feat: 增强编码能力 - 新增代码编辑器、代码搜索、Git集成
- 新增 plugins/code_editor.py: 精准代码编辑（搜索替换、批量编辑、diff对比） - 新增 plugins/code_search.py: 代码搜索与理解（跨文件搜索、符号提取、依赖分析） - 新增 plugins/git_tools.py: Git 版本控制（status/diff/log/commit/branch） - 新增 core/ 模块: 配置管理、引擎管理、对话处理、显示输出、代码沙箱 - 修复 requirements.txt: 使用正确的 pip 包名（Pillow、opencv-python 等） - 更新 unified_tool_manager.py: 兼容 Plugin 和 Liugin 两种类名 - 更新系统提示词: 增强编程工作流指导 本次更新由 mimo 自动化执行，与开发者无关。
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import os
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import json
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import requests
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import threading
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import time
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from colorama import Fore, Style
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class QwqAI:
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"""基于QwQ-32B模型的AI引擎"""
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def get_help_info(self):
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"""获取引擎帮助信息"""
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help_text = """
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QwQ-32B AI引擎插件帮助信息
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==========================
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引擎名称: QwQ
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描述: 基于QwQ-32B模型的AI引擎，支持复杂推理和数学计算
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可用命令:
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无特定命令，直接与AI对话即可
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使用说明:
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1. 此引擎使用QwQ-32B云API
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2. 使用/engine.switch qwq切换到此引擎
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3. 直接输入自然语言与AI对话
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4. 特别适合处理数学、逻辑推理等复杂任务
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"""
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return help_text
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def __init__(self):
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self.name = "qwq"
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self.cli = None # 引用CLI实例以访问插件
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# 直接从 config.json 读取配置
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import json
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import os
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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config_file = os.path.join(project_root, "config.json")
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try:
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with open(config_file, 'r', encoding='utf-8') as f:
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config = json.load(f)
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engine_config = config.get("api", {}).get("engines", {}).get("qwq", {})
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self.api_key = engine_config.get("api_key", "")
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self.url = engine_config.get("base_url", "")
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self.model = engine_config.get("model", "")
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except Exception as e:
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# 如果读取失败，使用空值
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print(f"{Fore.YELLOW}警告: 读取配置文件失败: {e}{Style.RESET_ALL}")
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self.api_key = ""
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self.url = ""
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self.model = ""
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self.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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# 初始化对话历史
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self.conversation_history = []
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# 设置最大历史记录数
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try:
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with open(config_file, 'r', encoding='utf-8') as f:
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config = json.load(f)
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self.max_history = config.get("system", {}).get("max_history", 10)
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except:
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self.max_history = 10
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# 设置思考标记
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self.thinking_start_marker = ""
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self.thinking_end_marker = ""
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# 共享对话历史引用（由CLI设置）
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self.shared_conversation_history = None
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def generate_response(self, user_input, tool_results=None, system_prompt=None):
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"""生成AI响应 - 使用QwQ API"""
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try:
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# 如果有工具结果，将其作为上下文返回给AI处理
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if tool_results:
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tool_result_text = f"工具执行结果: {tool_results.get('result', '无结果')}"
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# 构建消息列表
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messages = self._build_messages_with_history(system_prompt, tool_result_text)
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# 构建请求体
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body = {
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"model": self.model,
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"messages": messages,
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"stream": False # 使用非流式响应以便于处理
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}
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# 发送请求到QwQ API
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response = requests.post(url=self.url, json=body, headers=self.headers, timeout=None)
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if response.status_code == 200:
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response_data = response.json()
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if "choices" in response_data and len(response_data["choices"]) > 0:
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ai_response = response_data["choices"][0]["message"]["content"]
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# 处理思考标记
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processed_response = self._process_thinking_content(ai_response)
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# 返回处理后的AI回复
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return processed_response
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else:
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return "QwQ API返回格式错误"
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else:
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return f"QwQ API调用失败: {response.status_code} - {response.text}"
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# 构建消息列表
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messages = self._build_messages_with_history(system_prompt, user_input)
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# 构建请求体
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body = {
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"model": self.model,
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"messages": messages,
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"stream": False # 使用非流式响应以便于处理
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}
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# 发送请求到QwQ API
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response = requests.post(url=self.url, json=body, headers=self.headers, timeout=None)
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if response.status_code == 200:
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response_data = response.json()
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if "choices" in response_data and len(response_data["choices"]) > 0:
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ai_response = response_data["choices"][0]["message"]["content"]
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# 处理思考标记
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processed_response = self._process_thinking_content(ai_response)
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# 返回处理后的AI回复
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return processed_response
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else:
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return "QwQ API返回格式错误"
