From 582857ad3da9fe0e168364323c313e17993e9a17 Mon Sep 17 00:00:00 2001
From: Administrator <15274802129@163.com>
Date: Tue, 26 Aug 2025 11:03:49 +0800
Subject: [PATCH] fix(ai): 优化 AI 服务调用失败时的错误处理- 在 AiServiceImpl 类中,改进了流式调用 AI 服务失败时的错误响应 - 使用 FebsResponse 的 fail() 方法创建失败响应,增加了错误状态 - 修改了两处错误处理逻辑,提高了错误信息的准确性和可读性

---
 src/main/java/cc/mrbird/febs/ai/service/impl/AiServiceImpl.java |  393 +++++++++++++++++++++++++++++++++++++++++++++++++-------
 1 files changed, 344 insertions(+), 49 deletions(-)

diff --git a/src/main/java/cc/mrbird/febs/ai/service/impl/AiServiceImpl.java b/src/main/java/cc/mrbird/febs/ai/service/impl/AiServiceImpl.java
index 3ab9f2c..cccfe88 100644
--- a/src/main/java/cc/mrbird/febs/ai/service/impl/AiServiceImpl.java
+++ b/src/main/java/cc/mrbird/febs/ai/service/impl/AiServiceImpl.java
@@ -1,11 +1,22 @@
 package cc.mrbird.febs.ai.service.impl;
 
+import cc.mrbird.febs.ai.entity.AiTalkItem;
+import cc.mrbird.febs.ai.enumerates.AiTypeEnum;
 import cc.mrbird.febs.ai.entity.AiProductRole;
+import cc.mrbird.febs.ai.req.ai.AiMessage;
 import cc.mrbird.febs.ai.req.ai.AiRequest;
+import cc.mrbird.febs.ai.req.talk.AiTalkAnswerStream;
 import cc.mrbird.febs.ai.res.ai.AiResponse;
+import cc.mrbird.febs.ai.res.ai.RadarDataItem;
 import cc.mrbird.febs.ai.res.ai.Report;
+import cc.mrbird.febs.ai.res.memberTalk.ApiMemberTalkStreamVo;
 import cc.mrbird.febs.ai.service.AiProductRoleService;
 import cc.mrbird.febs.ai.service.AiService;
+import cc.mrbird.febs.ai.service.AiTalkItemService;
+import cc.mrbird.febs.common.entity.FebsResponse;
+import cn.hutool.core.collection.CollUtil;
+import cn.hutool.core.util.StrUtil;
+import cn.hutool.json.JSONUtil;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.JsonNode;
 import com.fasterxml.jackson.databind.ObjectMapper;
@@ -18,12 +29,15 @@
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.stereotype.Service;
 import org.springframework.util.StringUtils;
+import reactor.core.publisher.Flux;
 
 import javax.annotation.PostConstruct;
 import javax.annotation.PreDestroy;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 import java.util.concurrent.TimeUnit;
+import java.util.function.Consumer;
 import java.util.regex.Matcher;
 import java.util.regex.Pattern;
 import java.util.stream.Collectors;
@@ -37,11 +51,29 @@
 public class AiServiceImpl implements AiService {
 
     private static final String CODE_SUCCESS = "200";
+    private static final String CODE_GOING_ON = "199";
     private static final String CODE_NOT_FOUND = "201";
     private static final String CODE_ERROR = "500";
 
+    private static final String SCHEMA_JSON = "{\n" +
+            "     \"radar_data\": {\n" +
+            "       \"problem_understanding\": \"object\",\n" +
+            "       \"fluency\": \"object\",\n" +
+            "       \"principle_adherence\": \"object\",\n" +
+            "       \"logicality\": \"object\",\n" +
+            "       \"knowledge_mastery\": \"object\"\n" +
+            "     },\n" +
+            "     \"evaluation\": {\n" +
+            "       \"highlight\": \"object\",\n" +
+            "       \"suggestion\": \"object\",\n" +
+            "       \"reference_answer\": \"object\",\n" +
+            "       \"key_knowledge\": \"object\"\n" +
+            "     }\n" +
+            "   }";
+
     private final AiProductRoleService aiProductRoleService;
     private final ObjectMapper objectMapper;
+    private final AiTalkItemService aiTalkItemService;
 
