基础用法
- Python
- JavaScript
- cURL
- Go
from openai import OpenAI
client = OpenAI(
base_url="https://ai.megallm.io/v1",
api_key="your-api-key"
)
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the weather like?"}
],
temperature=0.7,
max_tokens=150
)
print(response.choices[0].message.content)
import OpenAI from 'openai';
const openai = new OpenAI({
baseURL: 'https://ai.megallm.io/v1',
apiKey: process.env.MEGALLM_API_KEY,
});
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: "What's the weather like?" }
],
temperature: 0.7,
max_tokens: 150
});
console.log(response.choices[0].message.content);
curl https://ai.megallm.io/v1/chat/completions \
-H "Authorization: Bearer $MEGALLM_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What'\''s the weather like?"}
],
"temperature": 0.7,
"max_tokens": 150
}'
package main
import (
"context"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig("your-api-key")
config.BaseURL = "https://ai.megallm.io/v1"
client := openai.NewClientWithConfig(config)
resp, err := client.CreateChatCompletion(
context.Background(),
openai.ChatCompletionRequest{
Model: "gpt-4",
Messages: []openai.ChatCompletionMessage{
{
Role: "system",
Content: "You are a helpful assistant.",
},
{
Role: "user",
Content: "What's the weather like?",
},
},
Temperature: 0.7,
MaxTokens: 150,
},
)
if err != nil {
panic(err)
}
println(resp.Choices[0].Message.Content)
}
高级功能
消息角色
API 支持不同的消息角色用于对话上下文：| 角色 | 描述 | 示例 |
|---|---|---|
system | 设置行为和上下文 | ”你是一个有帮助的助手” |
user | 用户输入/问题 | ”法国的首都是什么？“ |
assistant | AI 响应 | ”法国的首都是巴黎” |
tool | 工具/函数结果 | 函数执行结果 |
温度控制
使用温度参数调整响应的创造性：# 更确定性（0.0 - 0.3）
response = client.chat.completions.create(
model="gpt-4",
messages=[...],
temperature=0.2 # 更专注、一致的响应
)
# 平衡（0.4 - 0.7）
response = client.chat.completions.create(
model="gpt-4",
messages=[...],
temperature=0.5 # 平衡创造力和连贯性
)
# 更有创意（0.8 - 1.0）
response = client.chat.completions.create(
model="gpt-4",
messages=[...],
temperature=0.9 # 更多样化、更有创意的响应
)
多轮对话
在多次交互中保持上下文：messages = [
{"role": "system", "content": "你是一个数学导师。"},
{"role": "user", "content": "15 * 12 是多少？"},
{"role": "assistant", "content": "15 * 12 = 180"},
{"role": "user", "content": "现在把它除以 6"}
]
response = client.chat.completions.create(
model="gpt-4",
messages=messages
)
# 响应理解"它"指的是 180
视觉支持
同时处理图像和文本（需要支持视觉的模型）：response = client.chat.completions.create(
model="gpt-4-vision",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "这张图片里有什么？"},
{
"type": "image_url",
"image_url": {
"url": "https://example.com/image.jpg",
"detail": "high" # "low", "high", 或 "auto"
}
}
]
}
],
max_tokens=300
)
响应格式
标准响应
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1677858242,
"model": "gpt-4",
"usage": {
"prompt_tokens": 13,
"completion_tokens": 17,
"total_tokens": 30
},
"choices": [
{
"message": {
"role": "assistant",
"content": "法国的首都是巴黎。"
},
"finish_reason": "stop",
"index": 0
}
]
}
结束原因
| 原因 | 描述 |
|---|---|
stop | 自然完成 |
length | 达到 max_tokens 限制 |
tool_calls | 模型想要调用函数 |
content_filter | 内容被过滤 |
最佳实践
系统消息：始终包含清晰的系统消息来设置 AI 的行为和上下文。
Token 优化
# 发送前计算 token
import tiktoken
encoding = tiktoken.encoding_for_model("gpt-4")
tokens = encoding.encode("您的消息在这里")
print(f"Token 计数：{len(tokens)}")
# 如有需要进行截断
if len(tokens) > 1000:
truncated = encoding.decode(tokens[:1000])
错误处理
try:
response = client.chat.completions.create(...)
except openai.APIError as e:
print(f"API 错误：{e}")
except openai.RateLimitError as e:
print(f"达到速率限制：{e}")
# 实现指数退避
except openai.APIConnectionError as e:
print(f"连接错误：{e}")
# 使用退避重试
常见模式
摘要生成
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "你是一个摘要专家。"},
{"role": "user", "content": f"用 3 个要点总结这段文字：{long_text}"}
],
temperature=0.3,
max_tokens=150
)
分类
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "将情感分类为积极、消极或中性。"},
{"role": "user", "content": "我喜欢这个产品！它运行得很好。"}
],
temperature=0.0,
max_tokens=10
)
代码生成
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "你是一个 Python 专家。"},
{"role": "user", "content": "编写一个计算斐波那契数的函数"}
],
temperature=0.2,
max_tokens=500
)
速率限制
实现适当的速率限制以避免错误：import time
from typing import Optional
class RateLimiter:
def __init__(self, requests_per_minute: int = 60):
self.requests_per_minute = requests_per_minute
self.request_times = []
def wait_if_needed(self):
now = time.time()
# 删除超过 1 分钟的请求
self.request_times = [t for t in self.request_times if now - t < 60]
if len(self.request_times) >= self.requests_per_minute:
sleep_time = 60 - (now - self.request_times[0])
if sleep_time > 0:
time.sleep(sleep_time)
self.request_times.append(now)
# 使用方法
limiter = RateLimiter(60)
for prompt in prompts:
limiter.wait_if_needed()
response = client.chat.completions.create(...)