开始使用

Venice API 快速上手 —— 生成 API 密钥、发送您的第一个 chat completion，并在几分钟内探索图像、视频和音频端点。

在几分钟内启动并运行 Venice API。生成 API 密钥、发出您的第一个请求，并开始构建。

​

快速开始

1. 获取您的 API 密钥

   前往您的 Venice API 设置 并生成新的 API 密钥。

   如需详细的操作指南，请查看 API 密钥指南。

2. 设置您的 API 密钥

   将您的 API 密钥添加到您的环境中。您可以在 shell 中导出它：

   export VENICE_API_KEY='your-api-key-here'
   

   或将其添加到项目中的 .env 文件：

   VENICE_API_KEY=your-api-key-here
   
3. 安装 SDK

   Venice 与 OpenAI 兼容，因此您可以使用 OpenAI SDK。如果您更喜欢使用 cURL 或原始 HTTP 请求，可以跳过此步骤。

   Python

   pip install openai
   

   npm install openai
   
4. 发送您的第一个请求

   Python

   import os
   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.getenv("VENICE_API_KEY"),
       base_url="https://api.venice.ai/api/v1"
   )
   
   completion = client.chat.completions.create(
       model="zai-org-glm-5",
       messages=[
           {"role": "system", "content": "You are a helpful AI assistant"},
           {"role": "user", "content": "Why is privacy important?"}
       ]
   )
   
   print(completion.choices[0].message.content)
   

   import OpenAI from 'openai';
   
   const client = new OpenAI({
       apiKey: process.env.VENICE_API_KEY,
       baseURL: 'https://api.venice.ai/api/v1'
   });
   
   const completion = await client.chat.completions.create({
       model: 'zai-org-glm-5',
       messages: [
           { role: 'system', content: 'You are a helpful AI assistant' },
           { role: 'user', content: 'Why is privacy important?' }
       ]
   });
   
   console.log(completion.choices[0].message.content);
   

   curl https://api.venice.ai/api/v1/chat/completions \
     -H "Authorization: Bearer $VENICE_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "zai-org-glm-5",
       "messages": [
         {"role": "system", "content": "You are a helpful AI assistant"},
         {"role": "user", "content": "Why is privacy important?"}
       ]
     }'
   

   消息角色：

   • system - 模型应如何表现的指令
   • user - 您的 prompt 或问题
   • assistant - 之前的模型响应（用于多轮对话）
   • tool - 函数调用结果（使用工具时）
5. 通过更改模型 ID 切换模型

   每个请求都包含一个 model ID。要使用不同的模型，请更改请求中的 model 值。热门选择：

   • zai-org-glm-5 - 大多数用例的默认模型
   • kimi-k2-6 - 适用于更复杂任务的强大推理
   • claude-opus-4-8 - 适用于复杂任务的高智能模型
   • venice-uncensored-1-2 - Venice 的无审查模型

   查看所有模型

   浏览包含定价、能力和上下文限制的完整模型列表

6. 使用 Venice 参数

   您可以选择使用 venice_parameters 启用 Venice 特有的功能，如网页搜索：

   Python

   import os
   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.environ.get("VENICE_API_KEY"),
       base_url="https://api.venice.ai/api/v1"
   )
   
   completion = client.chat.completions.create(
       model="zai-org-glm-5",
       messages=[
           {"role": "user", "content": "What are the latest developments in AI?"}
       ],
       extra_body={
           "venice_parameters": {
               "enable_web_search": "auto",
               "include_venice_system_prompt": True
           }
       }
   )
   
   print(completion.choices[0].message.content)
   

   import OpenAI from 'openai';
   
   const client = new OpenAI({
       apiKey: process.env.VENICE_API_KEY,
       baseURL: 'https://api.venice.ai/api/v1'
   });
   
   const completion = await client.chat.completions.create({
       model: 'zai-org-glm-5',
       messages: [
           { role: 'user', content: 'What are the latest developments in AI?' }
       ],
       venice_parameters: {
           enable_web_search: 'auto',
           include_venice_system_prompt: true
       }
   });
   
   console.log(completion.choices[0].message.content);
   

   curl https://api.venice.ai/api/v1/chat/completions \
     -H "Authorization: Bearer $VENICE_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "zai-org-glm-5",
       "messages": [
         {"role": "user", "content": "What are the latest developments in AI?"}
       ],
       "venice_parameters": {
         "enable_web_search": "auto",
         "include_venice_system_prompt": true
       }
     }'
   

