Merge pull request #2742 from xinnan-tech/manager-web-logo-i18n

add:登录、注册、首页、忘记密码页的多语言logo显示判断
2026-07-21 10:55:38 +00:00 · 2025-12-23 15:35:20 +08:00
parent 38d984f225 1d6c4751de
commit fd4cff73ae
26 changed files with 1029 additions and 52 deletions
@@ -44,6 +44,7 @@ import xiaozhi.modules.agent.entity.AgentEntity;
 import xiaozhi.modules.agent.entity.AgentTemplateEntity;
 import xiaozhi.modules.agent.service.AgentChatAudioService;
 import xiaozhi.modules.agent.service.AgentChatHistoryService;
+import xiaozhi.modules.agent.service.AgentChatSummaryService;
 import xiaozhi.modules.agent.service.AgentContextProviderService;
 import xiaozhi.modules.agent.service.AgentPluginMappingService;
 import xiaozhi.modules.agent.service.AgentService;
@@ -66,6 +67,7 @@ public class AgentController {
    private final AgentChatAudioService agentChatAudioService;
    private final AgentPluginMappingService agentPluginMappingService;
    private final AgentContextProviderService agentContextProviderService;
+    private final AgentChatSummaryService agentChatSummaryService;
    private final RedisUtils redisUtils;

    @GetMapping("/list")
@@ -119,6 +121,27 @@ public class AgentController {
        return new Result<>();
    }

+    @PostMapping("/chat-summary/{sessionId}/save")
+    @Operation(summary = "根据会话ID生成聊天记录总结并保存（异步执行）")
+    public Result<Void> generateAndSaveChatSummary(@PathVariable String sessionId) {
+        try {
+            // 异步执行总结生成任务，立即返回成功响应
+            new Thread(() -> {
+                try {
+                    agentChatSummaryService.generateAndSaveChatSummary(sessionId);
+                    System.out.println("异步执行会话 " + sessionId + " 的聊天记录总结完成");
+                } catch (Exception e) {
+                    System.err.println("异步执行会话 " + sessionId + " 的聊天记录总结失败: " + e.getMessage());
+                }
+            }).start();
+
+            // 立即返回成功响应，不等待总结生成完成
+            return new Result<Void>().ok(null);
+        } catch (Exception e) {
+            return new Result<Void>().error("启动异步总结生成任务失败: " + e.getMessage());
+        }
+    }
+
    @PutMapping("/{id}")
    @Operation(summary = "更新智能体")
    @RequiresPermissions("sys:role:normal")
@@ -186,6 +209,7 @@ public class AgentController {
        List<AgentChatHistoryDTO> result = agentChatHistoryService.getChatHistoryBySessionId(id, sessionId);
        return new Result<List<AgentChatHistoryDTO>>().ok(result);
    }
+
    @GetMapping("/{id}/chat-history/user")
    @Operation(summary = "获取智能体聊天记录（用户）")
    @RequiresPermissions("sys:role:normal")
@@ -0,0 +1,45 @@
+package xiaozhi.modules.agent.dto;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+/**
+ * 智能体聊天记录总结DTO
+ */
+@Data
+@Schema(description = "智能体聊天记录总结对象")
+public class AgentChatSummaryDTO {
+
+    @Schema(description = "会话ID")
+    private String sessionId;
+
+    @Schema(description = "智能体ID")
+    private String agentId;
+
+    @Schema(description = "总结内容")
+    private String summary;
+
+    @Schema(description = "总结状态")
+    private boolean success;
+
+    @Schema(description = "错误信息")
+    private String errorMessage;
+
+    public AgentChatSummaryDTO() {
+        this.success = true;
+    }
+
+    public AgentChatSummaryDTO(String sessionId, String agentId, String summary) {
+        this.sessionId = sessionId;
+        this.agentId = agentId;
+        this.summary = summary;
+        this.success = true;
+    }
+
+    public AgentChatSummaryDTO(String sessionId, String errorMessage) {
+        this.sessionId = sessionId;
+        this.errorMessage = errorMessage;
+        this.success = false;
+    }
+
+}
@@ -0,0 +1,15 @@
+package xiaozhi.modules.agent.service;
+
+/**
+ * 智能体聊天记录总结服务接口
+ */
+public interface AgentChatSummaryService {
+
+    /**
+     * 根据会话ID生成聊天记录总结并保存到智能体记忆
+     * 
+     * @param sessionId 会话ID
+     * @return 保存结果
+     */
+    boolean generateAndSaveChatSummary(String sessionId);
+}
@@ -17,6 +17,7 @@ import xiaozhi.modules.agent.entity.AgentChatHistoryEntity;
 import xiaozhi.modules.agent.entity.AgentEntity;
 import xiaozhi.modules.agent.service.AgentChatAudioService;
 import xiaozhi.modules.agent.service.AgentChatHistoryService;
+import xiaozhi.modules.agent.service.AgentChatSummaryService;
 import xiaozhi.modules.agent.service.AgentService;
 import xiaozhi.modules.agent.service.biz.AgentChatHistoryBizService;
 import xiaozhi.modules.device.entity.DeviceEntity;
@@ -36,6 +37,7 @@ public class AgentChatHistoryBizServiceImpl implements AgentChatHistoryBizServic
    private final AgentService agentService;
    private final AgentChatHistoryService agentChatHistoryService;
    private final AgentChatAudioService agentChatAudioService;
+    private final AgentChatSummaryService agentChatSummaryService;
    private final RedisUtils redisUtils;
    private final DeviceService deviceService;

@@ -50,7 +52,8 @@ public class AgentChatHistoryBizServiceImpl implements AgentChatHistoryBizServic
    public Boolean report(AgentChatHistoryReportDTO report) {
        String macAddress = report.getMacAddress();
        Byte chatType = report.getChatType();
-        Long reportTimeMillis = null != report.getReportTime() ? report.getReportTime() * 1000 : System.currentTimeMillis();
+        Long reportTimeMillis = null != report.getReportTime() ? report.getReportTime() * 1000
+                : System.currentTimeMillis();
        log.info("小智设备聊天上报请求: macAddress={}, type={} reportTime={}", macAddress, chatType, reportTimeMillis);