     @Value("${ai.service.ak}")
     private String ak;
@@ -56,8 +88,13 @@
 
     @PostConstruct
     public void init() {
-        ConnectionPool connectionPool = new ConnectionPool(10, 30, TimeUnit.SECONDS);
+        // 增加连接池大小和存活时间
+        ConnectionPool connectionPool = new ConnectionPool(32, 60, TimeUnit.SECONDS);
         Dispatcher dispatcher = new Dispatcher();
+        // 增加并发请求数量
+        dispatcher.setMaxRequests(128);
+        dispatcher.setMaxRequestsPerHost(32);
+
         this.service = ArkService.builder()
                 .dispatcher(dispatcher)
                 .connectionPool(connectionPool)
@@ -75,7 +112,7 @@
     }
 
     @Override
-    public AiResponse start(String productRoleId, String content) {
+    public AiResponse start(List<AiMessage> aiMessageDtoList,Integer type,String productRoleId, String content, String question) {
         if (!StringUtils.hasText(productRoleId)) {
             log.warn("productRoleId 不能为空");
             return buildErrorResponse(CODE_NOT_FOUND, "AI陪练不存在");
@@ -87,20 +124,36 @@
             return buildErrorResponse(CODE_NOT_FOUND, "AI陪练不存在");
         }
 
-        String promptTemplate = aiProductRole.getPromptTemplate();
-        String linkId = aiProductRole.getLinkId();
 
-        if (!StringUtils.hasText(promptTemplate) || !StringUtils.hasText(linkId)) {
-            log.warn("角色配置不完整,promptTemplate 或 linkId 为空,productRoleId: {}", productRoleId);
+        String promptTemplate = "作为一个智能助手,请回答我提出的问题。";
+        if (AiTypeEnum.QUESTION.getCode() ==  type){
+            promptTemplate = aiProductRole.getPromptHead();
+        }
+        if (AiTypeEnum.ANSWER.getCode() ==  type){
+            promptTemplate = aiProductRole.getPromptTemplate()+question;
+        }
+        log.info("promptTemplate: {}", promptTemplate);
+        String linkId = aiProductRole.getLinkId();
+        String jsonTemplate = aiProductRole.getJsonTemplate();
+
+        if (
+                !StringUtils.hasText(promptTemplate)
+                || !StringUtils.hasText(linkId)
+                || !StringUtils.hasText(jsonTemplate)
+        ) {
+            log.warn("角色配置不完整,promptTemplate 或 linkId 或 jsonTemplate为空,productRoleId: {}", productRoleId);
             return buildErrorResponse(CODE_ERROR, "角色配置不完整");
         }
 
         AiRequest aiRequest = new AiRequest();
         aiRequest.setPromptTemplate(promptTemplate);
+        aiRequest.setJsonTemplate(jsonTemplate);
         aiRequest.setLinkId(linkId);
         aiRequest.setContent(content);
-
-        return question(aiRequest);
+        if (CollUtil.isNotEmpty(aiMessageDtoList)){
+            aiRequest.setAiMessageDtoList(aiMessageDtoList);
+        }
+        return this.question(aiRequest);
     }
 
     @Override
@@ -108,36 +161,34 @@
         String promptTemplate = aiRequest.getPromptTemplate();
         String linkId = aiRequest.getLinkId();
         String content = aiRequest.getContent();
-        if (!StringUtils.hasText(promptTemplate) || !StringUtils.hasText(linkId) || !StringUtils.hasText(content)) {
-            log.warn("请求参数不完整,promptTemplate: {}, linkId: {}, content: {}", promptTemplate, linkId, content);
+        String jsonTemplate = aiRequest.getJsonTemplate();
+        if (
+                !StringUtils.hasText(promptTemplate)
+                        || !StringUtils.hasText(linkId)
+                        || !StringUtils.hasText(content)
+                        || !StringUtils.hasText(jsonTemplate)
+        ) {
+            log.warn("请求参数不完整,promptTemplate: {}, linkId: {}, content: {}, jsonTemplate: {}", promptTemplate, linkId, content, jsonTemplate);
             return buildErrorResponse(CODE_ERROR, "请求参数不完整");
         }
 