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else:
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return f"QwQ API调用失败: {response.status_code} - {response.text}"
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except Exception as e:
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# 如果API调用失败，返回错误信息
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return f"QwQ API调用失败: {str(e)}"
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def _build_messages_with_history(self, system_prompt, user_input):
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"""构建包含历史记录的消息列表"""
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messages = []
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# 添加系统提示词
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if system_prompt:
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messages.append({
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"role": "system",
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"content": system_prompt
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})
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# 添加历史对话记录（使用共享历史）
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if self.shared_conversation_history is not None:
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# 确保历史记录格式正确
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for msg in self.shared_conversation_history[-self.max_history:]:
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if isinstance(msg, dict) and "role" in msg and "content" in msg:
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messages.append(msg)
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else:
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# 如果格式不正确，跳过该条记录
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continue
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# 添加当前用户输入
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messages.append({
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"role": "user",
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"content": user_input
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})
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return messages
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def _call_api_with_history(self, content, system_prompt=None):
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"""使用对话历史调用API"""
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# 构建消息列表
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messages = []
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# 添加系统提示词(如果提供)
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if system_prompt:
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messages.append({
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"role": "system",
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"content": system_prompt
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})
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# 添加历史对话记录（使用共享历史）
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if self.shared_conversation_history is not None:
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# 确保历史记录格式正确
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for msg in self.shared_conversation_history[-self.max_history:]:
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if isinstance(msg, dict) and "role" in msg and "content" in msg:
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messages.append(msg)
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else:
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# 如果格式不正确，跳过该条记录
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continue
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# 添加当前内容
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messages.append({
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"role": "tool",
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"content": content
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})
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# 构建请求体
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body = {
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"model": self.model,
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"messages": messages,
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"stream": False # 使用非流式响应以便于处理
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}
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# 发送请求到QwQ API
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response = requests.post(url=self.url, json=body, headers=self.headers, timeout=None)
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if response.status_code == 200:
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response_data = response.json()
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if "choices" in response_data and len(response_data["choices"]) > 0:
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ai_response = response_data["choices"][0]["message"]["content"]
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# 处理思考标记
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processed_response = self._process_thinking_content(ai_response)
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return processed_response
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else:
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return "QwQ API返回格式错误"
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else:
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return f"QwQ API调用失败: {response.status_code} - {response.text}"
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def handle_command(self, command):
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"""处理引擎特定命令"""
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# QwQ引擎目前不支持特定命令
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print(f"{Fore.RED}QwQ引擎不支持此命令{Style.RESET_ALL}")
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return False
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def _process_thinking_content(self, response):
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"""处理思考内容，将思考部分以灰色文本显示，只返回回复部分"""
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from colorama import Fore, Style
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import re
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# 这里可以设置思考标记，由用户自己定义
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thinking_start_marker = "<think>" # 用户可以设置开始标记
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thinking_end_marker = "</think>" # 用户可以设置结束标记
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# 如果用户设置了思考标记，则处理思考内容
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if thinking_start_marker and thinking_end_marker and thinking_start_marker in response and thinking_end_marker in response:
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# 分离思考内容和回复内容
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parts = response.split(thinking_start_marker)
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if len(parts) > 1:
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# 获取包含结束标记的部分
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remaining = parts[1]
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thinking_content = ""
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reply_content = ""
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# 检查结束标记
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if thinking_end_marker in remaining:
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thinking_part, reply_part = remaining.split(thinking_end_marker, 1)
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thinking_content = thinking_part.strip()
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reply_content = reply_part.strip()
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else:
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# 如果没有找到结束标记，将剩余部分都视为思考内容
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thinking_content = remaining.strip()
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reply_content = ""
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# 显示思考内容为灰色文本
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if thinking_content:
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print(f"{Fore.LIGHTBLACK_EX}[思考: {thinking_content}]{Style.RESET_ALL}")
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# 返回回复内容
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if reply_content:
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return reply_content
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else:
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return "" # 如果没有回复内容，返回空字符串
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else:
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# 如果没有设置思考标记或没有找到标记，直接返回原始响应
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return response
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