125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
"""
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LLM 统一调用客户端
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所有模型通过 SiliconFlow API 调用
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"""
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import os
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import requests
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from pathlib import Path
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from typing import Optional
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from dotenv import load_dotenv
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# 获取项目根目录
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PROJECT_ROOT = Path(__file__).parent.parent
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ENV_PATH = PROJECT_ROOT / ".env"
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class LLMClientError(Exception):
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"""LLM 客户端异常"""
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pass
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class LLMClient:
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"""
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统一的 LLM 调用客户端
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使用方式:
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client = LLMClient()
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response = client.chat(
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messages=[{"role": "user", "content": "你好"}],
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model="Qwen/Qwen2.5-7B-Instruct",
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temperature=0.7,
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max_tokens=1024
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)
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"""
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def __init__(self):
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load_dotenv(ENV_PATH)
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self.api_url = os.getenv("LLM_API_URL")
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self.api_key = os.getenv("LLM_API_KEY")
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if not self.api_url:
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raise LLMClientError("未配置 LLM_API_URL，请检查 .env 文件")
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if not self.api_key or self.api_key == "your_api_key_here":
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raise LLMClientError("未配置有效的 LLM_API_KEY，请检查 .env 文件")
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def chat(
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self,
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messages: list[dict],
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model: str,
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temperature: float = 0.7,
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max_tokens: int = 1024
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) -> str:
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"""
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调用 LLM 进行对话
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Args:
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messages: 消息列表，格式为 [{"role": "user/assistant/system", "content": "..."}]
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model: 模型名称
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temperature: 温度参数，控制随机性
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max_tokens: 最大生成 token 数
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Returns:
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LLM 生成的文本内容
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Raises:
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LLMClientError: 网络异常或 API 返回错误
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"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"stream": False,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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try:
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response = requests.post(
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self.api_url,
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headers=headers,
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json=payload,
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timeout=60
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)
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except requests.exceptions.Timeout:
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raise LLMClientError("请求超时，请检查网络连接")
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except requests.exceptions.ConnectionError:
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raise LLMClientError("网络连接失败，请检查网络设置")
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except requests.exceptions.RequestException as e:
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raise LLMClientError(f"网络请求异常: {str(e)}")
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if response.status_code != 200:
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error_msg = f"API 返回错误 (状态码: {response.status_code})"
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try:
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error_detail = response.json()
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if "error" in error_detail:
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error_msg += f": {error_detail['error']}"
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except:
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error_msg += f": {response.text[:200]}"
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raise LLMClientError(error_msg)
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try:
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result = response.json()
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content = result["choices"][0]["message"]["content"]
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return content
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except (KeyError, IndexError, TypeError) as e:
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raise LLMClientError(f"解析 API 响应失败: {str(e)}")
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# 全局单例（延迟初始化）
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_client: Optional[LLMClient] = None
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def get_client() -> LLMClient:
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"""获取 LLM 客户端单例"""
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global _client
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if _client is None:
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_client = LLMClient()
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return _client
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