fix: _extract_usage_from_response and CreateJobForm

Author: LuckyYC

frontend/public/locales/en/translation.json

@@ -405,25 +405,25 @@
       "userSpawnRate": "User Spawn Rate",
       "userSpawnRateTooltip": "Number of new virtual users started per second during ramp-up",
       "requestFieldMapping": "Request Field Mapping",
-      "promptFieldPath": "Prompt Field Path",
+      "promptFieldPath": "Prompt",
       "promptFieldPathTooltip": "The key in your request payload that contains the user prompt (needed for performance metrics calculation)",
-      "streamingResponseConfiguration": "Streaming Response Configuration",
+      "streamingResponseConfiguration": "Response Field Mapping",
       "streamLinePrefix": "Stream Line Prefix",
       "streamLinePrefixTooltip": "Text that appears at the beginning of each data line (e.g., \"data:\", \"event:\")",
       "dataFormatTooltip": "Format of the streaming data after removing the prefix",
       "jsonFormat": "JSON Format",
       "plainText": "Plain Text",
-      "contentFieldPath": "Content Field Path",
+      "contentFieldPath": "Content",
       "contentFieldPathTooltip": "Dot-notation path to the main content in each JSON chunk (e.g., choices.0.delta.content)",
-      "reasoningFieldPath": "Reasoning Field Path",
+      "reasoningFieldPath": "Reasoning Content",
       "reasoningFieldPathTooltip": "Dot-notation path to reasoning content in JSON (optional, for models that support reasoning)",
       "usageFieldPath": "Token Usage Field",
       "usageFieldPathTooltip": "Field path for token usage statistics (for token throughput calculation, if not filled or filled incorrectly, the built-in tokenizer will be used to estimate)",
-      "promptTokensFieldPath": "Prompt Tokens Field Path",
+      "promptTokensFieldPath": "Prompt Tokens",
       "promptTokensFieldPathTooltip": "Dot-notation path to prompt tokens count field (e.g., usage.prompt_tokens)",
-      "completionTokensFieldPath": "Completion Tokens Field Path",
+      "completionTokensFieldPath": "Completion Tokens",
       "completionTokensFieldPathTooltip": "Dot-notation path to completion tokens count field (e.g., usage.completion_tokens)",
-      "totalTokensFieldPath": "Total Tokens Field Path",
+      "totalTokensFieldPath": "Total Tokens",
       "totalTokensFieldPathTooltip": "Dot-notation path to total tokens count field (e.g., usage.total_tokens)",
       "streamTerminationConfiguration": "Stream Termination Configuration",
       "endLinePrefix": "End Line Prefix",
@@ -432,7 +432,7 @@
       "stopSignalTooltip": "Text content that indicates the stream has ended (e.g., [DONE], STOP, finished)",
       "endFieldPath": "End Field Path",
       "endFieldPathTooltip": "JSON path to a field that indicates completion (optional, e.g., choices.0.finish_reason)",
-      "nonStreamingResponseConfiguration": "Non-Streaming Response Configuration",
+      "nonStreamingResponseConfiguration": "Response Field Mapping",
       "nonStreamingContentFieldPathTooltip": "Dot-notation path to the main content in the response JSON (e.g., choices.0.message.content)",
       "nonStreamingReasoningFieldPathTooltip": "Dot-notation path to reasoning content (optional, for models with reasoning capabilities)",
       "fieldMappingDescription": "⚠️ Please accurately configure the mapping between the prompt and the response data field. This mapping directly affects the accuracy of data set replacement, load test execution, and performance metrics (such as response latency and token throughput). If the response does not contain the usage field, the built-in tokenizer will be used to estimate the token count.",

frontend/public/locales/zh/translation.json

@@ -407,7 +407,7 @@
       "requestFieldMapping": "请求字段映射",
       "promptFieldPath": "提示词字段路径",
       "promptFieldPathTooltip": "请求参数中包含用户提示词的键（性能指标计算需要）",
-      "streamingResponseConfiguration": "流式响应配置",
+      "streamingResponseConfiguration": "响应字段映射",
       "streamLinePrefix": "数据行流前缀",
       "streamLinePrefixTooltip": "出现在每个数据行开头的文本，如：data:、event:",
       "dataFormatTooltip": "移除前缀后的数据格式",
@@ -432,7 +432,7 @@
       "stopSignalTooltip": "流式输出结束的标志，如：[DONE]、STOP、finished",
       "endFieldPath": "结束字段路径",
       "endFieldPathTooltip": "指示完成的字段的JSON路径，如： choices.0.finish_reason",
-      "nonStreamingResponseConfiguration": "非流式响应配置",
+      "nonStreamingResponseConfiguration": "响应字段映射",
       "nonStreamingContentFieldPathTooltip": "content字段路径,使用点分割，如：choices.0.delta.content",
       "nonStreamingReasoningFieldPathTooltip": "reasoning_content字段路径,使用点分割，如：choices.0.delta.reasoning_content",
       "fieldMappingDescription": "⚠️ 请准确配置提示词（prompt）与响应数据字段的映射关系。该映射直接影响数据集替换、压测执行及性能指标（如 响应时延、token吞吐量）的统计准确性。若响应中未包含 usage 字段，系统将回退至内置 tokenizer 估算 token 数量。",

frontend/src/components/CreateJobForm.tsx

@@ -343,15 +343,17 @@ const CreateJobFormContent: React.FC<CreateJobFormProps> = ({
 
