本地大漠模型联调，阿里百炼token

src/main/java/com/rj/common/LocalLlmSummaryResult.java

@@ -0,0 +1,11 @@
+package com.rj.common;
+
+/**
+ * 本地 OpenAI 兼容接口生成总结的文本与 token 用量（来自 LangChain4j / 服务端 usage 字段）。
+ */
+public record LocalLlmSummaryResult(
+        String rawContent,
+        Integer totalTokens,
+        Integer inputTokens,
+        Integer outputTokens) {
+}

src/main/java/com/rj/controller/AudioManagementController.java

@@ -720,16 +720,7 @@ public class AudioManagementController {
 
 
             // 4. 通过业务层通用服务调用大模型生成总结并保存数据
-            AudioTextAnalysisFurniture furniture = audioTextAnalysisLlmService.generateSummaryAndSave(
+//            AudioTextAnalysisFurniture furniture = audioTextAnalysisLlmService.generateSummaryAndSave(
-                    sceneType,
-                    recordingText,
-                    audioManagement.getId(),
-                    audioManagement.getSalesName(),
-                    audioManagement.getSalesPhone(),
-                    audioManagement.getCustomerName(),
-                    audioManagement.getCustomerPhone()
-            );
-//            AudioTextAnalysisFurniture furniture = audioTextAnalysisLlmService.generateSummaryAndSave_V2(
 //                    sceneType,
 //                    recordingText,
 //                    audioManagement.getId(),
@@ -738,6 +729,15 @@ public class AudioManagementController {
 //                    audioManagement.getCustomerName(),
 //                    audioManagement.getCustomerPhone()
 //            );
+            AudioTextAnalysisFurniture furniture = audioTextAnalysisLlmService.generateSummaryAndSave_V2(
+                    sceneType,
+                    recordingText,
+                    audioManagement.getId(),
+                    audioManagement.getSalesName(),
+                    audioManagement.getSalesPhone(),
+                    audioManagement.getCustomerName(),
+                    audioManagement.getCustomerPhone()
+            );
             log.info("llm return  furniture Analysis   : ",furniture.toString() );
             if (recordingText == null || recordingText.trim().isEmpty()) {
                 result.put("success", false);

src/main/java/com/rj/dto/AiQaCustomerAskRequestDto.java

@@ -28,6 +28,6 @@ public class AiQaCustomerAskRequestDto {
     @Schema(description = "可选系统提示词，不传则使用默认助手设定")
     private String systemPrompt;
 
-    @Schema(description = "max_tokens，不传则使用配置 ai.qa.local.max-tokens（默认 512）")
+    @Schema(description = "max_tokens，不传则使用配置 ai.qa.local.max-tokens（默认 2048）")
     private Integer maxTokens;
 }

src/main/java/com/rj/entity/AudioManagement.java

@@ -189,6 +189,18 @@ public class AudioManagement implements Serializable {
     @TableField("summary")
     private String summary;
 
+    @Schema(description = "大模型调用总 token 数")
+    @TableField("total_tokens")
+    private Integer totalTokens;
+
+    @Schema(description = "大模型输入 token 数")
+    @TableField("input_tokens")
+    private Integer inputTokens;
+
+    @Schema(description = "大模型输出 token 数")
+    @TableField("output_tokens")
+    private Integer outputTokens;
+
     /**
      * 前端上传的录音文件
      * 此字段不保存在数据库中，仅用于接收前端上传的文件

src/main/java/com/rj/service/IAudioTextAnalysisLlmService.java

@@ -1,6 +1,8 @@
 package com.rj.service;
 
+import com.alibaba.dashscope.aigc.generation.GenerationResult;
 import com.rj.common.AudioAnalysisSceneType;
+import com.rj.common.LocalLlmSummaryResult;
 import com.rj.entity.AudioTextAnalysisFurniture;
 
 /**
@@ -19,7 +21,7 @@ public interface IAudioTextAnalysisLlmService {
      * @param recordingText 录音转写文本
      * @return 大模型返回的原始内容（一般是 JSON 字符串或结构化文本）
      */
-    String generateSummaryByLLM(AudioAnalysisSceneType sceneType, String recordingText);
+    GenerationResult generateSummaryByLLM(AudioAnalysisSceneType sceneType, String recordingText);
 
