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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@@ -0,0 +1,209 @@
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"""
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图像识别引擎模块
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支持多种图像识别引擎的切换和管理
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"""
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import os
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import sys
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import importlib
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import json
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from colorama import Fore, Style
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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 BaseImageEngine:
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"""图像识别引擎基类"""
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name = "base"
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description = "基础图像识别引擎"
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requires_api_key = False
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def __init__(self):
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self.config = {}
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def set_config(self, config):
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"""设置引擎配置"""
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self.config = config
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def is_available(self):
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"""检查引擎是否可用（子类实现）"""
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return False
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def analyze(self, image_path, prompt="请描述这张图片"):
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"""分析图片（子类实现）"""
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raise NotImplementedError("子类必须实现 analyze 方法")
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def stream_analyze(self, image_path, prompt="请描述这张图片"):
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"""流式分析图片（子类实现，可选）"""
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# 默认实现：调用 analyze 并一次性返回
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result = self.analyze(image_path, prompt)
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yield result
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def get_model_list(self):
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"""获取可用模型列表（子类实现，可选）"""
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return []
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def set_model(self, model_name):
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"""设置当前使用的模型（子类实现，可选）"""
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pass
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def get_help(self):
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"""获取引擎帮助信息"""
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return f"{self.name}: {self.description}"
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class ImageEngineManager:
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"""图像识别引擎管理器"""
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def __init__(self):
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self.engines = {}
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self.current_engine = None
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self.current_engine_name = None
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self.config = self._load_config()
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# 自动加载所有引擎
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self._load_engines()
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# 设置默认引擎
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self._set_default_engine()
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def _load_config(self):
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"""加载配置文件"""
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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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return json.load(f)
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except:
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return {"image": {"engines": {}, "default_engine": "ollama"}}
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def _save_config(self):
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"""保存配置文件"""
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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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full_config = json.load(f)
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except:
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full_config = {"api": {"engines": {}}, "system": {}, "image": {}}
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# 确保image配置存在
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if "image" not in full_config:
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full_config["image"] = {}
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full_config["image"]["default_engine"] = self.current_engine_name
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try:
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with open(config_file, 'w', encoding='utf-8') as f:
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json.dump(full_config, f, ensure_ascii=False, indent=2)
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except Exception as e:
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print(f"{Fore.RED}保存配置失败: {e}{Style.RESET_ALL}")
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def _load_engines(self):
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"""加载所有引擎"""
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engine_dir = os.path.dirname(__file__)
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for filename in os.listdir(engine_dir):
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if filename.endswith('_engine.py') and filename != '__init__.py':
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engine_name = filename[:-11] # 移除 _engine.py
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try:
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# 动态导入模块
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module_path = os.path.join(engine_dir, filename)
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spec = importlib.util.spec_from_file_location(f"image_engine.{engine_name}", module_path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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# 获取引擎类
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for attr_name in dir(module):
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attr = getattr(module, attr_name)
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if (isinstance(attr, type) and
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issubclass(attr, BaseImageEngine) and
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attr != BaseImageEngine):
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engine_instance = attr()
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# 加载引擎配置
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engine_config = self.config.get("image", {}).get("engines", {}).get(engine_name, {})
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engine_instance.set_config(engine_config)
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self.engines[engine_name] = engine_instance
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print(f"{Fore.GREEN}已加载图像引擎: {engine_name}{Style.RESET_ALL}")
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break
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except Exception as e:
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print(f"{Fore.RED}加载图像引擎 {engine_name} 失败: {e}{Style.RESET_ALL}")
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def _set_default_engine(self):
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"""设置默认引擎"""
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# 从配置读取默认引擎
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default = self.config.get("image", {}).get("default_engine", "ollama")
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if default in self.engines:
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engine = self.engines[default]
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if engine.is_available():
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self.current_engine = engine
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self.current_engine_name = default
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print(f"{Fore.GREEN}当前图像引擎: {default}{Style.RESET_ALL}")