        // 根据设备MAC地址查询对应的默认智能体，判断是否需要上报
@@ -105,7 +108,8 @@ public class AgentChatHistoryBizServiceImpl implements AgentChatHistoryBizServic
    /**
     * 组装上报数据
     */
-    private void saveChatText(AgentChatHistoryReportDTO report, String agentId, String macAddress, String audioId, Long reportTime) {
+    private void saveChatText(AgentChatHistoryReportDTO report, String agentId, String macAddress, String audioId,
+            Long reportTime) {
        // 构建聊天记录实体
        AgentChatHistoryEntity entity = AgentChatHistoryEntity.builder()
                .macAddress(macAddress)
@@ -0,0 +1,423 @@
+package xiaozhi.modules.agent.service.impl;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+import org.apache.commons.lang3.StringUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.springframework.stereotype.Service;
+
+import com.baomidou.mybatisplus.core.conditions.query.QueryWrapper;
+
+import lombok.RequiredArgsConstructor;
+import xiaozhi.modules.agent.dto.AgentChatHistoryDTO;
+import xiaozhi.modules.agent.dto.AgentChatSummaryDTO;
+import xiaozhi.modules.agent.dto.AgentMemoryDTO;
+import xiaozhi.modules.agent.dto.AgentUpdateDTO;
+import xiaozhi.modules.agent.entity.AgentChatHistoryEntity;
+import xiaozhi.modules.agent.service.AgentChatHistoryService;
+import xiaozhi.modules.agent.service.AgentChatSummaryService;
+import xiaozhi.modules.agent.service.AgentService;
+import xiaozhi.modules.agent.vo.AgentInfoVO;
+import xiaozhi.modules.device.entity.DeviceEntity;
+import xiaozhi.modules.device.service.DeviceService;
+import xiaozhi.modules.llm.service.LLMService;
+import xiaozhi.modules.model.entity.ModelConfigEntity;
+import xiaozhi.modules.model.service.ModelConfigService;
+
+/**
+ * 智能体聊天记录总结服务实现类
+ * 实现Python端mem_local_short.py中的总结逻辑
+ */
+@Service
+@RequiredArgsConstructor
+public class AgentChatSummaryServiceImpl implements AgentChatSummaryService {
+
+    private static final Logger log = LoggerFactory.getLogger(AgentChatSummaryServiceImpl.class);
+
+    private final AgentChatHistoryService agentChatHistoryService;
+    private final AgentService agentService;
+    private final DeviceService deviceService;
+    private final LLMService llmService;
+    private final ModelConfigService modelConfigService;
+
+    // 总结规则常量
+    private static final int MAX_SUMMARY_LENGTH = 1800; // 最大总结长度
+    private static final Pattern JSON_PATTERN = Pattern.compile("\\{.*?\\}", Pattern.DOTALL);
+    private static final Pattern DEVICE_CONTROL_PATTERN = Pattern.compile("设备控制|设备操作|控制设备|设备状态",
+            Pattern.CASE_INSENSITIVE);
+    private static final Pattern WEATHER_PATTERN = Pattern.compile("天气|温度|湿度|降雨|气象", Pattern.CASE_INSENSITIVE);
+    private static final Pattern DATE_PATTERN = Pattern.compile("日期|时间|星期|月份|年份", Pattern.CASE_INSENSITIVE);
+
+    private AgentChatSummaryDTO generateChatSummary(String sessionId) {
+        try {
+            System.out.println("开始生成会话 " + sessionId + " 的聊天记录总结");
+
+            // 1. 根据sessionId获取聊天记录
+            List<AgentChatHistoryDTO> chatHistory = getChatHistoryBySessionId(sessionId);
+            if (chatHistory == null || chatHistory.isEmpty()) {
+                return new AgentChatSummaryDTO(sessionId, "未找到该会话的聊天记录");
+            }
+
+            // 2. 获取智能体信息
+            String agentId = getAgentIdFromSession(sessionId, chatHistory);
+            if (StringUtils.isBlank(agentId)) {
+                return new AgentChatSummaryDTO(sessionId, "无法获取智能体信息");
+            }
+
+            // 3. 提取关键对话内容
+            List<String> meaningfulMessages = extractMeaningfulMessages(chatHistory);
+            if (meaningfulMessages.isEmpty()) {
+                return new AgentChatSummaryDTO(sessionId, "没有有效的对话内容可总结");
+            }
+
+            // 4. 生成总结（generateSummaryFromMessages方法已包含长度限制逻辑）
+            String summary = generateSummaryFromMessages(meaningfulMessages, agentId);
+
+            System.out.println("成功生成会话 " + sessionId + " 的聊天记录总结，长度: " + summary.length() + " 字符");
+            return new AgentChatSummaryDTO(sessionId, agentId, summary);
+
+        } catch (Exception e) {
+            System.err.println("生成会话 " + sessionId + " 的聊天记录总结时发生错误: " + e.getMessage());
+            return new AgentChatSummaryDTO(sessionId, "生成总结时发生错误: " + e.getMessage());
+        }
+    }
+
+    @Override
+    public boolean generateAndSaveChatSummary(String sessionId) {
+        try {
+            // 1. 生成总结
+            AgentChatSummaryDTO summaryDTO = generateChatSummary(sessionId);
+            if (!summaryDTO.isSuccess()) {
+                System.err.println("生成总结失败: " + summaryDTO.getErrorMessage());
+                return false;
+            }
+
+            // 2. 获取设备信息（通过会话关联的设备）
+            DeviceEntity device = getDeviceBySessionId(sessionId);
+            if (device == null) {
+                System.err.println("未找到与会话 " + sessionId + " 关联的设备");
+                return false;
+            }
+
+            // 3. 更新智能体记忆
+            AgentMemoryDTO memoryDTO = new AgentMemoryDTO();
+            memoryDTO.setSummaryMemory(summaryDTO.getSummary());
+
+            // 调用现有接口更新记忆
+            agentService.updateAgentById(device.getAgentId(),
+                    new AgentUpdateDTO() {
+                        {
+                            setSummaryMemory(summaryDTO.getSummary());
+                        }
+                    });
+
+            System.out.println("成功保存会话 " + sessionId + " 的聊天记录总结到智能体 " + device.getAgentId());
+            return true;
+
+        } catch (Exception e) {
+            System.err.println("保存会话 " + sessionId + " 的聊天记录总结时发生错误: " + e.getMessage());
+            return false;
+        }
+    }
+
+    /**
+     * 根据会话ID获取聊天记录
+     */
+    private List<AgentChatHistoryDTO> getChatHistoryBySessionId(String sessionId) {
+        try {
+            // 这里需要根据sessionId获取聊天记录
+            // 由于现有接口需要agentId，我们需要先找到关联的agentId
+            String agentId = findAgentIdBySessionId(sessionId);
+            if (StringUtils.isBlank(agentId)) {
+                return null;
+            }
+            return agentChatHistoryService.getChatHistoryBySessionId(agentId, sessionId);
+        } catch (Exception e) {
+            System.err.println("获取会话 " + sessionId + " 的聊天记录失败: " + e.getMessage());
+            return null;
+        }
+    }
+
+    /**
+     * 根据会话ID查找关联的智能体ID
+     */
+    private String findAgentIdBySessionId(String sessionId) {
+        try {
+            // 查询该会话的第一条记录获取agentId
+            QueryWrapper<AgentChatHistoryEntity> wrapper = new QueryWrapper<>();
+            wrapper.select("agent_id")
+                    .eq("session_id", sessionId)
+                    .last("LIMIT 1");
+
+            AgentChatHistoryEntity entity = agentChatHistoryService.getOne(wrapper);
+            return entity != null ? entity.getAgentId() : null;
+        } catch (Exception e) {
+            System.err.println("根据会话ID " + sessionId + " 查找智能体ID失败: " + e.getMessage());
+            return null;
+        }
+    }
+
+    /**
+     * 从会话中获取智能体ID
+     */
+    private String getAgentIdFromSession(String sessionId, List<AgentChatHistoryDTO> chatHistory) {
+        // 直接从数据库查询智能体ID
+        return findAgentIdBySessionId(sessionId);
+    }
+
+    /**
+     * 提取有意义的对话内容（只提取用户消息，排除AI回复）
+     */
+    private List<String> extractMeaningfulMessages(List<AgentChatHistoryDTO> chatHistory) {
+        List<String> meaningfulMessages = new ArrayList<>();
+
+        for (AgentChatHistoryDTO message : chatHistory) {
+            // 只处理用户消息（chatType = 1）
+            if (message.getChatType() != null && message.getChatType() == 1) {
+                String content = extractContentFromMessage(message);
+                if (isMeaningfulMessage(content)) {
+                    meaningfulMessages.add(content);