-        final List<ChatMessage> messages = new ArrayList<>();
-        final ChatMessage systemMessage = ChatMessage.builder().role(ChatMessageRole.SYSTEM).content(promptTemplate).build();
-        final ChatMessage userMessage = ChatMessage.builder().role(ChatMessageRole.USER).content(content).build();
+        List<ChatMessage> messages = new ArrayList<>();
+        ChatMessage systemMessage = ChatMessage.builder().role(ChatMessageRole.SYSTEM).content(promptTemplate).build();
+        ChatMessage userMessage = ChatMessage.builder().role(ChatMessageRole.USER).content(content).build();
         messages.add(systemMessage);
+        if (CollUtil.isNotEmpty(aiRequest.getAiMessageDtoList())){
+            aiRequest.getAiMessageDtoList().forEach(aiMessageDto -> {
+                ChatMessage message = ChatMessage.builder()
+                        .role(aiMessageDto.getRole())
+                        .content(aiMessageDto.getContent())
+                        .build();
+                messages.add(message);
+            });
+        }
         messages.add(userMessage);
 
-        // 生成 JSON Schema
-        String schemaJson = "{\n" +
-                "     \"radar_data\": {\n" +
-                "       \"problem_understanding\": \"object\",\n" +
-                "       \"fluency\": \"object\",\n" +
-                "       \"principle_adherence\": \"object\",\n" +
-                "       \"logicality\": \"object\",\n" +
-                "       \"knowledge_mastery\": \"object\"\n" +
-                "     },\n" +
-                "     \"evaluation\": {\n" +
-                "       \"highlight\": \"object\",\n" +
-                "       \"suggestion\": \"object\",\n" +
-                "       \"reference_answer\": \"object\",\n" +
-                "       \"key_knowledge\": \"object\"\n" +
-                "     }\n" +
-                "   }";
         try {
-            JsonNode schemaNode = objectMapper.readTree(schemaJson);
-            // 配置响应格式
+            JsonNode schemaNode = objectMapper.readTree(jsonTemplate);
             ChatCompletionRequest.ChatCompletionRequestResponseFormat responseFormat = new ChatCompletionRequest.ChatCompletionRequestResponseFormat(
                     "json_schema",
                     new ResponseFormatJSONSchemaJSONSchemaParam(
@@ -152,28 +203,136 @@
                     .messages(messages)
                     .stream(false)
                     .responseFormat(responseFormat)
-                    .temperature(1.0)
-                    .topP(0.7)
-                    .maxTokens(4096)
+                    .temperature(0.7) // 降低温度参数,提高确定性,可能提升速度
+                    .topP(0.9)        // 调整topP参数
+                    .maxTokens(2048)  // 减少最大token数
                     .frequencyPenalty(0.0)
                     .build();
+
             List<ChatCompletionChoice> choices = service.createChatCompletion(chatCompletionRequest).getChoices();
             String result = choices.stream()
                     .map(choice -> choice.getMessage().getContent())
                     .filter(contentObj -> contentObj != null)
                     .map(Object::toString)
                     .collect(Collectors.joining());
-
             Report report = this.extractReportData(result);
             return buildSuccessResponse(report, result);
         } catch (JsonProcessingException e) {
             log.error("初始化AI服务失败,JSON格式化输出初始化失败", e);
             return buildErrorResponse(CODE_ERROR, "AI服务调用失败");
         } catch (Exception e) {
-            log.error("调用AI服务失败,modelId: {}, content: {}", linkId, content, e);
+            log.error("调用AI服务失败,modelId: {}", linkId, e);
             return buildErrorResponse(CODE_ERROR, "AI服务调用失败");
         }
     }
+
+    public static void main(String[] args) {
+        Report report = new Report();
+        List<RadarDataItem> radarDataItems = new ArrayList<>();
+
+        RadarDataItem item1 = new RadarDataItem();
+        item1.setName("A");
+        item1.setCode("A");
+        item1.setScore("80");
+        radarDataItems.add(item1);
+
+        RadarDataItem item2 = new RadarDataItem();
+        item2.setName("A");
+        item2.setCode("A");
+        item2.setScore("80");
+        radarDataItems.add(item2);
+        report.setRadarDataItems(radarDataItems);
+
+        System.out.println(JSONUtil.parse( report));
