add nodes batch

src/memos/graph_dbs/base.py

@@ -250,3 +250,16 @@ def get_all_memory_items(self, scope: str, include_embedding: bool = False) -> l
         Returns:
             list[dict]: Full list of memory items under this scope.
         """
+
+    @abstractmethod
+    def add_nodes_batch(self, nodes: list[dict[str, Any]], user_name: str | None = None) -> None:
+        """
+        Batch add multiple memory nodes to the graph.
+
+        Args:
+            nodes: List of node dictionaries, each containing:
+                - id: str - Node ID
+                - memory: str - Memory content
+                - metadata: dict[str, Any] - Node metadata
+            user_name: Optional user name (will use config default if not provided)
+        """

src/memos/graph_dbs/neo4j.py

@@ -236,6 +236,110 @@ def add_node(
                 metadata=metadata,
             )
 
+    def add_nodes_batch(
+        self,
+        nodes: list[dict[str, Any]],
+        user_name: str | None = None,
+    ) -> None:
+        """
+        Batch add multiple memory nodes to the graph.
+
+        Args:
+            nodes: List of node dictionaries, each containing:
+                - id: str - Node ID
+                - memory: str - Memory content
+                - metadata: dict[str, Any] - Node metadata
+            user_name: Optional user name (will use config default if not provided)
+        """
+        if not nodes:
+            logger.warning("[add_nodes_batch] Empty nodes list, skipping")
+            return
+
+        logger.info(f"[add_nodes_batch] Adding {len(nodes)} nodes")
+
+        # user_name comes from parameter; fallback to config if missing
+        effective_user_name = user_name if user_name else self.config.user_name
+
+        # Prepare all nodes
+        prepared_nodes = []
+        for node_data in nodes:
+            try:
+                id = node_data["id"]
+                memory = node_data["memory"]
+                metadata = node_data.get("metadata", {})
+
+                logger.debug(f"[add_nodes_batch] Processing node id: {id}")
+
+                # Set user_name in metadata if needed
+                if not self.config.use_multi_db and (self.config.user_name or effective_user_name):
+                    metadata["user_name"] = effective_user_name
+
+                # Safely process metadata
+                metadata = _prepare_node_metadata(metadata)
+
+                # Flatten info fields to top level (for Neo4j flat structure)
+                metadata = _flatten_info_fields(metadata)
+
+                # Merge node and set metadata
+                created_at = metadata.pop("created_at")
+                updated_at = metadata.pop("updated_at")
+
+                # Serialization for sources
+                if metadata.get("sources"):
+                    for idx in range(len(metadata["sources"])):
+                        metadata["sources"][idx] = json.dumps(metadata["sources"][idx])
+
+                prepared_nodes.append(
+                    {
+                        "id": id,
+                        "memory": memory,
+                        "created_at": created_at,
+                        "updated_at": updated_at,
+                        "metadata": metadata,
+                    }
+                )
+            except Exception as e:
+                logger.error(
+                    f"[add_nodes_batch] Failed to prepare node {node_data.get('id', 'unknown')}: {e}",
+                    exc_info=True,
+                )
+                # Continue with other nodes
+                continue
+
+        if not prepared_nodes:
+            logger.warning("[add_nodes_batch] No valid nodes to insert after preparation")
+            return
+
+        # Batch insert using Neo4j UNWIND for better performance
+        query = """
+            UNWIND $nodes AS node
+            MERGE (n:Memory {id: node.id})
+            SET n.memory = node.memory,
+                n.created_at = datetime(node.created_at),
+                n.updated_at = datetime(node.updated_at),
+                n += node.metadata
+        """
+
+        # Prepare nodes data for UNWIND
+        nodes_data = [
+            {
+                "id": node["id"],
+                "memory": node["memory"],
+                "created_at": node["created_at"],
+                "updated_at": node["updated_at"],
+                "metadata": node["metadata"],
+            }
+            for node in prepared_nodes
+        ]
+
+        try:
+            with self.driver.session(database=self.db_name) as session:
+                session.run(query, nodes=nodes_data)
+                logger.info(f"[add_nodes_batch] Successfully inserted {len(prepared_nodes)} nodes")
+        except Exception as e:
+            logger.error(f"[add_nodes_batch] Failed to add nodes: {e}", exc_info=True)
+            raise
+
     def update_node(self, id: str, fields: dict[str, Any], user_name: str | None = None) -> None:
         """
         Update node fields in Neo4j, auto-converting `created_at` and `updated_at` to datetime type if present.

src/memos/graph_dbs/polardb.py

@@ -3226,6 +3226,190 @@ def add_node(
                 logger.info(f"In add node polardb: id-{id} memory-{memory} query-{insert_query}")
             self._return_connection(conn)
 
