From 574bc2e8dba9d1d11be124f2054ba2243fca125c Mon Sep 17 00:00:00 2001
From: Administrator <15274802129@163.com>
Date: Wed, 03 Sep 2025 13:43:21 +0800
Subject: [PATCH] feat(ai): 优化 prompt 构建逻辑并添加思考内容输出- 在 AliLlmStrategyServiceImpl 中添加了对思考内容的输出- 在 ApiMemberTalkStreamServiceImpl 中优化了 prompt 的构建逻辑 - 在 TestController 中调整了测试用例,使用枚举生成 prompt- 在 TestController 中添加了对思考内容的输出

---
 src/main/java/cc/mrbird/febs/ai/strategy/Impl/HsLlmStrategyServiceImpl.java |   99 ++++++++++++++++++++++++++++++++++---------------
 1 files changed, 68 insertions(+), 31 deletions(-)

diff --git a/src/main/java/cc/mrbird/febs/ai/strategy/Impl/HsLlmStrategyServiceImpl.java b/src/main/java/cc/mrbird/febs/ai/strategy/Impl/HsLlmStrategyServiceImpl.java
index 68f5b9c..4cb18dd 100644
--- a/src/main/java/cc/mrbird/febs/ai/strategy/Impl/HsLlmStrategyServiceImpl.java
+++ b/src/main/java/cc/mrbird/febs/ai/strategy/Impl/HsLlmStrategyServiceImpl.java
@@ -1,29 +1,24 @@
 package cc.mrbird.febs.ai.strategy.Impl;
 
-import cc.mrbird.febs.ai.entity.AiTalkItem;
-import cc.mrbird.febs.ai.res.ai.Report;
-import cc.mrbird.febs.ai.res.memberTalk.ApiMemberTalkStreamVo;
 import cc.mrbird.febs.ai.strategy.LlmStrategyService;
+import cc.mrbird.febs.ai.strategy.enumerates.LlmStrategyContextEnum;
 import cc.mrbird.febs.ai.strategy.param.LlmStrategyDto;
 import cc.mrbird.febs.common.entity.FebsResponse;
 import cc.mrbird.febs.common.exception.FebsException;
 import cn.hutool.core.collection.CollUtil;
+import cn.hutool.core.util.ObjectUtil;
 import cn.hutool.core.util.StrUtil;
-import com.alibaba.dashscope.common.Message;
-import com.alibaba.dashscope.common.Role;
-import com.fasterxml.jackson.core.JsonProcessingException;
-import com.fasterxml.jackson.databind.JsonNode;
 import com.volcengine.ark.runtime.model.completion.chat.*;
 import com.volcengine.ark.runtime.service.ArkService;
 import okhttp3.ConnectionPool;
 import okhttp3.Dispatcher;
 import org.springframework.stereotype.Component;
-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.HashMap;
 import java.util.List;
 import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
@@ -33,7 +28,14 @@
 
     private ArkService service;
 
+    private static final String ak = "AKLTZTQxZjMyZTUxMWJmNDEyNDkzNWExOGQ3ODllNzhhNmQ";
+    private static final String sk = "TmpFeE1qZ3haREExTW1JeE5HRTBZVGc1WlRRNVlqWXpORGd5TWpsak5HWQ==";
+    private static final String baseUrl = "https://ark.cn-beijing.volces.com/api/v3";
     private static final String LinkId = "ep-20250805124033-lhxbf";
+    private static final Double temperature = 0.7;
+    private static final Double topP = 0.9;
+    private static final Integer maxTokens = 2048;
+    private static final Double frequencyPenalty = 0.0;
 
     @PostConstruct
     public void init() {
@@ -47,9 +49,9 @@
         this.service = ArkService.builder()
                 .dispatcher(dispatcher)
                 .connectionPool(connectionPool)
-                .baseUrl("https://ark.cn-beijing.volces.com/api/v3")
-                .ak("AKLTZTQxZjMyZTUxMWJmNDEyNDkzNWExOGQ3ODllNzhhNmQ")
-                .sk("TmpFeE1qZ3haREExTW1JeE5HRTBZVGc1WlRRNVlqWXpORGd5TWpsak5HWQ")
+                .baseUrl(baseUrl)
+                .ak(ak)
+                .sk(sk)
                 .build();
     }
 
@@ -96,10 +98,10 @@
                     .model(LinkId)
                     .messages(messages)
                     .stream(false)
-                    .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)
+                    .temperature(temperature) // 降低温度参数,提高确定性,可能提升速度
+                    .topP(topP)        // 调整topP参数
+                    .maxTokens(maxTokens)  // 减少最大token数
+                    .frequencyPenalty(frequencyPenalty)
                     .build();
 