       // Always preserve original values when copying
       dataToFill.field_mapping = originalFieldMapping || {
-        prompt: '',
-        stream_prefix: '',
+        prompt: 'messages.0.content',
+        stream_prefix: 'data:',
         data_format: 'json',
-        content: '',
-        reasoning_content: '',
-        usage: '',
-        end_prefix: '',
-        stop_flag: '',
+        content: 'choices.0.message.content',
+        reasoning_content: 'choices.0.message.reasoning_content',
+        prompt_tokens: 'usage.prompt_tokens',
+        completion_tokens: 'usage.completion_tokens',
+        total_tokens: 'usage.total_tokens',
+        end_prefix: 'data:',
         end_field: '',
+        stop_flag: '[DONE]',
       };
       dataToFill.request_payload = originalRequestPayload;
 
@@ -2232,27 +2234,6 @@ const CreateJobFormContent: React.FC<CreateJobFormProps> = ({
                           </Form.Item>
                         </Col>
                       </Row>
-                      {/* <Col span={12}>
-                          <Form.Item
-                            name={['field_mapping', 'usage']}
-                            label={
-                              <span>
-                                {t('components.createJobForm.usageFieldPath')}
-                                <Tooltip
-                                  title={t(
-                                    'components.createJobForm.usageFieldPathTooltip'
-                                  )}
-                                >
-                                  <InfoCircleOutlined
-                                    style={{ marginLeft: 5 }}
-                                  />
-                                </Tooltip>
-                              </span>
-                            }
-                          >
-                            <Input placeholder='usage' />
-                          </Form.Item>
-                        </Col> */}
                     </>
                   )
                 );
@@ -2416,8 +2397,67 @@ const CreateJobFormContent: React.FC<CreateJobFormProps> = ({
               </Form.Item>
             </Col>
           </Row>
+          <Row gutter={16} style={{ marginTop: 16 }}>
+            <Col span={8}>
+              <Form.Item
+                name={['field_mapping', 'prompt_tokens']}
+                label={
+                  <span>
+                    {t('components.createJobForm.promptTokensFieldPath')}
+                    <Tooltip
+                      title={t(
+                        'components.createJobForm.promptTokensFieldPathTooltip'
+                      )}
+                    >
+                      <InfoCircleOutlined style={{ marginLeft: 5 }} />
+                    </Tooltip>
+                  </span>
+                }
+              >
+                <Input placeholder='usage.prompt_tokens' />
+              </Form.Item>
+            </Col>
 
-          <Row gutter={24} style={{ marginTop: 16 }}>
+            <Col span={8}>
+              <Form.Item
+                name={['field_mapping', 'completion_tokens']}
+                label={
+                  <span>
+                    {t('components.createJobForm.completionTokensFieldPath')}
+                    <Tooltip
+                      title={t(
+                        'components.createJobForm.completionTokensFieldPathTooltip'
+                      )}
+                    >
+                      <InfoCircleOutlined style={{ marginLeft: 5 }} />
+                    </Tooltip>
+                  </span>
+                }
+              >
+                <Input placeholder='usage.completion_tokens' />
+              </Form.Item>
+            </Col>
+            <Col span={8}>
+              <Form.Item
+                name={['field_mapping', 'total_tokens']}
+                label={
+                  <span>
+                    {t('components.createJobForm.totalTokensFieldPath')}
+                    <Tooltip
+                      title={t(
+                        'components.createJobForm.totalTokensFieldPathTooltip'
+                      )}
+                    >
+                      <InfoCircleOutlined style={{ marginLeft: 5 }} />
+                    </Tooltip>
+                  </span>
+                }
+              >
+                <Input placeholder='usage.total_tokens' />
+              </Form.Item>
+            </Col>
+          </Row>
+          {/* <Row gutter={24} style={{ marginTop: 16 }}>
             <Col span={12}>
               <Form.Item
                 name={['field_mapping', 'usage']}
@@ -2437,7 +2477,7 @@ const CreateJobFormContent: React.FC<CreateJobFormProps> = ({
                 <Input placeholder='usage' />
               </Form.Item>
             </Col>
-          </Row>
+          </Row> */}
         </div>
       )}
     </div>

st_engine/engine/request_processor.py

@@ -316,7 +316,7 @@ def process_stream_chunk(
                     EventManager.fire_metric_event(
                         "Time_to_first_output_token", ttfot, 0
                     )
-        return False, None, metrics  # Continue processing
+        return False, None, metrics
 
