feat:fix knowledge-gragh model context problem

Author: ruanrongman

docs/README.md

@@ -88,7 +88,7 @@ mysql,redis,emqx和influxdb环境。
 欢迎加入群聊一起交流讨论有关Aiot相关的话题，免费获取智控台的前端源码，链接过期了可以issue或email提醒一下作者。
 
 <div style="width: 250px;margin: 0 auto;">
-    <img src="./images/7324111a30d61b0b9a0cc63f2d1c8781.jpg" width="250px"/>
+    <img src="./images/0accee4cf2e6b999e567432173cbf62c.jpg" width="250px"/>
 </div>
 
 

docs/images/0accee4cf2e6b999e567432173cbf62c.jpg

@@ -116,7 +116,7 @@ mysql,redis,emqx和influxdb环境，安装详情请看官方文档。
 欢迎加入群聊一起交流讨论有关Aiot相关的话题，有机会获取项目的免费部署咨询，链接过期了可以issue或email提醒一下作者。
 
 <div style="width: 250px;margin: 0 auto;">
-    <img src="./docs/images/7324111a30d61b0b9a0cc63f2d1c8781.jpg" width="250px"/>
+    <img src="./docs/images/0accee4cf2e6b999e567432173cbf62c.jpg" width="250px"/>
 </div>
 
 ## 致谢

docs/images/7324111a30d61b0b9a0cc63f2d1c8781.jpg

@@ -22,8 +22,10 @@
 import com.alibaba.fastjson.JSONObject;
 import lombok.Data;
 import lombok.extern.slf4j.Slf4j;
+import org.jspecify.annotations.NonNull;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.beans.factory.annotation.Value;
+import org.springframework.scheduling.annotation.Async;
 import org.springframework.stereotype.Component;
 import top.rslly.iot.models.KnowledgeGraphicNodeEntity;
 import top.rslly.iot.param.request.KnowledgeGraphicNode;
@@ -42,7 +44,7 @@
 @Component
 @Data
 @Slf4j
-public class KnowledgeGraphicTool implements BaseTool<String> {
+public class KnowledgeGraphicTool {
   @Autowired
   private ProductService productService;
   @Autowired
@@ -53,40 +55,62 @@ public class KnowledgeGraphicTool implements BaseTool<String> {
   private KnowledgeGraphicPrompt knowledgeGraphicPrompt;
   @Value("${ai.knowledge_graphic_llm}")
   private String llmName;
+  @Value("${ai.knowledge_graphic.max_graphic_length:30000}")
+  private int maxGraphicLength;
   private String name = "knowledgeGraphicTool";
   private String description = """
       This tool is working for knowledge graphic generation to help LLM
       handle relations between entities.
       """;
 
