feat: add new SQL queries for embedding and keyword searches

apps/knowledge/sql/blend_search.sql

@@ -0,0 +1,26 @@
+SELECT
+	paragraph_id,
+	comprehensive_score,
+	comprehensive_score AS similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		( "paragraph_id" ) ( similarity ),* ,
+		similarity AS comprehensive_score
+	FROM
+		(
+		SELECT
+			*,
+			(( 1 - ( embedding.embedding <=>  %s ) )+ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS similarity
+		FROM
+			embedding ${embedding_query}
+		) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE
+	comprehensive_score >%s
+ORDER BY
+	comprehensive_score DESC
+	LIMIT %s

apps/knowledge/sql/embedding_search.sql

@@ -0,0 +1,17 @@
+SELECT
+    paragraph_id,
+	comprehensive_score,
+	comprehensive_score as similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+	FROM
+		( SELECT *, ( 1 - ( embedding.embedding <=>  %s ) ) AS similarity FROM embedding ${embedding_query}) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE comprehensive_score>%s
+ORDER BY comprehensive_score DESC
+LIMIT %s

apps/knowledge/sql/hit_test.sql

@@ -0,0 +1,17 @@
+SELECT
+	paragraph_id,
+	comprehensive_score,
+	comprehensive_score as similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+	FROM
+		( SELECT *, ( 1 - ( embedding.embedding <=>  %s ) ) AS similarity FROM embedding ${embedding_query} ) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE comprehensive_score>%s
+ORDER BY comprehensive_score DESC
+LIMIT %s

apps/knowledge/sql/keywords_search.sql

@@ -0,0 +1,17 @@
+SELECT
+    paragraph_id,
+	comprehensive_score,
+	comprehensive_score as similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+	FROM
+		( SELECT *,ts_rank_cd(embedding.search_vector,websearch_to_tsquery('simple',%s),32) AS similarity  FROM embedding ${keywords_query}) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE comprehensive_score>%s
+ORDER BY comprehensive_score DESC
+LIMIT %s

apps/knowledge/vector/pg_vector.py

@@ -165,7 +165,7 @@ class EmbeddingSearch(ISearch):
                search_mode: SearchMode):
         exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
                                                            select_string=get_file_content(
-                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                               os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'embedding_search.sql')),
                                                            with_table_name=True)
         embedding_model = select_list(exec_sql,
@@ -186,7 +186,7 @@ class KeywordsSearch(ISearch):
                search_mode: SearchMode):
         exec_sql, exec_params = generate_sql_by_query_dict({'keywords_query': query_set},
                                                            select_string=get_file_content(
-                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                               os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'keywords_search.sql')),
                                                            with_table_name=True)
         embedding_model = select_list(exec_sql,
@@ -207,7 +207,7 @@ class BlendSearch(ISearch):
                search_mode: SearchMode):
         exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
                                                            select_string=get_file_content(
-                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                               os.path.join(PROJECT_DIR, "apps", "knowledge", 'sql',
                                                                             'blend_search.sql')),
                                                            with_table_name=True)
         embedding_model = select_list(exec_sql,