feat:联网搜索和自动回复
This commit is contained in:
@@ -1606,6 +1606,7 @@ class AIChat(PluginBase):
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异步执行工具调用（不阻塞主流程）
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AI 已经先回复用户，这里异步执行工具，完成后发送结果
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支持 need_ai_reply 标记：工具结果回传给 AI 继续对话（保留上下文和人设）
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"""
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import json
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@@ -1644,10 +1645,14 @@ class AIChat(PluginBase):
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if tasks:
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# 收集需要 AI 回复的工具结果
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need_ai_reply_results = []
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# 处理每个工具的结果
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for i, result in enumerate(results):
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tool_info = tool_info_list[i]
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function_name = tool_info["function_name"]
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tool_call_id = tool_info["tool_call_id"]
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if isinstance(result, Exception):
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logger.error(f"[异步] 工具 {function_name} 执行异常: {result}")
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@@ -1658,6 +1663,15 @@ class AIChat(PluginBase):
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if result and result.get("success"):
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logger.success(f"[异步] 工具 {function_name} 执行成功")
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# 检查是否需要 AI 基于工具结果继续回复
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if result.get("need_ai_reply"):
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need_ai_reply_results.append({
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"tool_call_id": tool_call_id,
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"function_name": function_name,
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"result": result.get("message", "")
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})
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continue # 不直接发送，等待 AI 处理
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# 如果工具没有自己发送内容，且有消息需要发送
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if not result.get("already_sent") and result.get("message"):
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# 某些工具可能需要发送结果消息
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@@ -1675,6 +1689,13 @@ class AIChat(PluginBase):
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if result and result.get("message"):
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await bot.send_text(from_wxid, f"❌ {result.get('message')}")
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# 如果有需要 AI 回复的工具结果，调用 AI 继续对话
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if need_ai_reply_results:
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await self._continue_with_tool_results(
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need_ai_reply_results, bot, from_wxid, chat_id,
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nickname, is_group, messages, tool_calls_data
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)
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logger.info(f"[异步] 所有工具执行完成")
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except Exception as e:
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@@ -1686,6 +1707,131 @@ class AIChat(PluginBase):
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except:
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pass
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async def _continue_with_tool_results(self, tool_results: list, bot, from_wxid: str,
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chat_id: str, nickname: str, is_group: bool,
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messages: list, tool_calls_data: list):
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"""
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基于工具结果继续调用 AI 对话（保留上下文和人设）
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用于 need_ai_reply=True 的工具，如联网搜索等
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"""
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import json
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try:
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logger.info(f"[工具回传] 开始基于 {len(tool_results)} 个工具结果继续对话")
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# 构建包含工具调用和结果的消息
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# 1. 添加 assistant 的工具调用消息
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tool_calls_msg = []
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for tool_call in tool_calls_data:
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tool_call_id = tool_call.get("id", "")
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function_name = tool_call.get("function", {}).get("name", "")
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arguments_str = tool_call.get("function", {}).get("arguments", "{}")
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# 只添加需要 AI 回复的工具
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for tr in tool_results:
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if tr["tool_call_id"] == tool_call_id:
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tool_calls_msg.append({
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": function_name,
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"arguments": arguments_str
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}
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})
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break
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if tool_calls_msg:
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messages.append({
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"role": "assistant",
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"content": None,
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"tool_calls": tool_calls_msg
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})
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# 2. 添加工具结果消息
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for tr in tool_results:
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messages.append({
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"role": "tool",
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"tool_call_id": tr["tool_call_id"],
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"content": tr["result"]
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})
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# 3. 调用 AI 继续对话（不带 tools 参数，避免再次调用工具）
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api_config = self.config["api"]
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proxy_config = self.config.get("proxy", {})
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payload = {
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"model": api_config["model"],
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"messages": messages,
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"max_tokens": api_config.get("max_tokens", 4096),
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"stream": True
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_config['api_key']}"
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}
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proxy = None
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if proxy_config.get("enabled", False):
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proxy_type = proxy_config.get("type", "http")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
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timeout = aiohttp.ClientTimeout(total=api_config.get("timeout", 120))
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async with aiohttp.ClientSession(timeout=timeout) as session:
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async with session.post(
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api_config["url"],
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json=payload,
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headers=headers,
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proxy=proxy
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) as resp:
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if resp.status != 200:
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error_text = await resp.text()
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logger.error(f"[工具回传] AI API 错误: {resp.status}, {error_text}")
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await bot.send_text(from_wxid, "❌ AI 处理搜索结果失败")
