feat: Document vectorization supports processing based on status (#1984)

shaohuzhang1 · web-flow · commit 54381ffaf385 · 2025-01-07T11:15:10.000+08:00
diff --git a/apps/common/event/listener_manage.py b/apps/common/event/listener_manage.py
@@ -6,26 +6,22 @@
     @date：2023/10/20 14:01
     @desc:
 """
-import datetime
 import logging
 import os
 import threading
-import time
 import traceback
 from typing import List
 
 import django.db.models
-from django.db import models, transaction
 from django.db.models import QuerySet
 from django.db.models.functions import Substr, Reverse
 from langchain_core.embeddings import Embeddings
 
 from common.config.embedding_config import VectorStore
 from common.db.search import native_search, get_dynamics_model, native_update
-from common.db.sql_execute import sql_execute, update_execute
 from common.util.file_util import get_file_content
 from common.util.lock import try_lock, un_lock
-from common.util.page_utils import page
+from common.util.page_utils import page_desc
 from dataset.models import Paragraph, Status, Document, ProblemParagraphMapping, TaskType, State
 from embedding.models import SourceType, SearchMode
 from smartdoc.conf import PROJECT_DIR
@@ -162,7 +158,7 @@ def embedding_paragraph_apply(paragraph_list):
                 if is_the_task_interrupted():
                     break
                 ListenerManagement.embedding_by_paragraph(str(paragraph.get('id')), embedding_model)
-                post_apply()
+            post_apply()
 
         return embedding_paragraph_apply
 
@@ -241,13 +237,16 @@ def update_status(query_set: QuerySet, taskType: TaskType, state: State):
             lock.release()
 
     @staticmethod
-    def embedding_by_document(document_id, embedding_model: Embeddings):
+    def embedding_by_document(document_id, embedding_model: Embeddings, state_list=None):
         """
         向量化文档
+        @param state_list:
         @param document_id: 文档id
         @param embedding_model 向量模型
         :return: None
         """
+        if state_list is None:
+            state_list = [State.PENDING, State.SUCCESS, State.FAILURE, State.REVOKE, State.REVOKED]
         if not try_lock('embedding' + str(document_id)):
             return
         try:
@@ -268,11 +267,17 @@ def is_the_task_interrupted():
             VectorStore.get_embedding_vector().delete_by_document_id(document_id)
 
             # 根据段落进行向量化处理
-            page(QuerySet(Paragraph).filter(document_id=document_id).values('id'), 5,
-                 ListenerManagement.get_embedding_paragraph_apply(embedding_model, is_the_task_interrupted,
-                                                                  ListenerManagement.get_aggregation_document_status(
-                                                                      document_id)),
-                 is_the_task_interrupted)
+            page_desc(QuerySet(Paragraph)
+                      .annotate(
+                reversed_status=Reverse('status'),
+                task_type_status=Substr('reversed_status', TaskType.EMBEDDING.value,
+                                        1),
+            ).filter(task_type_status__in=state_list, document_id=document_id)
+                      .values('id'), 5,
+                      ListenerManagement.get_embedding_paragraph_apply(embedding_model, is_the_task_interrupted,
+                                                                       ListenerManagement.get_aggregation_document_status(
+                                                                           document_id)),
+                      is_the_task_interrupted)
         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()}')
         finally:
diff --git a/apps/common/util/page_utils.py b/apps/common/util/page_utils.py
@@ -26,3 +26,22 @@ def page(query_set, page_size, handler, is_the_task_interrupted=lambda: False):
         offset = i * page_size
         paragraph_list = query.all()[offset: offset + page_size]
         handler(paragraph_list)
+
+
+def page_desc(query_set, page_size, handler, is_the_task_interrupted=lambda: False):
+    """
+
+    @param query_set: 查询query_set
+    @param page_size: 每次查询大小
+    @param handler:   数据处理器
+    @param is_the_task_interrupted: 任务是否被中断
+    @return:
+    """
+    query = query_set.order_by("id")
+    count = query_set.count()
+    for i in sorted(range(0, ceil(count / page_size)), reverse=True):
+        if is_the_task_interrupted():
+            return
+        offset = i * page_size
+        paragraph_list = query.all()[offset: offset + page_size]
+        handler(paragraph_list)
diff --git a/apps/dataset/serializers/document_serializers.py b/apps/dataset/serializers/document_serializers.py
@@ -700,20 +700,24 @@ def edit(self, instance: Dict, with_valid=False):
             _document.save()
             return self.one()
 