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return
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# 如果配置的默认引擎不可用，找第一个可用的
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for name, engine in self.engines.items():
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if engine.is_available():
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self.current_engine = engine
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self.current_engine_name = name
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print(f"{Fore.GREEN}当前图像引擎: {name}{Style.RESET_ALL}")
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return
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print(f"{Fore.YELLOW}警告: 没有可用的图像引擎{Style.RESET_ALL}")
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def get_engine(self, name=None):
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"""获取指定引擎或当前引擎"""
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if name:
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return self.engines.get(name)
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return self.current_engine
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def switch_engine(self, name):
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"""切换引擎"""
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if name not in self.engines:
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available = list(self.engines.keys())
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return False, f"引擎 '{name}' 不存在。可用引擎: {', '.join(available)}"
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engine = self.engines[name]
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if not engine.is_available():
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return False, f"引擎 '{name}' 不可用，请检查配置"
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self.current_engine = engine
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self.current_engine_name = name
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self._save_config()
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return True, f"已切换到图像引擎: {name}"
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def list_engines(self):
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"""列出所有引擎"""
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result = f"{Fore.CYAN}图像识别引擎列表:{Style.RESET_ALL}\n"
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for name, engine in self.engines.items():
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status = f"{Fore.GREEN}✓ 可用{Style.RESET_ALL}" if engine.is_available() else f"{Fore.RED}✗ 不可用{Style.RESET_ALL}"
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current = f" {Fore.YELLOW}[当前]{Style.RESET_ALL}" if name == self.current_engine_name else ""
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result += f" - {name}: {engine.description} {status}{current}\n"
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return result
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def analyze(self, image_path, prompt="请描述这张图片", stream=True):
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"""使用当前引擎分析图片"""
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if not self.current_engine:
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raise Exception("没有可用的图像引擎")
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if stream and hasattr(self.current_engine, 'stream_analyze'):
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return self.current_engine.stream_analyze(image_path, prompt)
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else:
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return self.current_engine.analyze(image_path, prompt)
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def get_current_engine_name(self):
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"""获取当前引擎名称"""
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return self.current_engine_name
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# 全局引擎管理器实例
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_engine_manager = None
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def get_engine_manager():
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"""获取全局引擎管理器实例"""
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global _engine_manager
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if _engine_manager is None:
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_engine_manager = ImageEngineManager()
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return _engine_manager
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@@ -0,0 +1,204 @@
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"""
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Ollama 视觉引擎
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使用本地 Ollama 服务进行图像识别
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"""
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import os
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from colorama import Fore, Style
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from . import BaseImageEngine
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try:
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import ollama
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OLLAMA_AVAILABLE = True
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except ImportError:
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OLLAMA_AVAILABLE = False
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class OllamaImageEngine(BaseImageEngine):
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"""Ollama 视觉引擎"""
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name = "ollama"
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description = "Ollama本地视觉模型（支持多种视觉模型）"
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requires_api_key = False
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# 预设提示词
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PROMPTS = {
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"analyze": """请详细分析这张图片：
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1. 图像类型：照片/截图/插画/图表/文档等
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2. 主要内容：描述核心元素和场景
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3. 文字内容：如有文字，请完整转录
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4. 视觉细节：颜色、构图、风格等
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5. 总结：一句话概括
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请用中文简洁回答。""",
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"ocr": "请识别图片中的所有文字内容，按原文格式输出。如果没有文字，请描述图片内容。",
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"quick": "请用中文简要描述这张图片的内容。",
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"describe": "请详细描述这张图片的内容，包括所有可见的元素、颜色、文字等。"
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}
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# 推荐的视觉模型列表
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VISION_MODELS = [
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'qwen3-vl:4b', 'qwen3-vl:latest',
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'llava:7b', 'llava:13b', 'llava:34b',
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'llava-llama3:latest',
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'minicpm-v:latest',
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'deepseek-ocr:latest',
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'moondream:latest',
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'bakllava:latest',
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]
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def __init__(self):
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super().__init__()
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self.model = "qwen3-vl:4b"
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self.installed_models = []
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self._refresh_models()
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def set_config(self, config):
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"""设置引擎配置"""
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super().set_config(config)
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if 'model' in config:
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self.model = config['model']
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def _refresh_models(self):
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"""刷新已安装模型列表"""
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if not OLLAMA_AVAILABLE:
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self.installed_models = []
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return
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try:
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result = ollama.list()
