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。
401 lines
16 KiB
Python
401 lines
16 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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import threading
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import time
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from colorama import Fore, Style
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# 延迟导入 ollama，避免程序启动时卡住
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# 如果导入失败，ollama_client 将为 None
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try:
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import ollama
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ollama_client = ollama
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except Exception as e:
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print(f"{Fore.YELLOW}警告: 无法导入 ollama 库: {e}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}请确保已安装 ollama 包 (pip install ollama){Style.RESET_ALL}")
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ollama_client = None
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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 OllamaAI:
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"""基于Ollama的本地AI引擎"""
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requires_api_key = False # Ollama是本地引擎，不需要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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Ollama AI引擎插件帮助信息
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========================
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引擎名称: Ollama
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描述: 基于Ollama的本地AI引擎,支持多种大型语言模型
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可用命令:
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/engine.ollama models - 列出本地可用的模型
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/engine.ollama remote-models - 显示可下载的模型列表
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/engine.ollama search <关键词> - 搜索可下载的模型
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/engine.ollama set <模型名> - 设置当前使用的模型
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/engine.ollama pull <模型名> - 从Ollama库拉取模型
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注意: 本引擎强制使用流式输出，AI回复会实时显示。
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使用说明:
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1. 确保Ollama服务已在本地运行
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2. 使用/engine.switch ollama切换到此引擎
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3. 使用上述命令管理模型和与AI交互
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思考模式:
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- 支持深度思考模型（如 QwQ、DeepSeek-R1）
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- 思考过程会自动以灰色文本显示
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"""
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return help_text
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def __init__(self):
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self.name = "ollama"
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self.cli = None # 引用CLI实例以访问插件
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# 初始化对话历史
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self.conversation_history = []
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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("ollama", {})
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self.api_key = engine_config.get("api_key", "")
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self.base_url = engine_config.get("base_url", "")
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self.model = engine_config.get("model", "")
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self.max_history = config.get("system", {}).get("max_history", 10)
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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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self.max_history = 10
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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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# 直接启用思考标记，由主程序自动处理深度思考模型的响应
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self.thinking_start_marker = "<thinking>"
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self.thinking_end_marker = "</thinking>"
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# 检测Ollama服务是否运行
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self.is_service_running = self.check_ollama_service()
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if self.is_service_running:
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print(f"{Fore.GREEN}Ollama服务检测成功,引擎已启用{Style.RESET_ALL}")
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else:
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print(f"{Fore.RED}Ollama服务未运行,引擎将不可用{Style.RESET_ALL}")
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def check_ollama_service(self):
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"""检测Ollama服务是否运行"""
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# 检查 ollama 库是否可用
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if ollama_client is None:
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return False
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try:
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# 尝试调用Ollama API获取版本信息
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response = ollama_client.list()
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# 如果没有异常,说明服务运行正常
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return True
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except Exception as e:
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# 如果出现异常,说明服务未运行
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return False
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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Ollama API"""
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try:
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# 检查Ollama服务是否运行
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if not self.is_service_running:
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return "错误:Ollama服务未运行,请启动Ollama服务后再使用此引擎."
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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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# 使用Ollama Python库调用API
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response = ollama_client.chat(
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model=self.model,
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messages=messages,
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stream=False
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)
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# 返回AI的回复
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return response["message"]["content"]
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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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# 使用Ollama Python库调用API
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response = ollama_client.chat(
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model=self.model,
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messages=messages,
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stream=False
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)
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# 返回AI的回复
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content = response["message"]["content"]
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# 清理无效的 UTF-8 代理字符
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content = content.encode('utf-8', 'replace').decode('utf-8')
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return content
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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"Ollama 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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# 清理代理字符
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clean_prompt = system_prompt.encode('utf-8', 'replace').decode('utf-8')
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messages.append({
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"role": "system",
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"content": clean_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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# 清理内容中的代理字符
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clean_content = msg["content"].encode('utf-8', 'replace').decode('utf-8')
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messages.append({"role": msg["role"], "content": clean_content})
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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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# 清理用户输入
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if user_input:
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clean_input = user_input.encode('utf-8', 'replace').decode('utf-8')
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messages.append({
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"role": "user",
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"content": clean_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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if not self.is_service_running:
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return "错误:Ollama服务未运行,请启动Ollama服务后再使用此引擎."
