feat: enhance LocalAgent configuration and UI components
- Updated .env.example to provide clearer configuration instructions and API key setup. - Removed debug_env.py as it was no longer needed. - Refactored main.py to streamline application initialization and workspace setup. - Introduced a new HistoryManager for managing task execution history. - Enhanced UI components in chat_view.py and task_guide_view.py to improve user interaction and code preview functionality. - Added loading indicators and improved task history display in the UI. - Implemented unit tests for history management and intent classification.
This commit is contained in:
Mimikko-zeus
228
llm/client.py
228
llm/client.py
@@ -2,13 +2,15 @@
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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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支持自动重试机制
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"""
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import os
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import json
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import time
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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 typing import Optional, Generator, Callable, List, Dict, Any
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from dotenv import load_dotenv
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# 获取项目根目录
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@@ -40,29 +42,78 @@ class LLMClient:
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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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特性:
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- 自动重试：网络错误时自动重试（默认3次）
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- 指数退避：重试间隔逐渐增加
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"""
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def __init__(self):
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# 重试配置
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DEFAULT_MAX_RETRIES = 3
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DEFAULT_RETRY_DELAY = 1.0 # 初始重试延迟（秒）
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DEFAULT_RETRY_BACKOFF = 2.0 # 退避倍数
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def __init__(self, max_retries: int = DEFAULT_MAX_RETRIES):
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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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self.max_retries = max_retries
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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 _should_retry(self, exception: Exception) -> bool:
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"""判断是否应该重试"""
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# 网络连接错误、超时错误可以重试
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if isinstance(exception, (requests.exceptions.ConnectionError,
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requests.exceptions.Timeout)):
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return True
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# 服务器错误（5xx）可以重试
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if isinstance(exception, LLMClientError):
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error_msg = str(exception)
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if "状态码: 5" in error_msg or "502" in error_msg or "503" in error_msg or "504" in error_msg:
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return True
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return False
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def _do_request_with_retry(
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self,
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request_func: Callable,
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operation_name: str = "请求"
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):
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"""带重试的请求执行"""
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last_exception = None
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for attempt in range(self.max_retries + 1):
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try:
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return request_func()
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except Exception as e:
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last_exception = e
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# 判断是否应该重试
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if attempt < self.max_retries and self._should_retry(e):
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delay = self.DEFAULT_RETRY_DELAY * (self.DEFAULT_RETRY_BACKOFF ** attempt)
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print(f"[重试] {operation_name}失败，{delay:.1f}秒后重试 ({attempt + 1}/{self.max_retries})...")
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time.sleep(delay)
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continue
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else:
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raise
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# 所有重试都失败
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raise last_exception
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def chat(
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self,
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messages: list[dict],
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messages: List[Dict[str, str]],
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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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调用 LLM 进行对话（非流式，带自动重试）
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Args:
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messages: 消息列表
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@@ -74,60 +125,63 @@ class LLMClient:
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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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def do_request():
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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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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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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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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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return self._do_request_with_retry(do_request, "LLM调用")
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def chat_stream(
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self,
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messages: list[dict],
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messages: List[Dict[str, str]],
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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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调用 LLM 进行对话（流式，带自动重试）
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Args:
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messages: 消息列表
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@@ -139,43 +193,49 @@ class LLMClient:
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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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def do_request():
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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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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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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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return response
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# 流式请求的重试只在建立连接阶段
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response = self._do_request_with_retry(do_request, "流式LLM调用")
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# 解析 SSE 流
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for line in response.iter_lines():
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@@ -197,7 +257,7 @@ class LLMClient:
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def chat_stream_collect(
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self,
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messages: list[dict],
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messages: List[Dict[str, str]],
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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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@@ -108,7 +108,8 @@ import shutil
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from pathlib import Path
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# 工作目录（固定，不要修改）
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WORKSPACE = Path(__file__).parent
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# 代码保存在 workspace/codes/ 目录，向上一级是 workspace
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WORKSPACE = Path(__file__).parent.parent
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INPUT_DIR = WORKSPACE / "input"
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OUTPUT_DIR = WORKSPACE / "output"
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