     /**
      * 与 {@link #generateSummaryByLLM} 相同的提示词与场景逻辑，但通过本地 OpenAI 兼容接口调用大模型
@@ -28,9 +30,9 @@ public interface IAudioTextAnalysisLlmService {
      *
      * @param sceneType     业务场景类型
      * @param recordingText 录音转写文本
-     * @return 大模型返回的原始内容
+     * @return 大模型返回的原始内容及 token 用量（若接口未返回 usage 则对应字段可能为 null）
      */
-    String generateSummaryByLocalLLM(AudioAnalysisSceneType sceneType, String recordingText);
+    LocalLlmSummaryResult generateSummaryByLocalLLM(AudioAnalysisSceneType sceneType, String recordingText);
 
     /**
      * 处理完整的业务逻辑：调用大模型生成总结，解析JSON，保存数据

src/main/java/com/rj/service/impl/AiQaCustomerAskServiceImpl.java

@@ -57,7 +57,7 @@ public class AiQaCustomerAskServiceImpl implements IAiQaCustomerAskService {
             IAiQaItemService aiQaItemService,
             @Value("${ai.qa.local.chat-url:http://192.168.1.44:8000/v1/chat/completions}") String chatCompletionsUrl,
             @Value("${ai.qa.local.default-model:Qwen2.5-7B-Instruct}") String configDefaultModel,
-            @Value("${ai.qa.local.max-tokens:512}") int configMaxTokens) {
+            @Value("${ai.qa.local.max-tokens:2048}") int configMaxTokens) {
         this.restTemplate = restTemplate;
         this.objectMapper = objectMapper;
         this.aiQaMainService = aiQaMainService;

src/main/java/com/rj/service/impl/AudioTextAnalysisLlmServiceImpl.java

@@ -3,6 +3,7 @@ package com.rj.service.impl;
 import com.alibaba.dashscope.aigc.generation.Generation;
 import com.alibaba.dashscope.aigc.generation.GenerationParam;
 import com.alibaba.dashscope.aigc.generation.GenerationResult;
+import com.alibaba.dashscope.aigc.generation.GenerationUsage;
 import com.alibaba.dashscope.common.Message;
 import com.alibaba.dashscope.common.Role;
 import com.alibaba.dashscope.exception.InputRequiredException;
@@ -12,6 +13,7 @@ import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.JsonNode;
 import com.fasterxml.jackson.databind.ObjectMapper;
 import com.rj.common.AudioAnalysisSceneType;
+import com.rj.common.LocalLlmSummaryResult;
 import com.rj.entity.AudioManagement;
 import com.rj.entity.AudioTextAnalysisFurniture;
 import com.rj.entity.AudioTextAnalysisSop;
@@ -24,6 +26,7 @@ import com.rj.service.ITodoItemService;
 import dev.langchain4j.model.chat.response.ChatResponse;
 import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
 import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import dev.langchain4j.model.output.TokenUsage;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.beans.factory.annotation.Value;
@@ -50,6 +53,9 @@ import java.util.concurrent.atomic.AtomicReference;
 @Slf4j
 public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmService {
 
+    /** LangChain4j 流式调用结束后的文本与（可选）服务端 usage */
+    private record LocalChatOutcome(String text, TokenUsage tokenUsage) {}
+
     private static final Map<String, String> SYSTEM_PROMPT_CACHE = new ConcurrentHashMap<>();
     private static final Map<String, String> USER_PROMPT_CACHE = new ConcurrentHashMap<>();
 
@@ -75,7 +81,7 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
     private String localDefaultModel;
 
     /** 单次补全上限（OpenAI max_tokens）；与「模型总上下文 context-length」不是同一概念 */
-    @Value("${ai.qa.local.max-tokens:512}")
+    @Value("${ai.qa.local.max-tokens:2048}")
     private int localMaxTokens;
 
     /** 本地模型上下文长度（与推理侧 max context 一致，用于避免 input+max_tokens 超过上限；默认 16384） */
@@ -117,7 +123,7 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
     }
 
     @Override
-    public String generateSummaryByLLM(AudioAnalysisSceneType sceneType, String recordingText) {
+    public GenerationResult generateSummaryByLLM(AudioAnalysisSceneType sceneType, String recordingText) {
         long startTime = System.currentTimeMillis();
         log.info("开始调用大模型生成总结，场景: {}", sceneType);
 
@@ -145,8 +151,9 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
         try {
             GenerationResult call = gen.call(param);
             String rawContent = call.getOutput().getChoices().get(0).getMessage().getContent();
-            log.info("大模型生成总结完成，场景: {}, 结果长度: {}", sceneType, rawContent != null ? rawContent.length() : 0);
+            log.info("大模型生成总结完成，场景: {}, 输入长度：{},结果长度: {}", sceneType, recordingText.length(),rawContent != null ? rawContent.length() : 0);
-            return rawContent;
+
+            return call;
         } catch (NoApiKeyException e) {
             log.error("API密钥未配置", e);
             throw new RuntimeException("API密钥未配置: " + e.getMessage(), e);
@@ -170,7 +177,7 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
     }
 