-        @transaction.atomic
-        def refresh(self, with_valid=True):
+        def refresh(self, state_list, with_valid=True):
             if with_valid:
                 self.is_valid(raise_exception=True)
             document_id = self.data.get("document_id")
             ListenerManagement.update_status(QuerySet(Document).filter(id=document_id), TaskType.EMBEDDING,
                                              State.PENDING)
-            ListenerManagement.update_status(QuerySet(Paragraph).filter(document_id=document_id),
+            ListenerManagement.update_status(QuerySet(Paragraph).annotate(
+                reversed_status=Reverse('status'),
+                task_type_status=Substr('reversed_status', TaskType.EMBEDDING.value,
+                                        1),
+            ).filter(task_type_status__in=state_list, document_id=document_id)
+                                             .values('id'),
                                              TaskType.EMBEDDING,
                                              State.PENDING)
             ListenerManagement.get_aggregation_document_status(document_id)()
             embedding_model_id = get_embedding_model_id_by_dataset_id(dataset_id=self.data.get('dataset_id'))
             try:
-                embedding_by_document.delay(document_id, embedding_model_id)
+                embedding_by_document.delay(document_id, embedding_model_id, state_list)
             except AlreadyQueued as e:
                 raise AppApiException(500, "任务正在执行中,请勿重复下发")
 
@@ -1122,14 +1126,14 @@ def batch_refresh(self, instance: Dict, with_valid=True):
             if with_valid:
                 self.is_valid(raise_exception=True)
             document_id_list = instance.get("id_list")
-            with transaction.atomic():
-                dataset_id = self.data.get('dataset_id')
-                for document_id in document_id_list:
-                    try:
-                        DocumentSerializers.Operate(
-                            data={'dataset_id': dataset_id, 'document_id': document_id}).refresh()
-                    except AlreadyQueued as e:
-                        pass
+            state_list = instance.get("state_list")
+            dataset_id = self.data.get('dataset_id')
+            for document_id in document_id_list:
+                try:
+                    DocumentSerializers.Operate(
+                        data={'dataset_id': dataset_id, 'document_id': document_id}).refresh(state_list)
+                except AlreadyQueued as e:
+                    pass
 
     class GenerateRelated(ApiMixin, serializers.Serializer):
         document_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid("文档id"))
diff --git a/apps/dataset/swagger_api/document_api.py b/apps/dataset/swagger_api/document_api.py
@@ -51,3 +51,16 @@ def get_request_body_api():
                                            description="1|2|3 1:向量化|2:生成问题|3:同步文档", default=1)
                 }
             )
+
+    class EmbeddingState(ApiMixin):
+        @staticmethod
+        def get_request_body_api():
+            return openapi.Schema(
+                type=openapi.TYPE_OBJECT,
+                properties={
+                    'state_list': openapi.Schema(type=openapi.TYPE_ARRAY,
+                                                 items=openapi.Schema(type=openapi.TYPE_STRING),
+                                                 title="状态列表",
+                                                 description="状态列表")
+                }
+            )
diff --git a/apps/dataset/views/document.py b/apps/dataset/views/document.py
@@ -262,6 +262,7 @@ class Refresh(APIView):
         @action(methods=['PUT'], detail=False)
         @swagger_auto_schema(operation_summary="刷新文档向量库",
                              operation_id="刷新文档向量库",
+                             request_body=DocumentApi.EmbeddingState.get_request_body_api(),
                              manual_parameters=DocumentSerializers.Operate.get_request_params_api(),
                              responses=result.get_default_response(),
                              tags=["知识库/文档"]
@@ -272,6 +273,7 @@ class Refresh(APIView):
         def put(self, request: Request, dataset_id: str, document_id: str):
             return result.success(
                 DocumentSerializers.Operate(data={'document_id': document_id, 'dataset_id': dataset_id}).refresh(
+                    request.data.get('state_list')
                 ))
 
