原生 OpenAI 格式（ChatCompletions）

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "glm-5",
    "messages": [
      {"role": "user", "content": "上海的天气怎么样？"},
      {
        "role": "assistant",
        "tool_calls": [
          {
            "id": "call_xxx",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"city\":\"上海\"}"
            }
          }
        ]
      },
      {
        "role": "tool",
        "tool_call_id": "call_xxx",
        "content": "{\"temp\":\"22°C\",\"condition\":\"多云\",\"aqi\":53}"
      }
    ]
  }'

curl -N -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "qwen3-omni-flash",
    "stream": true,
    "enable_thinking": true,
    "parameters": {
      "incremental_output": true
    },
    "messages": [
      {"role": "system", "content": "你是一名优秀的数学家"},
      {"role": "user", "content": "汉诺塔的公式是啥"}
    ]
  }'

curl -N -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "qwen3-omni-flash",
    "stream": true,
    "enable_thinking": true,
    "gravitex_thinking_to_content": true,
    "parameters": {
      "incremental_output": true
    },
    "messages": [
      {"role": "user", "content": "汉诺塔的公式是啥"}
    ]
  }'

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {"role":"user","content":"给出一道中等难度的几何题并分步解析。"}
    ],
    "extra_body": {
      "google": {
        "thinking_config": { "thinking_budget": 6000, "include_thoughts": true }
      }
    },
    "stream": true
  }'

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "gemini-3-pro-preview",
    "messages": [
      {"role":"user","content":"Google 搜索一下今天广州的天气怎么样"}
    ],
    "generationConfig": {
      "thinkingConfig": { "thinkingLevel": "LOW" }
    },
    "tools": [
      { "type": "function", "function": { "name": "googleSearch" } }
    ],
    "stream": true
  }'

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "qwen3-omni-flash",
    "messages": [
      {"role": "user", "content": "你好"}
    ],
    "parameters": {
      "asr_options": {
        "language": "zh"
      }
    }
  }'

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "grok-4",
    "messages": [
      {"role": "user", "content": "2026年人工智能领域有什么最新进展？"}
    ],
    "search_parameters": {
      "mode": "auto"
    }
  }'

curl -X POST "https://api.gravitex.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "grok-4",
    "messages": [
      {"role": "user", "content": "最新的科技新闻有哪些？"}
    ],
    "search_parameters": {
      "mode": "on",
      "return_citations": true
    }
  }'

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai/v1"
)

completion = client.chat.completions.create(
    model="grok-4",
    messages=[
        {"role": "user", "content": "2026年人工智能领域有什么最新进展？"}
    ],
    extra_body={
        "search_parameters": {
            "mode": "auto"
        }
    }
)
print(completion.choices[0].message.content)

import base64
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai/v1"
)

with open("document.pdf", "rb") as f:
    data = f.read()

base64_string = base64.b64encode(data).decode("utf-8")

response = client.responses.create(
    model="gpt-5.2",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "filename": "document.pdf",
                    "file_data": f"data:application/pdf;base64,{base64_string}"
                },
                {
                    "type": "input_text",
                    "text": "这份文档的主要内容是什么？"
                }
            ]
        }
    ]
)
print(response.output_text)

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"doubao-seed-1-8-251228","choices":[{"index":0,"delta":{"content":"回"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"doubao-seed-1-8-251228","choices":[{"index":0,"delta":{"content":"复"},"finish_reason":null}]}

data: [DONE]