   查看所有可用参数。

7. 启用流式传输（可选）

   使用 stream=True 实时流式传输响应：

   Python

   import os
   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.environ.get("VENICE_API_KEY"),
       base_url="https://api.venice.ai/api/v1"
   )
   
   stream = client.chat.completions.create(
       model="zai-org-glm-5",
       messages=[{"role": "user", "content": "Write a short story about AI"}],
       stream=True
   )
   
   for chunk in stream:
       if chunk.choices and chunk.choices[0].delta.content is not None:
           print(chunk.choices[0].delta.content, end="")
   

   import OpenAI from 'openai';
   
   const client = new OpenAI({
       apiKey: process.env.VENICE_API_KEY,
       baseURL: 'https://api.venice.ai/api/v1'
   });
   
   const stream = await client.chat.completions.create({
       model: 'zai-org-glm-5',
       messages: [{ role: 'user', content: 'Write a short story about AI' }],
       stream: true
   });
   
   for await (const chunk of stream) {
       if (chunk.choices && chunk.choices[0]?.delta?.content) {
           process.stdout.write(chunk.choices[0].delta.content);
       }
   }
   

   curl https://api.venice.ai/api/v1/chat/completions \
     -H "Authorization: Bearer $VENICE_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "zai-org-glm-5",
       "messages": [
         {"role": "user", "content": "Write a short story about AI"}
       ],
       "stream": true
     }'
   
8. 自定义响应行为（可选）

   使用 temperature、max tokens 等参数控制模型的响应方式：

   Python

   import os
   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.environ.get("VENICE_API_KEY"),
       base_url="https://api.venice.ai/api/v1"
   )
   
   completion = client.chat.completions.create(
       model="zai-org-glm-5",
       messages=[
           {"role": "system", "content": "You are a creative storyteller"},
           {"role": "user", "content": "Tell me a creative story"}
       ],
       temperature=0.8,
       max_tokens=500,
       top_p=0.9,
       frequency_penalty=0.5,
       presence_penalty=0.5,
       extra_body={
           "venice_parameters": {
               "include_venice_system_prompt": False
           }
       }
   )
   
   print(completion.choices[0].message.content)
   

   import OpenAI from 'openai';
   
   const client = new OpenAI({
       apiKey: process.env.VENICE_API_KEY,
       baseURL: 'https://api.venice.ai/api/v1'
   });
   
   const completion = await client.chat.completions.create({
       model: 'zai-org-glm-5',
       messages: [
           { role: 'system', content: 'You are a creative storyteller' },
           { role: 'user', content: 'Tell me a creative story' }
       ],
       temperature: 0.8,
       max_tokens: 500,
       top_p: 0.9,
       frequency_penalty: 0.5,
       presence_penalty: 0.5,
       venice_parameters: {
           include_venice_system_prompt: false
       }
   });
   
   console.log(completion.choices[0].message.content);
   

   curl https://api.venice.ai/api/v1/chat/completions \
     -H "Authorization: Bearer $VENICE_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "zai-org-glm-5",
       "messages": [
         {"role": "system", "content": "You are a creative storyteller"},
         {"role": "user", "content": "Tell me a creative story"}
       ],
       "temperature": 0.8,
       "max_tokens": 500,
       "top_p": 0.9,
       "frequency_penalty": 0.5,
       "presence_penalty": 0.5,
       "stream": false,
       "venice_parameters": {
         "include_venice_system_prompt": false
       }
     }'
   

   有关所有支持参数的更多信息，请查看 Chat Completions 文档。

---

​

更多能力

​

图像生成

使用扩散模型从文本 prompt 创建图像：

Python

import os
import requests

url = "https://api.venice.ai/api/v1/image/generate"

payload = {
    "model": "venice-sd35",
    "prompt": "A cyberpunk city with neon lights and rain",
    "width": 1024,
    "height": 1024,
    "format": "webp"
}

headers = {
    "Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}",
    "Content-Type": "application/json"
}

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

print(response.json())

const url = 'https://api.venice.ai/api/v1/image/generate';

const options = {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        model: 'venice-sd35',
        prompt: 'A cyberpunk city with neon lights and rain',
        width: 1024,
        height: 1024,
        format: 'webp'
    })
};

try {
    const response = await fetch(url, options);
    const data = await response.json();
    console.log(data);
} catch (error) {
    console.error(error);
}

curl https://api.venice.ai/api/v1/image/generate \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "venice-sd35",
    "prompt": "A cyberpunk city with neon lights and rain",
    "width": 1024,
    "height": 1024
  }'

注意： 响应在 images 数组中返回 base64 编码的图像。解码 base64 字符串以保存或显示图像。

热门图像模型：

• qwen-image - 最高质量的图像生成
• venice-sd35 - 默认选择，适用于所有功能
• hidream - 用于生产用途的快速生成

查看所有图像模型

查看所有可用图像模型及其定价和能力

要了解更高级的参数选项，如 cfg_scale、negative_prompt、style_preset、seed、variants 等，请查看 图像 API 参考。