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self.installed_models = [m['model'] for m in result.get('models', [])]
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except:
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self.installed_models = []
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def is_available(self):
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"""检查引擎是否可用"""
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if not OLLAMA_AVAILABLE:
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return False
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try:
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ollama.list()
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return True
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except:
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return False
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def _find_model(self, model_name):
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"""查找模型（处理版本标签）"""
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for m in self.installed_models:
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if m == model_name or m.startswith(model_name + ':') or model_name in m:
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return m
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return None
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def get_model_list(self):
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"""获取可用模型列表"""
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self._refresh_models()
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result = f"{Fore.CYAN}已安装的模型:{Style.RESET_ALL}\n"
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for m in self.installed_models:
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marker = f"{Fore.GREEN}*{Style.RESET_ALL}" if any(vm.split(':')[0] in m for vm in ['vl', 'llava', 'vision', 'ocr', 'minicpm', 'moondream', 'qwen3-vl']) else " "
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result += f" {marker} {m}\n"
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result += f"\n{Fore.CYAN}推荐的视觉模型:{Style.RESET_ALL}\n"
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for vm in self.VISION_MODELS:
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status = f"{Fore.GREEN}[已安装]{Style.RESET_ALL}" if self._find_model(vm) else f"{Fore.YELLOW}[未安装]{Style.RESET_ALL}"
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result += f" - {vm} {status}\n"
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return result
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def set_model(self, model_name):
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"""设置当前使用的模型"""
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actual = self._find_model(model_name)
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if actual:
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self.model = actual
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return True, f"已切换到模型: {actual}"
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# 如果没找到，直接使用用户指定的名称（可能还没安装）
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self.model = model_name
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return True, f"已设置模型: {model_name} (未安装? 运行 ollama pull {model_name})"
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def analyze(self, image_path, prompt="请描述这张图片"):
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"""分析图片（非流式）"""
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if not OLLAMA_AVAILABLE:
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raise Exception("ollama 库未安装，请运行: pip install ollama")
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if not os.path.exists(image_path):
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raise Exception(f"文件不存在: {image_path}")
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# 使用预设提示词或自定义提示词
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actual_prompt = self.PROMPTS.get(prompt, prompt)
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try:
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response = ollama.chat(
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model=self.model,
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messages=[{
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'role': 'user',
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'content': actual_prompt,
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'images': [image_path]
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}]
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)
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return response.get('message', {}).get('content', '')
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except Exception as e:
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raise Exception(f"Ollama识别失败: {str(e)}")
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def stream_analyze(self, image_path, prompt="请描述这张图片"):
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"""流式分析图片"""
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if not OLLAMA_AVAILABLE:
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raise Exception("ollama 库未安装，请运行: pip install ollama")
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if not os.path.exists(image_path):
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raise Exception(f"文件不存在: {image_path}")
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# 使用预设提示词或自定义提示词
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actual_prompt = self.PROMPTS.get(prompt, prompt)
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# 检查模型是否已安装
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actual_model = self._find_model(self.model)
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if not actual_model:
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raise Exception(f"模型 {self.model} 未安装。请运行: ollama pull {self.model}")
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try:
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response = ollama.chat(
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model=actual_model,
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messages=[{
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'role': 'user',
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'content': actual_prompt,
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'images': [image_path]
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}],
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stream=False
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)
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content = response.get('message', {}).get('content', '')
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if content:
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yield content
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else:
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raise Exception("模型返回空内容")
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except Exception as e:
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error_msg = str(e)
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if "connection" in error_msg.lower():
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raise Exception("无法连接到 Ollama 服务，请确保服务正在运行 (ollama serve)")
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raise Exception(f"Ollama识别失败: {error_msg}")
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def get_help(self):
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"""获取引擎帮助信息"""
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return f"""
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Ollama 视觉引擎帮助
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==================
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当前模型: {self.model}
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使用说明:
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1. 确保 Ollama 服务正在运行
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2. 安装视觉模型: ollama pull qwen3-vl:4b
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3. 使用 /image model <模型名> 切换模型
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支持的操作:
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- analyze 详细分析
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- ocr 文字识别
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- quick 快速描述
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- describe 完整描述
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模型管理:
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- /image models 列出模型
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- /image model <名> 切换模型
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"""
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Block a user