+    @timed
+    def add_nodes_batch(
+        self,
+        nodes: list[dict[str, Any]],
+        user_name: str | None = None,
+    ) -> None:
+        """
+        Batch add multiple memory nodes to the graph.
+
+        Args:
+            nodes: List of node dictionaries, each containing:
+                - id: str - Node ID
+                - memory: str - Memory content
+                - metadata: dict[str, Any] - Node metadata
+            user_name: Optional user name (will use config default if not provided)
+        """
+        if not nodes:
+            logger.warning("[add_nodes_batch] Empty nodes list, skipping")
+            return
+
+        logger.info(f"[add_nodes_batch] Adding {len(nodes)} nodes")
+
+        # user_name comes from parameter; fallback to config if missing
+        effective_user_name = user_name if user_name else self.config.user_name
+
+        # Prepare all nodes
+        prepared_nodes = []
+        for node_data in nodes:
+            try:
+                id = node_data["id"]
+                memory = node_data["memory"]
+                metadata = node_data.get("metadata", {})
+
+                logger.debug(f"[add_nodes_batch] Processing node id: {id}")
+
+                # Set user_name in metadata
+                metadata["user_name"] = effective_user_name
+
+                metadata = _prepare_node_metadata(metadata)
+
+                # Merge node and set metadata
+                created_at = metadata.pop("created_at", datetime.utcnow().isoformat())
+                updated_at = metadata.pop("updated_at", datetime.utcnow().isoformat())
+
+                # Prepare properties
+                properties = {
+                    "id": id,
+                    "memory": memory,
+                    "created_at": created_at,
+                    "updated_at": updated_at,
+                    **metadata,
+                }
+
+                # Generate embedding if not provided
+                if "embedding" not in properties or not properties["embedding"]:
+                    properties["embedding"] = generate_vector(
+                        self._get_config_value("embedding_dimension", 1024)
+                    )
+
+                # Serialization - JSON-serialize sources and usage fields
+                for field_name in ["sources", "usage"]:
+                    if properties.get(field_name):
+                        if isinstance(properties[field_name], list):
+                            for idx in range(len(properties[field_name])):
+                                # Serialize only when element is not a string
+                                if not isinstance(properties[field_name][idx], str):
+                                    properties[field_name][idx] = json.dumps(
+                                        properties[field_name][idx]
+                                    )
+                        elif isinstance(properties[field_name], str):
+                            # If already a string, leave as-is
+                            pass
+
+                # Extract embedding for separate column
+                embedding_vector = properties.pop("embedding", [])
+                if not isinstance(embedding_vector, list):
+                    embedding_vector = []
+
+                # Select column name based on embedding dimension
+                embedding_column = "embedding"  # default column
+                if len(embedding_vector) == 3072:
+                    embedding_column = "embedding_3072"
+                elif len(embedding_vector) == 1024:
+                    embedding_column = "embedding"
+                elif len(embedding_vector) == 768:
+                    embedding_column = "embedding_768"
+
+                prepared_nodes.append(
+                    {
+                        "id": id,
+                        "memory": memory,
+                        "properties": properties,
+                        "embedding_vector": embedding_vector,
+                        "embedding_column": embedding_column,
+                    }
+                )
+            except Exception as e:
+                logger.error(
+                    f"[add_nodes_batch] Failed to prepare node {node_data.get('id', 'unknown')}: {e}",
+                    exc_info=True,
+                )
+                # Continue with other nodes
+                continue
+
+        if not prepared_nodes:
+            logger.warning("[add_nodes_batch] No valid nodes to insert after preparation")
+            return
+
+        # Group nodes by embedding column to optimize batch inserts
+        nodes_by_embedding_column = {}
+        for node in prepared_nodes:
+            col = node["embedding_column"]
+            if col not in nodes_by_embedding_column:
+                nodes_by_embedding_column[col] = []
+            nodes_by_embedding_column[col].append(node)
+
+        conn = None
+        try:
+            conn = self._get_connection()
+            with conn.cursor() as cursor:
+                # Process each group separately
+                for embedding_column, nodes_group in nodes_by_embedding_column.items():
+                    # Delete existing records first (batch delete)
+                    for node in nodes_group:
+                        delete_query = f"""
+                            DELETE FROM {self.db_name}_graph."Memory"
+                            WHERE id = ag_catalog._make_graph_id('{self.db_name}_graph'::name, 'Memory'::name, %s::text::cstring)
+                        """
+                        cursor.execute(delete_query, (node["id"],))
+
+                    # Insert nodes (batch insert using executemany for better performance)
+                    for node in nodes_group:
+                        # Get graph_id for this node
+                        get_graph_id_query = f"""
+                            SELECT ag_catalog._make_graph_id('{self.db_name}_graph'::name, 'Memory'::name, %s::text::cstring)
+                        """
+                        cursor.execute(get_graph_id_query, (node["id"],))
+                        graph_id = cursor.fetchone()[0]
+                        node["properties"]["graph_id"] = str(graph_id)
+
+                        # Insert node
+                        if node["embedding_vector"]:
+                            insert_query = f"""
+                                INSERT INTO {self.db_name}_graph."Memory"(id, properties, {embedding_column})
+                                VALUES (
+                                    ag_catalog._make_graph_id('{self.db_name}_graph'::name, 'Memory'::name, %s::text::cstring),
+                                    %s,
+                                    %s
+                                )
+                            """
+                            logger.info(
+                                f"[add_nodes_batch] Inserting node insert_query={insert_query}"
+                            )
+                            cursor.execute(
+                                insert_query,
+                                (
+                                    node["id"],
+                                    json.dumps(node["properties"]),
+                                    json.dumps(node["embedding_vector"]),
+                                ),
+                            )
+                        else:
+                            insert_query = f"""
+                                INSERT INTO {self.db_name}_graph."Memory"(id, properties)
+                                VALUES (
+                                    ag_catalog._make_graph_id('{self.db_name}_graph'::name, 'Memory'::name, %s::text::cstring),
+                                    %s
+                                )
+                            """
+                            cursor.execute(
+                                insert_query,
+                                (node["id"], json.dumps(node["properties"])),
+                            )
+
+                    logger.info(
+                        f"[add_nodes_batch] Inserted {len(nodes_group)} nodes with embedding_column={embedding_column}"
+                    )
+
+        except Exception as e:
+            logger.error(f"[add_nodes_batch] Failed to add nodes: {e}", exc_info=True)
+            raise
+        finally:
+            self._return_connection(conn)
+
     def _build_node_from_agtype(self, node_agtype, embedding=None):
         """
         Parse the cypher-returned column `n` (agtype or JSON string)