+
+    }
+
+    @Override
+    public void streamQuestion(AiRequest aiRequest, Consumer<AiResponse> callback) {
+
+        String promptTemplate = aiRequest.getPromptTemplate();
+        String linkId = aiRequest.getLinkId();
+        String content = aiRequest.getContent();
+        if (!StringUtils.hasText(promptTemplate) || !StringUtils.hasText(linkId) || !StringUtils.hasText(content)) {
+            log.warn("请求参数不完整,promptTemplate: {}, linkId: {}, content: {}", promptTemplate, linkId, content);
+        }
+
+        final List<ChatMessage> messages = new ArrayList<>();
+        final ChatMessage systemMessage = ChatMessage.builder().role(ChatMessageRole.SYSTEM).content(promptTemplate).build();
+        final ChatMessage userMessage = ChatMessage.builder().role(ChatMessageRole.USER).content(content).build();
+        messages.add(systemMessage);
+        messages.add(userMessage);
+
+        try {
+            JsonNode schemaNode = objectMapper.readTree(SCHEMA_JSON);
+            ChatCompletionRequest.ChatCompletionRequestResponseFormat responseFormat = new ChatCompletionRequest.ChatCompletionRequestResponseFormat(
+                    "json_schema",
+                    new ResponseFormatJSONSchemaJSONSchemaParam(
+                            "ai_response",
+                            "json数据响应",
+                            schemaNode,
+                            true
+                    )
+            );
+            ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
+                    .model(linkId)
+                    .messages(messages)
+                    .stream(true) // 启用流式响应
+                    .responseFormat(responseFormat)
+                    .temperature(0.7)
+                    .topP(0.9)
+                    .maxTokens(2048)
+                    .build();
+
+            service.streamChatCompletion(chatCompletionRequest)
+                    .doOnError(Throwable::printStackTrace) // 处理错误
+                    .blockingForEach(response -> {
+                        AiResponse partialResponse = new AiResponse();
+                        if (response.getChoices() != null && !response.getChoices().isEmpty()) {
+                            String responseStr = String.valueOf(response.getChoices().get(0).getMessage().getContent());
+                            if (responseStr != null) {
+                                // 构造部分响应并回调
+                                partialResponse = buildGOINGONResponse(responseStr);
+                            }
+                        }else{
+                            partialResponse = buildPartialResponse("成功");
+                        }
+                        callback.accept(partialResponse);
+                    });
+//            service.streamChatCompletion(chatCompletionRequest)
+//                    .doOnError(throwable -> {
+//                        log.error("流式调用AI服务失败", throwable);
+//                        callback.accept(buildErrorResponse(CODE_ERROR, "AI服务调用失败"));
+//                    })
+//                    .subscribe(chatCompletionChunk -> {
+//                        // 处理每个数据块
+//                        Object chunkContent = chatCompletionChunk.getChoices().get(0).getMessage().getContent();
+//                        // 构造部分响应并回调
+//                        AiResponse partialResponse = buildGOINGONResponse(chunkContent);
+//                        callback.accept(partialResponse);
+//                    });
+        } catch (Exception e) {
+            log.error("调用AI服务失败", e);
+            callback.accept(buildErrorResponse(CODE_ERROR, "AI服务调用失败"));
+        }
+    }
+
+    private AiResponse buildGOINGONResponse(Object chunkContent) {
+        AiResponse response = new AiResponse();
+        response.setCode(CODE_GOING_ON);
+        response.setDescription("成功");
+        response.setResContext(chunkContent.toString());
+        return response;
+    }
+
+    private AiResponse buildPartialResponse(Object chunkContent) {
+        AiResponse response = new AiResponse();
+        response.setCode(CODE_SUCCESS);
+        response.setDescription("成功");
+        response.setResContext(chunkContent.toString());
+        return response;
+    }
+
 
     private static final Pattern JSON_PATTERN = Pattern.compile(
             "<\\|FunctionCallBegin\\|>(.*?)<\\|FunctionCallEnd\\|>",
@@ -182,22 +341,19 @@
 
