update

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -4,9 +4,10 @@
 
 import sqlalchemy
 from pydantic import BaseModel, model_validator
-from sqlalchemy import JSON, TEXT, Column, DateTime, String, Table, create_engine, insert
+from sqlalchemy import JSON, TEXT, Column, DateTime, Float, Integer, String, Table, create_engine, insert
 from sqlalchemy import text as sql_text
 from sqlalchemy.orm import Session, declarative_base
+from sqlalchemy.sql.expression import bindparam
 
 from configs import dify_config
 from core.rag.datasource.vdb.field import Field
@@ -196,11 +197,11 @@ def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Doc
 
         docs = []
         if self._distance_func == "l2":
-            tidb_dist_func = "Vec_l2_distance"
+            tidb_dist_func = "VEC_L2_DISTANCE"
         elif self._distance_func == "cosine":
-            tidb_dist_func = "Vec_Cosine_distance"
+            tidb_dist_func = "VEC_COSINE_DISTANCE"
         else:
-            tidb_dist_func = "Vec_Cosine_distance"
+            tidb_dist_func = "VEC_COSINE_DISTANCE"
 
         with Session(self._engine) as session:
             select_statement = sql_text(f"""
@@ -212,7 +213,12 @@ def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Doc
                     WHERE distance <= :distance
                     ORDER BY distance
                     LIMIT :top_k
-                    """)
+                    """).bindparams(
+                bindparam("query_vector_str", type_=String),
+                bindparam("table_name", type_=String),
+                bindparam("top_k", type_=Integer),
+                bindparam("distance", type_=Float),
+            )
             res = session.execute(
                 statement=select_statement,
                 params={