: Detailed parameter configuration
✅ Error Handling: Includes exception handling logic
✅ Best Practices: Follows development standards of each language

We recommend developers to check the request examples in the backend first. These examples are updated in real-time according to the latest API version, ensuring code accuracy and availability.

Basic Information

API Endpoints

Primary Endpoint: https://api.apiyi.com/v1
Backup Endpoint: https://vip.apiyi.com/v1

Authentication

All API requests must include authentication information in the Header:

Authorization: Bearer YOUR_API_KEY

Request Format

Content-Type: application/json
Encoding: UTF-8
Request Method: POST (most interfaces)

Core Interfaces

1. Chat Completions

Create a chat completion request, supports multi-turn conversations. Request Endpoint

POST /v1/chat/completions

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	Model name, e.g. `gpt-3.5-turbo`
messages	array	Yes	Conversation message array
temperature	number	No	Sampling temperature, 0-2, default 1
max_tokens	integer	No	Maximum generated tokens
stream	boolean	No	Whether to stream response, default false
top_p	number	No	Nucleus sampling parameter, 0-1
n	integer	No	Number of generations, default 1
stop	string/array	No	Stop sequences
presence_penalty	number	No	Presence penalty, -2 to 2
frequency_penalty	number	No	Frequency penalty, -2 to 2

Message Format

{
  "role": "system|user|assistant",
  "content": "Message content"
}

Complete Code Examples

curl -X POST "https://api.apiyi.com/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "system", "content": "You are a helpful AI assistant."},
      {"role": "user", "content": "Hello! Please introduce yourself."}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
  }'

from openai import OpenAI

# Initialize client
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

# Send chat request
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Hello! Please introduce yourself."}
    ],
    temperature=0.7,
    max_tokens=1000
)

print(response.choices[0].message.content)

import requests
import json

url = "https://api.apiyi.com/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "model": "gpt-3.5-turbo",
    "messages": [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Hello! Please introduce yourself."}
    ],
    "temperature": 0.7,
    "max_tokens": 1000
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

if response.status_code == 200:
    print(result["choices"][0]["message"]["content"])
else:
    print(f"Error: {result}")

const OpenAI = require('openai');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.apiyi.com/v1'
});

async function chatCompletion() {
  try {
    const response = await client.chat.completions.create({
      model: 'gpt-3.5-turbo',
      messages: [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Hello! Please introduce yourself."}
      ],
      temperature: 0.7,
      max_tokens: 1000
    });

    console.log(response.choices[0].message.content);
  } catch (error) {
    console.error('API call error:', error);
  }
}

chatCompletion();

import okhttp3.*;
import com.google.gson.Gson;
import java.io.IOException;
import java.util.*;

public class APIYiExample {
    private static final String API_KEY = "YOUR_API_KEY";
    private static final String BASE_URL = "https://api.apiyi.com/v1";

    public static void main(String[] args) throws IOException {
        OkHttpClient client = new OkHttpClient();
        Gson gson = new Gson();

        // Build request body
        Map<String, Object> requestBody = new HashMap<>();
        requestBody.put("model", "gpt-3.5-turbo");
        requestBody.put("temperature", 0.7);
        requestBody.put("max_tokens", 1000);

        List<Map<String, String>> messages = Arrays.asList(
            Map.of("role", "system", "content", "You are a helpful AI assistant."),
            Map.of("role", "user", "content", "Hello! Please introduce yourself.")
        );
        requestBody.put("messages", messages);

        RequestBody body = RequestBody.create(
            gson.toJson(requestBody),
            MediaType.parse("application/json")
        );

        Request request = new Request.Builder()
            .url(BASE_URL + "/chat/completions")
            .addHeader("Authorization", "Bearer " + API_KEY)
            .addHeader("Content-Type", "application/json")
            .post(body)
            .build();

        try (Response response = client.newCall(request).execute()) {
            System.out.println(response.body().string());
        }
    }
}

using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
using Newtonsoft.Json;

class Program
{
    private static readonly string API_KEY = "YOUR_API_KEY";
    private static readonly string BASE_URL = "https://api.apiyi.com/v1";

    static async Task Main(string[] args)
    {
        using var client = new HttpClient();
        client.DefaultRequestHeaders.Add("Authorization", $"Bearer {API_KEY}");

        var requestBody = new
        {
            model = "gpt-3.5-turbo",
            messages = new[]
            {
                new { role = "system", content = "You are a helpful AI assistant." },
                new { role = "user", content = "Hello! Please introduce yourself." }
            },
            temperature = 0.7,
            max_tokens = 1000
        };

        var json = JsonConvert.SerializeObject(requestBody);
        var content = new StringContent(json, Encoding.UTF8, "application/json");

        try
        {
            var response = await client.PostAsync($"{BASE_URL}/chat/completions", content);
            var result = await response.Content.ReadAsStringAsync();
            Console.WriteLine(result);
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Error: {ex.Message}");
        }
    }
}

