新增测试方法

slibra-admin/src/main/java/com/slibra/web/controller/business/FrontController.java

@@ -77,6 +77,24 @@ public class FrontController extends BaseController {
         return getDataTable(xinyiChatRecordMapper.selectTXinyiChatRecordFirstList(tXinyiChatRecord));
     }
 
+    /**
+     * 不分页获取大模型的问答记录
+     * @param tXinyiChatRecord
+     * @return
+     */
+    @GetMapping("/bigModel/qa/noPageList")
+    public AjaxResult noPageList(TXinyiChatRecord tXinyiChatRecord)
+    {
+        log.info("进入了 不分页获取大模型的问答记录 接口");
+        //获取登录用户信息
+        Long userId = SecurityUtils.getUserId();
+        tXinyiChatRecord.setUserId(String.valueOf(userId));
+        return AjaxResult.success(xinyiChatRecordMapper.selectTXinyiChatRecordFirstList(tXinyiChatRecord));
+    }
+
+
+
+
 
 
     /**
@@ -142,14 +160,14 @@ public class FrontController extends BaseController {
 
     //报警+预警记录列表分页 【水质报警  1生化报警 2预测报警】
     /**
-     * 分页获取大模型的问答记录
+     * 报警+预警记录列表分页
      * @param tXinyiWarningRecord
      * @return
      */
     @GetMapping("/bigModel/warning/pageList")
     public TableDataInfo warningPageList(TXinyiWarningRecord tXinyiWarningRecord)
     {
-        log.info("进入了 分页获取大模型的问答记录 接口");
+        log.info("进入了 报警+预警记录列表分页 接口");
         startPage();
         return getDataTable(xinyiWarningRecordMapper.selectTXinyiWarningRecordList(tXinyiWarningRecord));
     }
@@ -157,14 +175,14 @@ public class FrontController extends BaseController {
 
     //报警+预警记录列表不分页 【水质报警  1生化报警 2预测报警】
     /**
-     * 不分页获取大模型的问答记录
+     * 报警+预警记录列表不分页
      * @param tXinyiWarningRecord
      * @return
      */
     @GetMapping("/bigModel/warning/noPageList")
     public AjaxResult noPageList(TXinyiWarningRecord tXinyiWarningRecord)
     {
-        log.info("进入了 不分页获取大模型的问答记录 接口");
+        log.info("进入了 报警+预警记录列表不分页 接口");
         return AjaxResult.success(xinyiWarningRecordMapper.selectTXinyiWarningRecordList(tXinyiWarningRecord));
     }
 

slibra-admin/src/main/java/com/slibra/web/controller/business/GRPCController.java

@@ -334,7 +334,7 @@ public class GRPCController extends BaseController {
      * ⼤模型问答的调⽤参数
      * @return
      */
-    @GetMapping(value = "/inferStreamCommon")
+    @PostMapping(value = "/inferStreamCommon")
 //    public void inferStreamCommon(HttpServletResponse response, @RequestBody ChatReq chatReq)
     public void inferStreamCommon(HttpServletResponse response, ChatReq chatReq)
     {
@@ -343,7 +343,7 @@ public class GRPCController extends BaseController {
         // 获取输出流
         OutputStream outputStream = null;
         ManagedChannel channel = null;
-        response.setContentType("text/plain");
+        response.setContentType("text/event-stream");
         response.setCharacterEncoding("utf-8");
         try {
             channel = ManagedChannelBuilder.forAddress("10.0.0.24", 17070)
@@ -469,7 +469,82 @@ public class GRPCController extends BaseController {
         // 获取输出流
         OutputStream outputStream = null;
         ManagedChannel channel = null;
-        response.setContentType("text/plain");
+        response.setContentType("text/event-stream");
+        response.setCharacterEncoding("utf-8");
+        String dataJson = "";
+        try {
+            channel = ManagedChannelBuilder.forAddress("10.0.0.24", 17070)
+                    .usePlaintext()
+                    .build();
+            InferenceAPIsServiceGrpc.InferenceAPIsServiceBlockingStub stub = InferenceAPIsServiceGrpc.newBlockingStub(channel);
+            dataJson = "{\"bot_id\":\"b00001\",\"exp_id\":\"721\",\"norm\":\"" + category + "\",\"feedback\":" + feedback + ",\"session_id\":" + "\"" + sessionId + "\"" + ",\"generate_args\":{\"max_new_tokens\":1024,\"max_length\":4096,\"num_beams\":1,\"do_sample\":true,\"top_p\":0.7,\"temperature\":0.95},\"extra\":{\"rows\":" +  rows + "}}";
+            log.info("请求大模型的决策的参数为{}", dataJson);
+            PredictionsRequest request = PredictionsRequest.newBuilder()
+                    .setModelName("slibra_bot")
+                    .putInput("method", ByteString.copyFrom("decision_stream", "utf-8"))//推理
+                    .putInput("data", ByteString.copyFrom(dataJson, "utf-8"))
+                    .buildPartial();
+            outputStream = response.getOutputStream();
+            Iterator<PredictionResponse> predictions = stub.streamPredictions(request);
+            outputStream.write("begin".getBytes());
+            outputStream.flush();
+            while (predictions.hasNext()) {
+                String responseStr = predictions.next().getPrediction().toStringUtf8();
+                log.info("决策流式返回的结果是{}", responseStr);
+                responseStr = JSON.parseObject(responseStr).getString("message");
+                if("complete".equals(responseStr)){
+                    System.out.println("结尾语句并且是非JSON，无需处理");
+                    //结束语句也流式输出，但是并不记录下来
+                    outputStream.write(responseStr.getBytes());
+                    outputStream.flush();
+                }else{
+//                    sb.append(responseStr);
+                    resultData.add(responseStr);
+                    outputStream.write(responseStr.getBytes());
+                    outputStream.flush();
+                }
+                // todo 类型判断处理
+            }
+        } catch (Exception e) {
+//            throw new RuntimeException(e);
+            log.error("处理大模型推理异常，异常信息为{}", JSON.toJSONString(e));
+        } finally {
+            System.out.println(JSON.toJSONString(resultData));
+            // 关闭输出流
+            try {
+                outputStream.close();
+            } catch (IOException e) {
+                throw new RuntimeException(e);
+            }finally {
+                channel.shutdown();
+            }
+        }
+    }
+
+
+
+
+    /**
+     * 调⽤通过输入数据获取大模型决策接口
+     * @return
+     */
+    @GetMapping(value = "/myData2DecisionStreamGet")
+    public void myData2DecisionStreamGet(HttpServletResponse response, ChatReq chatReq)
+//    public void decisionStream(HttpServletResponse response,  ChatReq chatReq)
+    {
+        log.info("进入了调⽤大模型决策接口");
+//        StringBuilder sb = new StringBuilder();
+        //大模型结果 放入一个结合中
+        List<String> resultData = new ArrayList<>();
+        //决策和问答不一样 没有历史的概念 所以sessionId都是新的  次数都是1
+        String sessionId = IdUtils.simpleUUID();
+        String feedback = chatReq.getFeedback();
+        String rows = chatReq.getRows();
+        String category = chatReq.getCategory();
+        // 获取输出流
+        OutputStream outputStream = null;
+        ManagedChannel channel = null;
+        response.setContentType("text/event-stream");
         response.setCharacterEncoding("utf-8");
         String dataJson = "";
         try {