+                }
+            }
+        }
+
+        return meaningfulMessages;
+    }
+
+    /**
+     * 从消息中提取内容（处理JSON格式）
+     */
+    private String extractContentFromMessage(AgentChatHistoryDTO message) {
+        String content = message.getContent();
+        if (StringUtils.isBlank(content)) {
+            return "";
+        }
+
+        // 处理JSON格式内容（与前端ChatHistoryDialog.vue逻辑一致）
+        Matcher matcher = JSON_PATTERN.matcher(content);
+        if (matcher.find()) {
+            String jsonContent = matcher.group();
+            // 简化处理：提取JSON中的文本内容
+            return extractTextFromJson(jsonContent);
+        }
+
+        return content;
+    }
+
+    /**
+     * 从JSON中提取文本内容
+     */
+    private String extractTextFromJson(String jsonContent) {
+        // 简化处理：提取"content"字段的值
+        Pattern contentPattern = Pattern.compile("\"content\"\s*:\s*\"([^\"]*)\"");
+        Matcher matcher = contentPattern.matcher(jsonContent);
+        if (matcher.find()) {
+            return matcher.group(1);
+        }
+        return jsonContent;
+    }
+
+    /**
+     * 判断是否为有意义的消息
+     */
+    private boolean isMeaningfulMessage(String content) {
+        if (StringUtils.isBlank(content)) {
+            return false;
+        }
+
+        // 排除设备控制信息
+        if (DEVICE_CONTROL_PATTERN.matcher(content).find()) {
+            return false;
+        }
+
+        // 排除日期天气等无关内容
+        if (WEATHER_PATTERN.matcher(content).find() || DATE_PATTERN.matcher(content).find()) {
+            return false;
+        }
+
+        // 排除过短的消息
+        return content.length() >= 5;
+    }
+
+    /**
+     * 从消息生成总结
+     */
+    private String generateSummaryFromMessages(List<String> messages, String agentId) {
+        if (messages.isEmpty()) {
+            return "本次对话内容较少，没有需要总结的重要信息。";
+        }
+
+        // 构建完整的对话内容
+        StringBuilder conversation = new StringBuilder();
+        for (int i = 0; i < messages.size(); i++) {
+            conversation.append("消息").append(i + 1).append(": ").append(messages.get(i)).append("\n");
+        }
+
+        try {
+            // 获取当前智能体的历史记忆
+            String historyMemory = getCurrentAgentMemory(agentId);
+
+            // 调用LLM服务进行智能总结，传递agentId以获取正确的模型配置
+            String summary = callJavaLLMForSummaryWithHistory(conversation.toString(), historyMemory, agentId);
+
+            // 应用总结规则：限制最大长度
+            if (summary.length() > MAX_SUMMARY_LENGTH) {
+                summary = summary.substring(0, MAX_SUMMARY_LENGTH) + "...";
+            }
+
+            return summary;
+        } catch (Exception e) {
+            System.err.println("调用Java端LLM服务失败: " + e.getMessage());
+            throw new RuntimeException("LLM服务不可用，无法生成聊天总结");
+        }
+    }
+
+    /**
+     * 获取当前智能体的历史记忆
+     */
+    private String getCurrentAgentMemory(String agentId) {
+        try {
+            if (StringUtils.isBlank(agentId)) {
+                return null;
+            }
+
+            // 获取智能体信息
+            AgentInfoVO agentInfo = agentService.getAgentById(agentId);
+            if (agentInfo == null) {
+                return null;
+            }
+
+            // 返回智能体的当前总结记忆
+            return agentInfo.getSummaryMemory();
+        } catch (Exception e) {
+            System.err.println("获取智能体历史记忆失败，agentId: " + agentId + ", 错误: " + e.getMessage());
+            return null;
+        }
+    }
+
+    /**
+     * 调用Java端LLM服务进行智能总结（支持历史记忆合并）
+     */
+    private String callJavaLLMForSummaryWithHistory(String conversation, String historyMemory, String agentId) {
+        try {
+            // 获取智能体配置，从中提取记忆总结的模型ID
+            String modelId = getMemorySummaryModelId(agentId);
+
+            if (StringUtils.isBlank(modelId)) {
+                System.out.println("未找到记忆总结的LLM模型配置，使用默认LLM服务");
+                return llmService.generateSummaryWithHistory(conversation, historyMemory, null, null);
+            }
+
+            // 使用指定的模型ID调用LLM服务（支持历史记忆合并）
+            String summary = llmService.generateSummaryWithHistory(conversation, historyMemory, null, modelId);
+
+            if (StringUtils.isNotBlank(summary) && !summary.equals("服务暂不可用") && !summary.equals("总结生成失败")) {
+                return summary;
+            }
+
+            throw new RuntimeException("Java端LLM服务返回异常: " + summary);
+
+        } catch (Exception e) {
+            System.err.println("调用Java端LLM服务异常，agentId: " + agentId + ", 错误: " + e.getMessage());
+            throw e;
+        }
+    }
+
+    /**
+     * 调用Java端LLM服务进行智能总结
+     */
+    private String callJavaLLMForSummary(String conversation, String agentId) {
+        try {
+            // 获取智能体配置，从中提取记忆总结的模型ID
+            String modelId = getMemorySummaryModelId(agentId);
+
+            if (StringUtils.isBlank(modelId)) {
+                System.out.println("未找到记忆总结的LLM模型配置，使用默认LLM服务");
+                return llmService.generateSummary(conversation);
+            }
+
+            // 使用指定的模型ID调用LLM服务
+            String summary = llmService.generateSummaryWithModel(conversation, modelId);
+
+            if (StringUtils.isNotBlank(summary) && !summary.equals("服务暂不可用") && !summary.equals("总结生成失败")) {
+                return summary;
+            }
+
+            throw new RuntimeException("Java端LLM服务返回异常: " + summary);
+
+        } catch (Exception e) {
+            System.err.println("调用Java端LLM服务异常，agentId: " + agentId + ", 错误: " + e.getMessage());
+            throw e;
+        }
+    }
+
+    /**
+     * 获取记忆总结的LLM模型ID
+     */
+    private String getMemorySummaryModelId(String agentId) {
+        try {
+            if (StringUtils.isBlank(agentId)) {
+                return null;
+            }
+
+            // 获取智能体信息
+            AgentInfoVO agentInfo = agentService.getAgentById(agentId);
+            if (agentInfo == null) {
+                return null;
+            }
+
+            // 获取智能体的记忆模型ID
+            String memModelId = agentInfo.getMemModelId();
+            if (StringUtils.isBlank(memModelId)) {
+                return null;
+            }
+
+            // 获取记忆模型配置
+            ModelConfigEntity memModelConfig = modelConfigService.getModelByIdFromCache(memModelId);
+            if (memModelConfig == null || memModelConfig.getConfigJson() == null) {
+                return null;
+            }
+
+            // 从记忆模型配置中提取对应的LLM模型ID
+            Map<String, Object> configMap = memModelConfig.getConfigJson();
+            String llmModelId = (String) configMap.get("llm");
+
+            if (StringUtils.isBlank(llmModelId)) {
+                // 如果记忆模型没有配置独立的LLM，则使用智能体的默认LLM模型
+                return agentInfo.getLlmModelId();
+            }
+
+            return llmModelId;
+        } catch (Exception e) {
+            System.err.println("获取记忆总结LLM模型ID失败，agentId: " + agentId + ", 错误: " + e.getMessage());
+            return null;
+        }
+    }
+
+    /**
+     * 根据会话ID获取设备信息
+     */
+    private DeviceEntity getDeviceBySessionId(String sessionId) {
+        try {
+            // 查询该会话的第一条记录获取macAddress
+            QueryWrapper<AgentChatHistoryEntity> wrapper = new QueryWrapper<>();
+            wrapper.select("mac_address")
+                    .eq("session_id", sessionId)
+                    .last("LIMIT 1");
+
+            AgentChatHistoryEntity entity = agentChatHistoryService.getOne(wrapper);
+            if (entity != null && StringUtils.isNotBlank(entity.getMacAddress())) {
+                return deviceService.getDeviceByMacAddress(entity.getMacAddress());
+            }
+            return null;
+        } catch (Exception e) {
+            System.err.println("根据会话ID " + sessionId + " 查找设备信息失败: " + e.getMessage());
+            return null;
+        }
+    }
+}
@@ -0,0 +1,70 @@
+package xiaozhi.modules.llm.service;