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)

type Message struct {
    Role    string `json:"role"`
    Content string `json:"content"`
}

type ChatRequest struct {
    Model       string    `json:"model"`
    Messages    []Message `json:"messages"`
    Temperature float64   `json:"temperature"`
    MaxTokens   int       `json:"max_tokens"`
}

func main() {
    apiKey := "YOUR_API_KEY"
    baseURL := "https://api.apiyi.com/v1"

    reqData := ChatRequest{
        Model: "gpt-3.5-turbo",
        Messages: []Message{
            {Role: "system", Content: "You are a helpful AI assistant."},
            {Role: "user", Content: "Hello! Please introduce yourself."},
        },
        Temperature: 0.7,
        MaxTokens:   1000,
    }

    jsonData, _ := json.Marshal(reqData)

    req, _ := http.NewRequest("POST", baseURL+"/chat/completions", bytes.NewBuffer(jsonData))
    req.Header.Set("Authorization", "Bearer "+apiKey)
    req.Header.Set("Content-Type", "application/json")

    client := &http.Client{}
    resp, err := client.Do(req)
    if err != nil {
        fmt.Printf("Request error: %v\n", err)
        return
    }
    defer resp.Body.Close()

    body, _ := ioutil.ReadAll(resp.Body)
    fmt.Println(string(body))
}

<?php
$api_key = 'YOUR_API_KEY';
$base_url = 'https://api.apiyi.com/v1';

$data = array(
    'model' => 'gpt-3.5-turbo',
    'messages' => array(
        array('role' => 'system', 'content' => 'You are a helpful AI assistant.'),
        array('role' => 'user', 'content' => 'Hello! Please introduce yourself.')
    ),
    'temperature' => 0.7,
    'max_tokens' => 1000
);

$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, $base_url . '/chat/completions');
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($ch, CURLOPT_HTTPHEADER, array(
    'Authorization: Bearer ' . $api_key,
    'Content-Type: application/json'
));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);

$response = curl_exec($ch);
$http_code = curl_getinfo($ch, CURLINFO_HTTP_CODE);
curl_close($ch);

if ($http_code == 200) {
    $result = json_decode($response, true);
    echo $result['choices'][0]['message']['content'];
} else {
    echo "Error: " . $response;
}
?>

require 'net/http'
require 'json'

api_key = 'YOUR_API_KEY'
base_url = 'https://api.apiyi.com/v1'

uri = URI("#{base_url}/chat/completions")
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true

request = Net::HTTP::Post.new(uri)
request['Authorization'] = "Bearer #{api_key}"
request['Content-Type'] = 'application/json'

request.body = {
  model: 'gpt-3.5-turbo',
  messages: [
    { role: 'system', content: 'You are a helpful AI assistant.' },
    { role: 'user', content: 'Hello! Please introduce yourself.' }
  ],
  temperature: 0.7,
  max_tokens: 1000
}.to_json

response = http.request(request)

if response.code == '200'
  result = JSON.parse(response.body)
  puts result['choices'][0]['message']['content']
else
  puts "Error: #{response.body}"
end

Response Example

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1699000000,
  "model": "gpt-3.5-turbo",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Hello! How can I help you today?"
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 10,
    "total_tokens": 30
  }
}

2. Text Completions

Retained for backward compatibility, recommend using Chat Completions. Request Endpoint

POST /v1/completions

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	Model name
prompt	string/array	Yes	Prompt text
max_tokens	integer	No	Maximum generation length
temperature	number	No	Sampling temperature
top_p	number	No	Nucleus sampling parameter
n	integer	No	Number of generations
stream	boolean	No	Streaming output
stop	string/array	No	Stop sequences

3. Embeddings

Convert text to vector representation. Request Endpoint

POST /v1/embeddings

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	Model name, e.g. `text-embedding-ada-002`
input	string/array	Yes	Input text
encoding_format	string	No	Encoding format, `float` or `base64`

Complete Code Examples

cURL
Python (SDK)
Python (requests)
Node.js

curl -X POST "https://api.apiyi.com/v1/embeddings" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-ada-002",
    "input": "This is a text example for vectorization"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

response = client.embeddings.create(
    model="text-embedding-ada-002",
    input="This is a text example for vectorization"
)