​

图像编辑

使用 Qwen-Image 模型通过 AI 驱动的修复修改现有图像：

Python

import os
import requests
import base64

url = "https://api.venice.ai/api/v1/image/edit"

with open("image.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode('utf-8')

payload = {
    "prompt": "Colorize",
    "image": image_base64
}

headers = {
    "Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}",
    "Content-Type": "application/json"
}

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

with open("edited_image.png", "wb") as f:
    f.write(response.content)

import fs from 'fs';

const imageBuffer = fs.readFileSync('image.jpg');
const imageBase64 = imageBuffer.toString('base64');

const options = {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        prompt: 'Colorize',
        image: imageBase64
    })
};

const response = await fetch('https://api.venice.ai/api/v1/image/edit', options);
const imageData = await response.arrayBuffer();
fs.writeFileSync('edited_image.png', Buffer.from(imageData));

curl --request POST \
  --url https://api.venice.ai/api/v1/image/edit \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "prompt": "Colorize",
    "image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAAIGNIUk0A..."
  }'

注意： 图像编辑器使用 Qwen-Image 模型，是一个实验性端点。将输入图像作为 base64 编码字符串发送，API 以二进制数据返回编辑后的图像。

有关所有参数，请参阅 Image Edit API。

​

图像放大

将图像增强并放大到更高分辨率：

Python

import os
import requests
import base64

url = "https://api.venice.ai/api/v1/image/upscale"

with open("image.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode('utf-8')

payload = {
    "image": image_base64,
    "scale": 2
}

headers = {
    "Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}",
    "Content-Type": "application/json"
}

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

with open("upscaled_image.png", "wb") as f:
    f.write(response.content)

import fs from 'fs';

const imageBuffer = fs.readFileSync('image.jpg');
const imageBase64 = imageBuffer.toString('base64');

const options = {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        image: imageBase64,
        scale: 2
    })
};

const response = await fetch('https://api.venice.ai/api/v1/image/upscale', options);
const imageData = await response.arrayBuffer();
fs.writeFileSync('upscaled_image.png', Buffer.from(imageData));

curl --request POST \
  --url https://api.venice.ai/api/v1/image/upscale \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAAIGNIUk0A...",
    "scale": 2
  }'

注意： 将输入图像作为 base64 编码字符串发送，API 以二进制数据返回放大的图像。

有关所有参数，请参阅 Image Upscale API。

​

文本转语音

使用 50+ 种多语言声音将文本转换为音频：

Python

import os
import requests

response = requests.post(
    "https://api.venice.ai/api/v1/audio/speech",
    headers={
        "Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}",
        "Content-Type": "application/json"
    },
    json={
        "input": "Hello, welcome to Venice Voice.",
        "model": "tts-kokoro",
        "voice": "af_sky"
    }
)

with open("speech.mp3", "wb") as f:
    f.write(response.content)

import fs from 'fs';

const response = await fetch('https://api.venice.ai/api/v1/audio/speech', {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        input: 'Hello, welcome to Venice Voice.',
        model: 'tts-kokoro',
        voice: 'af_sky'
    })
});

const audioBuffer = await response.arrayBuffer();
fs.writeFileSync('speech.mp3', Buffer.from(audioBuffer));

curl --request POST \
  --url https://api.venice.ai/api/v1/audio/speech \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "input": "Hello, welcome to Venice Voice.",
    "model": "tts-kokoro",
    "voice": "af_sky"
  }' \
  --output speech.mp3

tts-kokoro 模型支持 50+ 种多语言声音，包括 af_sky、af_nova、am_liam、bf_emma、zf_xiaobei 和 jm_kumo。

有关所有声音选项，请参阅 TTS API。

​

语音转文本

将音频文件转录为文本：

Python

import os
import requests

url = "https://api.venice.ai/api/v1/audio/transcriptions"

with open("audio.mp3", "rb") as f:
    response = requests.post(
        url,
        headers={"Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}"},
        files={"file": f},
        data={
            "model": "nvidia/parakeet-tdt-0.6b-v3",
            "response_format": "json"
        }
    )

print(response.json())

import fs from 'fs';
import FormData from 'form-data';

const form = new FormData();
form.append('file', fs.createReadStream('audio.mp3'));
form.append('model', 'nvidia/parakeet-tdt-0.6b-v3');
form.append('response_format', 'json');

const response = await fetch('https://api.venice.ai/api/v1/audio/transcriptions', {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        ...form.getHeaders()
    },
    body: form
});

const data = await response.json();
console.log(data);

curl --request POST \
  --url https://api.venice.ai/api/v1/audio/transcriptions \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --form file=@audio.mp3 \
  --form model=nvidia/parakeet-tdt-0.6b-v3 \
  --form response_format=json

