> ## Documentation Index
> Fetch the complete documentation index at: https://veniceai-mintlify-d2fddb8a.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

<Steps>
  <Step title="获取您的 API 密钥">
    前往您的 [Venice API 设置](https://venice.ai/settings/api) 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](/guides/getting-started/generating-api-key)。
  </Step>

  <Step title="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：

    ```bash theme={null}
    export VENICE_API_KEY='your-api-key-here'
    ```

    或将其添加到项目中的 `.env` 文件：

    ```bash theme={null}
    VENICE_API_KEY=your-api-key-here
    ```
  </Step>

  <Step title="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。

    <CodeGroup>
      ```bash Python theme={null}
      pip install openai
      ```

      ```bash Node.js theme={null}
      npm install openai
      ```
    </CodeGroup>
  </Step>

  <Step title="发送您的第一个请求">
    <CodeGroup>
      ```python Python theme={null}
      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)
      ```

      ```javascript Node.js theme={null}
      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);
      ```

      ```bash cURL theme={null}
      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?"}
          ]
        }'
      ```
    </CodeGroup>

    **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）
  </Step>

  <Step title="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

    <Card title="查看所有模型" icon="database" href="/overview/models">
      浏览包含定价、能力和上下文限制的完整模型列表
    </Card>
  </Step>

  <Step title="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：

    <CodeGroup>
      ```python Python theme={null}
      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)
      ```

      ```javascript Node.js theme={null}
      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);
      ```

      ```bash cURL theme={null}
      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
          }
        }'
      ```
    </CodeGroup>

    查看所有[可用参数](https://docs.venice.ai/api-reference/api-spec#venice-parameters)。
  </Step>

  <Step title="启用流式传输（可选）">
    使用 `stream=True` 实时流式传输响应：

    <CodeGroup>
      ```python Python theme={null}
      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="")
      ```

      ```javascript Node.js theme={null}
      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);
          }
      }
      ```

      ```bash cURL theme={null}
      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
        }'
      ```
    </CodeGroup>
  </Step>

  <Step title="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：

    <CodeGroup>
      ```python Python theme={null}
      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)
      ```

      ```javascript Node.js theme={null}
      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);
      ```

      ```bash cURL theme={null}
      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
          }
        }'
      ```
    </CodeGroup>

    有关所有支持参数的更多信息，请查看 [Chat Completions 文档](/api-reference/endpoint/chat/completions)。
  </Step>
</Steps>

***

## 更多能力

### 图像生成

使用扩散模型从文本 prompt 创建图像：

<CodeGroup>
  ```python Python theme={null}
  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())
  ```

  ```javascript Node.js theme={null}
  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);
  }
  ```

  ```bash cURL theme={null}
  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
    }'
  ```
</CodeGroup>

**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

<Card title="查看所有图像模型" icon="image" href="/overview/models#image-models">
  查看所有可用图像模型及其定价和能力
</Card>

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](/api-reference/endpoint/image/generate)。

### 图像编辑

使用 Qwen-Image 模型通过 AI 驱动的修复修改现有图像：

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```javascript Node.js theme={null}
  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));
  ```

  ```bash cURL theme={null}
  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..."
    }'
  ```
</CodeGroup>

**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](/api-reference/endpoint/image/edit)。

### 图像放大

将图像增强并放大到更高分辨率：

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```javascript Node.js theme={null}
  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));
  ```

  ```bash cURL theme={null}
  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
    }'
  ```
</CodeGroup>

**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](/api-reference/endpoint/image/upscale)。

### 文本转语音

使用 50+ 种多语言声音将文本转换为音频：

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```javascript Node.js theme={null}
  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));
  ```

  ```bash cURL theme={null}
  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
  ```
</CodeGroup>

`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](/api-reference/endpoint/audio/speech)。

### 语音转文本

将音频文件转录为文本：

<CodeGroup>
  ```python Python theme={null}
  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())
  ```

  ```javascript Node.js theme={null}
  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);
  ```

  ```bash cURL theme={null}
  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
  ```
</CodeGroup>

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](/api-reference/endpoint/audio/transcriptions)。

### Embeddings

为语义搜索、RAG 和推荐生成向量嵌入：

<CodeGroup>
  ```python Python theme={null}
  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())
  ```

  ```javascript Node.js theme={null}
  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);
  }
  ```

  ```bash cURL theme={null}
  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"
    }'
  ```
</CodeGroup>

有关批处理和高级选项，请参阅 [Embeddings API](/api-reference/endpoint/embeddings/generate)。

### 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：

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```javascript Node.js theme={null}
  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);
  ```

  ```bash cURL theme={null}
  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"
              }
            }
          ]
        }
      ]
    }'
  ```
</CodeGroup>

### 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：

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```javascript Node.js theme={null}
  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);
  ```

  ```bash cURL theme={null}
  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"]
            }
          }
        }
      ]
    }'
  ```
</CodeGroup>

***

## 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：

<CardGroup cols={2}>
  <Card title="浏览模型" icon="database" href="/overview/models">
    比较所有可用模型及其能力、定价和上下文限制
  </Card>

  <Card title="API 参考" icon="code" href="/api-reference/api-spec">
    探索包含所有端点和参数的详细 API 文档
  </Card>

  <Card title="结构化响应" icon="brackets-curly" href="/guides/features/structured-responses">
    了解如何获取具有保证 schema 的 JSON 响应
  </Card>

  <Card title="AI Agents 指南" icon="robot" href="/guides/integrations/ai-agents">
    使用 agent 应用、编码 agent、MCP 工具、skill 和加密货币工作流进行构建
  </Card>
</CardGroup>

### 其他资源

<CardGroup cols={2}>
  <Card title="速率限制" icon="gauge" href="/api-reference/rate-limiting">
    了解速率限制和生产使用的最佳实践
  </Card>

  <Card title="错误代码" icon="triangle-exclamation" href="/api-reference/error-codes">
    处理 API 错误和故障排查的参考
  </Card>

  <Card title="Postman Collection" icon="bolt" href="/guides/getting-started/postman">
    导入我们的完整 Postman collection 以方便测试
  </Card>

  <Card title="隐私与安全" icon="shield" href="/overview/privacy">
    了解 Venice 的隐私优先架构和数据处理
  </Card>
</CardGroup>

***

## 需要帮助？

* **Discord 社区**：加入我们的 [Discord 服务器](https://discord.gg/askvenice) 获取支持和讨论
* **文档**：浏览我们的 [完整 API 参考](/api-reference/api-spec)
* **状态页**：在 [veniceai-status.com](https://veniceai-status.com) 检查服务状态
* **Twitter**：关注 [@AskVenice](https://x.com/AskVenice) 获取更新

<Resources />