大模型API

# 查询支持的模型列表

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

models = client.models.list()
for model in models.data:
    print(model.id)

# install from PyPI
pip install openai

import os
from openai import OpenAI

client = OpenAI(
     api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
     base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

chat_completion = client.chat.completions.create(
    messages=[
        {'role': 'system', 'content': '你是 AI Studio 实训AI开发平台的开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。'},
        {'role': 'user', 'content': '你好，请介绍一下AI Studio'}
    ],
    model="ernie-3.5-8k",
)

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

import os
from openai import OpenAI
import base64

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

# 生成的图片以链接形式返回
images_url = client.images.generate(prompt="一只白猫, 红帽子", model="Stable-Diffusion-XL", response_format="url")
print(images_url.data[0].url)

# 生成的图片以 base64 形式返回
images_base64 = client.images.generate(prompt="一只黑猫, 蓝帽子", model="Stable-Diffusion-XL", response_format="b64_json")
# 保存生成的图片
for i, image in enumerate(images_base64.data):
    with open("image_{}.png".format(i), "wb") as f:
        f.write(base64.b64decode(image.b64_json))

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

embeddings = client.embeddings.create(
        model="embedding-v1",
        input=[
            "推荐一些美食",
            "给我讲个故事"
        ]
    )
print(embeddings)

import os
from openai import OpenAI

def get_response(messages):
    client = OpenAI(
        api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
        base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
    )
    completion = client.chat.completions.create(model="ernie-3.5-8k", messages=messages)
    return completion

messages = [
    {
        "role": "system",
        "content": "你是 AI Studio 开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。",
    }
]

assistant_output = "您好，我是AI Studio 开发者助理，请问有什么能帮助你的吗？"
print(f"""输入："结束"，结束对话\n""")
print(f"模型输出：{assistant_output}\n")
user_input = ""
while "结束" not in user_input:
    user_input = input("请输入：")
    # 将用户问题信息添加到messages列表中
    messages.append({"role": "user", "content": user_input})
    assistant_output = get_response(messages).choices[0].message.content
    # 将大模型的回复信息添加到messages列表中
    messages.append({"role": "assistant", "content": assistant_output})
    print(f"模型输出：{assistant_output}")
    print("\n")

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
    model="ernie-3.5-8k",
    messages=[
        {'role': 'system', 'content': '你是 AI Studio 实训AI开发平台的开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。'},
        {'role': 'user', 'content': '你好，请介绍一下AI Studio'}
    ],
    stream=True,
)

for chunk in completion:
    print(chunk.choices[0].delta.content or "", end="")

import os
from openai import AsyncOpenAI
import asyncio

client = AsyncOpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

async def main() -> None:
    chat_completion = await client.chat.completions.create(
        messages=[
            {'role': 'system', 'content': '你是 AI Studio 实训AI开发平台的开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。'},
            {'role': 'user', 'content': '你好，请介绍一下AI Studio'}
        ],
        model="ernie-3.5-8k",
    )
    print(chat_completion.choices[0].message.content)
    

asyncio.run(main())

{
    "web_search": {
        "enable": true,
        "enable_citation": true,
        "enable_trace": true,
        "enable_status": true,
        "search_num": 10,
        "reference_num": 5
    }
}

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.0-turbo-8k",
        messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": "2024年奥运会乒乓球男单冠军是谁"
                        }
                    ]
                }
            ],
            extra_body={
                "web_search": {
                    "enable": True,
                    "enable_trace": True
                }
            },
        stream=True,
    )

search_result = []
for chunk in completion:
    if (len(chunk.choices) > 0):
        if (hasattr(chunk, 'search_results')):
            search_result.extend(chunk.search_results)
        print(chunk.choices[0].delta.content, end="", flush=True)
    
unique_dict = {}
for item in search_result:
    unique_dict[item["index"]] = item
print("\n参考资料：\n")
for result in list(unique_dict.values()):
    print(str(result["index"]) + ". " + result["title"] + ". " + result["url"] + "\n")

{
  "model": "ernie-3.5-8k",
  "messages": [
    {
      "role": "user",
      "content": "今天上海天气"
    }
  ],
  "response_format": {
    "type": "text" //可替换成json_object、json_schema
  }
}

