release: v6.1.0 - TUI 体验全面升级 + 背景自定义
TUI 界面改进: - 全面中文化: CommandPalette/HelpPanel/Footer/Chatbox 边框标题等所有用户可见英文汉化 - 辅助中心 (Ctrl+P/F9): 原"设置"重命名, 含 6 项可切换设置 - 独立帮助浮层 (? 键): 重写帮助页面逻辑, 全中文, 带滑入淡入动画 - 背景切换: 辅助中心 [5] 预设背景, [6] 自定义背景; 新增 /bg 命令支持图片提取主色调 - 换行修复: Ctrl+J 作为主推换行键 (Windows 终端不区分 Shift+Enter) - 引擎切换修复: 解决 call_from_thread 在主线程崩溃的 RuntimeError 技术改进: - 文本工具调用解析: 检测模型用 <tool_call> 标签返回的工具调用 - 网络工具优化: network_tools.py 和 ai_search.py 改进请求头/重试/编码 - 流式输出修复: append_chunk 的 refresh 改为 layout=True - 取消响应修复: 引擎层 _cancelled 标志 + TUI 层 worker 身份跟踪 融合 v6.0.0 工作: - 保留 terminal 模拟器/operit 桥接/深度思考/工作模式/记忆模式等 v6.0.0 全部功能 - 在此基础上叠加本会话的 TUI 改进
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@@ -22,6 +22,26 @@ project_root = os.path.dirname(current_dir)
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sys.path.insert(0, project_root)
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def _safe_get(obj, key, default=None):
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"""兼容 dict 和 pydantic 对象的安全取值（新版 ollama 返回 pydantic 模型，不支持 .get()）"""
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if obj is None:
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return default
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# 1. 先尝试 dict 访问
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try:
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return obj[key]
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except (KeyError, TypeError):
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pass
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# 2. 尝试属性访问（pydantic 模型）
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val = getattr(obj, key, None)
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if val is not None:
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return val
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# 3. 尝试 model_dump 后的 dict 访问
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try:
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return obj.model_dump()[key]
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except (AttributeError, KeyError, TypeError):
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return default
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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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@@ -80,7 +100,10 @@ Ollama AI引擎插件帮助信息
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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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# 取消标志
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self._cancelled = False
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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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@@ -93,6 +116,33 @@ Ollama AI引擎插件帮助信息
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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 cancel(self):
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"""取消正在进行的请求"""
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self._cancelled = True
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def apply_config(self, base_url=None, api_key=None, model=None):
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"""应用新配置到当前引擎实例（内存），并持久化到 config.json"""
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if base_url is not None:
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self.base_url = base_url.rstrip('/')
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if api_key is not None:
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self.api_key = api_key
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if model is not None:
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self.model = model
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# 持久化到 config.json
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try:
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import os, json
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config_file = os.path.join(project_root, "config.json")
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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_cfg = config.setdefault("api", {}).setdefault("engines", {}).setdefault("ollama", {})
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engine_cfg["base_url"] = self.base_url
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engine_cfg["api_key"] = self.api_key
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engine_cfg["model"] = self.model
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with open(config_file, 'w', encoding='utf-8') as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.warning(f"持久化引擎配置失败: {e}")
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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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@@ -107,7 +157,7 @@ Ollama AI引擎插件帮助信息
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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, tools=None):
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def generate_response(self, user_input, tool_results=None, system_prompt=None, tools=None, stream_start_event=None):
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"""
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生成AI响应 - 使用Ollama API
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@@ -137,10 +187,20 @@ Ollama AI引擎插件帮助信息
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messages = self._build_messages_with_history(system_prompt, user_input)
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# 构建调用参数
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# 重置取消标志
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self._cancelled = False
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# 获取流式回调
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stream_cb = None
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thinking_cb = None
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if self.cli and hasattr(self.cli, 'tui_stream_callback'):
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stream_cb = self.cli.tui_stream_callback
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if self.cli and hasattr(self.cli, 'tui_thinking_callback'):
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thinking_cb = self.cli.tui_thinking_callback
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kwargs = {
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"model": self.model,
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"messages": messages,
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"stream": False
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"stream": True if stream_cb else False
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}
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# ── Function Calling: 注入工具定义 ──
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@@ -150,16 +210,45 @@ Ollama AI引擎插件帮助信息
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# 使用Ollama Python库调用API
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response = ollama_client.chat(**kwargs)
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# ── 流式模式：逐块读取 ──
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if stream_cb:
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full_content = ""
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reasoning_content = ""
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for chunk in response:
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if self._cancelled:
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break
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msg = _safe_get(chunk, "message", {})
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# 检查 tool_calls
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tc_raw = _safe_get(msg, "tool_calls")
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if tc_raw:
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# 工具调用 — 走原逻辑
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message = msg
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tool_calls_raw = tc_raw
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break
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# 深度思考（Ollama thinking 字段）
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rc = _safe_get(msg, "thinking", "")
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if rc and thinking_cb:
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reasoning_content += rc
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thinking_cb(rc)
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content = _safe_get(msg, "content", "")
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if content:
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full_content += content
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stream_cb(content)
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else:
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# 流式完成，无工具调用
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return full_content.encode('utf-8', 'replace').decode('utf-8')
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else:
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message = _safe_get(response, "message", {})
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tool_calls_raw = _safe_get(message, "tool_calls")
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# ── Function Calling: 检查 tool_calls ──
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message = response.get("message", {})
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tool_calls_raw = message.get("tool_calls")
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if tool_calls_raw:
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# Ollama 的 tool_calls 格式: [{"function": {"name": ..., "arguments": {...}}}]
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fc_calls = []
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for i, tc in enumerate(tool_calls_raw):
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func = tc.get("function", {})
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name = func.get("name", "")
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args = func.get("arguments", {})
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func = _safe_get(tc, "function", {})
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name = _safe_get(func, "name", "")
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args = _safe_get(func, "arguments", {})
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call_id = f"call_{name}_{i}"
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# 统一为小狸内部格式
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@@ -189,7 +278,7 @@ Ollama AI引擎插件帮助信息
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}, ensure_ascii=False)
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# 普通文本响应
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content = message.get("content", "")
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content = _safe_get(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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@@ -306,11 +395,16 @@ Ollama AI引擎插件帮助信息
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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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models_list = _safe_get(response, "models", [])
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result = []
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for model in models_list:
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name = _safe_get(model, "model", "") or _safe_get(model, "name", "")
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if name:
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result.append(name)
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return result if result else ["未找到本地模型"]
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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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@@ -392,12 +486,14 @@ Ollama AI引擎插件帮助信息
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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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status = _safe_get(part, "status", None)
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if 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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completed = _safe_get(part, "completed", None)
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total = _safe_get(part, "total", None)
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if completed is not None and total is not None:
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if completed == 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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