     class BatchRefresh(APIView):
diff --git a/apps/embedding/task/embedding.py b/apps/embedding/task/embedding.py
@@ -56,22 +56,28 @@ def embedding_by_paragraph_list(paragraph_id_list, model_id):
 
 
 @celery_app.task(base=QueueOnce, once={'keys': ['document_id']}, name='celery:embedding_by_document')
-def embedding_by_document(document_id, model_id):
+def embedding_by_document(document_id, model_id, state_list=None):
     """
     向量化文档
+    @param state_list:
     @param document_id: 文档id
     @param model_id 向量模型
     :return: None
     """
 
+    if state_list is None:
+        state_list = [State.PENDING.value, State.STARTED.value, State.SUCCESS.value, State.FAILURE.value,
+                      State.REVOKE.value,
+                      State.REVOKED.value, State.IGNORED.value]
+
     def exception_handler(e):
         ListenerManagement.update_status(QuerySet(Document).filter(id=document_id), TaskType.EMBEDDING,
                                          State.FAILURE)
         max_kb_error.error(
             f'获取向量模型失败：{str(e)}{traceback.format_exc()}')
 
     embedding_model = get_embedding_model(model_id, exception_handler)
-    ListenerManagement.embedding_by_document(document_id, embedding_model)
+    ListenerManagement.embedding_by_document(document_id, embedding_model, state_list)
 