+
+/**
+ * LLM服务接口
+ * 支持多种大模型调用
+ */
+public interface LLMService {
+
+    /**
+     * 生成聊天记录总结
+     * 
+     * @param conversation   对话内容
+     * @param promptTemplate 提示词模板
+     * @return 总结结果
+     */
+    String generateSummary(String conversation, String promptTemplate);
+
+    /**
+     * 生成聊天记录总结（使用默认提示词）
+     * 
+     * @param conversation 对话内容
+     * @return 总结结果
+     */
+    String generateSummary(String conversation);
+
+    /**
+     * 生成聊天记录总结（指定模型ID）
+     * 
+     * @param conversation 对话内容
+     * @param modelId      模型ID
+     * @return 总结结果
+     */
+    String generateSummaryWithModel(String conversation, String modelId);
+
+    /**
+     * 生成聊天记录总结（指定模型ID和提示词模板）
+     * 
+     * @param conversation   对话内容
+     * @param promptTemplate 提示词模板
+     * @param modelId        模型ID
+     * @return 总结结果
+     */
+    String generateSummary(String conversation, String promptTemplate, String modelId);
+
+    /**
+     * 生成聊天记录总结（包含历史记忆合并）
+     * 
+     * @param conversation   对话内容
+     * @param historyMemory  历史记忆
+     * @param promptTemplate 提示词模板
+     * @param modelId        模型ID
+     * @return 总结结果
+     */
+    String generateSummaryWithHistory(String conversation, String historyMemory, String promptTemplate, String modelId);
+
+    /**
+     * 检查服务是否可用
+     * 
+     * @return 是否可用
+     */
+    boolean isAvailable();
+
+    /**
+     * 检查指定模型的服务是否可用
+     * 
+     * @param modelId 模型ID
+     * @return 是否可用
+     */
+    boolean isAvailable(String modelId);
+}
@@ -0,0 +1,305 @@
+package xiaozhi.modules.llm.service.impl;
+
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.http.HttpEntity;
+import org.springframework.http.HttpHeaders;
+import org.springframework.http.HttpMethod;
+import org.springframework.http.MediaType;
+import org.springframework.http.ResponseEntity;
+import org.springframework.stereotype.Service;
+import org.springframework.web.client.RestTemplate;
+
+import cn.hutool.json.JSONArray;
+import cn.hutool.json.JSONObject;
+import cn.hutool.json.JSONUtil;
+import lombok.extern.slf4j.Slf4j;
+import xiaozhi.modules.llm.service.LLMService;
+import xiaozhi.modules.model.entity.ModelConfigEntity;
+import xiaozhi.modules.model.service.ModelConfigService;
+
+/**
+ * OpenAI风格API的LLM服务实现
+ * 支持阿里云、DeepSeek、ChatGLM等兼容OpenAI API的模型
+ */
+@Slf4j
+@Service
+public class OpenAIStyleLLMServiceImpl implements LLMService {
+
+    @Autowired
+    private ModelConfigService modelConfigService;
+
+    private final RestTemplate restTemplate = new RestTemplate();
+
+    private static final String DEFAULT_SUMMARY_PROMPT = "你是一个经验丰富的记忆总结者，擅长将对话内容进行总结摘要，遵循以下规则：\n1、总结用户的重要信息，以便在未来的对话中提供更个性化的服务\n2、不要重复总结，不要遗忘之前记忆，除非原来的记忆超过了1800字，否则不要遗忘、不要压缩用户的历史记忆\n3、用户操控的设备音量、播放音乐、天气、退出、不想对话等和用户本身无关的内容，这些信息不需要加入到总结中\n4、聊天内容中的今天的日期时间、今天的天气情况与用户事件无关的数据，这些信息如果当成记忆存储会影响后续对话，这些信息不需要加入到总结中\n5、不要把设备操控的成果结果和失败结果加入到总结中，也不要把用户的一些废话加入到总结中\n6、不要为了总结而总结，如果用户的聊天没有意义，请返回原来的历史记录也是可以的\n7、只需要返回总结摘要，严格控制在1800字内\n8、不要包含代码、xml，不需要解释、注释和说明，保存记忆时仅从对话提取信息，不要混入示例内容\n9、如果提供了历史记忆，请将新对话内容与历史记忆进行智能合并，保留有价值的历史信息，同时添加新的重要信息\n\n历史记忆：\n{history_memory}\n\n新对话内容：\n{conversation}";
+
+    @Override
+    public String generateSummary(String conversation) {
+        return generateSummary(conversation, null, null);
+    }
+
+    @Override
+    public String generateSummaryWithModel(String conversation, String modelId) {
+        return generateSummary(conversation, null, modelId);
+    }
+
+    @Override
+    public String generateSummary(String conversation, String promptTemplate, String modelId) {
+        if (!isAvailable()) {
+            log.warn("LLM服务不可用，无法生成总结");
+            return "LLM服务不可用，无法生成总结";
+        }
+
+        try {
+            // 从智控台获取LLM模型配置
+            ModelConfigEntity llmConfig;
+            if (modelId != null && !modelId.trim().isEmpty()) {
+                // 通过具体模型ID获取配置
+                llmConfig = modelConfigService.getModelByIdFromCache(modelId);
+            } else {
+                // 保持向后兼容，使用默认配置
+                llmConfig = getDefaultLLMConfig();
+            }
+
+            if (llmConfig == null || llmConfig.getConfigJson() == null) {
+                log.error("未找到可用的LLM模型配置，modelId: {}", modelId);
+                return "未找到可用的LLM模型配置";
+            }
+
+            JSONObject configJson = llmConfig.getConfigJson();
+            String baseUrl = configJson.getStr("base_url");
+            String model = configJson.getStr("model_name");
+            String apiKey = configJson.getStr("api_key");
+            Double temperature = configJson.getDouble("temperature");
+            Integer maxTokens = configJson.getInt("max_tokens");
+
+            if (StringUtils.isBlank(baseUrl) || StringUtils.isBlank(apiKey)) {
+                log.error("LLM配置不完整，baseUrl或apiKey为空");
+                return "LLM配置不完整，无法生成总结";
+            }
+
+            // 构建提示词
+            String prompt = (promptTemplate != null ? promptTemplate : DEFAULT_SUMMARY_PROMPT).replace("{conversation}",
+                    conversation);
+
+            // 构建请求体
+            Map<String, Object> requestBody = new HashMap<>();
+            requestBody.put("model", model != null ? model : "gpt-3.5-turbo");
+
+            Map<String, Object>[] messages = new Map[1];
+            Map<String, Object> message = new HashMap<>();
+            message.put("role", "user");
+            message.put("content", prompt);
+            messages[0] = message;
+
+            requestBody.put("messages", messages);
+            requestBody.put("temperature", temperature != null ? temperature : 0.7);
+            requestBody.put("max_tokens", maxTokens != null ? maxTokens : 2000);
+
+            // 发送HTTP请求
+            HttpHeaders headers = new HttpHeaders();
+            headers.setContentType(MediaType.APPLICATION_JSON);
+            headers.set("Authorization", "Bearer " + apiKey);
+
+            HttpEntity<Map<String, Object>> entity = new HttpEntity<>(requestBody, headers);
+
+            // 构建完整的API URL
+            String apiUrl = baseUrl;
+            if (!apiUrl.endsWith("/chat/completions")) {
+                if (!apiUrl.endsWith("/")) {
+                    apiUrl += "/";
+                }
+                apiUrl += "chat/completions";
+            }
+
+            ResponseEntity<String> response = restTemplate.exchange(
+                    apiUrl, HttpMethod.POST, entity, String.class);
+
+            if (response.getStatusCode().is2xxSuccessful()) {
+                JSONObject responseJson = JSONUtil.parseObj(response.getBody());
+                JSONArray choices = responseJson.getJSONArray("choices");
+                if (choices != null && choices.size() > 0) {
+                    JSONObject choice = choices.getJSONObject(0);
+                    JSONObject messageObj = choice.getJSONObject("message");
+                    return messageObj.getStr("content");
+                }
+            } else {
+                log.error("LLM API调用失败，状态码：{}，响应：{}", response.getStatusCode(), response.getBody());
+            }
+        } catch (Exception e) {
+            log.error("调用LLM服务生成总结时发生异常，modelId: {}", modelId, e);
+        }
+
+        return "生成总结失败，请稍后重试";
+    }
+
+    @Override
+    public String generateSummary(String conversation, String promptTemplate) {
+        return generateSummary(conversation, promptTemplate, null);
+    }
+
+    @Override
+    public String generateSummaryWithHistory(String conversation, String historyMemory, String promptTemplate,
+            String modelId) {
+        if (!isAvailable()) {
+            log.warn("LLM服务不可用，无法生成总结");
+            return "LLM服务不可用，无法生成总结";
+        }
+
+        try {
+            // 从智控台获取LLM模型配置
+            ModelConfigEntity llmConfig;
+            if (modelId != null && !modelId.trim().isEmpty()) {