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return
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# 流式读取响应
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full_content = ""
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async for line in resp.content:
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line = line.decode("utf-8").strip()
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if not line or not line.startswith("data: "):
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continue
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if line == "data: [DONE]":
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break
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try:
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data = json.loads(line[6:])
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delta = data.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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full_content += content
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except:
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continue
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# 发送 AI 的回复
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if full_content.strip():
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await bot.send_text(from_wxid, full_content.strip())
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logger.success(f"[工具回传] AI 回复完成，长度: {len(full_content)}")
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# 保存到历史记录
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if chat_id:
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self._add_to_memory(chat_id, "assistant", full_content.strip())
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else:
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logger.warning("[工具回传] AI 返回空内容")
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except Exception as e:
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logger.error(f"[工具回传] 继续对话失败: {e}")
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import traceback
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logger.error(f"详细错误: {traceback.format_exc()}")
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try:
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await bot.send_text(from_wxid, "❌ 处理搜索结果时出错")
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except:
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pass
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async def _execute_tools_async_with_image(self, tool_calls_data: list, bot, from_wxid: str,
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chat_id: str, nickname: str, is_group: bool,
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messages: list, image_base64: str):
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@@ -113,50 +113,4 @@ clr_all_context - 抛开那些非瑞依式的沉闷乏味，顽皮地拥抱自
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- "你好" → 直接回复，**不要**调用任何工具
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- "在干嘛" → 直接回复，**不要**调用任何工具
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---
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## 支持的工具函数
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### 1. **SignIn 插件 - 签到功能**
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* `user_signin`：用户签到并获取积分奖励
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- **何时使用**：**仅当**用户明确说"签到"、"打卡"、"我要签到"等签到相关词汇时才调用
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- **不要调用**：用户只是打招呼（如"早上好"、"你好"、"在吗"）时**绝对不要**调用签到
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* `check_profile`：查看个人信息（积分、连续签到天数等）
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* `register_city`：注册或更新用户城市信息
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### 2. **DeerCheckin 插件 - 鹿打卡功能**
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* `deer_checkin`：鹿打卡，可记录今天的鹿数量（支持指定数量）
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* `view_calendar`：查看本月鹿打卡日历
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* `makeup_checkin`：补签指定日期的鹿打卡记录
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### 3. **ChatRoomSummary 插件 - 群聊总结功能**
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* `generate_summary`：生成群聊总结（支持今日/昨日选择）
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### 4. **PlayletSearch 插件 - 短剧搜索功能**
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* `search_playlet`：搜索短剧并获取视频链接
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### 5. **Grok_video 插件 - 视频生成功能**
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* `generate_video`：生成视频
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### 6. **Weather 插件 - 天气查询功能**
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* `query_weather`：查询天气预报信息
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- **何时使用**：当用户询问天气、气温、会不会下雨、天气怎么样等天气相关问题时，**立即调用此函数**
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- **参数说明**：
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- `city`（可选）：城市名称。如果用户明确指定了城市（如"北京天气"），则传入城市名；如果用户没有指定城市（如"今天天气怎么样"），则不传此参数，系统会自动使用用户设置的默认城市
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- **使用示例**：
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- 用户："帮我查下天气" → 调用 `query_weather()` 不传参数
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- 用户："北京今天会下雨吗" → 调用 `query_weather(city="北京")`
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- 用户："今天气温多少度" → 调用 `query_weather()` 不传参数
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- **重要**：不要询问用户城市，直接调用函数即可，函数会自动处理
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### 7. **RandomVideo 插件 - 随机视频功能**
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* `get_random_video()`：随机视频
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### 8. **RandomImage 插件 - 随机图片功能**
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* `get_random_image`：随机图片
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---
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@@ -6,15 +6,16 @@ AutoReply 插件 - 基于双LLM架构的智能自动回复
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import json
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import time
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import asyncio
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import tomllib
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import aiohttp
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from pathlib import Path
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from datetime import datetime, date
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from dataclasses import dataclass
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from typing import Dict
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from dataclasses import dataclass, field
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from typing import Dict, Optional
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from loguru import logger
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from utils.plugin_base import PluginBase
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from utils.decorators import on_text_message, schedule
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from utils.decorators import on_text_message
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try:
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from aiohttp_socks import ProxyConnector
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@@ -51,259 +52,183 @@ class AutoReply(PluginBase):
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description = "基于双LLM架构的智能自动回复插件"
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author = "ShiHao"
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version = "1.0.0"
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version = "1.1.0"
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def __init__(self):
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super().__init__()
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self.config = None
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self.chat_states: Dict[str, ChatState] = {}
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self.weights = {}
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self.last_judge_time: Dict[str, float] = {} # 记录每个群最后判断时间
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self.judging: Dict[str, bool] = {} # 记录是否正在判断中
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self.last_history_size: Dict[str, int] = {} # 记录每个群的history大小
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self.pending_judge: Dict[str, bool] = {} # 记录是否有待判断的消息
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self.whitelist_normalized = set() # 归一化后的白名单ID（与history文件名一致）
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self.last_judge_time: Dict[str, float] = {}
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self.judging: Dict[str, bool] = {}