     @Override
     public Report extractReportData(String modelOutput) {
-        // 提取JSON部分
         Matcher matcher = JSON_PATTERN.matcher(modelOutput);
         if (!matcher.find()) {
-            log.warn("未匹配到FunctionCall内容,原始输出: {}", modelOutput);
+            log.warn("未匹配到FunctionCall内容,原始输出长度: {}", modelOutput);
             return null;
         }
 
         String jsonContent = matcher.group(1);
-        log.debug("提取到的JSON内容: {}", jsonContent);
+        log.debug("提取到的JSON内容长度: {}", jsonContent.length());
 
-        // 解析JSON到Report对象
         try {
             return objectMapper.readValue(jsonContent, Report.class);
         } catch (JsonProcessingException e) {
-            log.error("JSON解析失败,原始内容: {}", jsonContent, e);
-            // 尝试修复截断的JSON(可选)
+            log.error("JSON解析失败,原始内容长度: {}", jsonContent.length(), e);
             Report repairedReport = tryRepairTruncatedJson(jsonContent);
             if (repairedReport != null) {
                 log.info("成功修复截断的JSON");
@@ -207,13 +363,152 @@
         }
     }
 
-    /**
-     * 尝试修复截断的JSON字符串
-     * @param truncatedJson 可能被截断的JSON字符串
-     * @return 修复后的Report对象,如果无法修复则返回null
-     */
+        // 修改服务实现
+    @Override
+    public Flux<FebsResponse> answerStream(String question) {
+        log.info("----- standard request -----");
+
+        final ChatMessage systemMessage = ChatMessage.builder()
+                .role(ChatMessageRole.SYSTEM)
+                .content("你是豆包,是由字节跳动开发的 AI 人工智能助手")
+                .build();
+
+        final ChatMessage userMessage = ChatMessage.builder()
+                .role(ChatMessageRole.USER)
+                .content(question)
+                .build();
+
+        List<ChatMessage> messages = Arrays.asList(systemMessage, userMessage);
+
+        ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
+                .model("ep-20250805124033-lhxbf")
+                .messages(messages)
+                .stream(true)
+                .thinking(new ChatCompletionRequest.ChatCompletionRequestThinking("enabled"))
+                .temperature(0.7)
+                .topP(0.9)
+                .maxTokens(2048)
+                .frequencyPenalty(0.0)
+                .build();
+
+        return Flux.from(service.streamChatCompletion(chatCompletionRequest))
+                .map(response -> {
+                    if (response == null || response.getChoices() == null || response.getChoices().isEmpty()) {
+                        return new FebsResponse().success().data("END");
+                    }
+
+                    ChatCompletionChoice choice = response.getChoices().get(0);
+                    if (choice == null || choice.getMessage() == null) {
+                        return new FebsResponse().success().data("END");
+                    }
+
+
+                    ApiMemberTalkStreamVo apiMemberTalkStreamVo = new ApiMemberTalkStreamVo();
+                    // 判断是否触发深度思考,触发则打印模型输出的思维链内容
+                    ChatMessage message = choice.getMessage();
+                    if (message.getReasoningContent()!= null &&!message.getReasoningContent().isEmpty()) {
+                        apiMemberTalkStreamVo.setReasoningContent(message.getReasoningContent());
+//                        System.out.print(message.getReasoningContent());
+                    }
+
+                    String content = message.getContent() == null ? "" : message.getContent().toString();
+                    apiMemberTalkStreamVo.setContent(content);
+                    System.out.print(content);
+                    return new FebsResponse().success().data(apiMemberTalkStreamVo);
+                })
+                .onErrorResume(throwable -> {
+                    log.error("流式调用AI服务失败,问题输入: {}", question, throwable);
+                    FebsResponse errorResponse = new FebsResponse().fail().message("AI服务调用失败");
+                    return Flux.just(errorResponse);
+                });
+    }
+
+    @Override
+    public Flux<FebsResponse> answerStreamV2(AiTalkAnswerStream dto) {
+        String question = dto.getQuestion();
+        log.info("----- standard request -----");
+
+
+        List<ChatMessage> messages = new ArrayList<>();