+                // 通过具体模型ID获取配置
+                llmConfig = modelConfigService.getModelByIdFromCache(modelId);
+            } else {
+                // 保持向后兼容，使用默认配置
+                llmConfig = getDefaultLLMConfig();
+            }
+
+            if (llmConfig == null || llmConfig.getConfigJson() == null) {
+                log.error("未找到可用的LLM模型配置，modelId: {}", modelId);
+                return "未找到可用的LLM模型配置";
+            }
+
+            JSONObject configJson = llmConfig.getConfigJson();
+            String baseUrl = configJson.getStr("base_url");
+            String model = configJson.getStr("model_name");
+            String apiKey = configJson.getStr("api_key");
+
+            if (StringUtils.isBlank(baseUrl) || StringUtils.isBlank(apiKey)) {
+                log.error("LLM配置不完整，baseUrl或apiKey为空");
+                return "LLM配置不完整，无法生成总结";
+            }
+
+            // 构建提示词，包含历史记忆
+            String prompt = (promptTemplate != null ? promptTemplate : DEFAULT_SUMMARY_PROMPT)
+                    .replace("{history_memory}", historyMemory != null ? historyMemory : "无历史记忆")
+                    .replace("{conversation}", conversation);
+
+            // 构建请求体
+            Map<String, Object> requestBody = new HashMap<>();
+            requestBody.put("model", model != null ? model : "gpt-3.5-turbo");
+
+            Map<String, Object>[] messages = new Map[1];
+            Map<String, Object> message = new HashMap<>();
+            message.put("role", "user");
+            message.put("content", prompt);
+            messages[0] = message;
+
+            requestBody.put("messages", messages);
+            requestBody.put("temperature", 0.2);
+            requestBody.put("max_tokens", 2000);
+
+            // 发送HTTP请求
+            HttpHeaders headers = new HttpHeaders();
+            headers.setContentType(MediaType.APPLICATION_JSON);
+            headers.set("Authorization", "Bearer " + apiKey);
+
+            HttpEntity<Map<String, Object>> entity = new HttpEntity<>(requestBody, headers);
+
+            // 构建完整的API URL
+            String apiUrl = baseUrl;
+            if (!apiUrl.endsWith("/chat/completions")) {
+                if (!apiUrl.endsWith("/")) {
+                    apiUrl += "/";
+                }
+                apiUrl += "chat/completions";
+            }
+
+            ResponseEntity<String> response = restTemplate.exchange(
+                    apiUrl, HttpMethod.POST, entity, String.class);
+
+            if (response.getStatusCode().is2xxSuccessful()) {
+                JSONObject responseJson = JSONUtil.parseObj(response.getBody());
+                JSONArray choices = responseJson.getJSONArray("choices");
+                if (choices != null && choices.size() > 0) {
+                    JSONObject choice = choices.getJSONObject(0);
+                    JSONObject messageObj = choice.getJSONObject("message");
+                    return messageObj.getStr("content");
+                }
+            } else {
+                log.error("LLM API调用失败，状态码：{}，响应：{}", response.getStatusCode(), response.getBody());
+            }
+        } catch (Exception e) {
+            log.error("调用LLM服务生成总结时发生异常，modelId: {}", modelId, e);
+        }
+
+        return "生成总结失败，请稍后重试";
+    }
+
+    @Override
+    public boolean isAvailable() {
+        try {
+            ModelConfigEntity defaultLLMConfig = getDefaultLLMConfig();
+            if (defaultLLMConfig == null || defaultLLMConfig.getConfigJson() == null) {
+                return false;
+            }
+
+            JSONObject configJson = defaultLLMConfig.getConfigJson();
+            String baseUrl = configJson.getStr("base_url");
+            String apiKey = configJson.getStr("api_key");
+
+            return baseUrl != null && !baseUrl.trim().isEmpty() &&
+                    apiKey != null && !apiKey.trim().isEmpty();
+        } catch (Exception e) {
+            log.error("检查LLM服务可用性时发生异常：", e);
+            return false;
+        }
+    }
+
+    @Override
+    public boolean isAvailable(String modelId) {
+        try {
+            if (modelId == null || modelId.trim().isEmpty()) {
+                return isAvailable();
+            }
+
+            // 通过具体模型ID获取配置
+            ModelConfigEntity modelConfig = modelConfigService.getModelByIdFromCache(modelId);
+            if (modelConfig == null || modelConfig.getConfigJson() == null) {
+                log.warn("未找到指定的LLM模型配置，modelId: {}", modelId);
+                return false;
+            }
+
+            JSONObject configJson = modelConfig.getConfigJson();
+            String baseUrl = configJson.getStr("base_url");
+            String apiKey = configJson.getStr("api_key");
+
+            return baseUrl != null && !baseUrl.trim().isEmpty() &&
+                    apiKey != null && !apiKey.trim().isEmpty();
+        } catch (Exception e) {
+            log.error("检查LLM服务可用性时发生异常，modelId: {}", modelId, e);
+            return false;
+        }
+    }
+
+    /**
+     * 从智控台获取默认的LLM模型配置
+     */
+    private ModelConfigEntity getDefaultLLMConfig() {
+        try {
+            // 获取所有启用的LLM模型配置
+            List<ModelConfigEntity> llmConfigs = modelConfigService.getEnabledModelsByType("LLM");
+            if (llmConfigs == null || llmConfigs.isEmpty()) {
+                return null;
+            }
+
+            // 优先返回默认配置，如果没有默认配置则返回第一个启用的配置
+            for (ModelConfigEntity config : llmConfigs) {
+                if (config.getIsDefault() != null && config.getIsDefault() == 1) {
+                    return config;
+                }
+            }
+
+            return llmConfigs.get(0);
+        } catch (Exception e) {
+            log.error("获取LLM模型配置时发生异常：", e);
+            return null;
+        }
+    }
+}
@@ -55,4 +55,12 @@ public interface ModelConfigService extends BaseService<ModelConfigEntity> {
     * @return TTS平台列表(id和modelName)
     */
    List<Map<String, Object>> getTtsPlatformList();
+
+    /**
+     * 根据模型类型获取所有启用的模型配置
+     *
+     * @param modelType 模型类型（如：LLM, TTS, ASR等）
+     * @return 启用的模型配置列表
+     */
+    List<ModelConfigEntity> getEnabledModelsByType(String modelType);
 }
@@ -502,4 +502,22 @@ public class ModelConfigServiceImpl extends BaseServiceImpl<ModelConfigDao, Mode
    public List<Map<String, Object>> getTtsPlatformList() {
        return modelConfigDao.getTtsPlatformList();
    }
+
+    /**
+     * 根据模型类型获取所有启用的模型配置
+     */
+    @Override
+    public List<ModelConfigEntity> getEnabledModelsByType(String modelType) {
+        if (StringUtils.isBlank(modelType)) {
+            return null;
+        }
+
+        List<ModelConfigEntity> entities = modelConfigDao.selectList(
+                new QueryWrapper<ModelConfigEntity>()
+                        .eq("model_type", modelType)
+                        .eq("is_enabled", 1)
+                        .orderByAsc("sort"));
+
+        return entities;
+    }
 }
@@ -89,7 +89,7 @@ public class ShiroConfig {
        filterMap.put("/config/**", "server");
        filterMap.put("/agent/chat-history/report", "server");
        filterMap.put("/agent/chat-history/download/**", "anon");
-        filterMap.put("/agent/saveMemory/**", "server");
+        filterMap.put("/agent/chat-summary/**", "server");
        filterMap.put("/agent/play/**", "anon");
        filterMap.put("/voiceClone/play/**", "anon");
        filterMap.put("/**", "oauth2");
@@ -4,7 +4,7 @@
      <!-- 左侧元素 -->
      <div class="header-left" @click="goHome">
        <img loading="lazy" alt="" src="@/assets/xiaozhi-logo.png" class="logo-img" />
-        <img loading="lazy" alt="" src="@/assets/xiaozhi-ai.png" class="brand-img" />
+        <img loading="lazy" alt="" :src="xiaozhiAiIcon" class="brand-img" />
      </div>

