fix: 修复索引中的文档,知识库删除后依然再执行 (#934)

apps/common/event/listener_manage.py

@@ -110,11 +110,16 @@ class ListenerManagement:
     @embedding_poxy
     def embedding_by_paragraph_data_list(data_list, paragraph_id_list, embedding_model: Embeddings):
         max_kb.info(f'开始--->向量化段落:{paragraph_id_list}')
+        status = Status.success
         try:
             # 删除段落
             VectorStore.get_embedding_vector().delete_by_paragraph_ids(paragraph_id_list)
+
+            def is_save_function():
+                return QuerySet(Paragraph).filter(id__in=paragraph_id_list).exists()
+
             # 批量向量化
-            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model)
+            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model, is_save_function)
         except Exception as e:
             max_kb_error.error(f'向量化段落:{paragraph_id_list}出现错误{str(e)}{traceback.format_exc()}')
             status = Status.error
@@ -141,8 +146,12 @@ class ListenerManagement:
                     os.path.join(PROJECT_DIR, "apps", "common", 'sql', 'list_embedding_text.sql')))
             # 删除段落
             VectorStore.get_embedding_vector().delete_by_paragraph_id(paragraph_id)
+
+            def is_save_function():
+                return QuerySet(Paragraph).filter(id=paragraph_id).exists()
+
             # 批量向量化
-            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model)
+            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model, is_save_function)
         except Exception as e:
             max_kb_error.error(f'向量化段落:{paragraph_id}出现错误{str(e)}{traceback.format_exc()}')
             status = Status.error
@@ -175,8 +184,12 @@ class ListenerManagement:
                     os.path.join(PROJECT_DIR, "apps", "common", 'sql', 'list_embedding_text.sql')))
             # 删除文档向量数据
             VectorStore.get_embedding_vector().delete_by_document_id(document_id)
+
+            def is_save_function():
+                return QuerySet(Document).filter(id=document_id).exists()
+
             # 批量向量化
-            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model)
+            VectorStore.get_embedding_vector().batch_save(data_list, embedding_model, is_save_function)
         except Exception as e:
             max_kb_error.error(f'向量化文档:{document_id}出现错误{str(e)}{traceback.format_exc()}')
             status = Status.error

apps/embedding/vector/base_vector.py

@@ -84,9 +84,9 @@ class BaseVectorStore(ABC):
         chunk_list = chunk_data(data)
         result = sub_array(chunk_list)
         for child_array in result:
-            self._batch_save(child_array, embedding)
+            self._batch_save(child_array, embedding, lambda: True)
 
-    def batch_save(self, data_list: List[Dict], embedding: Embeddings):
+    def batch_save(self, data_list: List[Dict], embedding: Embeddings, is_save_function):
         # 获取锁
         lock.acquire()
         try:
@@ -100,7 +100,10 @@ class BaseVectorStore(ABC):
             chunk_list = chunk_data_list(data_list)
             result = sub_array(chunk_list)
             for child_array in result:
-                self._batch_save(child_array, embedding)
+                if is_save_function():
+                    self._batch_save(child_array, embedding, is_save_function)
+                else:
+                    break
         finally:
             # 释放锁
             lock.release()
@@ -113,7 +116,7 @@ class BaseVectorStore(ABC):
         pass
 
     @abstractmethod
-    def _batch_save(self, text_list: List[Dict], embedding: Embeddings):
+    def _batch_save(self, text_list: List[Dict], embedding: Embeddings, is_save_function):
         pass
 
     def search(self, query_text, dataset_id_list: list[str], exclude_document_id_list: list[str],

apps/embedding/vector/pg_vector.py

@@ -55,7 +55,7 @@ class PGVector(BaseVectorStore):
         embedding.save()
         return True
 
-    def _batch_save(self, text_list: List[Dict], embedding: Embeddings):
+    def _batch_save(self, text_list: List[Dict], embedding: Embeddings, is_save_function):
         texts = [row.get('text') for row in text_list]
         embeddings = embedding.embed_documents(texts)
         embedding_list = [Embedding(id=uuid.uuid1(),
@@ -68,7 +68,8 @@ class PGVector(BaseVectorStore):
                                     embedding=embeddings[index],
                                     search_vector=to_ts_vector(text_list[index]['text'])) for index in
                           range(0, len(text_list))]
-        QuerySet(Embedding).bulk_create(embedding_list) if len(embedding_list) > 0 else None
+        if is_save_function():
+            QuerySet(Embedding).bulk_create(embedding_list) if len(embedding_list) > 0 else None
         return True
 
     def hit_test(self, query_text, dataset_id_list: list[str], exclude_document_id_list: list[str], top_number: int,