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self.bot_wxid: str = ""
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self.bot_nickname: str = ""
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async def async_init(self):
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"""异步初始化"""
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config_path = Path(__file__).parent / "config.toml"
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with open(config_path, "rb") as f:
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self.config = tomllib.load(f)
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try:
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config_path = Path(__file__).parent / "config.toml"
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with open(config_path, "rb") as f:
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self.config = tomllib.load(f)
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# 加载权重配置
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self.weights = {
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"relevance": self.config["weights"]["relevance"],
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"willingness": self.config["weights"]["willingness"],
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"social": self.config["weights"]["social"],
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"timing": self.config["weights"]["timing"],
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"continuity": self.config["weights"]["continuity"]
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}
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# 加载权重配置
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self.weights = {
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"relevance": self.config["weights"]["relevance"],
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"willingness": self.config["weights"]["willingness"],
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"social": self.config["weights"]["social"],
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"timing": self.config["weights"]["timing"],
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"continuity": self.config["weights"]["continuity"]
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}
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# 检查权重和
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weight_sum = sum(self.weights.values())
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if abs(weight_sum - 1.0) > 1e-6:
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logger.warning(f"判断权重和不为1，当前和为{weight_sum}，已自动归一化")
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self.weights = {k: v / weight_sum for k, v in self.weights.items()}
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# 检查权重和
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weight_sum = sum(self.weights.values())
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if abs(weight_sum - 1.0) > 1e-6:
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logger.warning(f"[AutoReply] 判断权重和不为1，当前和为{weight_sum}，已自动归一化")
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self.weights = {k: v / weight_sum for k, v in self.weights.items()}
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# 预处理白名单（与history文件名的归一化规则保持一致）
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self.whitelist_normalized = {
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self._normalize_chat_id(cid) for cid in self.config.get("whitelist", {}).get("chat_list", [])
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}
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# 加载机器人信息
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self._load_bot_info()
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logger.info(f"AutoReply 插件已加载，判断模型: {self.config['basic']['judge_model']}")
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logger.info(f"AutoReply 配置: enabled={self.config['basic']['enabled']}, priority=90")
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logger.info(f"AutoReply 监听模式: 每{self.config.get('rate_limit', {}).get('check_interval', 5)}秒检查history变化")
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logger.warning("⚠️ AutoReply插件已启动，等待消息...")
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logger.success(f"[AutoReply] 插件已加载，判断模型: {self.config['basic']['judge_model']}")
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logger.info(f"[AutoReply] 回复阈值: {self.config['basic']['reply_threshold']}, 最小间隔: {self.config['rate_limit']['min_interval']}秒")
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except Exception as e:
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logger.error(f"[AutoReply] 初始化失败: {e}")
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self.config = None
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def _load_bot_info(self):
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"""加载机器人信息"""
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try:
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with open("main_config.toml", "rb") as f:
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main_config = tomllib.load(f)
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self.bot_wxid = main_config.get("Bot", {}).get("wxid", "")
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self.bot_nickname = main_config.get("Bot", {}).get("nickname", "")
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except Exception as e:
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logger.warning(f"[AutoReply] 加载机器人信息失败: {e}")
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def _normalize_chat_id(self, chat_id: str) -> str:
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"""将群ID转成history文件使用的安全文件名"""
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return (chat_id or "").replace("@", "_").replace(":", "_")
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def _is_chat_allowed(self, raw_chat_id: str) -> bool:
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"""白名单判断，兼容原始ID与归一化ID"""
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if not self.config["whitelist"]["enabled"]:
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def _is_chat_allowed(self, chat_id: str) -> bool:
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"""白名单判断"""
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whitelist_config = self.config.get("whitelist", {})
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if not whitelist_config.get("enabled", False):
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return True
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safe_id = self._normalize_chat_id(raw_chat_id)
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return raw_chat_id in self.config["whitelist"]["chat_list"] or safe_id in self.whitelist_normalized
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@schedule('interval', seconds=5)
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async def check_history_changes(self, *args, **kwargs):
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"""定时检查history文件变化"""
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if not self.config["basic"]["enabled"]:
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logger.debug("[AutoReply] 插件未启用，跳过检查")
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return
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chat_list = whitelist_config.get("chat_list", [])
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safe_id = self._normalize_chat_id(chat_id)
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# 检查是否启用监听模式
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if not self.config.get("rate_limit", {}).get("monitor_mode", True):
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logger.debug("[AutoReply] 监听模式未启用，跳过检查")
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return
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try:
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# 获取AIChat插件的history目录
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from utils.plugin_manager import PluginManager
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plugin_manager = PluginManager() # 单例模式，直接实例化
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aichat_plugin = plugin_manager.plugins.get("AIChat")
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||||
|
||||
if not aichat_plugin:
|
||||
logger.debug("[AutoReply] 未找到AIChat插件")
|
||||
return
|
||||
|
||||
if not hasattr(aichat_plugin, 'history_dir'):
|
||||
logger.debug("[AutoReply] AIChat插件没有history_dir属性")
|
||||
return
|
||||
|
||||
history_dir = aichat_plugin.history_dir
|
||||
if not history_dir.exists():
|
||||
logger.debug(f"[AutoReply] History目录不存在: {history_dir}")
|
||||
return
|
||||
|
||||
logger.debug(f"[AutoReply] 开始检查history目录: {history_dir}")
|
||||
|
||||
# 遍历所有history文件
|
||||
for history_file in history_dir.glob("*.json"):