 
 # === REQUEST HANDLERS ===
@@ -735,6 +735,81 @@ def handle_stream_request(
             return "", "", usage
         return metrics.reasoning_content, metrics.content, metrics.usage
 
+    @staticmethod
+    def _extract_usage_from_response(
+        resp_json: Dict[str, Any], field_mapping: FieldMapping
+    ) -> Dict[str, Optional[int]]:
+        """
+        Extract usage from response JSON using FieldMapping.
+        Similar to extract_metrics_from_chunk but for non-streaming responses.
+        """
+        usage: Dict[str, Optional[int]] = {
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "total_tokens": 0,
+        }
+
+        # Update prompt tokens if field mapping exists
+        if field_mapping.prompt_tokens:
+            prompt_tokens_value = safe_int_convert(
+                StreamProcessor.get_field_value(resp_json, field_mapping.prompt_tokens)
+            )
+            if prompt_tokens_value > 0:
+                usage["prompt_tokens"] = prompt_tokens_value
+
+        # Update completion tokens if field mapping exists
+        if field_mapping.completion_tokens:
+            completion_tokens_value = safe_int_convert(
+                StreamProcessor.get_field_value(
+                    resp_json, field_mapping.completion_tokens
+                )
+            )
+            if completion_tokens_value > 0:
+                usage["completion_tokens"] = completion_tokens_value
+
+        # Update total tokens if field mapping exists
+        if field_mapping.total_tokens:
+            total_tokens_value = safe_int_convert(
+                StreamProcessor.get_field_value(resp_json, field_mapping.total_tokens)
+            )
+            if total_tokens_value > 0:
+                usage["total_tokens"] = total_tokens_value
+
+        # Fallback: try to extract from usage field if mappings are not provided
+        if (
+            usage["prompt_tokens"] == 0
+            and usage["completion_tokens"] == 0
+            and usage["total_tokens"] == 0
+        ):
+            if "usage" in resp_json and isinstance(resp_json["usage"], dict):
+                response_usage = resp_json["usage"]
+                if "prompt_tokens" in response_usage:
+                    usage["prompt_tokens"] = safe_int_convert(
+                        response_usage["prompt_tokens"]
+                    )
+                if "input_tokens" in response_usage:
+                    usage["prompt_tokens"] = safe_int_convert(
+                        response_usage["input_tokens"]
+                    )
+                if "completion_tokens" in response_usage:
+                    usage["completion_tokens"] = safe_int_convert(
+                        response_usage["completion_tokens"]
+                    )
+                if "output_tokens" in response_usage:
+                    usage["completion_tokens"] = safe_int_convert(
+                        response_usage["output_tokens"]
+                    )
+                if "total_tokens" in response_usage:
+                    usage["total_tokens"] = safe_int_convert(
+                        response_usage["total_tokens"]
+                    )
+                if "all_tokens" in response_usage:
+                    usage["total_tokens"] = safe_int_convert(
+                        response_usage["all_tokens"]
+                    )
+
+        return usage
+
     def handle_non_stream_request(
         self, client, base_request_kwargs: Dict[str, Any], start_time: float
     ) -> Tuple[str, str, Dict[str, Optional[int]]]:
@@ -767,7 +842,6 @@ def handle_non_stream_request(
                     "total_tokens": 0,
                 },
             )
-        self.task_logger.info(f"base_request_kwargs: {base_request_kwargs}")
 
         request_kwargs = {**base_request_kwargs, "stream": False}
         content, reasoning_content = "", ""
@@ -827,30 +901,16 @@ def handle_non_stream_request(
                     0,
                 )
 
-                # Extract token counts from usage field if available
-                if "usage" in resp_json and isinstance(resp_json["usage"], dict):
-                    usage = resp_json["usage"]
-                self.task_logger.debug(f"usage: {usage}")
+                # Extract usage and content using FieldMapping
+                usage = self._extract_usage_from_response(resp_json, field_mapping)
 
-                if usage["total_tokens"] is None:
-                    content = (
-                        StreamProcessor.get_field_value(
-                            resp_json, field_mapping.content
-                        )
-                        if field_mapping.content
-                        else ""
-                    )
-                    content = str(content) if content else ""
-
-                    reasoning_content = (
-                        StreamProcessor.get_field_value(
-                            resp_json, field_mapping.reasoning_content
-                        )
-                        if field_mapping.reasoning_content
-                        else ""
+                if field_mapping.content:
+                    content = StreamProcessor.get_field_value(
+                        resp_json, field_mapping.content
                     )
-                    reasoning_content = (
-                        str(reasoning_content) if reasoning_content else ""
+                if field_mapping.reasoning_content:
+                    reasoning_content = StreamProcessor.get_field_value(
+                        resp_json, field_mapping.reasoning_content
                     )
                 response.success()
                 return reasoning_content, content, usage