feat: add dataset index creation and deletion functions

apps/common/event/listener_manage.py

@@ -24,6 +24,7 @@ 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_desc
 from dataset.models import Paragraph, Status, Document, ProblemParagraphMapping, TaskType, State
+from dataset.serializers.common_serializers import create_dataset_index
 from embedding.models import SourceType, SearchMode
 from smartdoc.conf import PROJECT_DIR
 from django.utils.translation import gettext_lazy as _
@@ -281,6 +282,8 @@ class ListenerManagement:
                                                                        ListenerManagement.get_aggregation_document_status(
                                                                            document_id)),
                       is_the_task_interrupted)
+            # 检查是否存在索引
+            create_dataset_index(document_id=document_id)
         except Exception as e:
             max_kb_error.error(_('Vectorized document: {document_id} error {error} {traceback}').format(
                 document_id=document_id, error=str(e), traceback=traceback.format_exc()))

apps/dataset/serializers/common_serializers.py

@@ -18,13 +18,13 @@ from rest_framework import serializers
 
 from common.config.embedding_config import ModelManage
 from common.db.search import native_search
-from common.db.sql_execute import update_execute
+from common.db.sql_execute import update_execute, sql_execute
 from common.exception.app_exception import AppApiException
 from common.mixins.api_mixin import ApiMixin
 from common.util.field_message import ErrMessage
 from common.util.file_util import get_file_content
 from common.util.fork import Fork
-from dataset.models import Paragraph, Problem, ProblemParagraphMapping, DataSet, File, Image
+from dataset.models import Paragraph, Problem, ProblemParagraphMapping, DataSet, File, Image, Document
 from setting.models_provider import get_model
 from smartdoc.conf import PROJECT_DIR
 from django.utils.translation import gettext_lazy as _
@@ -224,6 +224,46 @@ def get_embedding_model_id_by_dataset_id_list(dataset_id_list: List):
     return str(dataset_list[0].embedding_mode_id)
 
 
+
+def create_dataset_index(dataset_id=None, document_id=None):
+    if dataset_id is None and document_id is None:
+        raise AppApiException(500, _('Dataset ID or Document ID must be provided'))
+
+    if dataset_id is not None:
+        k_id = dataset_id
+    else:
+        document = QuerySet(Document).filter(id=document_id).first()
+        k_id = document.dataset_id
+
+    sql = f"SELECT indexname, indexdef FROM pg_indexes WHERE tablename = 'embedding' AND indexname = 'embedding_hnsw_idx_{k_id}'"
+    index = sql_execute(sql, [])
+    if not index:
+        sql = f"SELECT vector_dims(embedding) AS dims FROM embedding WHERE dataset_id = '{k_id}' LIMIT 1"
+        result = sql_execute(sql, [])
+        if len(result) == 0:
+            return
+        dims = result[0]['dims']
+        sql = f"""CREATE INDEX "embedding_hnsw_idx_{k_id}" ON embedding USING hnsw ((embedding::vector({dims})) vector_cosine_ops) WHERE dataset_id = '{k_id}'"""
+        update_execute(sql, [])
+
+
+def drop_dataset_index(dataset_id=None, document_id=None):
+    if dataset_id is None and document_id is None:
+        raise AppApiException(500, _('Dataset ID or Document ID must be provided'))
+
+    if dataset_id is not None:
+        k_id = dataset_id
+    else:
+        document = QuerySet(Document).filter(id=document_id).first()
+        k_id = document.dataset_id
+
+    sql = f"SELECT indexname, indexdef FROM pg_indexes WHERE tablename = 'embedding' AND indexname = 'embedding_hnsw_idx_{k_id}'"
+    index = sql_execute(sql, [])
+    if index:
+        sql = f'DROP INDEX "embedding_hnsw_idx_{k_id}"'
+        update_execute(sql, [])
+
+
 class GenerateRelatedSerializer(ApiMixin, serializers.Serializer):
     model_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_('Model id')))
     prompt = serializers.CharField(required=True, error_messages=ErrMessage.uuid(_('Prompt word')))

apps/dataset/serializers/dataset_serializers.py

@@ -44,7 +44,7 @@ from dataset.models.data_set import DataSet, Document, Paragraph, Problem, Type,
     State, File, Image
 from dataset.serializers.common_serializers import list_paragraph, MetaSerializer, ProblemParagraphManage, \
     get_embedding_model_by_dataset_id, get_embedding_model_id_by_dataset_id, write_image, zip_dir, \
-    GenerateRelatedSerializer
+    GenerateRelatedSerializer, drop_dataset_index
 from dataset.serializers.document_serializers import DocumentSerializers, DocumentInstanceSerializer
 from dataset.task import sync_web_dataset, sync_replace_web_dataset, generate_related_by_dataset_id
 from embedding.models import SearchMode
@@ -788,6 +788,7 @@ class DataSetSerializers(serializers.ModelSerializer):
             QuerySet(ProblemParagraphMapping).filter(dataset=dataset).delete()
             QuerySet(Paragraph).filter(dataset=dataset).delete()
             QuerySet(Problem).filter(dataset=dataset).delete()
+            drop_dataset_index(knowledge_id=dataset.id)
             dataset.delete()
             delete_embedding_by_dataset(self.data.get('id'))
             return True