|
||||
chat_id = history_file.stem # 文件名就是chat_id
|
||||
|
||||
# 检查白名单
|
||||
if self.config["whitelist"]["enabled"]:
|
||||
if chat_id not in self.whitelist_normalized:
|
||||
continue
|
||||
|
||||
try:
|
||||
with open(history_file, "r", encoding="utf-8") as f:
|
||||
history = json.load(f)
|
||||
|
||||
current_size = len(history)
|
||||
last_size = self.last_history_size.get(chat_id, 0)
|
||||
|
||||
# 如果有新消息
|
||||
if current_size > last_size:
|
||||
# 获取新增的消息
|
||||
new_messages = history[last_size:]
|
||||
|
||||
# 检查新消息中是否有非机器人的消息
|
||||
with open("main_config.toml", "rb") as f:
|
||||
main_config = tomllib.load(f)
|
||||
bot_nickname = main_config.get("Bot", {}).get("nickname", "机器人")
|
||||
|
||||
has_user_message = any(
|
||||
msg.get('nickname') != bot_nickname
|
||||
for msg in new_messages
|
||||
)
|
||||
|
||||
if has_user_message:
|
||||
logger.debug(f"[AutoReply] 检测到群聊 {chat_id[:20]}... 有新消息")
|
||||
# 标记为待判断
|
||||
self.pending_judge[chat_id] = True
|
||||
|
||||
# 更新记录的大小
|
||||
self.last_history_size[chat_id] = current_size
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"读取history文件失败: {history_file.name}, {e}")
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"检查history变化失败: {e}")
|
||||
|
||||
@on_text_message(priority=90) # 高优先级，在AIChat之前执行
|
||||
async def handle_message(self, bot, message: dict):
|
||||
"""处理消息"""
|
||||
try:
|
||||
logger.debug(f"[AutoReply] 收到消息，开始处理")
|
||||
|
||||
# 检查是否启用
|
||||
if not self.config["basic"]["enabled"]:
|
||||
logger.debug("AutoReply插件未启用，跳过处理")
|
||||
return True
|
||||
|
||||
# 只处理群聊消息
|
||||
is_group = message.get('IsGroup', False)
|
||||
if not is_group:
|
||||
logger.debug("AutoReply只处理群聊消息，跳过私聊")
|
||||
return True
|
||||
|
||||
# 群聊消息：FromWxid是群ID，SenderWxid是发送者ID
|
||||
from_wxid = message.get('FromWxid') # 群聊ID
|
||||
sender_wxid = message.get('SenderWxid') # 发送者ID
|
||||
chat_id = self._normalize_chat_id(from_wxid) # 归一化ID，匹配history文件名
|
||||
content = (message.get('msg') or message.get('Content', '')).strip()
|
||||
|
||||
# 跳过空消息
|
||||
if not content:
|
||||
logger.debug("AutoReply跳过空消息")
|
||||
return True
|
||||
|
||||
# 检查白名单（使用from_wxid作为群聊ID）
|
||||
if not self._is_chat_allowed(from_wxid):
|
||||
logger.debug(f"AutoReply白名单模式，群聊 {from_wxid[:20]}... 不在白名单中")
|
||||
return True
|
||||
|
||||
# 跳过已被@的消息（让AIChat正常处理）
|
||||
if self._is_at_bot(message):
|
||||
logger.debug("AutoReply跳过@消息，交由AIChat处理")
|
||||
return True
|
||||
|
||||
# 监听模式：只在检测到待判断标记时才判断
|
||||
monitor_mode = self.config.get("rate_limit", {}).get("monitor_mode", True)
|
||||
if monitor_mode:
|
||||
if not self.pending_judge.get(chat_id, False):
|
||||
logger.debug(f"AutoReply监听模式，群聊 {from_wxid[:20]}... 无待判断标记")
|
||||
return True
|
||||
# 清除待判断标记
|
||||
self.pending_judge[chat_id] = False
|
||||
|
||||
# 频率限制：检查是否正在判断中
|
||||
if self.config.get("rate_limit", {}).get("skip_if_judging", True):
|
||||
if self.judging.get(chat_id, False):
|
||||
logger.debug(f"AutoReply跳过消息，群聊 {from_wxid[:20]}... 正在判断中")
|
||||
return True
|
||||
|
||||
# 频率限制：检查距离上次判断的时间间隔
|
||||
min_interval = self.config.get("rate_limit", {}).get("min_interval", 10)
|
||||
last_time = self.last_judge_time.get(chat_id, 0)
|
||||
current_time = time.time()
|
||||
if current_time - last_time < min_interval:
|
||||
logger.debug(f"AutoReply跳过消息，距离上次判断仅 {current_time - last_time:.1f}秒")
|
||||
# 监听模式下，如果时间间隔不够，重新标记为待判断
|
||||
if monitor_mode:
|
||||
self.pending_judge[chat_id] = True
|
||||
return True
|
||||
|
||||
logger.info(f"AutoReply开始判断消息: {content[:30]}...")
|
||||
|
||||
# 标记正在判断中
|
||||
self.judging[chat_id] = True
|
||||
self.last_judge_time[chat_id] = current_time
|
||||
|
||||
# 使用小模型判断是否需要回复
|
||||
judge_result = await self._judge_with_small_model(bot, message)
|
||||
|
||||
# 清除判断中标记
|
||||
self.judging[chat_id] = False
|
||||
|
||||
if judge_result.should_reply:
|
||||
logger.info(f"🔥 AutoReply触发 | {from_wxid[:20]}... | 评分:{judge_result.overall_score:.2f} | {judge_result.reasoning[:50]}")
|
||||
|
||||
# 更新状态
|
||||
self._update_active_state(chat_id, judge_result)
|
||||
|
||||
# 修改消息，让AIChat认为需要回复
|
||||
message['_auto_reply_triggered'] = True
|
||||
|
||||
return True # 继续传递给AIChat
|
||||
else:
|
||||
logger.debug(f"AutoReply不触发 | {from_wxid[:20]}... | 评分:{judge_result.overall_score:.2f}")
|
||||
self._update_passive_state(chat_id, judge_result)
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"AutoReply处理异常: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
# 异常时也要清除判断中标记
|
||||
if 'chat_id' in locals():
|
||||
self.judging[chat_id] = False
|
||||
elif 'from_wxid' in locals():
|
||||
self.judging[self._normalize_chat_id(from_wxid)] = False
|
||||
return True
|
||||
# 同时检查原始ID和归一化ID
|
||||
return chat_id in chat_list or safe_id in chat_list
|
||||
|
||||
def _is_at_bot(self, message: dict) -> bool:
|
||||
"""检查是否@了机器人"""
|
||||
content = message.get('Content', '')
|
||||
# 规范化后的消息使用 Ats 字段
|
||||
# 优先检查 Ats 列表
|
||||
at_list = message.get('Ats', [])
|
||||
# 检查是否有@列表或内容中包含@标记
|
||||
return len(at_list) > 0 or '@' in content or '＠' in content
|
||||
if at_list:
|
||||
# 检查机器人wxid是否在@列表中
|
||||
if self.bot_wxid and self.bot_wxid in at_list:
|
||||
return True
|
||||
|
||||
async def _judge_with_small_model(self, bot, message: dict) -> JudgeResult:
|
||||
"""使用小模型判断是否需要回复"""
|
||||
# 规范化后的消息：FromWxid是群ID，SenderWxid是发送者ID，Content是内容
|
||||
from_wxid = message.get('FromWxid') # 群聊ID
|
||||
chat_id = self._normalize_chat_id(from_wxid)
|
||||
# 备用：检查内容中是否包含@机器人昵称
|
||||
content = message.get('Content', '')
|
||||
sender_wxid = message.get('SenderWxid', '')
|
||||
if self.bot_nickname and f"@{self.bot_nickname}" in content:
|
||||
return True
|
||||
|
||||
# 获取群聊状态
|
||||
return False
|
||||
|
||||
def _is_bot_message(self, message: dict) -> bool:
|
||||
"""检查是否是机器人自己的消息"""
|
||||
sender_wxid = message.get('SenderWxid', '')
|
||||
return sender_wxid == self.bot_wxid if self.bot_wxid else False
|
||||
|
||||
@on_text_message(priority=90)
|
||||
async def handle_message(self, bot, message: dict):
|
||||
"""处理消息"""
|
||||
try:
|
||||
# 检查是否启用
|
||||
if not self.config or not self.config["basic"]["enabled"]:
|
||||
return True
|
||||
|
||||
# 只处理群聊消息
|
||||
if not message.get('IsGroup', False):
|
||||
return True
|
||||
|
||||
from_wxid = message.get('FromWxid', '') # 群聊ID
|
||||
sender_wxid = message.get('SenderWxid', '') # 发送者ID
|
||||
content = message.get('Content', '').strip()
|
||||
|
||||
# 跳过空消息
|
||||
if not content:
|
||||
return True
|
||||
|
||||
# 跳过机器人自己的消息
|
||||
if self._is_bot_message(message):
|
||||
return True
|
||||
|
||||
# 检查白名单
|
||||
if not self._is_chat_allowed(from_wxid):
|
||||
return True
|
||||
|
||||
# 跳过@机器人的消息（让AIChat正常处理）
|
||||
if self._is_at_bot(message):
|
||||
logger.debug(f"[AutoReply] 跳过@消息，交由AIChat处理")
|
||||
return True
|
||||
|
||||
chat_id = self._normalize_chat_id(from_wxid)
|
||||
current_time = time.time()
|
||||
|
||||
# 频率限制：检查是否正在判断中
|
||||
if self.judging.get(chat_id, False):
|
||||
logger.debug(f"[AutoReply] 群聊 {from_wxid[:15]}... 正在判断中，跳过")
|
||||
return True
|
||||
|
||||
# 频率限制：检查时间间隔
|
||||
min_interval = self.config.get("rate_limit", {}).get("min_interval", 10)
|
||||
last_time = self.last_judge_time.get(chat_id, 0)
|
||||
if current_time - last_time < min_interval:
|
||||
logger.debug(f"[AutoReply] 距离上次判断仅 {current_time - last_time:.1f}秒，跳过")
|
||||
return True
|
||||
|
||||
# 标记正在判断
|
||||
self.judging[chat_id] = True
|
||||
self.last_judge_time[chat_id] = current_time
|
||||
|
||||
try:
|
||||
# 使用小模型判断
|
||||
judge_result = await self._judge_with_small_model(from_wxid, content)
|
||||
|
||||
if judge_result.should_reply:
|
||||
logger.info(f"[AutoReply] 触发回复 | 群:{from_wxid[:15]}... | 评分:{judge_result.overall_score:.2f} | {judge_result.reasoning[:30]}")
|
||||
|
||||
# 更新状态
|
||||
self._update_state(chat_id, replied=True)
|
||||
|
||||
# 设置触发标记，让AIChat处理
|
||||
message['_auto_reply_triggered'] = True
|
||||
else:
|
||||
logger.debug(f"[AutoReply] 不触发 | 群:{from_wxid[:15]}... | 评分:{judge_result.overall_score:.2f}")
|
||||
self._update_state(chat_id, replied=False)
|
||||
|
||||
finally:
|
||||
# 清除判断中标记
|
||||
self.judging[chat_id] = False
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[AutoReply] 处理异常: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
# 清除判断中标记
|
||||
if 'chat_id' in locals():
|
||||
self.judging[chat_id] = False
|
||||
return True
|
||||
|
||||
async def _judge_with_small_model(self, from_wxid: str, content: str) -> JudgeResult:
|
||||
"""使用小模型判断是否需要回复"""
|
||||
chat_id = self._normalize_chat_id(from_wxid)
|
||||
chat_state = self._get_chat_state(chat_id)
|
||||
|
||||
# 获取最近消息历史
|
||||
@@ -311,60 +236,55 @@ class AutoReply(PluginBase):
|
||||
last_bot_reply = await self._get_last_bot_reply(chat_id)
|
||||
|
||||
# 构建判断提示词
|
||||
reasoning_part = ""
|
||||
if self.config["judge"]["include_reasoning"]:
|
||||
reasoning_part = ',\n "reasoning": "详细分析原因"'
|
||||
