Xiaoli Dev
e22652fbbb
- 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。
356 lines
14 KiB
Python
356 lines
14 KiB
Python
import os
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import sys
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import json
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import requests
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from colorama import Fore, Style
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from openai import OpenAI
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import logging
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from datetime import datetime
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# 设置日志
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logger = logging.getLogger(__name__)
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# 添加项目根目录到sys.path
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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sys.path.insert(0, project_root)
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class MimoAI:
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"""小米MiMoAI引擎 - 使用MiMo-v2-flash模型 - OpenAI格式"""
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requires_api_key = True # MiMo引擎需要API密钥
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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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MiMo AI引擎插件帮助信息
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==========================
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引擎名称: MiMo
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描述: 基于MiMo模型的AI引擎，由小米公司开发
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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. 此引擎使用小米MiMo云API
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2. 使用/engine.switch mimo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. 支持思考模式和多轮对话
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"""
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return help_text
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def __init__(self):
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self.name = "mimo"
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self.cli = None
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# 直接从 config.json 读取配置
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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("mimo", {})
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self.api_key = engine_config.get("api_key", "")
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base_url_raw = engine_config.get("base_url", "")
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self.base_url = base_url_raw.rstrip('/') + '/chat/completions' if base_url_raw else ""
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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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self.api_key = ""
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self.base_url = ""
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self.model = ""
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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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# 初始化 headers（用于 API 请求）
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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 OpenAI 客户端
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try:
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if self.api_key and self.base_url:
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url.replace('/chat/completions', ''),
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timeout=None
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)
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else:
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self.client = None
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except Exception as e:
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logger.warning(f"初始化 MiMo OpenAI 客户端失败: {e}")
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self.client = 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MiMo-v2-flash - OpenAI格式"""
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if not self.client:
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return "MiMo引擎未正确初始化，无法调用API"
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try:
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# 获取当前日期和星期
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now = datetime.now()
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date_str = now.strftime("%Y年%m月%d日")
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week_str = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"][now.weekday()]
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current_datetime = f"{date_str} {week_str}"
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# 设置默认系统提示词（如果未提供）
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if not system_prompt:
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system_prompt = f"You are MiMo, an AI assistant developed by Xiaomi. Today is date: {current_datetime}. Your knowledge cutoff date is December 2024."
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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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# 构建包含历史记录的消息列表
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messages = self._build_messages_with_history(system_prompt, tool_result_text)
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# 构建请求体，使用MiMo特定参数
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body = {
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"model": self.model,
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2048,
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"top_p": 0.95,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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# MiMo特定参数
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"extra_body": {
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"thinking": {"type": "disabled"} # 禁用思考模式
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}
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}
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# 直接使用requests调用MiMo API，因为需要特定的header格式
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import requests
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response = requests.post(
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url=self.base_url,
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json=body,
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headers=self.headers,
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timeout=None
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)
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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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# 更新对话历史
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self._update_conversation_history(user_input if not tool_results else tool_result_text, ai_response)
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# 返回处理后的AI回复，前面加上✦
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return processed_response
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else:
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return "MiMo-v2-flash API返回格式错误"
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elif response.status_code == 401:
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return "MiMo-v2-flash API认证失败，请检查API密钥是否正确配置"
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else:
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return f"MiMo-v2-flash API调用失败: {response.status_code} - {response.text}"
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# 构建包含历史记录的消息列表
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messages = self._build_messages_with_history(system_prompt, user_input)
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# 构建请求体，使用MiMo特定参数
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body = {
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"model": self.model,
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2048,
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"top_p": 0.95,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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# MiMo特定参数
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"extra_body": {
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"thinking": {"type": "disabled"} # 禁用思考模式
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}
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}
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# 直接使用requests调用MiMo API，因为需要特定的header格式
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import requests
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response = requests.post(
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url=self.base_url,
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json=body,
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headers=self.headers,
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timeout=None
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)
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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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# 更新对话历史
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self._update_conversation_history(user_input, 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 "MiMo-v2-flash API返回格式错误"
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elif response.status_code == 401:
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return "MiMo-v2-flash API认证失败，请检查API密钥是否正确配置"
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else:
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return f"MiMo-v2-flash 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"MiMo-v2-flash 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 _update_conversation_history(self, user_input, ai_response):
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"""更新对话历史"""
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# 添加用户输入到历史记录
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self.conversation_history.append({
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"role": "user",
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"content": user_input
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})
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# 添加AI回复到历史记录
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self.conversation_history.append({
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"role": "assistant",
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"content": ai_response
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})
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# 限制历史记录数量
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if len(self.conversation_history) > self.max_history * 2:
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self.conversation_history = self.conversation_history[-self.max_history * 2:]
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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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# 获取当前日期和星期
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now = datetime.now()
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date_str = now.strftime("%Y年%m月%d日")
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week_str = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"][now.weekday()]
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current_datetime = f"{date_str} {week_str}"
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# 设置默认系统提示词（如果未提供）
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if not system_prompt:
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system_prompt = f"You are MiMo, an AI assistant developed by Xiaomi. Today is date: {current_datetime}. Your knowledge cutoff date is December 2024."
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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": "user",
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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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"max_completion_tokens": 1024,
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"temperature": 0.3,
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"top_p": 0.95,
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"stream": False,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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"extra_body": {
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"thinking": {"type": "disabled"}
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}
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}
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# 发送请求到MiMo-v2-flash API
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response = requests.post(url=self.base_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 "MiMo-v2-flash API返回格式错误"
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else:
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return f"MiMo-v2-flash 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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# MiMo引擎目前不支持特定命令
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print(f"{Fore.RED}MiMo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 = "<thinking>"
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thinking_end_marker = "</thinking>"
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# 如果响应中包含思考标记，则提取思考内容和回复内容
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if thinking_start_marker in response and thinking_end_marker in response:
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# 提取思考内容
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thinking_match = re.search(f'{thinking_start_marker}(.*?){thinking_end_marker}', response, re.DOTALL)
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if thinking_match:
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thinking_content = thinking_match.group(1).strip()
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# 打印思考内容（灰色）
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print(f"{Fore.LIGHTBLACK_EX}思考: {thinking_content}{Style.RESET_ALL}")
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# 提取回复内容
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response_content = re.split(f'{thinking_end_marker}', response, maxsplit=1)[-1].strip()
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return response_content
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# 如果没有思考标记，直接返回响应
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return response
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