1ba5f0f7d6
- Updated .env.example to include API key placeholder and configuration instructions. - Refactored main.py to support streaming responses from the LLM, improving user experience during chat interactions. - Enhanced LLMClient to include methods for streaming chat and collecting responses. - Modified safety review process to pass static analysis warnings to the LLM for better code safety evaluation. - Improved UI components in chat_view.py to handle streaming messages effectively.
247 lines
7.5 KiB
Python
247 lines
7.5 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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支持流式和非流式两种模式
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"""
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import os
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import json
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import requests
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from pathlib import Path
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from typing import Optional, Generator, Callable
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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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# 非流式调用
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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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)
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# 流式调用
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for chunk in client.chat_stream(
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messages=[{"role": "user", "content": "你好"}],
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model="Qwen/Qwen2.5-7B-Instruct"
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):
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print(chunk, end="", flush=True)
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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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timeout: int = 180
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) -> str:
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"""
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调用 LLM 进行对话（非流式）
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Args:
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messages: 消息列表
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大生成 token 数
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timeout: 超时时间（秒），默认 180 秒
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Returns:
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LLM 生成的文本内容
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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=timeout
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)
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except requests.exceptions.Timeout:
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raise LLMClientError(f"请求超时（{timeout}秒），请检查网络连接或稍后重试")
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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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def chat_stream(
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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 = 2048,
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timeout: int = 180
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) -> Generator[str, None, None]:
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"""
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调用 LLM 进行对话（流式）
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Args:
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messages: 消息列表
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大生成 token 数
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timeout: 超时时间（秒）
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Yields:
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逐个返回生成的文本片段
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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": True,
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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=timeout,
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stream=True
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)
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except requests.exceptions.Timeout:
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raise LLMClientError(f"请求超时（{timeout}秒），请检查网络连接或稍后重试")
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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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# 解析 SSE 流
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for line in response.iter_lines():
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if line:
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line = line.decode('utf-8')
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if line.startswith('data: '):
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data = line[6:] # 去掉 "data: " 前缀
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if data == '[DONE]':
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break
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try:
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chunk = json.loads(data)
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if 'choices' in chunk and len(chunk['choices']) > 0:
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delta = chunk['choices'][0].get('delta', {})
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content = delta.get('content', '')
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if content:
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yield content
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except json.JSONDecodeError:
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continue
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def chat_stream_collect(
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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 = 2048,
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timeout: int = 180,
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on_chunk: Optional[Callable[[str], None]] = None
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) -> str:
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"""
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流式调用并收集完整结果
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Args:
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messages: 消息列表
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大生成 token 数
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timeout: 超时时间（秒）
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on_chunk: 每收到一个片段时的回调函数
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Returns:
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完整的生成文本
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"""
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full_content = []
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for chunk in self.chat_stream(
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messages=messages,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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timeout=timeout
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):
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full_content.append(chunk)
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if on_chunk:
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on_chunk(chunk)
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return ''.join(full_content)
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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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