reasoning_part = ',\n "reasoning": "简短分析原因(20字内)"' if self.config["judge"]["include_reasoning"] else ""
|
||||
|
||||
judge_prompt = f"""你是群聊机器人的决策系统，判断是否应该主动回复。
|
||||
|
||||
## 当前群聊情况
|
||||
- 群聊ID: {from_wxid}
|
||||
- 精力水平: {chat_state.energy:.1f}/1.0
|
||||
## 当前状态
|
||||
- 精力: {chat_state.energy:.1f}/1.0
|
||||
- 上次发言: {self._get_minutes_since_last_reply(chat_id)}分钟前
|
||||
|
||||
## 最近{self.config['context']['messages_count']}条对话
|
||||
## 最近对话
|
||||
{recent_messages}
|
||||
|
||||
## 上次机器人回复
|
||||
{last_bot_reply if last_bot_reply else "暂无"}
|
||||
{last_bot_reply or "暂无"}
|
||||
|
||||
## 待判断消息
|
||||
内容: {content}
|
||||
时间: {datetime.now().strftime('%H:%M:%S')}
|
||||
{content}
|
||||
|
||||
## 评估要求
|
||||
从以下5个维度评估（0-10分）：
|
||||
1. **内容相关度**(0-10)：消息是否有趣、有价值、适合回复
|
||||
2. **回复意愿**(0-10)：基于当前精力水平的回复意愿
|
||||
3. **社交适宜性**(0-10)：在当前群聊氛围下回复是否合适
|
||||
4. **时机恰当性**(0-10)：回复时机是否恰当
|
||||
5. **对话连贯性**(0-10)：当前消息与上次回复的关联程度
|
||||
## 评估维度(0-10分)
|
||||
1. relevance: 内容是否有趣、值得回复
|
||||
2. willingness: 基于精力的回复意愿
|
||||
3. social: 回复是否社交适宜
|
||||
4. timing: 时机是否恰当
|
||||
5. continuity: 与上次回复的关联度
|
||||
|
||||
**回复阈值**: {self.config['basic']['reply_threshold']}
|
||||
回复阈值: {self.config['basic']['reply_threshold']}
|
||||
|
||||
请以JSON格式回复：
|
||||
仅返回JSON:
|
||||
{{
|
||||
"relevance": 分数,
|
||||
"willingness": 分数,
|
||||
"social": 分数,
|
||||
"timing": 分数,
|
||||
"continuity": 分数{reasoning_part}
|
||||
}}
|
||||
}}"""
|
||||
|
||||
**注意：你的回复必须是完整的JSON对象，不要包含任何其他内容！**"""
|
||||
|
||||
# 调用小模型API
|
||||
max_retries = self.config["judge"]["max_retries"] + 1
|
||||
for attempt in range(max_retries):
|
||||
# 调用API
|
||||
max_retries = self.config["judge"].get("max_retries", 2)
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
result = await self._call_judge_api(judge_prompt)
|
||||
|
||||
# 解析JSON
|
||||
content_text = result.strip()
|
||||
if content_text.startswith("```json"):
|
||||
content_text = content_text.replace("```json", "").replace("```", "").strip()
|
||||
elif content_text.startswith("```"):
|
||||
content_text = content_text.replace("```", "").strip()
|
||||
# 移除可能的markdown代码块标记
|
||||
if content_text.startswith("```"):
|
||||
content_text = content_text.split("```")[1]
|
||||
if content_text.startswith("json"):
|
||||
content_text = content_text[4:]
|
||||
content_text = content_text.strip()
|
||||
|
||||
judge_data = json.loads(content_text)
|
||||
|
||||
@@ -385,18 +305,17 @@ class AutoReply(PluginBase):
|
||||
social=judge_data.get("social", 0),
|
||||
timing=judge_data.get("timing", 0),
|
||||
continuity=judge_data.get("continuity", 0),
|
||||
reasoning=judge_data.get("reasoning", "") if self.config["judge"]["include_reasoning"] else "",
|
||||
reasoning=judge_data.get("reasoning", ""),
|
||||
should_reply=should_reply,
|
||||
overall_score=overall_score
|
||||
)
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"小模型返回JSON解析失败 (尝试 {attempt + 1}/{max_retries}): {str(e)}")
|
||||
if attempt == max_retries - 1:
|
||||
logger.warning(f"[AutoReply] JSON解析失败 (尝试 {attempt + 1}/{max_retries + 1}): {e}")
|
||||
if attempt == max_retries:
|
||||
return JudgeResult(should_reply=False, reasoning="JSON解析失败")
|
||||
continue
|
||||
except Exception as e:
|
||||
logger.error(f"小模型判断异常: {e}")
|
||||
logger.error(f"[AutoReply] 判断异常: {e}")
|
||||
return JudgeResult(should_reply=False, reasoning=f"异常: {str(e)}")
|
||||
|
||||
return JudgeResult(should_reply=False, reasoning="重试失败")
|
||||
@@ -415,80 +334,112 @@ class AutoReply(PluginBase):
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": [
|
||||
{"role": "system", "content": "你是一个专业的群聊回复决策系统。你必须严格按照JSON格式返回结果。"},
|
||||
{"role": "system", "content": "你是群聊回复决策系统。严格按JSON格式返回，不要输出其他内容。"},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
"temperature": 0.7
|
||||
"temperature": 0.5,
|
||||
"max_tokens": 200
|
||||
}
|
||||
|
||||
# 配置代理
|
||||
connector = None
|
||||
if self.config["proxy"]["enabled"] and PROXY_SUPPORT:
|
||||
proxy_type = self.config["proxy"]["type"]
|
||||
proxy_host = self.config["proxy"]["host"]
|
||||
proxy_port = self.config["proxy"]["port"]
|
||||
proxy_config = self.config.get("proxy", {})
|
||||
if proxy_config.get("enabled", False) and PROXY_SUPPORT:
|
||||
proxy_type = proxy_config.get("type", "http")
|
||||
proxy_host = proxy_config.get("host", "127.0.0.1")
|
||||
proxy_port = proxy_config.get("port", 7890)
|
||||
proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
|
||||
connector = ProxyConnector.from_url(proxy_url)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
async with session.post(api_url, headers=headers, json=payload, timeout=aiohttp.ClientTimeout(total=30)) as response:
|
||||
timeout = aiohttp.ClientTimeout(total=30)
|
||||
async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
|
||||
async with session.post(api_url, headers=headers, json=payload) as response:
|
||||
if response.status != 200:
|
||||
raise Exception(f"API调用失败: {response.status}")
|
||||
error_text = await response.text()
|
||||
raise Exception(f"API调用失败: {response.status}, {error_text[:100]}")
|
||||
|
||||
result = await response.json()
|
||||
return result["choices"][0]["message"]["content"]
|
||||
|
||||
async def _get_history(self, chat_id: str) -> list:
|
||||
"""获取群聊历史记录（优先 Redis，降级到文件）"""
|
||||
try:
|
||||
from utils.plugin_manager import PluginManager
|
||||
aichat_plugin = PluginManager().plugins.get("AIChat")
|
||||
|
||||
if not aichat_plugin:
|
||||
return []
|
||||
|
||||
# 优先使用 Redis（与 AIChat 保持一致）
|
||||
try:
|
||||
from utils.redis_cache import get_cache
|
||||
redis_cache = get_cache()
|
||||
if redis_cache and redis_cache.enabled:
|
||||
aichat_config = aichat_plugin.config or {}
|
||||
redis_config = aichat_config.get("redis", {})
|
||||
if redis_config.get("use_redis_history", True):
|
||||
max_history = aichat_config.get("history", {}).get("max_history", 100)
|
||||
history = redis_cache.get_group_history(chat_id, max_history)
|
||||
if history:
|
||||
return history
|
||||
except Exception as e:
|
||||
logger.debug(f"[AutoReply] Redis 获取历史失败: {e}")
|
||||
|
||||
# 降级到文件存储
|
||||
if hasattr(aichat_plugin, 'history_dir') and aichat_plugin.history_dir:
|
||||
history_file = aichat_plugin.history_dir / f"{chat_id}.json"
|
||||
if history_file.exists():
|
||||
with open(history_file, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"[AutoReply] 获取历史失败: {e}")
|
||||
|
||||
return []
|
||||
|
||||
async def _get_recent_messages(self, chat_id: str) -> str:
|
||||
"""获取最近消息历史"""
|
||||
try:
|
||||
# 尝试从AIChat插件获取历史记录
|
||||
from utils.plugin_manager import PluginManager
|
||||
plugin_manager = PluginManager() # 单例模式，直接实例化
|
||||
aichat_plugin = plugin_manager.plugins.get("AIChat")
|
||||
history = await self._get_history(chat_id)
|
||||
if not history:
|
||||
return "暂无对话历史"
|
||||
|
||||
if aichat_plugin and hasattr(aichat_plugin, 'history_dir'):
|
||||
history_file = aichat_plugin.history_dir / f"{chat_id}.json"
|
||||
if history_file.exists():
|
||||
with open(history_file, "r", encoding="utf-8") as f:
|
||||
history = json.load(f)
|
||||
count = self.config.get('context', {}).get('messages_count', 5)
|
||||
recent = history[-count:] if len(history) > count else history
|
||||
|
||||
# 获取最近N条
|
||||
recent = history[-self.config['context']['messages_count']:]
|
||||
messages = []
|
||||
for record in recent:
|
||||
nickname = record.get('nickname', '未知')
|
||||
content = record.get('content', '')
|
||||
messages.append(f"{nickname}: {content}")
|
||||
messages = []
|
||||
for record in recent:
|
||||
nickname = record.get('nickname', '未知')
|
||||
content = record.get('content', '')
|
||||
# 限制单条消息长度
|
||||
if len(content) > 100:
|
||||
content = content[:100] + "..."
|
||||
messages.append(f"{nickname}: {content}")
|
||||
|
||||
return "\n".join(messages) if messages else "暂无对话历史"
|
||||
|
||||
return "\n".join(messages) if messages else "暂无对话历史"
|
||||
except Exception as e:
|
||||
logger.debug(f"获取消息历史失败: {e}")
|
||||
logger.debug(f"[AutoReply] 获取消息历史失败: {e}")
|
||||
|
||||
return "暂无对话历史"
|
||||
|
||||
async def _get_last_bot_reply(self, chat_id: str) -> str:
|
||||
async def _get_last_bot_reply(self, chat_id: str) -> Optional[str]:
|
||||
"""获取上次机器人回复"""
|
||||
try:
|
||||
from utils.plugin_manager import PluginManager
|
||||
plugin_manager = PluginManager() # 单例模式，直接实例化
|
||||
aichat_plugin = plugin_manager.plugins.get("AIChat")
|
||||
history = await self._get_history(chat_id)
|
||||
if not history:
|
||||
return None
|
||||
|
||||
if aichat_plugin and hasattr(aichat_plugin, 'history_dir'):
|
||||
history_file = aichat_plugin.history_dir / f"{chat_id}.json"
|
||||
if history_file.exists():
|
||||
with open(history_file, "r", encoding="utf-8") as f:
|
||||
history = json.load(f)
|
||||
# 从后往前查找机器人回复
|
||||
for record in reversed(history):
|
||||
if record.get('nickname') == self.bot_nickname:
|
||||
content = record.get('content', '')
|
||||
if len(content) > 100:
|
||||
content = content[:100] + "..."