      <!-- 中间导航菜单 -->
@@ -257,6 +257,24 @@ export default {
          return this.$t("language.zhCN");
      }
    },
+    // 根据当前语言获取对应的xiaozhi-ai图标
+    xiaozhiAiIcon() {
+      const currentLang = this.currentLanguage;
+      switch (currentLang) {
+        case "zh_CN":
+          return require("@/assets/xiaozhi-ai.png");
+        case "zh_TW":
+          return require("@/assets/xiaozhi-ai_zh_TW.png");
+        case "en":
+          return require("@/assets/xiaozhi-ai_en.png");
+        case "de":
+          return require("@/assets/xiaozhi-ai_de.png");
+        case "vi":
+          return require("@/assets/xiaozhi-ai_vi.png");
+        default:
+          return require("@/assets/xiaozhi-ai.png");
+      }
+    },
    // 用户菜单选项
    userMenuOptions() {
      return [
@@ -10,7 +10,7 @@
            gap: 10px;
          ">
          <img loading="lazy" alt="" src="@/assets/xiaozhi-logo.png" style="width: 45px; height: 45px" />
-          <img loading="lazy" alt="" src="@/assets/xiaozhi-ai.png" style="height: 18px" />
+          <img loading="lazy" alt="" :src="xiaozhiAiIcon" style="height: 18px" />
        </div>
      </el-header>
      <div class="login-person">
@@ -192,6 +192,24 @@ export default {
          return this.$t("language.zhCN");
      }
    },
+    // 根据当前语言获取对应的xiaozhi-ai图标
+    xiaozhiAiIcon() {
+      const currentLang = this.currentLanguage;
+      switch (currentLang) {
+        case "zh_CN":
+          return require("@/assets/xiaozhi-ai.png");
+        case "zh_TW":
+          return require("@/assets/xiaozhi-ai_zh_TW.png");
+        case "en":
+          return require("@/assets/xiaozhi-ai_en.png");
+        case "de":
+          return require("@/assets/xiaozhi-ai_de.png");
+        case "vi":
+          return require("@/assets/xiaozhi-ai_vi.png");
+        default:
+          return require("@/assets/xiaozhi-ai.png");
+      }
+    },
  },
  data() {
    return {
@@ -5,7 +5,7 @@
      <el-header>
        <div style="display: flex;align-items: center;margin-top: 15px;margin-left: 10px;gap: 10px;">
          <img loading="lazy" alt="" src="@/assets/xiaozhi-logo.png" style="width: 45px;height: 45px;" />
-          <img loading="lazy" alt="" src="@/assets/xiaozhi-ai.png" style="height: 18px;" />
+          <img loading="lazy" alt="" :src="xiaozhiAiIcon" style="height: 18px;" />
        </div>
      </el-header>
      <div class="login-person">
@@ -108,7 +108,7 @@
          <div style="font-size: 14px;color: #979db1;">
            {{ $t('register.agreeTo') }}
            <div style="display: inline-block;color: #5778FF;cursor: pointer;">{{ $t('register.userAgreement') }}</div>
-            {{ $t('register.and') }}
+            {{ $t('login.and') }}
            <div style="display: inline-block;color: #5778FF;cursor: pointer;">{{ $t('register.privacyPolicy') }}</div>
          </div>
        </div>
@@ -127,6 +127,7 @@ import Api from '@/apis/api';
 import VersionFooter from '@/components/VersionFooter.vue';
 import { getUUID, goToPage, showDanger, showSuccess, sm2Encrypt, validateMobile } from '@/utils';
 import { mapState } from 'vuex';
+import i18n from '@/i18n';

