OpenRouter、Anthropic、火山引擎、Siliconflow 使用指南

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

# 使用 Claude Sonnet 进行复杂推理（适合 Agent 的深度思考）
completion = client.chat.completions.create(
  model="anthropic/claude-sonnet-4",
  messages=[
    {
      "role": "user",
      "content": "请解释什么是递归算法，并给出一个 Python 示例"
    }
  ]
)
print(completion.choices[0].message.content)

# 多模态示例：图片理解（适合需要视觉能力的 Agent）
completion = client.chat.completions.create(
  model="anthropic/claude-sonnet-4",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is 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"
          }
        }
      ]
    }
  ]
)
print(completion.choices[0].message.content)

response = client.chat.completions.create(
    model="doubao-seed-1-6-flash-250615",
    messages=[
        {
            "role": "user",
            "content": "今天天气怎么样？"
        }
    ],
    stream=True  # 流式输出，减少延迟
)

response = client.chat.completions.create(
    model="doubao-seed-1-6-thinking-250615",
    messages=[
        {
            "role": "user",
            "content": "请详细解释相对论的基本原理"
        }
    ],
    stream=True  # 流式输出中间思考过程
)

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://ark.cn-beijing.volces.com/api/v3",
    api_key=os.environ.get("ARK_API_KEY"),
)

response = client.chat.completions.create(
    model="doubao-seed-1-6-250615",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://ark-project.tos-cn-beijing.ivolces.com/images/view.jpeg"
                    },
                },
                {"type": "text", "text": "这是哪里？"},
            ],
        }
    ],
)

print(response.choices[0])

import requests

url = "https://api.siliconflow.cn/v1/chat/completions"

payload = {
    "model": "moonshotai/Kimi-K2-Instruct",
    "messages": [
        {
            "role": "user",
            "content": "What opportunities and challenges will the Chinese large model industry face in 2025?"
        }
    ],
    "stream": True  # Agent 开发推荐使用流式输出
}
headers = {
    "Authorization": "Bearer <SILICONFLOW_API_KEY>",
    "Content-Type": "application/json"
}

response = requests.request("POST", url, json=payload, headers=headers, stream=True)
# 处理流式响应...

import requests
import os

url = "https://api.siliconflow.cn/v1/audio/speech"

payload = {
    "model": "fishaudio/fish-speech-1.5",
    "input": "Nice to meet you!",
    "voice": "fishaudio/fish-speech-1.5:alex",
    "response_format": "mp3",
    "speed": 1.0
}
headers = {
    "Authorization": "Bearer " + os.environ['SILICONFLOW_API_KEY'],
    "Content-Type": "application/json"
}

response = requests.request("POST", url, json=payload, headers=headers)

# 保存音频文件
with open("output.mp3", "wb") as f:
    f.write(response.content)

from openai import OpenAI
import os

client = OpenAI(
  base_url="https://api.siliconflow.cn/v1",
  api_key=os.environ['SILICONFLOW_API_KEY'],
)

def transcribe_audio(speech_file_path):
    with open(speech_file_path, "rb") as audio_file:
        transcription = client.audio.transcriptions.create(
            model="FunAudioLLM/SenseVoiceSmall", 
            file=audio_file,
            language="zh",  # 指定语言可以提高准确率
            prompt="这是一段对话录音"  # 提供上下文
        )
    return transcription.text

# 在实时语音 Agent 中的使用示例
def process_audio_chunk(audio_chunk):
    # 1. 使用 VAD 检测语音结束（推荐 Silero VAD）
    # 2. 调用 ASR 识别
    text = transcribe_audio(audio_chunk)
    # 3. 传递给 LLM 处理
    return text