From 19c6aaa53da008270a1c4d3da81332338b1238b2 Mon Sep 17 00:00:00 2001
From: vasilije <vas.markovic@gmail.com>
Date: Wed, 9 Jul 2025 22:13:49 +0200
Subject: [PATCH] added lint

---
 .../hybrid/falkordb/FalkorDBAdapter.py        | 82 +++++++++----------
 1 file changed, 41 insertions(+), 41 deletions(-)

diff --git a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py
index 9d6d01df1..b7dc0d886 100644
--- a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py
+++ b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py
@@ -341,47 +341,47 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
 
         return collection_name in collections
 
-    async def create_data_points(self, data_points: list[DataPoint]):
-        """
-        Add a list of data points to the graph database via batching.
-
-        Can raise exceptions if there are issues during the database operations.
-
-        Parameters:
-        -----------
-
-            - data_points (list[DataPoint]): A list of DataPoint instances to be inserted into
-              the database.
-        """
-        embeddable_values = []
-        vector_map = {}
-
-        for data_point in data_points:
-            property_names = DataPoint.get_embeddable_property_names(data_point)
-            key = str(data_point.id)
-            vector_map[key] = {}
-
-            for property_name in property_names:
-                property_value = getattr(data_point, property_name, None)
-
-                if property_value is not None:
-                    vector_map[key][property_name] = len(embeddable_values)
-                    embeddable_values.append(property_value)
-                else:
-                    vector_map[key][property_name] = None
-
-        vectorized_values = await self.embed_data(embeddable_values)
-
-        for data_point in data_points:
-            vectorized_data = [
-                vectorized_values[vector_map[str(data_point.id)][property_name]]
-                if vector_map[str(data_point.id)][property_name] is not None
-                else None
-                for property_name in DataPoint.get_embeddable_property_names(data_point)
-            ]
-
-            query, params = await self.create_data_point_query(data_point, vectorized_data)
-            self.query(query, params)
+    # async def create_data_points(self, data_points: list[DataPoint]):
+    #     """
+    #     Add a list of data points to the graph database via batching.
+    #
+    #     Can raise exceptions if there are issues during the database operations.
+    #
+    #     Parameters:
+    #     -----------
+    #
+    #         - data_points (list[DataPoint]): A list of DataPoint instances to be inserted into
+    #           the database.
+    #     """
+    #     embeddable_values = []
+    #     vector_map = {}
+    #
+    #     for data_point in data_points:
+    #         property_names = DataPoint.get_embeddable_property_names(data_point)
+    #         key = str(data_point.id)
+    #         vector_map[key] = {}
+    #
+    #         for property_name in property_names:
+    #             property_value = getattr(data_point, property_name, None)
+    #
+    #             if property_value is not None:
+    #                 vector_map[key][property_name] = len(embeddable_values)
+    #                 embeddable_values.append(property_value)
+    #             else:
+    #                 vector_map[key][property_name] = None
+    #
+    #     vectorized_values = await self.embed_data(embeddable_values)
+    #
+    #     for data_point in data_points:
+    #         vectorized_data = [
+    #             vectorized_values[vector_map[str(data_point.id)][property_name]]
+    #             if vector_map[str(data_point.id)][property_name] is not None
+    #             else None
+    #             for property_name in DataPoint.get_embeddable_property_names(data_point)
+    #         ]
+    #
+    #         query, params = await self.create_data_point_query(data_point, vectorized_data)
+    #         self.query(query, params)
 
     async def create_vector_index(self, index_name: str, index_property_name: str):
         """