|
||||
return content
|
||||
|
||||
# 从后往前查找机器人回复
|
||||
with open("main_config.toml", "rb") as f:
|
||||
main_config = tomllib.load(f)
|
||||
bot_nickname = main_config.get("Bot", {}).get("nickname", "机器人")
|
||||
|
||||
for record in reversed(history):
|
||||
if record.get('nickname') == bot_nickname:
|
||||
return record.get('content', '')
|
||||
except Exception as e:
|
||||
logger.debug(f"获取上次回复失败: {e}")
|
||||
logger.debug(f"[AutoReply] 获取上次回复失败: {e}")
|
||||
|
||||
return None
|
||||
|
||||
@@ -497,9 +448,10 @@ class AutoReply(PluginBase):
|
||||
if chat_id not in self.chat_states:
|
||||
self.chat_states[chat_id] = ChatState()
|
||||
|
||||
today = date.today().isoformat()
|
||||
state = self.chat_states[chat_id]
|
||||
today = date.today().isoformat()
|
||||
|
||||
# 每日重置精力
|
||||
if state.last_reset_date != today:
|
||||
state.last_reset_date = today
|
||||
state.energy = min(1.0, state.energy + 0.2)
|
||||
@@ -508,21 +460,21 @@ class AutoReply(PluginBase):
|
||||
|
||||
def _get_minutes_since_last_reply(self, chat_id: str) -> int:
|
||||
"""获取距离上次回复的分钟数"""
|
||||
chat_state = self._get_chat_state(chat_id)
|
||||
if chat_state.last_reply_time == 0:
|
||||
state = self._get_chat_state(chat_id)
|
||||
if state.last_reply_time == 0:
|
||||
return 999
|
||||
return int((time.time() - chat_state.last_reply_time) / 60)
|
||||
return int((time.time() - state.last_reply_time) / 60)
|
||||
|
||||
def _update_active_state(self, chat_id: str, judge_result: JudgeResult):
|
||||
"""更新主动回复状态"""
|
||||
chat_state = self._get_chat_state(chat_id)
|
||||
chat_state.last_reply_time = time.time()
|
||||
chat_state.total_replies += 1
|
||||
chat_state.total_messages += 1
|
||||
chat_state.energy = max(0.1, chat_state.energy - self.config["energy"]["decay_rate"])
|
||||
def _update_state(self, chat_id: str, replied: bool):
|
||||
"""更新群聊状态"""
|
||||
state = self._get_chat_state(chat_id)
|
||||
state.total_messages += 1
|
||||
|
||||
def _update_passive_state(self, chat_id: str, judge_result: JudgeResult):
|
||||
"""更新被动状态"""
|
||||
chat_state = self._get_chat_state(chat_id)
|
||||
chat_state.total_messages += 1
|
||||
chat_state.energy = min(1.0, chat_state.energy + self.config["energy"]["recovery_rate"])
|
||||
if replied:
|
||||
state.last_reply_time = time.time()
|
||||
state.total_replies += 1
|
||||
decay = self.config.get("energy", {}).get("decay_rate", 0.1)
|
||||
state.energy = max(0.1, state.energy - decay)
|
||||
else:
|
||||
recovery = self.config.get("energy", {}).get("recovery_rate", 0.02)
|
||||
state.energy = min(1.0, state.energy + recovery)
|
||||
|
||||
3
plugins/TavilySearch/__init__.py
Normal file
3
plugins/TavilySearch/__init__.py
Normal file
@@ -0,0 +1,3 @@
|
||||
from .main import TavilySearch
|
||||
|
||||
__all__ = ["TavilySearch"]
|
||||
202
plugins/TavilySearch/main.py
Normal file
202
plugins/TavilySearch/main.py
Normal file
@@ -0,0 +1,202 @@
|
||||
"""
|
||||
TavilySearch 联网搜索插件
|
||||
|
||||
基于 Tavily API 的联网搜索功能，仅作为 LLM Tool 供 AIChat 调用
|
||||
支持多 API Key 轮询，搜索结果返回给 AIChat 的 AI 处理（带上下文和人设）
|
||||
"""
|
||||
|
||||
import tomllib
|
||||
import aiohttp
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
from loguru import logger
|
||||
from utils.plugin_base import PluginBase
|
||||
|
||||
|
||||
class TavilySearch(PluginBase):
|
||||
"""Tavily 联网搜索插件 - 仅作为 LLM Tool"""
|
||||
|
||||
description = "Tavily 联网搜索 - 支持多 Key 轮询的搜索工具"
|
||||
author = "Assistant"
|
||||
version = "1.0.0"
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.config = None
|
||||
self.api_keys = []
|
||||
self.current_key_index = 0
|
||||
|
||||
async def async_init(self):
|
||||
"""异步初始化"""
|
||||
try:
|
||||
config_path = Path(__file__).parent / "config.toml"
|
||||
if not config_path.exists():
|
||||
logger.error(f"TavilySearch 配置文件不存在: {config_path}")
|
||||
return
|
||||
|
||||
with open(config_path, "rb") as f:
|
||||
self.config = tomllib.load(f)
|
||||
|
||||
self.api_keys = [k for k in self.config["tavily"]["api_keys"] if k and not k.startswith("#")]
|
||||
if not self.api_keys:
|
||||
logger.warning("TavilySearch: 未配置有效的 API Key")
|
||||
else:
|
||||
logger.success(f"TavilySearch 已加载，共 {len(self.api_keys)} 个 API Key")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"TavilySearch 初始化失败: {e}")
|
||||
self.config = None
|
||||
|
||||
def _get_next_api_key(self) -> str:
|
||||
"""轮询获取下一个 API Key"""
|
||||
if not self.api_keys:
|
||||
return ""
|
||||
key = self.api_keys[self.current_key_index]
|
||||
self.current_key_index = (self.current_key_index + 1) % len(self.api_keys)
|
||||
return key
|
||||
|
||||
async def _search_tavily(self, query: str) -> Optional[dict]:
|
||||
"""调用 Tavily API 进行搜索"""
|
||||
api_key = self._get_next_api_key()
|
||||
if not api_key:
|
||||
logger.error("没有可用的 Tavily API Key")
|
||||
return None
|
||||
|
||||
tavily_config = self.config["tavily"]
|
||||
proxy_config = self.config.get("proxy", {})
|
||||
|
||||
payload = {
|
||||
"api_key": api_key,
|
||||
"query": query,
|
||||
"search_depth": tavily_config.get("search_depth", "basic"),
|
||||
"max_results": tavily_config.get("max_results", 5),
|
||||
"include_raw_content": tavily_config.get("include_raw_content", False),
|
||||
"include_images": tavily_config.get("include_images", False),
|
||||
}
|
||||
|
||||
proxy = None
|
||||
if proxy_config.get("enabled", False):
|
||||
proxy_type = proxy_config.get("type", "http")
|
||||
proxy_host = proxy_config.get("host", "127.0.0.1")
|
||||
proxy_port = proxy_config.get("port", 7890)
|
||||
proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
|
||||
|
||||
try:
|
||||
import ssl
|
||||
timeout = aiohttp.ClientTimeout(total=30)
|
||||
|
||||
# SSL 配置
|
||||
ssl_config = self.config.get("ssl", {})
|
||||
ssl_verify = ssl_config.get("verify", True)
|
||||
|
||||
connector = None
|
||||
if not ssl_verify:
|
||||
# 跳过 SSL 验证
|
||||
ssl_context = ssl.create_default_context()
|
||||
ssl_context.check_hostname = False
|
||||
ssl_context.verify_mode = ssl.CERT_NONE
|
||||
connector = aiohttp.TCPConnector(ssl=ssl_context)
|
||||
|
||||
async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
|
||||
async with session.post(
|
||||
"https://api.tavily.com/search",
|
||||
json=payload,
|
||||
proxy=proxy
|
||||
) as resp:
|
||||
if resp.status == 200:
|
||||
result = await resp.json()
|
||||
logger.info(f"Tavily 搜索成功: {query[:30]}...")
|
||||
logger.info(f"Tavily 原始返回: {result}")
|
||||
return result
|
||||
else:
|
||||
error_text = await resp.text()
|
||||
logger.error(f"Tavily API 错误: {resp.status}, {error_text}")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Tavily 搜索失败: {e}")
|
||||
return None
|
||||
|
||||
def _format_search_results(self, results: dict) -> str:
|
||||
"""格式化搜索结果供 AI 处理"""
|
||||
if not results or "results" not in results:
|
||||
return "未找到相关搜索结果"
|
||||
|
||||
formatted = []
|
||||
for i, item in enumerate(results["results"], 1):
|
||||
title = item.get("title", "无标题")
|
||||
content = item.get("content", "")
|
||||
url = item.get("url", "")
|
||||
formatted.append(f"【结果 {i}】\n标题: {title}\n内容: {content}\n来源: {url}\n")
|
||||
|
||||
return "\n".join(formatted)
|
||||
|
||||
def get_llm_tools(self) -> List[dict]:
|
||||
"""返回 LLM 工具定义"""
|
||||
if not self.config or not self.config["behavior"]["enabled"]:
|
||||
return []
|
||||
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "tavily_web_search",
|
||||
"description": "使用 Tavily 进行联网搜索，获取最新的网络信息。适用于需要查询实时信息、新闻、知识等场景。",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "搜索关键词或问题，建议使用简洁明确的搜索词"
|
||||
}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
async def execute_llm_tool(self, tool_name: str, arguments: dict, bot, from_wxid: str) -> dict:
|
||||
"""
|
||||
执行 LLM 工具调用
|
||||
|
||||
只负责搜索，返回结果给 AIChat 的 AI 处理（带上下文和人设）
|
||||
"""
|
||||
if tool_name != "tavily_web_search":
|
||||
return None
|
||||
|
||||
if not self.config or not self.config["behavior"]["enabled"]:
|
||||
return {"success": False, "message": "TavilySearch 插件未启用"}
|
||||
|
||||
if not self.api_keys:
|
||||
return {"success": False, "message": "未配置 Tavily API Key"}
|
||||
|
||||
query = arguments.get("query", "")
|
||||
if not query:
|
||||
return {"success": False, "message": "搜索关键词不能为空"}
|
||||
|
||||
try:
|
||||
logger.info(f"开始 Tavily 搜索: {query}")
|
||||
|
||||
# 调用 Tavily 搜索
|
||||
search_results = await self._search_tavily(query)
|
||||
if not search_results:
|
||||
return {"success": False, "message": "搜索失败，请稍后重试"}
|
||||
|
||||
# 格式化搜索结果
|
||||
formatted_results = self._format_search_results(search_results)
|
||||
|
||||
logger.success(f"Tavily 搜索完成: {query[:30]}...")