apps/embedding/sql/blend_search.sql

@@ -5,15 +5,17 @@ SELECT
 FROM
 	(
 	SELECT DISTINCT ON
-		( "paragraph_id" ) ( similarity ),* ,
-		similarity AS comprehensive_score
+		( "paragraph_id" ) ( 1 - distince + ts_similarity ) as similarity, *,
+		(1 - distince + ts_similarity) AS comprehensive_score
 	FROM
 		(
 		SELECT
 			*,
-			(( 1 - ( embedding.embedding <=>  %s ) )+ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS similarity
+			(embedding.embedding::vector(%s) <=>  %s) as distince,
+			(ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS ts_similarity
 		FROM
 			embedding ${embedding_query}
+		    ORDER BY distince
 		) TEMP
 	ORDER BY
 		paragraph_id,

apps/embedding/sql/embedding_search.sql

@@ -5,12 +5,12 @@ SELECT
 FROM
 	(
 	SELECT DISTINCT ON
-		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+		("paragraph_id") ( 1 - distince ),* ,(1 - distince) AS comprehensive_score
 	FROM
-		( SELECT *, ( 1 - ( embedding.embedding <=>  %s ) ) AS similarity FROM embedding ${embedding_query}) TEMP
+		( SELECT *, ( embedding.embedding::vector(%s) <=>  %s ) AS distince FROM embedding ${embedding_query} ORDER BY distince) TEMP
 	ORDER BY
 		paragraph_id,
-		similarity DESC
+		distince
 	) DISTINCT_TEMP
 WHERE comprehensive_score>%s
 ORDER BY comprehensive_score DESC

apps/embedding/task/embedding.py

@@ -17,6 +17,7 @@ from common.config.embedding_config import ModelManage
 from common.event import ListenerManagement, UpdateProblemArgs, UpdateEmbeddingDatasetIdArgs, \
     UpdateEmbeddingDocumentIdArgs
 from dataset.models import Document, TaskType, State
+from dataset.serializers.common_serializers import drop_dataset_index
 from ops import celery_app
 from setting.models import Model
 from setting.models_provider import get_model
@@ -110,6 +111,7 @@ def embedding_by_dataset(dataset_id, model_id):
     max_kb.info(_('Start--->Vectorized dataset: {dataset_id}').format(dataset_id=dataset_id))
     try:
         ListenerManagement.delete_embedding_by_dataset(dataset_id)
+        drop_dataset_index(dataset_id=dataset_id)
         document_list = QuerySet(Document).filter(dataset_id=dataset_id)
         max_kb.info(_('Dataset documentation: {document_names}').format(
             document_names=", ".join([d.name for d in document_list])))

apps/embedding/vector/pg_vector.py

@@ -12,7 +12,6 @@ import uuid
 from abc import ABC, abstractmethod
 from typing import Dict, List
 
-import jieba
 from django.contrib.postgres.search import SearchVector
 from django.db.models import QuerySet, Value
 from langchain_core.embeddings import Embeddings
@@ -169,8 +168,13 @@ class EmbeddingSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
                                                                             'embedding_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(query_embedding), *exec_params, similarity, top_number])
+        embedding_model = select_list(exec_sql, [
+            len(query_embedding),
+            json.dumps(query_embedding),
+            *exec_params,
+            similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):
@@ -190,8 +194,12 @@ class KeywordsSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
                                                                             'keywords_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [to_query(query_text), *exec_params, similarity, top_number])
+        embedding_model = select_list(exec_sql, [
+            to_query(query_text),
+            *exec_params,
+            similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):
@@ -211,9 +219,14 @@ class BlendSearch(ISearch):
                                                                os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
                                                                             'blend_search.sql')),
                                                            with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(query_embedding), to_query(query_text), *exec_params, similarity,
-                                       top_number])
+        embedding_model = select_list(exec_sql, [
+            len(query_embedding),
+            json.dumps(query_embedding),
+            to_query(query_text),
+            *exec_params,
+            similarity,
+            top_number
+        ])
         return embedding_model
 
     def support(self, search_mode: SearchMode):