-  @Override
   public String run(String question) {
     return null;
   }
 
-  @Override
-  public String run(String question, Map<String, Object> globalMessage) {
+  @Async("taskExecutor")
+  public void run(String question, Map<String, Object> globalMessage) {
     int productId = (int) globalMessage.get("productId");
     if (productService.findAllById(productId).isEmpty())
-      return "";
+      return;
     LLM llm = llmDiyUtility.getDiyLlm(productId, llmName, "knowledgeGraphic");
     List<ModelMessage> memory =
         Optional.ofNullable((List<ModelMessage>) globalMessage.get("memory"))
             .orElse(Collections.emptyList());
     List<ModelMessage> messages = new ArrayList<>();
+    String knowledgeSystemPrompt =
+        knowledgeGraphicPrompt.getKnowledgeGraphicPrompt(productId) + memory;
     ModelMessage systemMessage = new ModelMessage(ModelMessageRole.SYSTEM.value(),
-        knowledgeGraphicPrompt.getKnowledgeGraphicPrompt(productId) + memory);
-    ModelMessage userMessage = new ModelMessage(ModelMessageRole.USER.value(),
-        "Start generate knowledge graphic.");
+        knowledgeSystemPrompt);
+    ModelMessage userMessage = getUserMessage(knowledgeSystemPrompt);
     messages.add(systemMessage);
     messages.add(userMessage);
+    if (knowledgeSystemPrompt.length() > maxGraphicLength) {
+      log.error("Knowledge Graphic extract error:\n{}", "Knowledge Graphic is too long.");
+      return;
+    }
     var obj = llm.jsonChat(question, messages, true).getJSONObject("action");
     try {
       process_llm_result(obj, productId);
     } catch (Exception e) {
       log.error("Knowledge Graphic extract error:\n{}", e.getMessage());
     }
-    return null;
+  }
+
+  private @NonNull ModelMessage getUserMessage(String knowledgeSystemPrompt) {
+    ModelMessage userMessage;
+    if (knowledgeSystemPrompt.length() > maxGraphicLength / 2) {
+      log.info("Knowledge start summary");
+      userMessage = new ModelMessage(ModelMessageRole.USER.value(),
+          """
+              The graph has reached its maximum capacity.
+              Summarize the graph immediately, ensuring that the most recent content is not lost,
+              and begin generating the graph.
+              """);
+    } else {
+      userMessage = new ModelMessage(ModelMessageRole.USER.value(),
+          "Start generate knowledge graphic.");
+    }
+    return userMessage;
   }
 
   private void process_llm_result(JSONObject jsonObject, int productId) {

readme.md

@@ -277,10 +277,6 @@ public void dealWithAudio(List<byte[]> audioList, String chatId, int productId,
         }
         while ((res != null && !res.isDone()) || Router.queueMap.containsKey(chatId)
             && Router.queueMap.get(chatId).size() > 0) {
-          if (!Router.queueMap.containsKey(chatId)) {
-            log.warn("queueMap已被释放，停止处理: chatId={}", chatId);
-            break;
-          }
           if (emotionRes != null && emotionRes.isDone() && !emotionFlag) {
             try {
               Map<String, String> emotionResult = emotionRes.get();

src/main/java/top/rslly/iot/utility/ai/tools/KnowledgeGraphicTool.java

@@ -123,7 +123,7 @@ js:
   local:
     use_js_sandbox: true
     monitor_thread_pool_size: 4
-    max_cpu_time: 300000
+    max_cpu_time: 10000
     max_black_list_duration_sec: 60
     max_requests_timeout: 10
     js_thread_pool_size: 16
@@ -198,6 +198,8 @@ ai:
   longMemoryTool-llm: silicon-Qwen/Qwen2.5-14B-Instruct
   wx_smart_robot_llm: glm
   knowledge_graphic_llm: silicon-Qwen/Qwen3-Next-80B-A3B-Instruct
+  knowledge_graphic:
+    max_graphic_length: 30000
   mcp:
     agent-llm: silicon-Qwen/Qwen3-235B-A22B-Instruct-2507
     agent-speedUp: true

src/main/java/top/rslly/iot/utility/smartVoice/XiaoZhiUtil.java

@@ -1,4 +1,4 @@
 VITE_MODE='development'
 VITE_BASE_PATH='/'
-VITE_BASE_URL ='http://localhost:8080'
+VITE_BASE_URL ='https://api-ic.atdak.com/'
 VITE_LOCAL_TEST_URL="http://localhost:3002/"

src/main/resources/application.yaml

@@ -1,4 +1,4 @@
 VITE_MODE='production'
 VITE_BASE_PATH='/'
-VITE_BASE_URL ='http://localhost:8080'
+VITE_BASE_URL ='https://api-ic.atdak.com/'
 VITE_LOCAL_TEST_URL="http://localhost:3002/"

web/.env.development

@@ -132,7 +132,7 @@ function fetchProducts() {
     if (errorCode === 200 && data && Array.isArray(data)) {
       products.value = data.map((item, index) => {
         return {
-          key: item.keyvalue ? item.keyvalue : index,
+          key: item.id,
           value: item.id,
           label: item.productName,
         }