|
||||
|
||||
# 返回搜索结果，标记需要 AI 继续处理
|
||||
return {
|
||||
"success": True,
|
||||
"message": formatted_results,
|
||||
"need_ai_reply": True # 标记需要 AI 基于此结果继续回复
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Tavily 搜索执行失败: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
return {"success": False, "message": f"搜索失败: {str(e)}"}
|
||||
@@ -1,416 +0,0 @@
|
||||
"""
|
||||
联网搜索插件
|
||||
|
||||
支持命令触发和LLM工具调用
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import tomllib
|
||||
import aiohttp
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
from loguru import logger
|
||||
from utils.plugin_base import PluginBase
|
||||
from utils.decorators import on_text_message
|
||||
from WechatHook import WechatHookClient
|
||||
|
||||
|
||||
class WebSearch(PluginBase):
|
||||
"""联网搜索插件"""
|
||||
|
||||
description = "联网搜索插件 - 支持实时信息查询和LLM工具调用"
|
||||
author = "ShiHao"
|
||||
version = "1.0.0"
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.config = None
|
||||
|
||||
async def async_init(self):
|
||||
"""异步初始化"""
|
||||
config_path = Path(__file__).parent / "config.toml"
|
||||
with open(config_path, "rb") as f:
|
||||
self.config = tomllib.load(f)
|
||||
|
||||
logger.success(f"联网搜索插件初始化完成")
|
||||
|
||||
async def search(self, query: str, max_results: int = None) -> dict:
|
||||
"""
|
||||
执行搜索
|
||||
|
||||
Args:
|
||||
query: 搜索关键词
|
||||
max_results: 最大结果数量
|
||||
|
||||
Returns:
|
||||
{"success": bool, "results": List[dict], "message": str}
|
||||
"""
|
||||
api_config = self.config["api"]
|
||||
behavior_config = self.config["behavior"]
|
||||
|
||||
if max_results is None:
|
||||
max_results = behavior_config["max_results"]
|
||||
|
||||
try:
|
||||
url = f"{api_config['base_url']}/search"
|
||||
params = {
|
||||
"q": query,
|
||||
"format": "json",
|
||||
"language": api_config["language"]
|
||||
}
|
||||
|
||||
logger.info(f"搜索请求: {query}")
|
||||
|
||||
async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=api_config["timeout"])) as session:
|
||||
async with session.get(url, params=params) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
results = data.get("results", [])
|
||||
|
||||
if not results:
|
||||
return {
|
||||
"success": False,
|
||||
"results": [],
|
||||
"message": "未找到相关结果"
|
||||
}
|
||||
|
||||
# 限制结果数量
|
||||
results = results[:max_results]
|
||||
|
||||
logger.success(f"搜索成功，找到 {len(results)} 条结果")
|
||||
return {
|
||||
"success": True,
|
||||
"results": results,
|
||||
"message": f"找到 {len(results)} 条结果"
|
||||
}
|
||||
else:
|
||||
error_text = await response.text()
|
||||
logger.error(f"搜索API请求失败: {response.status}, {error_text[:200]}")
|
||||
return {
|
||||
"success": False,
|
||||
"results": [],
|
||||
"message": f"搜索失败: HTTP {response.status}"
|
||||
}
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f"搜索请求超时")
|
||||
return {
|
||||
"success": False,
|
||||
"results": [],
|
||||
"message": "搜索超时，请稍后重试"
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"搜索异常: {e}")
|
||||
return {
|
||||
"success": False,
|
||||
"results": [],
|
||||
"message": f"搜索失败: {str(e)}"
|
||||
}
|
||||
|
||||
def format_results(self, results: List[dict], max_results: int = 5) -> str:
|
||||
"""
|
||||
格式化搜索结果为美化文本
|
||||
|
||||
Args:
|
||||
results: 搜索结果列表
|
||||
max_results: 最大显示数量
|
||||
|
||||
Returns:
|
||||
格式化后的文本
|
||||
"""
|
||||
if not results:
|
||||
return "❌ 未找到相关结果"
|
||||
|
||||
output = f"🔍 搜索结果（共 {len(results)} 条）\n"
|
||||
output += "━━━━━━━━━━━━━━━━━━━━\n\n"
|
||||
|
||||
for i, result in enumerate(results[:max_results], 1):
|
||||
title = result.get("title", "无标题")
|
||||
url = result.get("url", "")
|
||||
content = result.get("content", "")
|
||||
|
||||
# 截断过长的内容
|
||||
if len(content) > 120:
|
||||
content = content[:120] + "..."
|
||||
|
||||
output += f"📌 {i}. {title}\n"
|
||||
if content:
|
||||
output += f"💬 {content}\n"
|
||||
output += f"🔗 {url}\n"
|
||||
|
||||
if i < min(len(results), max_results):
|
||||
output += "\n" + "─" * 30 + "\n\n"
|
||||
|
||||
return output.strip()
|
||||
|
||||
async def send_results_as_chat_record(self, bot: WechatHookClient, to_wxid: str, query: str, results: List[dict], max_results: int = 5):
|
||||
"""
|
||||
以聊天记录格式发送搜索结果
|
||||
|
||||
Args:
|
||||
bot: 机器人客户端
|
||||
to_wxid: 接收者 wxid
|
||||
query: 搜索关键词
|
||||
results: 搜索结果列表
|
||||
max_results: 最大发送数量
|
||||
"""
|
||||
if not results:
|
||||
await bot.send_text(to_wxid, "❌ 未找到相关结果")
|
||||
return
|
||||
|
||||
# 构建聊天记录内容（单个字符串）
|
||||
content_lines = []
|
||||
|
||||
for i, result in enumerate(results[:max_results], 1):
|
||||
title = result.get("title", "无标题")
|
||||
url = result.get("url", "")
|
||||
desc = result.get("content", "")
|
||||
|
||||
# 截断过长的描述
|
||||
if len(desc) > 150:
|
||||
desc = desc[:150] + "..."
