API调用示例
本教程提供丰富的 OpenAI API 调用示例，帮助你快速上手开发。
基础调用
GPT-3.5 文本对话
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "你是一位专业的Python讲师"},
{"role": "user", "content": "请解释什么是装饰器，以及如何使用它"}
],
temperature=0.7,
max_tokens=1000
)
print(response.choices[0].message.content)GPT-4 高级对话
javascript
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
const response = await client.chat.completions.create({
model: "gpt-4",
messages: [
{
role: "system",
content: "你是一位资深架构师，擅长系统设计和性能优化"
},
{
role: "user",
content: "如何设计一个日活千万级用户的即时通讯系统？"
}
],
temperature: 0.5,
max_tokens: 2000
});
console.log(response.choices[0].message.content);流式输出
对于长文本生成，流式输出可以提供更好的用户体验：
python
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
stream = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "写一首关于人工智能的诗"}
],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)javascript
import OpenAI from 'openai';
const client = new OpenAI();
const stream = await client.chat.completions.create({
model: "gpt-3.5-turbo",
messages: [{ role: "user", content: "用Python实现一个快速排序" }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || "");
}上下文对话（多轮）
简单上下文管理
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
# 初始化对话历史
messages = [
{"role": "system", "content": "你是一位友好的助手"}
]
while True:
user_input = input("你: ")
if user_input.lower() in ["exit", "quit", "q"]:
break
messages.append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=messages
)
assistant_message = response.choices[0].message.content
print(f"助手: {assistant_message}")
messages.append({"role": "assistant", "content": assistant_message})自动摘要旧消息（节省 token）
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
MAX_TOKENS = 3000 # 留出空间给新消息
def summarize_if_needed(messages):
total_tokens = sum(len(m.split()) * 1.3 for m in messages) # 粗略估算
if total_tokens > MAX_TOKENS:
# 摘要前几条消息
summary_response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "请用简洁的语言总结以下对话的要点："},
*[{"role": "user", "content": messages[1]["content"]}]
]
)
summary = summary_response.choices[0].message.content
return [
messages[0], # 保留 system prompt
{"role": "system", "content": f"之前的对话摘要：{summary}"}
]
return messages函数调用（Function Calling）
GPT-4 可以调用你定义的函数，实现更强大的功能：
python
from openai import OpenAI
import json
client = OpenAI(api_key="your-api-key")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "获取指定城市的天气信息",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "城市名称，如：北京、上海"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "温度单位"
}
},
"required": ["city"]
}
}
}
]
messages = [
{"role": "user", "content": "北京今天天气怎么样？需要穿什么衣服？"}
]
response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
messages=messages,
tools=tools,
tool_choice="auto"
)
response_message = response.choices[0].message
# 检查是否需要调用函数
if response_message.tool_calls:
tool_call = response_message.tool_calls[0]
function_name = tool_call.function.name
arguments = json.loads(tool_call.function.arguments)
print(f"需要调用函数: {function_name}")
print(f"参数: {arguments}")
# 模拟函数调用结果
if function_name == "get_weather":
weather_result = f"北京今天晴，25°C，适合穿短袖"
messages.append(response_message)
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": weather_result
})
# 再次调用，获取最终回复
final_response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
messages=messages
)
print(f"最终回复: {final_response.choices[0].message.content}")文本嵌入（Embeddings）
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
# 获取文本嵌入
response = client.embeddings.create(
model="text-embedding-3-small",
input="人工智能将改变未来的工作方式"
)
embedding = response.data[0].embedding
print(f"嵌入向量维度: {len(embedding)}")
print(f"前5个值: {embedding[:5]}")语义搜索示例
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
documents = [
"Python是一种高级编程语言，适合初学者学习",
"JavaScript主要用于网页前端开发",
"机器学习是人工智能的一个分支",
"深度学习使用神经网络模拟人脑工作",
"React是一个用于构建用户界面的JavaScript库"
]
# 获取文档嵌入
doc_embeddings = []
for doc in documents:
response = client.embeddings.create(
model="text-embedding-3-small",
input=doc
)
doc_embeddings.append(response.data[0].embedding)
def cosine_similarity(a, b):
return sum(x * y for x, y in zip(a, b)) / (
(sum(x * x for x in a) ** 0.5) * (sum(x * x for x in b) ** 0.5)
)
query = "我想学网页开发"
query_response = client.embeddings.create(
model="text-embedding-3-small",
input=query
)
query_embedding = query_response.data[0].embedding
# 计算相似度
similarities = []
for i, doc_emb in enumerate(doc_embeddings):
sim = cosine_similarity(query_embedding, doc_emb)
similarities.append((i, sim, documents[i]))
# 排序输出
similarities.sort(key=lambda x: x[1], reverse=True)
print(f"查询: {query}\n最相关的结果:")
for i, sim, doc in similarities[:3]:
print(f" [{sim:.3f}] {doc}")图像生成（DALL-E API）
python
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
response = client.images.generate(
model="dall-e-3",
prompt="一个可爱的机器人正在咖啡馆里写代码，赛博朋克风格",
size="1024x1024",
quality="standard",
n=1,
)
image_url = response.data[0].url
print(f"生成的图片: {image_url}")错误处理
python
from openai import OpenAI
from openai import RateLimitError, APIError
client = OpenAI(api_key="your-api-key")
def call_with_retry(messages, max_retries=3):
for attempt in range(max_retries):
try:
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=messages
)
return response
except RateLimitError:
print(f"触达速率限制，等待后重试... ({attempt + 1}/{max_retries})")
import time
time.sleep(2 ** attempt) # 指数退避
except APIError as e:
print(f"API错误: {e}")
if attempt == max_retries - 1:
raise
raise Exception("达到最大重试次数")
# 使用
response = call_with_retry([
{"role": "user", "content": "你好"}
])
print(response.choices[0].message.content)常用参数说明
| 参数 | 说明 | 推荐值 |
|---|---|---|
model | 使用的模型 | gpt-3.5-turbo / gpt-4 |
temperature | 创造性（0-2） | 0.7（日常）、0.2（精确） |
max_tokens | 最大输出 token 数 | 根据需求设置 |
top_p | 核采样 | 通常与 temperature 二选一 |
frequency_penalty | 频率惩罚 | -2.0 到 2.0 |
presence_penalty | 存在惩罚 | -2.0 到 2.0 |
下一步
- OpenAI API申请 - 还没 API Key？先申请
- Prompt工程入门 - 写出更好的提示词