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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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# 使用Ollama Python库调用API
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response = ollama_client.chat(
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model=self.model,
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messages=messages,
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stream=False
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)
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return response["message"]["content"]
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def set_model(self, model_name):
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"""设置要使用的模型"""
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if not self.is_service_running:
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print(f"{Fore.RED}错误:Ollama服务未运行,无法切换模型{Style.RESET_ALL}")
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return
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self.model = model_name
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print(f"{Fore.GREEN}已切换Ollama模型为: {model_name}{Style.RESET_ALL}")
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def list_models(self):
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"""列出本地可用的模型"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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response = ollama_client.list()
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models = [model["name"] for model in response["models"]]
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return models
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except Exception as e:
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return [f"获取模型列表失败: {str(e)}"]
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def list_remote_models(self, limit=10):
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"""获取可下载的模型列表(仅展示前N个)"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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# 使用Ollama Python库获取可下载的模型列表
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# 注意:Ollama库本身没有直接获取可下载模型列表的API
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# 这里返回一些常见的模型作为示例
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# 实际应用中可能需要从Ollama官网或其他来源获取此信息
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common_models = [
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"llama3.1:latest",
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"llama3:latest",
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"mistral:latest",
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"gemma2:latest",
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"phi3:latest",
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"qwen2:latest",
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"command-r:latest",
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"dolphin-mistral:latest",
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"nous-hermes2:latest",
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"llava:latest",
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"codellama:latest",
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"mxbai-embed-large:latest",
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"all-minilm:latest",
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"stable-diffusion:latest",
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"moondream:latest"
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]
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return common_models[:limit]
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except Exception as e:
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return [f"获取远程模型列表失败: {str(e)}"]
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def search_models(self, query, limit=10):
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"""模糊搜索可下载的模型"""
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if not self.is_service_running:
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return ["错误:Ollama服务未运行"]
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try:
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# 使用Ollama Python库获取可下载的模型列表
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# 注意:Ollama库本身没有直接获取可下载模型列表的API
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all_models = [
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"llama3.1:latest", "llama3:latest", "llama2:latest",
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"mistral:latest", "mistral-openorca:latest",
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"gemma2:latest", "gemma:latest",
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"phi3:latest", "phi2:latest",
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"qwen2:latest", "qwen:latest",
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"command-r:latest", "command-r-plus:latest",
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"dolphin-mistral:latest", "dolphin-llama3:latest",
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"nous-hermes2:latest", "nous-hermes:latest",
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"llava:latest", "bakllava:latest",
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"codellama:latest", "codeup:latest",
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"mxbai-embed-large:latest", "nomic-embed-text:latest",
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"all-minilm:latest", "bge-m3:latest",
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"stable-diffusion:latest", "moondream:latest",
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"llama3-groq:latest", "eva-qwen2:latest"
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]
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# 进行模糊搜索(简单匹配,包含查询词的模型)
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matched_models = [model for model in all_models if query.lower() in model.lower()]
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# 限制返回数量
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return matched_models[:limit]
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except Exception as e:
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return [f"搜索模型失败: {str(e)}"]
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def pull_model(self, model_name):
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"""从Ollama库拉取模型"""
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if not self.is_service_running:
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print(f"{Fore.RED}错误:Ollama服务未运行,无法拉取模型{Style.RESET_ALL}")
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return False
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try:
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print(f"{Fore.YELLOW}正在拉取模型 {model_name}...{Style.RESET_ALL}")
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# 使用Ollama Python库拉取模型
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response = ollama_client.pull(model_name, stream=True)
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# 逐块处理响应
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for part in response:
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if 'status' in part:
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status = part['status']
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print(f"{Fore.YELLOW}{status}{Style.RESET_ALL}")
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# 检查是否完成
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if 'completed' in part and 'total' in part:
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if part['completed'] == part['total']:
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print(f"{Fore.GREEN}模型 {model_name} 拉取完成!{Style.RESET_ALL}")
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print(f"{Fore.GREEN}模型 {model_name} 拉取成功!{Style.RESET_ALL}")
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return True
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except Exception as e:
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print(f"{Fore.RED}拉取模型 {model_name} 失败: {str(e)}{Style.RESET_ALL}")
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return False
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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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if not self.is_service_running:
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print(f"{Fore.RED}Ollama服务未运行,无法执行命令{Style.RESET_ALL}")
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return False
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if command == "models":
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# 显示本地可用模型
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models = self.list_models()
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print(f"{Fore.GREEN}本地可用的Ollama模型:{Style.RESET_ALL}")
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for model in models:
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status = " (当前使用)" if model == self.model else ""
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print(f"{Fore.GREEN} - {model}{status}{Style.RESET_ALL}")
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return True
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elif command == "remote-models":
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# 显示可下载的模型(前10个)
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models = self.list_remote_models()
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print(f"{Fore.GREEN}可下载的Ollama模型(前10个):{Style.RESET_ALL}")
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for i, model in enumerate(models, 1):
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print(f"{Fore.GREEN} {i}. {model}{Style.RESET_ALL}")
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return True
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elif command.startswith("search "):
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# 搜索模型
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query = command[7:].strip() # 移除 'search '
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if query:
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models = self.search_models(query)
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if models:
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print(f"{Fore.GREEN}搜索 '{query}' 的结果(前10个):{Style.RESET_ALL}")
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for i, model in enumerate(models, 1):
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print(f"{Fore.GREEN} {i}. {model}{Style.RESET_ALL}")
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else:
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print(f"{Fore.YELLOW}未找到与 '{query}' 匹配的模型{Style.RESET_ALL}")
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return True
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else:
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print(f"{Fore.RED}请提供搜索关键词.用法: /engine.search <关键词>{Style.RESET_ALL}")
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return False
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elif command.startswith("set "):
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# 设置模型
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model_name = command[4:].strip() # 移除 'set '
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if model_name:
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self.set_model(model_name)
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return True
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else:
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print(f"{Fore.RED}请提供模型名称.用法: /engine.set <模型名>{Style.RESET_ALL}")
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return False
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elif command.startswith("pull "):
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# 拉取模型
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model_name = command[5:].strip() # 移除 'pull '
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if model_name:
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success = self.pull_model(model_name)
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return success
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else:
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print(f"{Fore.RED}请提供模型名称.用法: /engine.pull <模型名>{Style.RESET_ALL}")
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return False
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else:
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print(f"{Fore.RED}未知的Ollama命令.可用命令: models, remote-models, search, set, pull{Style.RESET_ALL}")
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return False |