|
||||
|
||||
# 格式化每条结果
|
||||
content_lines.append(f"📌 {i}. {title}")
|
||||
if desc:
|
||||
content_lines.append(f"💬 {desc}")
|
||||
content_lines.append(f"🔗 {url}")
|
||||
content_lines.append("") # 空行分隔
|
||||
|
||||
# 合并为单个字符串
|
||||
content = "\n".join(content_lines)
|
||||
|
||||
# 使用聊天记录格式发送
|
||||
title = f"🔍 搜索：{query}"
|
||||
await self._send_chat_records(bot, to_wxid, title, content)
|
||||
|
||||
async def _send_chat_records(self, bot, from_wxid: str, title: str, content: str):
|
||||
"""发送聊天记录格式消息"""
|
||||
try:
|
||||
import uuid
|
||||
import time
|
||||
import hashlib
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
is_group = from_wxid.endswith("@chatroom")
|
||||
|
||||
# 自动分割内容（与总结插件相同）
|
||||
max_length = 800
|
||||
content_parts = []
|
||||
|
||||
if len(content) <= max_length:
|
||||
content_parts = [content]
|
||||
else:
|
||||
lines = content.split('\n')
|
||||
current_part = ""
|
||||
|
||||
for line in lines:
|
||||
if len(current_part + line + '\n') > max_length:
|
||||
if current_part:
|
||||
content_parts.append(current_part.strip())
|
||||
current_part = line + '\n'
|
||||
else:
|
||||
content_parts.append(line[:max_length])
|
||||
current_part = line[max_length:] + '\n'
|
||||
else:
|
||||
current_part += line + '\n'
|
||||
|
||||
if current_part.strip():
|
||||
content_parts.append(current_part.strip())
|
||||
|
||||
recordinfo = ET.Element("recordinfo")
|
||||
info_el = ET.SubElement(recordinfo, "info")
|
||||
info_el.text = title
|
||||
is_group_el = ET.SubElement(recordinfo, "isChatRoom")
|
||||
is_group_el.text = "1" if is_group else "0"
|
||||
datalist = ET.SubElement(recordinfo, "datalist")
|
||||
datalist.set("count", str(len(content_parts)))
|
||||
desc_el = ET.SubElement(recordinfo, "desc")
|
||||
desc_el.text = title
|
||||
fromscene_el = ET.SubElement(recordinfo, "fromscene")
|
||||
fromscene_el.text = "3"
|
||||
|
||||
for i, part in enumerate(content_parts):
|
||||
di = ET.SubElement(datalist, "dataitem")
|
||||
di.set("datatype", "1")
|
||||
di.set("dataid", uuid.uuid4().hex)
|
||||
|
||||
src_local_id = str((int(time.time() * 1000) % 90000) + 10000)
|
||||
new_msg_id = str(int(time.time() * 1000) + i)
|
||||
create_time = str(int(time.time()) - len(content_parts) + i)
|
||||
|
||||
ET.SubElement(di, "srcMsgLocalid").text = src_local_id
|
||||
ET.SubElement(di, "sourcetime").text = time.strftime("%Y-%m-%d %H:%M", time.localtime(int(create_time)))
|
||||
ET.SubElement(di, "fromnewmsgid").text = new_msg_id
|
||||
ET.SubElement(di, "srcMsgCreateTime").text = create_time
|
||||
ET.SubElement(di, "sourcename").text = "搜索助手"
|
||||
ET.SubElement(di, "sourceheadurl").text = ""
|
||||
ET.SubElement(di, "datatitle").text = part
|
||||
ET.SubElement(di, "datadesc").text = part
|
||||
ET.SubElement(di, "datafmt").text = "text"
|
||||
ET.SubElement(di, "ischatroom").text = "1" if is_group else "0"
|
||||
|
||||
dataitemsource = ET.SubElement(di, "dataitemsource")
|
||||
ET.SubElement(dataitemsource, "hashusername").text = hashlib.sha256(from_wxid.encode("utf-8")).hexdigest()
|
||||
|
||||
record_xml = ET.tostring(recordinfo, encoding="unicode")
|
||||
|
||||
appmsg_parts = [
|
||||
"<appmsg appid=\"\" sdkver=\"0\">",
|
||||
f"<title>{title}</title>",
|
||||
f"<des>{title}</des>",
|
||||
"<type>19</type>",
|
||||
"<url>https://support.weixin.qq.com/cgi-bin/mmsupport-bin/readtemplate?t=page/favorite_record__w_unsupport</url>",
|
||||
"<appattach><cdnthumbaeskey></cdnthumbaeskey><aeskey></aeskey></appattach>",
|
||||
f"<recorditem><![CDATA[{record_xml}]]></recorditem>",
|
||||
"<percent>0</percent>",
|
||||
"</appmsg>"
|
||||
]
|
||||
appmsg_xml = "".join(appmsg_parts)
|
||||
|
||||
await bot._send_data_async(11214, {"to_wxid": from_wxid, "content": appmsg_xml})
|
||||
logger.success(f"已发送聊天记录: {title}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送聊天记录失败: {e}")
|
||||
|
||||
@on_text_message(priority=70)
|
||||
async def handle_message(self, bot: WechatHookClient, message: dict):
|
||||
"""处理文本消息"""
|
||||
if not self.config["behavior"]["enable_command"]:
|
||||
return True
|
||||
|
||||
content = message.get("Content", "").strip()
|
||||
from_wxid = message.get("FromWxid", "")
|
||||
is_group = message.get("IsGroup", False)
|
||||
|
||||
# 检查群聊/私聊开关
|
||||
if is_group and not self.config["behavior"]["enable_group"]:
|
||||
return True
|
||||
if not is_group and not self.config["behavior"]["enable_private"]:
|
||||
return True
|
||||
|
||||
# 检查是否是搜索命令（精确匹配命令+空格+关键词）
|
||||
keywords = self.config["behavior"]["command_keywords"]
|
||||
matched_keyword = None
|
||||
for keyword in keywords:
|
||||
if content.startswith(keyword + " "):
|
||||
matched_keyword = keyword
|
||||
break
|
||||
|
||||
if not matched_keyword:
|
||||
return True
|
||||
|
||||
# 提取搜索关键词
|
||||
query = content[len(matched_keyword):].strip()
|
||||
if not query:
|
||||
await bot.send_text(from_wxid, "❌ 请提供搜索关键词\n用法: /搜 <关键词>")
|
||||
return False
|
||||
|
||||
logger.info(f"收到搜索请求: {query}")
|
||||
|
||||
# 发送处理中提示
|
||||
await bot.send_text(from_wxid, "🔍 正在搜索，请稍候...")
|
||||
|
||||
try:
|
||||
# 执行搜索
|
||||
result = await self.search(query)
|
||||
|
||||
if result["success"]:
|
||||
# 根据配置选择发送方式
|
||||
send_as_cards = self.config["behavior"].get("send_as_cards", True)
|
||||
|
||||
if send_as_cards:
|
||||
# 以聊天记录形式发送
|
||||
await self.send_results_as_chat_record(bot, from_wxid, query, result["results"])
|
||||
else:
|
||||
# 以格式化文本形式发送
|
||||
formatted_text = self.format_results(result["results"])
|
||||
await bot.send_text(from_wxid, formatted_text)
|
||||
|
||||
logger.success(f"搜索成功，已发送结果")
|
||||
else:
|
||||
await bot.send_text(from_wxid, f"❌ {result['message']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"搜索处理失败: {e}")
|
||||
await bot.send_text(from_wxid, f"❌ 处理失败: {str(e)}")
|
||||
|
||||
return False
|
||||
|
||||
def get_llm_tools(self) -> List[dict]:
|
||||
"""
|
||||
返回LLM工具定义
|
||||
供AIChat插件调用
|
||||
"""
|
||||
if not self.config["llm_tool"]["enabled"]:
|
||||
return []
|
||||
|
||||
return [{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": self.config["llm_tool"]["tool_name"],
|
||||
"description": self.config["llm_tool"]["tool_description"],
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "搜索关键词，描述想要查询的内容"
|
||||
},
|
||||
"max_results": {
|
||||
"type": "integer",
|
||||
"description": "返回的最大结果数量，默认5条",
|
||||
"default": 5
|
||||
}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
}
|
||||
}]
|
||||
|
||||
async def execute_llm_tool(self, tool_name: str, arguments: dict, bot: WechatHookClient, from_wxid: str) -> dict:
|
||||
"""
|
||||
执行LLM工具调用
|
||||
供AIChat插件调用
|
||||
|
||||
Returns:
|
||||
{"success": bool, "message": str, "results": List[dict]}
|
||||
"""
|
||||
expected_tool_name = self.config["llm_tool"]["tool_name"]
|
||||
logger.info(f"WebSearch工具检查: 收到={tool_name}, 期望={expected_tool_name}")
|
||||
|
||||
if tool_name != expected_tool_name:
|
||||
return None # 不是本插件的工具，返回None让其他插件处理
|
||||
|
||||
try:
|
||||
query = arguments.get("query")
|
||||
if not query:
|
||||
return {"success": False, "message": "缺少搜索关键词参数"}
|
||||
|
||||
max_results = arguments.get("max_results", 5)
|
||||
|
||||
logger.info(f"LLM工具调用搜索: {query}")
|
||||
|
||||
# 执行搜索
|
||||
result = await self.search(query, max_results)
|
||||
|
||||
if result["success"]:
|
||||
# 直接发送聊天记录，不经过 AI 总结
|
||||
await self.send_results_as_chat_record(bot, from_wxid, query, result["results"], max_results)
|
||||
|
||||
# 返回简短说明给 AI
|
||||
return {
|
||||
"success": True,
|
||||
"message": f"已为用户搜索「{query}」并发送了 {len(result['results'])} 条搜索结果的聊天记录卡片。",
|
||||
"no_reply": True # 不需要 AI 再回复
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success": False,
|
||||
"message": result["message"]
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"LLM工具执行失败: {e}")
|
||||
return {"success": False, "message": f"执行失败: {str(e)}"}
|
||||
Reference in New Issue
Block a user