+        final ChatMessage systemMessage = ChatMessage.builder()
+                .role(ChatMessageRole.SYSTEM)
+                .content("你是豆包,是由字节跳动开发的 AI 人工智能助手")
+                .build();
+        messages.add(systemMessage);
+        //获取消息记录
+        if (StrUtil.isNotEmpty(dto.getTalkId())){
+            List<AiTalkItem> aiTalkItems = aiTalkItemService.getListByTalkId(dto.getTalkId());
+            if(CollUtil.isNotEmpty(aiTalkItems)){
+                for (AiTalkItem aiTalkItem : aiTalkItems){
+                    if (aiTalkItem.getType() == 1){
+                        ChatMessage memberMessage = ChatMessage.builder()
+                                .role(ChatMessageRole.USER)
+                                .content(aiTalkItem.getContext())
+                                .build();
+                        messages.add(memberMessage);
+                    }
+                    if (aiTalkItem.getType() == 2){
+                        ChatMessage assistantMessage = ChatMessage.builder()
+                                .role(ChatMessageRole.ASSISTANT)
+                                .content(aiTalkItem.getContext())
+                                .build();
+                        messages.add(assistantMessage);
+                    }
+                }
+            }
+        }
+
+        final ChatMessage userMessage = ChatMessage.builder()
+                .role(ChatMessageRole.USER)
+                .content(question)
+                .build();
+        messages.add(userMessage);
+
+        ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
+                .model("ep-20250805124033-lhxbf")
+                .messages(messages)
+                .stream(true)
+                .thinking(new ChatCompletionRequest.ChatCompletionRequestThinking("enabled"))
+                .temperature(0.7)
+                .topP(0.9)
+                .maxTokens(2048)
+                .frequencyPenalty(0.0)
+                .build();
+
+        return Flux.from(service.streamChatCompletion(chatCompletionRequest))
+                .map(response -> {
+                    if (response == null || response.getChoices() == null || response.getChoices().isEmpty()) {
+                        return new FebsResponse().success().data("END");
+                    }
+
+                    ChatCompletionChoice choice = response.getChoices().get(0);
+                    if (choice == null || choice.getMessage() == null) {
+                        return new FebsResponse().success().data("END");
+                    }
+
+
+                    ApiMemberTalkStreamVo apiMemberTalkStreamVo = new ApiMemberTalkStreamVo();
+                    // 判断是否触发深度思考,触发则打印模型输出的思维链内容
+                    ChatMessage message = choice.getMessage();
+                    if (message.getReasoningContent()!= null &&!message.getReasoningContent().isEmpty()) {
+                        apiMemberTalkStreamVo.setReasoningContent(message.getReasoningContent());
+                        System.out.print(message.getReasoningContent());
+                    }
+
+                    String content = message.getContent() == null ? "" : message.getContent().toString();
+                    apiMemberTalkStreamVo.setContent(content);
+                    System.out.print(content);
+                    return new FebsResponse().success().data(apiMemberTalkStreamVo);
+                })
+                .onErrorResume(throwable -> {
+                    log.error("流式调用AI服务失败,问题输入: {}", question, throwable);
+                    FebsResponse errorResponse = new FebsResponse().fail().message("AI服务调用失败");
+                    return Flux.just(errorResponse);
+                });
+    }
+
+
     private Report tryRepairTruncatedJson(String truncatedJson) {
-        // 简单的修复策略:尝试添加缺失的结束括号
         String[] repairAttempts = {
                 truncatedJson + "\"}}}",
                 truncatedJson + "}}}",
@@ -229,7 +524,7 @@
             }
         }
 
-        log.warn("无法修复截断的JSON: {}", truncatedJson);
+        log.warn("无法修复截断的JSON,原始内容长度: {}", truncatedJson.length());
         return null;
     }
 

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