     @Override
-    public String generateSummaryByLocalLLM(AudioAnalysisSceneType sceneType, String recordingText) {
+    public LocalLlmSummaryResult generateSummaryByLocalLLM(AudioAnalysisSceneType sceneType, String recordingText) {
         long startTime = System.currentTimeMillis();
         log.info("开始调用本地大模型生成总结，场景: {}", sceneType);
 
@@ -181,14 +188,25 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
 
         try {
             int effectiveMaxTokens = computeEffectiveLocalMaxTokens(systemPrompt, userPrompt);
-            String rawContent = callLocalOpenAiChatCompletionsByLangChain4j(
+            LocalChatOutcome outcome = callLocalOpenAiChatCompletionsByLangChain4j(
                     systemPrompt,
                     userPrompt,
                     localDefaultModel,
                     effectiveMaxTokens);
+            String rawContent = outcome.text();
+            TokenUsage usage = outcome.tokenUsage();
+            Integer total = usage != null ? usage.totalTokenCount() : null;
+            Integer input = usage != null ? usage.inputTokenCount() : null;
+            Integer output = usage != null ? usage.outputTokenCount() : null;
+            log.info(
+                    "本地大模型生成总结完成，token花费，总token: {}, 输入token: {}, 输出token: {}, 输出token详情: {}",
+                    total,
+                    input,
+                    output,
+                    usage != null ? usage.toString() : "无");
             log.info("本地大模型生成总结完成，场景: {}, 结果长度: {}",
                     sceneType, rawContent != null ? rawContent.length() : 0);
-            return rawContent;
+            return new LocalLlmSummaryResult(rawContent, total, input, output);
         } catch (Exception e) {
             log.error("调用本地大模型生成总结失败, 场景: {}", sceneType, e);
             throw new RuntimeException("调用本地大模型失败: " + e.getMessage(), e);
@@ -208,9 +226,9 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
     /**
      * 与 {@link AiQaCustomerAskServiceImpl} 中非流式问答路径一致：LangChain4j + OpenAI 兼容接口，流式聚合为完整文本。
      */
-    private String callLocalOpenAiChatCompletionsByLangChain4j(
+    private LocalChatOutcome callLocalOpenAiChatCompletionsByLangChain4j(
             String systemPrompt, String userContent, String model, int maxTokens) {
-        log.info("音频分析本地 LLM：model={}, chatCompletionsUrl={}", model, chatCompletionsUrl);
+        log.info("音频分析本地 LLM：model={}, chatCompletionsUrl={}, maxTokens={}", model, chatCompletionsUrl,maxTokens);
         String openAiBaseUrl = normalizeOpenAiBaseUrl(chatCompletionsUrl);
         OpenAiStreamingChatModel chatModel = OpenAiStreamingChatModel.builder()
                 .baseUrl(openAiBaseUrl)
@@ -223,6 +241,7 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
         StringBuilder answerBuffer = new StringBuilder();
         CountDownLatch done = new CountDownLatch(1);
         AtomicReference<Throwable> errorRef = new AtomicReference<>();
+        AtomicReference<TokenUsage> usageRef = new AtomicReference<>();
 
         chatModel.chat(prompt, new StreamingChatResponseHandler() {
             @Override
@@ -234,6 +253,9 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
 
             @Override
             public void onCompleteResponse(ChatResponse completeResponse) {
+                if (completeResponse != null) {
+                    usageRef.set(completeResponse.tokenUsage());
+                }
                 done.countDown();
             }
 
@@ -262,7 +284,7 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
         if (answer.isEmpty()) {
             throw new IllegalStateException("本地大模型流式调用返回空响应");
         }
-        return answer;
+        return new LocalChatOutcome(answer, usageRef.get());
     }
 
     private String normalizeOpenAiBaseUrl(String rawUrl) {
@@ -429,8 +451,11 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
             String customerName,
             String customerPhone) {
         try {
+            GenerationResult generationResult = generateSummaryByLLM(sceneType, recordingText);
+            log.info("大模型生成总结完成，token花费，总token: {}, 输入token: {}, 输出token: {}, 输出token详情: {}",
+                    generationResult.getUsage().getTotalTokens(),generationResult.getUsage().getInputTokens(),generationResult.getUsage().getOutputTokens(),generationResult.getUsage().getOutputTokensDetails());
             // 1. 调用大模型生成总结
-            String rawContent = generateSummaryByLLM(sceneType, recordingText);
+            String rawContent = generationResult.getOutput().getChoices().get(0).getMessage().getContent();
             if (rawContent == null || rawContent.trim().isEmpty()) {
                 log.warn("大模型返回内容为空");
                 return null;
@@ -443,10 +468,11 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
             // 3. 解析JSON并填充对象
             applyStructuredResult(furniture, rawContent, parentId, ownerName, ownerPhone, customerName, customerPhone);
 
