temp upload

applications/api/nlp_api.py

@@ -5,7 +5,6 @@ import requests
 import traceback
 from requests.exceptions import RequestException, JSONDecodeError
 
-from applications.feishuBotApi import bot
 from applications.aliyunLogApi import log
 
 
@@ -37,6 +36,7 @@ def similarity_between_title_list(target_title_list: list[str], base_title_list:
             task="article_exit_with_title",
             function="similarity_between_title_list",
             status="fail",
+            message="nlp server web error",
             data={
                 "e": str(e),
                 "error_msg": traceback.format_exc()
@@ -46,13 +46,15 @@ def similarity_between_title_list(target_title_list: list[str], base_title_list:
         response = requests.post(url_backup, json=body, timeout=120)
 
     if response.status_code != 200:
-        bot(
-            title='NLP API 接口异常',
-            detail={
+        log(
+            task="article_exit_with_title",
+            function="similarity_between_title_list",
+            status="fail",
+            message='nlp server request error',
+            data={
                 "status_code": response.status_code,
                 "response_text": response.text[:200]  # 截取部分内容避免过大
-            },
-            mention=False
+            }
         )
         return []
 
@@ -60,15 +62,16 @@ def similarity_between_title_list(target_title_list: list[str], base_title_list:
         response_json = response.json()
         score_array = response_json['score_list_list']
     except (JSONDecodeError, KeyError) as e:
-        print("NLP响应数据异常")
-        # bot(
-        #     title='NLP响应数据异常',
-        #     detail={
-        #         "error_type": type(e).__name__,
-        #         "raw_response": response.text[:200]
-        #     },
-        #     mention=False
-        # )
+        log(
+            task="article_exit_with_title",
+            function="similarity_between_title_list",
+            status="fail",
+            message='nlp server response error',
+            data={
+                "error_type": type(e).__name__,
+                "raw_response": response.text[:200]
+            }
+        )
         return []
 
     return score_array