 // 导入语言切换功能

@@ -142,6 +143,28 @@ export default {
      mobileAreaList: state => state.pubConfig.mobileAreaList,
      sm2PublicKey: state => state.pubConfig.sm2PublicKey,
    }),
+    // 获取当前语言
+    currentLanguage() {
+      return i18n.locale || "zh_CN";
+    },
+    // 根据当前语言获取对应的xiaozhi-ai图标
+    xiaozhiAiIcon() {
+      const currentLang = this.currentLanguage;
+      switch (currentLang) {
+        case "zh_CN":
+          return require("@/assets/xiaozhi-ai.png");
+        case "zh_TW":
+          return require("@/assets/xiaozhi-ai_zh_TW.png");
+        case "en":
+          return require("@/assets/xiaozhi-ai_en.png");
+        case "de":
+          return require("@/assets/xiaozhi-ai_de.png");
+        case "vi":
+          return require("@/assets/xiaozhi-ai_vi.png");
+        default:
+          return require("@/assets/xiaozhi-ai.png");
+      }
+    },
    canSendMobileCaptcha() {
      return this.countdown === 0 && validateMobile(this.form.mobile, this.form.areaCode);
    }
@@ -5,7 +5,7 @@
      <el-header>
        <div style="display: flex;align-items: center;margin-top: 15px;margin-left: 10px;gap: 10px;">
          <img loading="lazy" alt="" src="@/assets/xiaozhi-logo.png" style="width: 45px;height: 45px;" />
-          <img loading="lazy" alt="" src="@/assets/xiaozhi-ai.png" style="height: 18px;" />
+          <img loading="lazy" alt="" :src="xiaozhiAiIcon" style="height: 18px;" />
        </div>
      </el-header>
      <div class="login-person">
@@ -83,7 +83,7 @@
            <div style="font-size: 14px;color: #979db1;">
              {{ $t('retrievePassword.agreeTo') }}
              <div style="display: inline-block;color: #5778FF;cursor: pointer;">{{ $t('register.userAgreement') }}</div>
-              {{ $t('register.and') }}
+              {{ $t('login.and') }}
              <div style="display: inline-block;color: #5778FF;cursor: pointer;">{{ $t('register.privacyPolicy') }}</div>
            </div>
          </div>
@@ -103,6 +103,7 @@ import Api from '@/apis/api';
 import VersionFooter from '@/components/VersionFooter.vue';
 import { getUUID, goToPage, showDanger, showSuccess, validateMobile, sm2Encrypt } from '@/utils';
 import { mapState } from 'vuex';
+import i18n from '@/i18n';

 // 导入语言切换功能
 import { changeLanguage } from '@/i18n';
@@ -118,6 +119,28 @@ export default {
      mobileAreaList: state => state.pubConfig.mobileAreaList,
      sm2PublicKey: state => state.pubConfig.sm2PublicKey
    }),
+    // 获取当前语言
+    currentLanguage() {
+      return i18n.locale || "zh_CN";
+    },
+    // 根据当前语言获取对应的xiaozhi-ai图标
+    xiaozhiAiIcon() {
+      const currentLang = this.currentLanguage;
+      switch (currentLang) {
+        case "zh_CN":
+          return require("@/assets/xiaozhi-ai.png");
+        case "zh_TW":
+          return require("@/assets/xiaozhi-ai_zh_TW.png");
+        case "en":
+          return require("@/assets/xiaozhi-ai_en.png");
+        case "de":
+          return require("@/assets/xiaozhi-ai_de.png");
+        case "vi":
+          return require("@/assets/xiaozhi-ai_vi.png");
+        default:
+          return require("@/assets/xiaozhi-ai.png");
+      }
+    },
    canSendMobileCaptcha() {
      return this.countdown === 0 && validateMobile(this.form.mobile, this.form.areaCode);
    }
@@ -53,6 +53,7 @@ class ManageApiClient:
    async def _ensure_async_client(cls):
        """确保异步客户端已创建（为每个事件循环创建独立的客户端）"""
        import asyncio
+
        try:
            loop = asyncio.get_running_loop()
            loop_id = id(loop)
@@ -115,6 +116,7 @@ class ManageApiClient:
    async def _execute_async_request(cls, method: str, endpoint: str, **kwargs) -> Dict:
        """带重试机制的异步请求执行器"""
        import asyncio
+
        retry_count = 0

