Update oracle_impl.py

lightrag/kg/oracle_impl.py

@@ -503,79 +503,6 @@ class OracleGraphStorage(BaseGraphStorage):
                 #print("Node Edge not exist!",self.db.workspace, source_node_id)
                 return []
 
-    #################### INSERT method ################
-    async def upsert_node(self, node_id: str, node_data: dict[str, str]):
-        """插入或更新节点"""
-        #print("go into upsert node method")
-        entity_name = node_id
-        entity_type = node_data["entity_type"]
-        description = node_data["description"]
-        source_id   = node_data["source_id"]
-        content = entity_name+description
-        contents = [content]
-        batches = [
-            contents[i: i + self._max_batch_size]
-            for i in range(0, len(contents), self._max_batch_size)
-        ]
-        embeddings_list = await asyncio.gather(
-            *[self.embedding_func(batch) for batch in batches]
-        )
-        embeddings = np.concatenate(embeddings_list)
-        content_vector = embeddings[0]
-        merge_sql = SQL_TEMPLATES["merge_node"].format(
-            workspace=self.db.workspace,name=entity_name, source_chunk_id=source_id
-        )
-        #print(merge_sql)
-        await self.db.execute(merge_sql, [self.db.workspace,entity_name,entity_type,description,source_id,content,content_vector])
-        #self._graph.add_node(node_id, **node_data)
-
-    async def upsert_edge(
-        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
-    ):
-        """插入或更新边"""
-        #print("go into upsert edge method")
-        source_name = source_node_id
-        target_name = target_node_id
-        weight = edge_data["weight"]
-        keywords = edge_data["keywords"]
-        description = edge_data["description"]
-        source_chunk_id = edge_data["source_id"]
-        content = keywords+source_name+target_name+description
-        contents = [content]
-        batches = [
-            contents[i: i + self._max_batch_size]
-            for i in range(0, len(contents), self._max_batch_size)
-        ]
-        embeddings_list = await asyncio.gather(
-            *[self.embedding_func(batch) for batch in batches]
-        )
-        embeddings = np.concatenate(embeddings_list)
-        content_vector = embeddings[0]
-        merge_sql = SQL_TEMPLATES["merge_edge"].format(
-            workspace=self.db.workspace,source_name=source_name, target_name=target_name, source_chunk_id=source_chunk_id
-        )
-        #print(merge_sql)
-        await self.db.execute(merge_sql, [self.db.workspace,source_name,target_name,weight,keywords,description,source_chunk_id,content,content_vector])
-        #self._graph.add_edge(source_node_id, target_node_id, **edge_data)
-
-    async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
-        """为节点生成向量"""
-        if algorithm not in self._node_embed_algorithms:
-            raise ValueError(f"Node embedding algorithm {algorithm} not supported")
-        return await self._node_embed_algorithms[algorithm]()
-
-    async def _node2vec_embed(self):
-        """为节点生成向量"""
-        from graspologic import embed
-
-        embeddings, nodes = embed.node2vec_embed(
-            self._graph,
-            **self.config["node2vec_params"],
-        )
-
-        nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
-        return embeddings, nodes_ids
-
 
 N_T = {
     "full_docs": "LIGHTRAG_DOC_FULL",