支持的格式：WAV、FLAC、MP3、M4A、AAC、MP4。启用 timestamps=true 以获取字级时间数据。

有关所有选项，请参阅 Transcriptions API。

​

Embeddings

为语义搜索、RAG 和推荐生成向量嵌入：

Python

import os
import requests

url = "https://api.venice.ai/api/v1/embeddings"

payload = {
    "model": "text-embedding-bge-m3",
    "input": "Privacy-first AI infrastructure for semantic search",
    "encoding_format": "float"
}

headers = {
    "Authorization": f"Bearer {os.getenv('VENICE_API_KEY')}",
    "Content-Type": "application/json"
}

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

print(response.json())

const url = 'https://api.venice.ai/api/v1/embeddings';

const options = {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${process.env.VENICE_API_KEY}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        model: 'text-embedding-bge-m3',
        input: 'Privacy-first AI infrastructure for semantic search',
        encoding_format: 'float'
    })
};

try {
    const response = await fetch(url, options);
    const data = await response.json();
    console.log(data);
} catch (error) {
    console.error(error);
}

curl --request POST \
  --url https://api.venice.ai/api/v1/embeddings \
  --header "Authorization: Bearer $VENICE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "text-embedding-bge-m3",
    "input": "Privacy-first AI infrastructure for semantic search",
    "encoding_format": "float"
  }'

有关批处理和高级选项，请参阅 Embeddings API。

​

Vision（多模态）

使用支持视觉的模型如 qwen3-vl-235b-a22b 与文本一起分析图像：

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("VENICE_API_KEY"),
    base_url="https://api.venice.ai/api/v1"
)

response = client.chat.completions.create(
    model="qwen3-vl-235b-a22b",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://www.gstatic.com/webp/gallery/1.jpg"}
                }
            ]
        }
    ]
)

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

import OpenAI from 'openai';

const client = new OpenAI({
    apiKey: process.env.VENICE_API_KEY,
    baseURL: 'https://api.venice.ai/api/v1'
});

const response = await client.chat.completions.create({
    model: 'qwen3-vl-235b-a22b',
    messages: [
        {
            role: 'user',
            content: [
                { type: 'text', text: 'What is in this image?' },
                {
                    type: 'image_url',
                    image_url: { url: 'https://www.gstatic.com/webp/gallery/1.jpg' }
                }
            ]
        }
    ]
});

console.log(response.choices[0].message.content);

curl https://api.venice.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-vl-235b-a22b",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://www.gstatic.com/webp/gallery/1.jpg"
            }
          }
        ]
      }
    ]
  }'

​

函数调用

定义模型可以调用以与外部工具和 API 交互的函数：

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("VENICE_API_KEY"),
    base_url="https://api.venice.ai/api/v1"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="zai-org-glm-5",
    messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
    tools=tools
)

print(response.choices[0].message)

import OpenAI from 'openai';

const client = new OpenAI({
    apiKey: process.env.VENICE_API_KEY,
    baseURL: 'https://api.venice.ai/api/v1'
});

const tools = [
    {
        type: 'function',
        function: {
            name: 'get_weather',
            description: 'Get the current weather in a location',
            parameters: {
                type: 'object',
                properties: {
                    location: {
                        type: 'string',
                        description: 'The city and state'
                    }
                },
                required: ['location']
            }
        }
    }
];

const response = await client.chat.completions.create({
    model: 'zai-org-glm-5',
    messages: [{ role: 'user', content: "What's the weather in San Francisco?" }],
    tools: tools
});

console.log(response.choices[0].message);

curl https://api.venice.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org-glm-5",
    "messages": [
      {
        "role": "user",
        "content": "What'\''s the weather in San Francisco?"
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather in a location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state"
              }
            },
            "required": ["location"]
          }
        }
      }
    ]
  }'

---

​

下一步

既然您已经发出了第一个请求，请进一步探索 Venice API 提供的更多内容：

浏览模型

比较所有可用模型及其能力、定价和上下文限制

API 参考

探索包含所有端点和参数的详细 API 文档

结构化响应

了解如何获取具有保证 schema 的 JSON 响应

AI Agents 指南

使用 agent 应用、编码 agent、MCP 工具、skill 和加密货币工作流进行构建

​

其他资源

速率限制

了解速率限制和生产使用的最佳实践

错误代码

处理 API 错误和故障排查的参考

Postman Collection

导入我们的完整 Postman collection 以方便测试

隐私与安全

了解 Venice 的隐私优先架构和数据处理

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​

需要帮助？

• Discord 社区：加入我们的 Discord 服务器 获取支持和讨论
• 文档：浏览我们的 完整 API 参考
• 状态页：在 veniceai-status.com 检查服务状态
• Twitter：关注 @AskVenice 获取更新