openai_llm.py
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"""
OpenAI LLM实现
使用OpenAI API进行文本生成
"""
import os
from typing import Optional, Dict, Any
from openai import OpenAI
from .base import BaseLLM
class OpenAILLM(BaseLLM):
"""OpenAI LLM实现类"""
def __init__(self, api_key: Optional[str] = None, model_name: Optional[str] = None):
"""
初始化OpenAI客户端
Args:
api_key: OpenAI API密钥，如果不提供则从环境变量读取
model_name: 模型名称，默认使用gpt-4o-mini
"""
if api_key is None:
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OpenAI API Key未找到！请设置OPENAI_API_KEY环境变量或在初始化时提供")
super().__init__(api_key, model_name)
# 初始化OpenAI客户端
self.client = OpenAI(api_key=self.api_key)
self.default_model = model_name or self.get_default_model()
def get_default_model(self) -> str:
"""获取默认模型名称"""
return "gpt-4o-mini"
def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
"""
调用OpenAI API生成回复
Args:
system_prompt: 系统提示词
user_prompt: 用户输入
**kwargs: 其他参数，如temperature、max_tokens等
Returns:
OpenAI生成的回复文本
"""
try:
# 构建消息
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
]
# 设置默认参数
params = {
"model": self.default_model,
"messages": messages,
"temperature": kwargs.get("temperature", 0.7),
"max_tokens": kwargs.get("max_tokens", 4000)
}
# 调用API
response = self.client.chat.completions.create(**params)
# 提取回复内容
if response.choices and response.choices[0].message:
content = response.choices[0].message.content
return self.validate_response(content)
else:
return ""
except Exception as e:
print(f"OpenAI API调用错误: {str(e)}")
raise e
def get_model_info(self) -> Dict[str, Any]:
"""
获取当前模型信息
Returns:
模型信息字典
"""
return {
"provider": "OpenAI",
"model": self.default_model,
"api_base": "https://api.openai.com"
}