        while retry_count <= cls.max_retries:
@@ -138,6 +140,7 @@ class ManageApiClient:
    def safe_close(cls):
        """安全关闭所有异步连接池"""
        import asyncio
+
        for client in list(cls._async_clients.values()):
            try:
                asyncio.run(client.aclose())
@@ -149,7 +152,9 @@ class ManageApiClient:

 async def get_server_config() -> Optional[Dict]:
    """获取服务器基础配置"""
-    return await ManageApiClient._instance._execute_async_request("POST", "/config/server-base")
+    return await ManageApiClient._instance._execute_async_request(
+        "POST", "/config/server-base"
+    )


 async def get_agent_models(
@@ -167,17 +172,15 @@ async def get_agent_models(
    )


-async def save_mem_local_short(mac_address: str, short_momery: str) -> Optional[Dict]:
+async def generate_and_save_chat_summary(session_id: str) -> Optional[Dict]:
+    """生成并保存聊天记录总结"""
    try:
        return await ManageApiClient._instance._execute_async_request(
-            "PUT",
-            f"/agent/saveMemory/" + mac_address,
-            json={
-                "summaryMemory": short_momery,
-            },
+            "POST",
+            f"/agent/chat-summary/{session_id}/save",
        )
    except Exception as e:
-        print(f"存储短期记忆到服务器失败: {e}")
+        print(f"生成并保存聊天记录总结失败: {e}")
        return None


@@ -244,7 +244,9 @@ class ConnectionHandler:
                        loop = asyncio.new_event_loop()
                        asyncio.set_event_loop(loop)
                        loop.run_until_complete(
-                            self.memory.save_memory(self.dialogue.dialogue)
+                            self.memory.save_memory(
+                                self.dialogue.dialogue, self.session_id
+                            )
                        )
                    except Exception as e:
                        self.logger.bind(tag=TAG).error(f"保存记忆失败: {e}")
@@ -14,7 +14,7 @@ class MemoryProviderBase(ABC):
        self.llm = llm

    @abstractmethod
-    async def save_memory(self, msgs):
+    async def save_memory(self, msgs, session_id=None):
        """Save a new memory for specific role and return memory ID"""
        print("this is base func", msgs)

@@ -28,7 +28,7 @@ class MemoryProvider(MemoryProviderBase):
            logger.bind(tag=TAG).error(f"详细错误: {traceback.format_exc()}")
            self.use_mem0 = False

-    async def save_memory(self, msgs):
+    async def save_memory(self, msgs, session_id=None):
        if not self.use_mem0:
            return None
        if len(msgs) < 2:
@@ -41,9 +41,7 @@ class MemoryProvider(MemoryProviderBase):
                for message in msgs
                if message.role != "system"
            ]
-            result = self.client.add(
-                messages, user_id=self.role_id
-            )
+            result = self.client.add(messages, user_id=self.role_id)
            logger.bind(tag=TAG).debug(f"Save memory result: {result}")
        except Exception as e:
            logger.bind(tag=TAG).error(f"保存记忆失败: {str(e)}")
@@ -4,7 +4,7 @@ import json
 import os
 import yaml
 from config.config_loader import get_project_dir
-from config.manage_api_client import save_mem_local_short
+from config.manage_api_client import generate_and_save_chat_summary
 import asyncio
 from core.utils.util import check_model_key

@@ -75,18 +75,6 @@ short_term_memory_prompt = """
 ```
 """

-short_term_memory_prompt_only_content = """
-你是一个经验丰富的记忆总结者，擅长将对话内容进行总结摘要，遵循以下规则：
-1、总结user的重要信息，以便在未来的对话中提供更个性化的服务
-2、不要重复总结，不要遗忘之前记忆，除非原来的记忆超过了1800字内，否则不要遗忘、不要压缩用户的历史记忆
-3、用户操控的设备音量、播放音乐、天气、退出、不想对话等和用户本身无关的内容，这些信息不需要加入到总结中
-4、聊天内容中的今天的日期时间、今天的天气情况与用户事件无关的数据，这些信息如果当成记忆存储会影响后序对话，这些信息不需要加入到总结中
-5、不要把设备操控的成果结果和失败结果加入到总结中，也不要把用户的一些废话加入到总结中
-6、不要为了总结而总结，如果用户的聊天没有意义，请返回原来的历史记录也是可以的
-7、只需要返回总结摘要，严格控制在1800字内
-8、不要包含代码、xml，不需要解释、注释和说明，保存记忆时仅从对话提取信息，不要混入示例内容
-"""
-

 def extract_json_data(json_code):
    start = json_code.find("```json")
@@ -144,7 +132,7 @@ class MemoryProvider(MemoryProviderBase):
        with open(self.memory_path, "w", encoding="utf-8") as f:
            yaml.dump(all_memory, f, allow_unicode=True)

-    async def save_memory(self, msgs):
+    async def save_memory(self, msgs, session_id=None):
        # 打印使用的模型信息
        model_info = getattr(self.llm, "model_name", str(self.llm.__class__.__name__))
        logger.bind(tag=TAG).debug(f"使用记忆保存模型: {model_info}")
@@ -188,20 +176,12 @@ class MemoryProvider(MemoryProviderBase):
            except Exception as e:
                print("Error:", e)
        else:
-            result = self.llm.response_no_stream(
-                short_term_memory_prompt_only_content,
-                msgStr,
-                max_tokens=2000,
-                temperature=0.2,
-            )
-            # 使用异步版本，需要在事件循环中运行
-            try:
-                loop = asyncio.get_running_loop()
-                loop.create_task(save_mem_local_short(self.role_id, result))
-            except RuntimeError:
-                # 如果没有运行中的事件循环，创建一个新的
-                asyncio.run(save_mem_local_short(self.role_id, result))
-        logger.bind(tag=TAG).info(f"Save memory successful - Role: {self.role_id}")
+            # 当save_to_file为False时，调用Java端的聊天记录总结接口
+            summary_id = session_id if session_id else self.role_id
+            await generate_and_save_chat_summary(summary_id)
+        logger.bind(tag=TAG).info(
+            f"Save memory successful - Role: {self.role_id}, Session: {session_id}"
+        )

        return self.short_memory

@@ -11,7 +11,7 @@ class MemoryProvider(MemoryProviderBase):
    def __init__(self, config, summary_memory=None):
        super().__init__(config)

-    async def save_memory(self, msgs):
+    async def save_memory(self, msgs, session_id=None):
        logger.bind(tag=TAG).debug("nomem mode: No memory saving is performed.")
        return None