# Get vector
embedding = response.data[0].embedding
print(f"Vector dimension: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")

import requests
import json

url = "https://api.apiyi.com/v1/embeddings"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "model": "text-embedding-ada-002",
    "input": "This is a text example for vectorization"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

if response.status_code == 200:
    embedding = result["data"][0]["embedding"]
    print(f"Vector dimension: {len(embedding)}")
    print(f"Vector values: {embedding[:5]}")  # Show first 5 values
else:
    print(f"Error: {result}")

const OpenAI = require('openai');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.apiyi.com/v1'
});

async function getEmbedding() {
  try {
    const response = await client.embeddings.create({
      model: 'text-embedding-ada-002',
      input: 'This is a text example for vectorization'
    });

    const embedding = response.data[0].embedding;
    console.log(`Vector dimension: ${embedding.length}`);
    console.log(`First 5 values: ${embedding.slice(0, 5)}`);
  } catch (error) {
    console.error('API call error:', error);
  }
}

getEmbedding();

4. Image Generation

Generate, edit, or transform images. Generate Images

POST /v1/images/generations

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	Model name, recommend `gpt-image-1`
prompt	string	Yes	Image description prompt
n	integer	No	Number to generate, default 1
size	string	No	Image size: `1024x1024`, `1792x1024`, `1024x1792`
quality	string	No	Quality: `standard` or `hd`
style	string	No	Style: `vivid` or `natural`

Recommend using the gpt-image-1 model for image generation. For more image generation features and parameter descriptions, see GPT Image Generation Detailed Documentation.

Complete Code Examples

cURL
Python (SDK)
Node.js

curl -X POST "https://api.apiyi.com/v1/images/generations" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-1",
    "prompt": "A cute orange kitten sitting in a sunny garden",
    "n": 1,
    "size": "1024x1024",
    "quality": "hd"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

response = client.images.generate(
    model="gpt-image-1",  # Recommend using gpt-image-1
    prompt="A cute orange kitten sitting in a sunny garden",
    n=1,
    size="1024x1024",
    quality="hd"
)

# Get image URL
image_url = response.data[0].url
print(f"Generated image: {image_url}")

# Download image
import requests
img_response = requests.get(image_url)
with open("generated_image.png", "wb") as f:
    f.write(img_response.content)
print("Image saved as generated_image.png")

const OpenAI = require('openai');
const fs = require('fs');

const client = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://api.apiyi.com/v1'
});

async function generateImage() {
  try {
    const response = await client.images.generate({
      model: 'gpt-image-1',  // Recommend using gpt-image-1
      prompt: 'A cute orange kitten sitting in a sunny garden',
      n: 1,
      size: '1024x1024',
      quality: 'hd'
    });

    const imageUrl = response.data[0].url;
    console.log('Generated image:', imageUrl);

    // Download image
    const fetch = require('node-fetch');
    const imgResponse = await fetch(imageUrl);
    const buffer = await imgResponse.buffer();
    fs.writeFileSync('generated_image.png', buffer);
    console.log('Image saved');

  } catch (error) {
    console.error('Image generation error:', error);
  }
}

generateImage();

5. Audio Transcription

Speech recognition and transcription. Transcribe Audio

POST /v1/audio/transcriptions

Request Parameters (Form-Data)

Parameter	Type	Required	Description
file	file	Yes	Audio file
model	string	Yes	Model name, e.g. `whisper-1`
language	string	No	Language code
prompt	string	No	Guidance prompt
response_format	string	No	Response format
temperature	number	No	Sampling temperature

6. Model List

Get available model list. Request Endpoint

GET /v1/models

Response Example

{
  "object": "list",
  "data": [
    {
      "id": "gpt-3.5-turbo",
      "object": "model",
      "created": 1677610602,
      "owned_by": "openai"
    },
    {
      "id": "gpt-4o",
      "object": "model",
      "created": 1687882411,
      "owned_by": "openai"
    }
  ]
}

Streaming Response

Enable Streaming Output

Set stream: true in your request:

{
  "model": "gpt-3.5-turbo",
  "messages": [{"role": "user", "content": "Hello"}],
  "stream": true
}

Streaming Response Format

Response will be returned in Server-Sent Events (SSE) format:

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1699000000,"model":"gpt-3.5-turbo","choices":[{"delta":{"content":"Hello"},"index":0}]}

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1699000000,"model":"gpt-3.5-turbo","choices":[{"delta":{"content":" there"},"index":0}]}

data: [DONE]

Handle Streaming Response

Python
JavaScript

import requests
import json

response = requests.post(
    'https://api.apiyi.com/v1/chat/completions',
    headers={
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json'
    },
    json={
        'model': 'gpt-3.5-turbo',
        'messages': [{'role': 'user', 'content': 'Hello'}],
        'stream': True
    },
    stream=True
)

for line in response.iter_lines():
    if line:
        line = line.decode('utf-8')
        if line.startswith('data: '):
            data = line[6:]
            if data != '[DONE]':
                chunk = json.loads(data)
                content = chunk['choices'][0]['delta'].get('content', '')
                print(content, end='')

const response = await fetch('https://api.apiyi.com/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-3.5-turbo',
    messages: [{role: 'user', content: 'Hello'}],
    stream: true
  })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const {done, value} = await reader.read();
  if (done) break;

  const chunk = decoder.decode(value);
  const lines = chunk.split('\n');

