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OpenAI API

访问 GPT、DALL-E、Whisper 模型 - 函数调用、嵌入向量、最先进 AI

TL;DR

是什么：访问 OpenAI 的 GPT 模型、DALL-E、Whisper 等的 API。

为什么用：最先进的 AI 模型、易于集成、函数调用、嵌入向量。

Quick Start

安装：

pip install openai

Hello OpenAI：

from openai import OpenAI

client = OpenAI()  # 使用 OPENAI_API_KEY 环境变量

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

Cheatsheet

模型用途
gpt-4o最新多模态
gpt-4o-mini快速便宜
gpt-4-turbo上一代旗舰
text-embedding-3-small嵌入向量
dall-e-3图像生成
whisper-1语音转文字
tts-1文字转语音

Gotchas

聊天补全

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Python?"},
        {"role": "assistant", "content": "Python is a programming language."},
        {"role": "user", "content": "What can I do with it?"}
    ],
    temperature=0.7,
    max_tokens=500
)

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

流式输出

stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

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

函数调用

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools
)

if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

嵌入向量

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello world"
)

embedding = response.data[0].embedding
print(f"Dimension: {len(embedding)}")  # 1536

图像生成

response = client.images.generate(
    model="dall-e-3",
    prompt="A white cat sitting on a windowsill",
    size="1024x1024",
    quality="standard",
    n=1
)

image_url = response.data[0].url
print(image_url)

视觉

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }]
)

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

Next Steps