Optimize the memory usage of Tencent Vector Database (#22079)

Co-authored-by: wlleiiwang <wlleiiwang@tencent.com>

api/core/rag/datasource/vdb/tencent/tencent_vector.py

@@ -122,7 +122,6 @@ class TencentVector(BaseVector):
                 metric_type,
                 params,
             )
-            index_text = vdb_index.FilterIndex(self.field_text, enum.FieldType.String, enum.IndexType.FILTER)
             index_metadate = vdb_index.FilterIndex(self.field_metadata, enum.FieldType.Json, enum.IndexType.FILTER)
             index_sparse_vector = vdb_index.SparseIndex(
                 name="sparse_vector",
@@ -130,7 +129,7 @@ class TencentVector(BaseVector):
                 index_type=enum.IndexType.SPARSE_INVERTED,
                 metric_type=enum.MetricType.IP,
             )
-            indexes = [index_id, index_vector, index_text, index_metadate]
+            indexes = [index_id, index_vector, index_metadate]
             if self._enable_hybrid_search:
                 indexes.append(index_sparse_vector)
             try:
@@ -149,7 +148,7 @@ class TencentVector(BaseVector):
                 index_metadate = vdb_index.FilterIndex(
                     self.field_metadata, enum.FieldType.String, enum.IndexType.FILTER
                 )
-                indexes = [index_id, index_vector, index_text, index_metadate]
+                indexes = [index_id, index_vector, index_metadate]
                 if self._enable_hybrid_search:
                     indexes.append(index_sparse_vector)
                 self._client.create_collection(