  由于天气信息实时更新，我无法直接提供今天上海的精确天气情况。\n\n为了获取最新的上海天气信息，我建议您查看天气预报应用、访问气象局的官方网站或使用其他可靠的天气信息来源。这些平台通常会提供实时的气温、湿度、风速、降水概率等详细天气数据，以及未来几天的天气预测。\n\n希望这些建议对您有所帮助！

  "{\n    \"上海今天天气\": \"由于我无法实时获取天气信息，因此无法提供上海今天的确切天气情况。\"\n}\n\n为了获取上海今天的实时天气，我建议您查看手机上的天气应用、访问气象局的官方网站或使用其他可靠的天气信息来源。这些渠道通常会提供最新的天气状况、温度、湿度、风速等详细信息。"

from openai import OpenAI

client = OpenAI(
     api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
     base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
          },
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
        },
        "required": ["location"],
      },
    }
  }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
completion = client.chat.completions.create(
  model="deepseek-v3",
  messages=messages,
  tools=tools,
  tool_choice="auto"
)

print(completion)

import os
from openai import OpenAI

client = OpenAI(
     api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
     base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

chat_completion = client.chat.completions.create(
    messages=[
        {'role': 'system', 'content': '你是 AI Studio 实训AI开发平台的开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。'},
        {'role': 'user', 'content': '你好，请介绍一下AI Studio'}
    ],
    model="deepseek-r1",
)

print(chat_completion.choices[0].message.reasoning_content)
print(chat_completion.choices[0].message.content)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
    model="deepseek-r1",
    messages=[
        {'role': 'system', 'content': '你是 AI Studio 实训AI开发平台的开发者助理，你精通开发相关的知识，负责给开发者提供搜索帮助建议。'},
        {'role': 'user', 'content': '你好，请介绍一下AI Studio'}
    ],
    stream=True,
)

for chunk in completion:
    if (len(chunk.choices) > 0):
        if hasattr(chunk.choices[0].delta, 'reasoning_content') and chunk.choices[0].delta.reasoning_content:
            print(chunk.choices[0].delta.reasoning_content, end="", flush=True)
        else:
            print(chunk.choices[0].delta.content, end="", flush=True)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.5-8k-preview",
        messages=[
            {
                'role': 'user', 'content': [
                    {
                        "type": "text",
                        "text": "介绍几个北京著名景点"
                    }
                ]   
            }
        ]
    )

print(completion.choices[0].message.content or "")

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.5-8k-preview",
        messages=[
            {
                'role': 'user', 'content': [
                    {
                        "type": "text",
                        "text": "介绍几个北京著名景点"
                    }
                ]   
            }
        ]
    )

for chunk in completion:
    if (len(chunk.choices) > 0):
        print(chunk.choices[0].delta.content, end="", flush=True)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.5-8k-preview",
        messages=[
            {
                'role': 'user', 'content': [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://testimage.bj.bcebos.com/image1.jpg"
                        }
                    }
                ]   
            }
        ],
        stream=True,
    )

for chunk in completion:
    if (len(chunk.choices) > 0):
        print(chunk.choices[0].delta.content, end="", flush=True)

import os
from openai import OpenAI


def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")
        
# Path to your image
image_path = "/image_1.png"

# Getting the Base64 string
base64_image = encode_image(image_path)

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.5-8k-preview",
        messages=[
            {
                'role': 'user', 'content': [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]   
            }
        ],
        stream=True,
    )

for chunk in completion:
    if (len(chunk.choices) > 0):
        print(chunk.choices[0].delta.content, end="", flush=True)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("AI_STUDIO_API_KEY"),  # 含有 AI Studio 访问令牌的环境变量，https://aistudio.baidu.com/account/accessToken,
    base_url="https://aistudio.baidu.com/llm/lmapi/v3",  # aistudio 大模型 api 服务域名
)

completion = client.chat.completions.create(
        model="ernie-4.5-8k-preview",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "图片里有什么？这两张图片有什么不同？请用中文回答"
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://testimage.bj.bcebos.com/image1.jpg"
                        }
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://testimage.bj.bcebos.com/image2.png"
                        }
                    }
                ]
            }
        ],
        stream=True,
    )

for chunk in completion:
    if (len(chunk.choices) > 0):
        print(chunk.choices[0].delta.content, end="", flush=True)