             List<ChatCompletionChoice> choices = service.createChatCompletion(chatCompletionRequest).getChoices();
@@ -115,7 +117,7 @@
     }
 
     @Override
-    public Flux<FebsResponse> llmInvokeStreaming(List<LlmStrategyDto> dto) {
+    public Flux<FebsResponse> llmInvokeStreamingWithThink(List<LlmStrategyDto> dto) {
         if (CollUtil.isEmpty(dto)){
             throw new FebsException("火山大模型初始化异常");
         }
@@ -126,10 +128,10 @@
                 .messages(messages)
                 .stream(true)
                 .thinking(new ChatCompletionRequest.ChatCompletionRequestThinking("enabled"))
-                .temperature(0.7)
-                .topP(0.9)
-                .maxTokens(2048)
-                .frequencyPenalty(0.0)
+                .temperature(temperature) // 降低温度参数,提高确定性,可能提升速度
+                .topP(topP)        // 调整topP参数
+                .maxTokens(maxTokens)  // 减少最大token数
+                .frequencyPenalty(frequencyPenalty)
                 .build();
 
         return Flux.from(service.streamChatCompletion(chatCompletionRequest))
@@ -144,21 +146,56 @@
                     }
 
                     ChatMessage message = choice.getMessage();
-                    ApiMemberTalkStreamVo apiMemberTalkStreamVo = new ApiMemberTalkStreamVo();
 
-                    // 处理 reasoning content
-                    String reasoningContent = message.getReasoningContent();
-                    if (StrUtil.isNotEmpty(reasoningContent)) {
-                        apiMemberTalkStreamVo.setReasoningContent(reasoningContent);
+
+                    HashMap<String, String> stringStringHashMap = new HashMap<>();
+                    if (ObjectUtil.isNotEmpty(message.getReasoningContent())) {
+                        stringStringHashMap.put(LlmStrategyContextEnum.THINK.name(),message.getReasoningContent().toString());
+                    }
+                    if (ObjectUtil.isNotEmpty(message.getContent())) {
+                        stringStringHashMap.put(LlmStrategyContextEnum.CONTENT.name(),message.getContent().toString());
+                    }
+                    return new FebsResponse().success().data(stringStringHashMap);
+                })
+                .onErrorResume(throwable -> {
+                    throw new FebsException(StrUtil.format("火山大模型流式调用AI服务失:{}",throwable));
+                });
+    }
+
+    @Override
+    public Flux<FebsResponse> llmInvokeStreamingNoThink(List<LlmStrategyDto> dto) {
+        if (CollUtil.isEmpty(dto)){
+            throw new FebsException("火山大模型初始化异常");
+        }
+        List<ChatMessage> messages = getMessages(dto);
+
+        ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder()
+                .model(LinkId)
+                .messages(messages)
+                .stream(true)
+                .temperature(temperature) // 降低温度参数,提高确定性,可能提升速度
+                .topP(topP)        // 调整topP参数
+                .maxTokens(maxTokens)  // 减少最大token数
+                .frequencyPenalty(frequencyPenalty)
+                .build();
+
+        return Flux.from(service.streamChatCompletion(chatCompletionRequest))
+                .map(response -> {
+                    if (response == null || response.getChoices() == null || response.getChoices().isEmpty()) {
+                        return new FebsResponse().success().data("未响应,请重试");
                     }
 
-                    // 安全处理 content
-                    String content = "";
-                    if (message.getContent() != null) {
-                        content = message.getContent().toString();
+                    ChatCompletionChoice choice = response.getChoices().get(0);
+                    if (choice == null || choice.getMessage() == null) {
+                        return new FebsResponse().success().data("END");
                     }
-                    apiMemberTalkStreamVo.setContent(content);
-                    return new FebsResponse().success().data(apiMemberTalkStreamVo);
+                    ChatMessage message = choice.getMessage();
+
+                    HashMap<String, String> stringStringHashMap = new HashMap<>();
+                    if (ObjectUtil.isNotEmpty(message.getContent())) {
+                        stringStringHashMap.put(LlmStrategyContextEnum.CONTENT.name(),message.getContent().toString());
+                    }
+                    return new FebsResponse().success().data(stringStringHashMap);
                 })
                 .onErrorResume(throwable -> {
                     throw new FebsException(StrUtil.format("火山大模型流式调用AI服务失:{}",throwable));

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