  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const data = line.slice(6);
      if (data !== '[DONE]') {
        const json = JSON.parse(data);
        const content = json.choices[0].delta.content || '';
        process.stdout.write(content);
      }
    }
  }
}

Error Handling

Error Response Format

{
  "error": {
    "message": "Invalid API key provided",
    "type": "invalid_request_error",
    "param": null,
    "code": "invalid_api_key"
  }
}

Common Error Codes

Error Code	HTTP Status	Description
invalid_api_key	401	Invalid API key
insufficient_quota	429	Insufficient quota
model_not_found	404	Model not found
invalid_request_error	400	Invalid request parameters
server_error	500	Internal server error
rate_limit_exceeded	429	Request rate too high

Error Handling Example

try:
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello"}]
    )
except Exception as e:
    if hasattr(e, 'status_code'):
        if e.status_code == 401:
            print("Invalid API key")
        elif e.status_code == 429:
            print("Request too frequent or insufficient quota")
        elif e.status_code == 500:
            print("Server error, please try again later")
    else:
        print(f"Unknown error: {str(e)}")

Best Practices

1. Request Optimization

Set max_tokens reasonably: Avoid unnecessarily long outputs
Use temperature: Control output randomness
Batch processing: Merge multiple requests to reduce calls

2. Error Retry

Implement exponential backoff retry mechanism:

import time
import random

def retry_with_backoff(func, max_retries=3):
    for i in range(max_retries):
        try:
            return func()
        except Exception as e:
            if i == max_retries - 1:
                raise e
            wait_time = (2 ** i) + random.uniform(0, 1)
            time.sleep(wait_time)

3. Security Recommendations

Protect API keys: Use environment variables for storage
Limit permissions: Create different keys for different applications
Monitor usage: Regularly check API usage logs

4. Performance Optimization

Use streaming output: Improve user experience
Cache responses: Cache results for identical requests
Concurrency control: Reasonably control concurrent request count

Rate Limits

APIYI implements the following rate limits:

Limit Type	Limit Value	Description
RPM (Requests Per Minute)	3000	Per API key
TPM (Tokens Per Minute)	1000000	Per API key
Concurrent Requests	100	Simultaneous requests

When limits are exceeded, a 429 error will be returned. Please control request frequency reasonably.

Need Help?

View Complete API Documentation
Visit APIYI Website
Contact technical support: [email protected]

This manual is continuously updated. Please follow the latest version for new features and improvements.

Basics

Basic API

Video API

Image API

Multimodal Understanding API

Text API

Platform Features

OpenAI Compatible Mode

Feature Support Scope

Easy Model Switching

Quick Start

Get API Key

View Request Examples

Basic Information

API Endpoints

Authentication

Request Format

Core Interfaces

1. Chat Completions

2. Text Completions

3. Embeddings

4. Image Generation

5. Audio Transcription

6. Model List

Streaming Response

Enable Streaming Output

Streaming Response Format

Handle Streaming Response

Error Handling

Error Response Format

Common Error Codes

Error Handling Example

Best Practices

1. Request Optimization

2. Error Retry

3. Security Recommendations

4. Performance Optimization

Rate Limits

Need Help?

Basics

Basic API

Video API

Image API

Multimodal Understanding API

Text API

​Platform Features

​OpenAI Compatible Mode

​Feature Support Scope

​Easy Model Switching

​Quick Start

​Get API Key

​View Request Examples

​Basic Information

​API Endpoints

​Authentication

​Request Format

​Core Interfaces

​1. Chat Completions

​2. Text Completions

​3. Embeddings

​4. Image Generation

​5. Audio Transcription

​6. Model List

​Streaming Response

​Enable Streaming Output

​Streaming Response Format

​Handle Streaming Response

​Error Handling

​Error Response Format

​Common Error Codes

​Error Handling Example

​Best Practices

​1. Request Optimization

​2. Error Retry

​3. Security Recommendations

​4. Performance Optimization

​Rate Limits

​Need Help?

Platform Features

OpenAI Compatible Mode

Feature Support Scope

Easy Model Switching

Quick Start

Get API Key

View Request Examples

Basic Information

API Endpoints

Authentication

Request Format

Core Interfaces

1. Chat Completions

2. Text Completions

3. Embeddings

4. Image Generation

5. Audio Transcription

6. Model List

Streaming Response

Enable Streaming Output

Streaming Response Format

Handle Streaming Response

Error Handling

Error Response Format

Common Error Codes

Error Handling Example

Best Practices

1. Request Optimization

2. Error Retry

3. Security Recommendations

4. Performance Optimization

Rate Limits

Need Help?