火山方舟模型调用 API 与 OpenAI API 协议兼容，您可以使用兼容 OpenAI API 协议的多语言社区 SDK 调用火山方舟大模型或应用。您可以很方便地迁移模型服务至方舟平台以及 Doubao 大模型。

调用之前，您需要获取以下信息：

• <ARK_API_KEY>：获取您的方舟 API Key，可参考创建 API Key。
• <Model>：获取 Model ID（或 获取 Endpoint ID）。
• <Base Url >：
  • 大模型调用：https://ark.cn-beijing.volces.com/api/v3/
  • 应用调用：https://ark.cn-beijing.volces.com/api/v3/bots/

• Python版本：3.7及以上。
• OpenAI SDK：1.0版本及以上。您可以使用下面命令安装。

pip install --upgrade "openai>=1.0"

单轮

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- standard request -----")
completion = client.chat.completions.create(
    model="<Model>",
    messages = [
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "常见的十字花科植物有哪些？"},
    ],
)
print(completion.choices[0].message.content)

多轮

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- standard request -----")
completion = client.chat.completions.create(
    model="<Model>",
    messages=[
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "花椰菜是什么？"},
        {"role": "assistant", "content": "花椰菜又称菜花、花菜，是一种常见的蔬菜。"},
        {"role": "user", "content": "再详细点"},
    ],
)
print(completion.choices[0].message.content)

流式

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- streaming request -----")
stream = client.chat.completions.create(
    model="<Model>",
    messages=[
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "常见的十字花科植物有哪些？"},
    ],
    stream=True,
)
for chunk in stream:
    if not chunk.choices:
        continue

    print(chunk.choices[0].delta.content, end="")
print()

异步

import asyncio
import os

from openai import AsyncOpenAI

client = AsyncOpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

async def main() -> None:
    stream = await client.chat.completions.create(
        model="<Model>",
        messages=[
            {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
            {"role": "user", "content": "常见的十字花科植物有哪些？"},
        ],
        stream=True,
    )
    async for completion in stream:
        print(completion.choices[0].delta.content, end="")
    print()
    
asyncio.run(main())

Function calling

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- function call request -----")
completion = client.chat.completions.create(
    model="<Model>",
    messages = [
        {"role": "user", "content": "北京今天天气如何？"},
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "获取给定地点的天气",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "地点的位置信息，比如北京"
                        },
                        "unit": {
                            "type": "string",
                            "enum": [
                                "摄氏度",
                                "华氏度"
                            ]
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        }
    ]
)
print(completion.choices[0])

设置自定义header

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- standard request -----")
completion = client.chat.completions.create(
    model="<Model>",
    messages = [
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "常见的十字花科植物有哪些？"},
    ],
    # 自定义request id
    extra_headers={"X-Client-Request-Id": "202406251728190000B7EA7A9648AC08D9"}
)
print(completion.choices[0].message.content)

Embeddings

from openai import OpenAI
import os

client = OpenAI(
    # 替换为您需要调用的模型服务Base Url
    base_url="<BASE_URL>",
    # 环境变量中配置您的API Key
    api_key=os.environ.get("ARK_API_KEY")
)

print("----- embeddings request -----")
resp = client.embeddings.create(
    model="<Model>",
    input=["花椰菜又称菜花、花菜，是一种常见的蔬菜。"]
)
print(resp)

LangChain OpenAI SDK：您可以使用下面命令安装。

pip install langchain-openai

示例代码

from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
import os

llm = ChatOpenAI(
    # 环境变量中配置您的API Key
    openai_api_key=os.environ.get("ARK_API_KEY"), 
    # 替换为您需要调用的模型服务Base Url
    openai_api_base="<BASE_URL>",
    # 替换为您创建推理接入点 ID
    model_name="<Model>"
)

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain = prompt | llm

print(llm_chain.invoke(question))