-            // 4. 更新AudioManagement的summary字段
+            // 4. 更新AudioManagement的summary与 token 用量
             AudioManagement audioManagement = new AudioManagement();
             audioManagement.setId(parentId);
-            audioManagement.setSummary(furniture.getSummarySentence());
+            audioManagement.setSummary(rawContent);
+            applyDashScopeUsageToAudioManagement(audioManagement, generationResult);
             audioManagementService.updateById(audioManagement);
 
             // 5. 保存或更新AudioTextAnalysisFurniture记录
@@ -491,21 +517,47 @@ public class AudioTextAnalysisLlmServiceImpl implements IAudioTextAnalysisLlmSer
             String ownerPhone,
             String customerName,
             String customerPhone) {
-        // 1. 调用大模型生成总结
+        LocalLlmSummaryResult llmResult = generateSummaryByLocalLLM(sceneType, recordingText);
-        String rawContent = generateSummaryByLocalLLM(sceneType, recordingText);
+        String rawContent = llmResult.rawContent();
         if (rawContent == null || rawContent.trim().isEmpty()) {
             log.warn("大模型返回内容为空");
             return null;
         }
         AudioTextAnalysisFurniture furniture = new AudioTextAnalysisFurniture();
-        // 4. 更新AudioManagement的summary字段
         AudioManagement audioManagement = new AudioManagement();
         audioManagement.setId(parentId);
         audioManagement.setSummary(rawContent);
+        applyLocalLlmTokenCountsToAudioManagement(audioManagement, llmResult);
         audioManagementService.updateById(audioManagement);
 
         return furniture;
     }
+
+    private void applyDashScopeUsageToAudioManagement(AudioManagement target, GenerationResult result) {
+        if (result == null || result.getUsage() == null) {
+            return;
+        }
+        GenerationUsage usage = result.getUsage();
+        target.setTotalTokens(usage.getTotalTokens());
+        target.setInputTokens(usage.getInputTokens());
+        target.setOutputTokens(usage.getOutputTokens());
+    }
+
+    private void applyLocalLlmTokenCountsToAudioManagement(AudioManagement target, LocalLlmSummaryResult result) {
+        if (result == null) {
+            return;
+        }
+        if (result.totalTokens() != null) {
+            target.setTotalTokens(result.totalTokens());
+        }
+        if (result.inputTokens() != null) {
+            target.setInputTokens(result.inputTokens());
+        }
+        if (result.outputTokens() != null) {
+            target.setOutputTokens(result.outputTokens());
+        }
+    }
+
     /**
      * 解析大模型返回的JSON结果并填充到AudioTextAnalysisFurniture对象
      *

src/main/resources/prompts/audio_text_analysis_summary_user_prompts.txt

@@ -3,7 +3,7 @@
 字段要求：
 1. style_type：主题类型，仅可填「会议纪要」「课堂纪要」「面试纪要」。
 2. members：团队成员，描述 团队成员组成 ，比如： 经理，员工， 面试者，面试管，老师 ， 重点反应每类人员的数量，姓名， 优点，贡献大小 等。
-4. summary：一句话总结，应该简洁明了总结关键信息， 重点列举5到15个重点信息， 等关键信息, summary应该是树形结构，总共分3层 ，比如 summary是第一层 ，第二层 类似： summary1，summary2，summary3，summary[N] ,第三层 类似：summary1_1,summary1_2,summary1_3,summary1_[N]。
+4. summary：一句话总结,生成的描述不少于200汉字，应该简洁明了总结关键信息， 重点列举5到15个重点信息， 等关键信息, summary应该是树形结构，总共分3层 ，比如 summary是第一层 ，第二层 类似： summary1，summary2，summary3，summary[N] ,第三层 类似：summary1_1,summary1_2,summary1_3,summary1_[N]。
 5.summary1:  对summary  提供支持，summary1是对summary的详细解释的第一个理由，  summary1是对summary深入分析的一部分 。
 6.summary1_1:   对summary1提供支持，summary1_1是对summary1的详细解释的第一个理由，  summary1_1是对summary1深入分析的一部分 。
 7.summary1_[N]:   对summary1提供支持，summary1_[N]是对summary1的详细解释的第一个理由，  summary1_[N]是对summary1深入分析的一部分 ,[N] 这里的N不能超过数字 10。