fix: ai_search改用s.jina.ai搜索端点，删除napcat_qq和ollama_image
- ai_search: 搜索从r.jina.ai嵌套必应改为s.jina.ai直接搜索，解决旧时间戳问题 - 删除 napcat_qq.py (52KB, 导入即启后台线程) - 删除 ollama_image.py (冗余图像插件)
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@@ -60,7 +60,7 @@ ai_search <操作> <参数>/r
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def get_tool_info(self):
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return {
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"name": "ai_search",
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"description": "AI搜索工具，使用jina.ai服务进行智能搜索，支持网址内容提取和网络搜索。当用户询问天气、新闻、汇率、股票、航班、实时信息、网络内容搜索等问题时，应该直接使用此工具。使用格式：ai_search search <搜索词> 或 ai_search url <网址>",
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"description": "AI搜索工具，使用jina.ai服务进行智能搜索，支持网址内容提取和实时网络搜索。当用户询问天气、新闻、汇率、股票、航班、实时信息、网络内容搜索等问题时，应该直接使用此工具。使用格式：ai_search search <搜索词> 或 ai_search url <网址>",
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"keywords": ["搜索", "search", "网址", "url", "web", "AI搜索", "网络搜索", "内容提取", "天气", "新闻", "实时信息"],
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"usage": """ai_search 工具使用说明：
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JSON格式示例：
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@@ -144,17 +144,12 @@ JSON格式示例：
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return f"网址搜索错误: {str(e)}"
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def _search_web(self, query):
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"""普通搜索（使用jina.ai + 必应搜索）"""
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"""普通搜索（使用 jina.ai 搜索端点 s.jina.ai，返回实时结果）"""
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try:
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# 对搜索词进行URL编码，确保中文等特殊字符正确编码
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# 使用safe=''确保所有非字母数字字符都被编码
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encoded_query = quote(query, safe='')
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# 构建必应搜索URL
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bing_url = f"https://cn.bing.com/search?q={encoded_query}"
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# 构建jina.ai的URL - 直接使用必应URL，不需要二次编码
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jina_url = f"https://r.jina.ai/{bing_url}"
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# s.jina.ai 是 jina.ai 的搜索端点，直接返回实时搜索结果
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# 不再嵌套必应URL，避免返回旧时间戳的缓存内容
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jina_url = f"https://s.jina.ai/{encoded_query}"
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result = self._execute_curl(jina_url)
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File diff suppressed because it is too large.
Load diff
@@ -1,198 +0,0 @@
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"""
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Ollama图像识别插件
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支持多种图像识别引擎的切换
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"""
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import os
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import sys
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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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from image_engine import get_engine_manager
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class Liugin:
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"""图像识别插件 - 支持多引擎切换"""
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def __init__(self):
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self.usage = """图像识别插件使用方法：
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/image <操作> <参数>
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操作命令：
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- analyze <图片路径> - 详细分析图片
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- ocr <图片路径> - 快速OCR文字识别
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- quick <图片路径> - 快速图片描述
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- describe <图片路径> - 完整描述图片
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- model [模型名] - 查看/切换模型
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- models - 列出可用的视觉模型
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- engine [引擎名] - 查看/切换识别引擎
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- engines - 列出所有识别引擎
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示例：
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- image analyze C:\\photo.png
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- image ocr C:\\screenshot.png
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- image model llava:13b
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- image engine openai
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JSON调用示例：
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{"action": "use_tool", "tool": "image", "args": "analyze c:\\\\test.png"}
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"""
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self.cli = None
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self.engine_manager = None
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# 支持的图片格式
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self.supported_formats = ['png', 'jpg', 'jpeg', 'gif', 'webp', 'bmp']
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def set_cli(self, cli):
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"""设置CLI实例引用"""
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self.cli = cli
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# 注册插件命令
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self.cli.register_liugin_command('image', self.command_handler)
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self.cli.register_liugin_command('img', self.command_handler)
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def command_handler(self, args):
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"""处理命令"""
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parts = args.strip().split(maxsplit=1)
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if len(parts) < 1:
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return self._get_help()
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operation = parts[0].lower()
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remaining_args = parts[1] if len(parts) > 1 else ""
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return self.handle(f"{operation} {remaining_args}")
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def get_tool_info(self):
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return {
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"name": "image",
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"description": "图像识别工具，支持多引擎切换，使用本地或云端模型进行图像分析",
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"keywords": ["图像", "图片", "image", "analyze", "分析", "视觉", "识别", "OCR"],
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"usage": """image 工具使用说明：
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- analyze <图片路径> - 详细分析
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- ocr <图片路径> - OCR文字识别
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- quick <图片路径> - 快速描述
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- engine <引擎名> - 切换引擎
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- engines - 列出所有引擎
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支持格式：PNG, JPG, JPEG, GIF, WEBP, BMP"""
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}
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def handle(self, args):
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"""处理请求"""
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# 延迟初始化引擎管理器
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if self.engine_manager is None:
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self.engine_manager = get_engine_manager()
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try:
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parts = args.strip().split(maxsplit=1)
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if len(parts) < 1:
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return self._get_help()
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operation = parts[0].lower()
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image_path = parts[1].strip() if len(parts) > 1 else ""
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# 引擎管理命令
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if operation == "engines":
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return self.engine_manager.list_engines()
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elif operation == "engine":
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if image_path:
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success, msg = self.engine_manager.switch_engine(image_path)
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return msg
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return f"当前引擎: {self.engine_manager.get_current_engine_name()}"
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elif operation == "models":
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engine = self.engine_manager.get_engine()
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if engine and hasattr(engine, 'get_model_list'):
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return engine.get_model_list()
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return "当前引擎不支持模型列表"
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elif operation == "model":
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engine = self.engine_manager.get_engine()
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if not image_path:
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if engine and hasattr(engine, 'model'):
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return f"当前模型: {engine.model}"
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return "当前引擎不支持模型切换"
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if engine and hasattr(engine, 'set_model'):
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success, msg = engine.set_model(image_path)
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return msg
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return "当前引擎不支持模型切换"
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# 图像分析命令
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elif operation in ["analyze", "ocr", "quick", "describe"]:
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if not image_path:
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return f"错误：请提供图片路径。用法: {operation} <图片路径>"
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return self._analyze_image(image_path, operation)
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else:
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# 兼容旧用法，直接当作图片路径处理
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return self._analyze_image(operation, "analyze")
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except Exception as e:
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return f"错误: {str(e)}"
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def _get_help(self):
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"""获取帮助信息"""
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current = self.engine_manager.get_current_engine_name() if self.engine_manager else "未初始化"
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return f"""图像识别插件命令:
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- analyze <图片路径> 详细分析图片
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- ocr <图片路径> OCR文字识别
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- quick <图片路径> 快速描述
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- describe <图片路径> 完整描述
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- model [模型名] 查看/切换模型
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- models 列出视觉模型
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- engine [引擎名] 查看/切换引擎
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- engines 列出所有引擎
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当前引擎: {current}"""
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def _analyze_image(self, image_path, mode="analyze"):
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"""使用引擎分析图片"""
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# 检查文件
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if not os.path.exists(image_path):
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return f"文件不存在: {image_path}"
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ext = os.path.splitext(image_path)[1].lower().lstrip('.')
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if ext not in self.supported_formats:
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return f"不支持的图片格式: {ext}。支持: {', '.join(self.supported_formats)}"
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# 检查引擎
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engine = self.engine_manager.get_engine()
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if not engine:
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return "没有可用的图像引擎"
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file_size = os.path.getsize(image_path)
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engine_name = self.engine_manager.get_current_engine_name()
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# 获取模型名（如果支持）
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model_info = ""
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if hasattr(engine, 'model'):
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model_info = f" | 模型: {engine.model}"
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# 文件大小警告
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size_warning = ""
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if file_size > 1024 * 1024: # 大于1MB
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size_warning = f" {Fore.YELLOW}(较大图片，处理可能需要较长时间){Style.RESET_ALL}"
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print(f"{Fore.CYAN}📷 图像识别: {os.path.basename(image_path)}{Style.RESET_ALL}{size_warning}")
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print(f"{Fore.YELLOW}📦 大小: {file_size/1024:.1f}KB | 引擎: {engine_name}{model_info}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}⏳ 正在识别，请耐心等待...{Style.RESET_ALL}")
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try:
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# 使用引擎分析
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full_response = ""
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for chunk in self.engine_manager.analyze(image_path, mode, stream=True):
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if chunk:
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print(chunk, end='', flush=True)
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full_response += chunk
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print() # 换行
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print(f"{Fore.GREEN}✓ 识别完成{Style.RESET_ALL}")
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return full_response if full_response else "识别结果为空"
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except Exception as e:
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error_msg = str(e)
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print(f"{Fore.RED}✗ {error_msg}{Style.RESET_ALL}")
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return f"识别失败: {error_msg}"
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