 
 @celery_app.task(name='celery:embedding_by_document_list')
diff --git a/ui/src/api/document.ts b/ui/src/api/document.ts
@@ -129,11 +129,12 @@ const delMulDocument: (
 const batchRefresh: (
   dataset_id: string,
   data: any,
+  stateList: Array<string>,
   loading?: Ref<boolean>
-) => Promise<Result<boolean>> = (dataset_id, data, loading) => {
+) => Promise<Result<boolean>> = (dataset_id, data, stateList, loading) => {
   return put(
     `${prefix}/${dataset_id}/document/batch_refresh`,
-    { id_list: data },
+    { id_list: data, state_list: stateList },
     undefined,
     loading
   )
@@ -157,11 +158,12 @@ const getDocumentDetail: (dataset_id: string, document_id: string) => Promise<Re
 const putDocumentRefresh: (
   dataset_id: string,
   document_id: string,
+  state_list: Array<string>,
   loading?: Ref<boolean>
-) => Promise<Result<any>> = (dataset_id, document_id, loading) => {
+) => Promise<Result<any>> = (dataset_id, document_id, state_list, loading) => {
   return put(
     `${prefix}/${dataset_id}/document/${document_id}/refresh`,
-    undefined,
+    { state_list },
     undefined,
     loading
   )
diff --git a/ui/src/views/document/component/EmbeddingContentDialog.vue b/ui/src/views/document/component/EmbeddingContentDialog.vue
@@ -0,0 +1,41 @@
+<template>
+  <el-dialog v-model="dialogVisible" title="选择向量化内容" width="500" :before-close="close">
+    <el-radio-group v-model="state">
+      <el-radio value="error" size="large">向量化未成功的分段</el-radio>
+      <el-radio value="all" size="large">全部分段</el-radio>
+    </el-radio-group>
+    <template #footer>
+      <div class="dialog-footer">
+        <el-button @click="close">取消</el-button>
+        <el-button type="primary" @click="submit"> 提交 </el-button>
+      </div>
+    </template>
+  </el-dialog>
+</template>
+<script setup lang="ts">
+import { ref } from 'vue'
+const dialogVisible = ref<boolean>(false)
+const state = ref<'all' | 'error'>('error')
+const stateMap = {
+  all: ['0', '1', '2', '3', '4', '5', 'n'],
+  error: ['0', '1', '3', '4', '5', 'n']
+}
+const submit_handle = ref<(stateList: Array<string>) => void>()
+const submit = () => {
+  if (submit_handle.value) {
+    submit_handle.value(stateMap[state.value])
+  }
+  close()
+}
+
+const open = (handle: (stateList: Array<string>) => void) => {
+  submit_handle.value = handle
+  dialogVisible.value = true
+}
+const close = () => {
+  submit_handle.value = undefined
+  dialogVisible.value = false
+}
+defineExpose({ open, close })
+</script>
+<style lang="scss" scoped></style>
diff --git a/ui/src/views/document/index.vue b/ui/src/views/document/index.vue
@@ -422,6 +422,7 @@
       </el-text>
       <el-button class="ml-16" type="primary" link @click="clearSelection"> 清空 </el-button>
     </div>
+    <EmbeddingContentDialog ref="embeddingContentDialogRef"></EmbeddingContentDialog>
   </LayoutContainer>
 </template>
 <script setup lang="ts">
@@ -439,6 +440,7 @@ import { MsgSuccess, MsgConfirm, MsgError } from '@/utils/message'
 import useStore from '@/stores'
 import StatusVlue from '@/views/document/component/Status.vue'
 import GenerateRelatedDialog from '@/components/generate-related-dialog/index.vue'
+import EmbeddingContentDialog from '@/views/document/component/EmbeddingContentDialog.vue'
 import { TaskType, State } from '@/utils/status'
 const router = useRouter()
 const route = useRoute()
@@ -469,7 +471,7 @@ onBeforeRouteLeave((to: any) => {
 })
 const beforePagination = computed(() => common.paginationConfig[storeKey])
 const beforeSearch = computed(() => common.search[storeKey])
-
+const embeddingContentDialogRef = ref<InstanceType<typeof EmbeddingContentDialog>>()
 const SyncWebDialogRef = ref()
 const loading = ref(false)
 let interval: any
@@ -621,10 +623,14 @@ function syncDocument(row: any) {
       .catch(() => {})
   }
 }
+
 function refreshDocument(row: any) {
-  documentApi.putDocumentRefresh(row.dataset_id, row.id).then(() => {
-    getList()
-  })
+  const embeddingDocument = (stateList: Array<string>) => {
+    return documentApi.putDocumentRefresh(row.dataset_id, row.id, stateList).then(() => {
+      getList()
+    })
+  }
+  embeddingContentDialogRef.value?.open(embeddingDocument)
 }
 
 function rowClickHandle(row: any, column: any) {
@@ -691,19 +697,16 @@ function deleteMulDocument() {
 }
 
 function batchRefresh() {
-  const arr: string[] = []
-  multipleSelection.value.map((v) => {
-    if (v) {
-      arr.push(v.id)
-    }
-  })
-  documentApi.batchRefresh(id, arr, loading).then(() => {
-    MsgSuccess('批量向量化成功')
-    multipleTableRef.value?.clearSelection()
-  })
+  const arr: string[] = multipleSelection.value.map((v) => v.id)
+  const embeddingBatchDocument = (stateList: Array<string>) => {
+    documentApi.batchRefresh(id, arr, stateList, loading).then(() => {
+      MsgSuccess('批量向量化成功')
+      multipleTableRef.value?.clearSelection()
+    })
+  }
+  embeddingContentDialogRef.value?.open(embeddingBatchDocument)
 }
 
-
 function deleteDocument(row: any) {
   MsgConfirm(
     `是否删除文档：${row.name} ?`,
