get_node added and all to base.py and to neo4j_impl.py file

lightrag/operate.py

@@ -1233,16 +1233,20 @@ async def _get_node_data(
 
     if not len(results):
         return "", "", ""
-    # get entity information
-    node_datas, node_degrees = await asyncio.gather(
-        asyncio.gather(
-            *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results]
-        ),
-        asyncio.gather(
-            *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results]
-        ),
+    
+    # Extract all entity IDs from your results list
+    node_ids = [r["entity_name"] for r in results]
+
+    # Call the batch node retrieval and degree functions concurrently.
+    nodes_dict, degrees_dict = await asyncio.gather(
+        knowledge_graph_inst.get_nodes_batch(node_ids), 
+        knowledge_graph_inst.node_degrees_batch(node_ids)
     )
 
+    # Now, if you need the node data and degree in order:
+    node_datas = [nodes_dict.get(nid) for nid in node_ids]
+    node_degrees = [degrees_dict.get(nid, 0) for nid in node_ids]
+
     if not all([n is not None for n in node_datas]):
         logger.warning("Some nodes are missing, maybe the storage is damaged")
 
@@ -1374,9 +1378,10 @@ async def _find_most_related_text_unit_from_entities(
         all_one_hop_nodes.update([e[1] for e in this_edges])
 
     all_one_hop_nodes = list(all_one_hop_nodes)
-    all_one_hop_nodes_data = await asyncio.gather(
-        *[knowledge_graph_inst.get_node(e) for e in all_one_hop_nodes]
-    )
+    
+    # Batch retrieve one-hop node data using get_nodes_batch
+    all_one_hop_nodes_data_dict = await knowledge_graph_inst.get_nodes_batch(all_one_hop_nodes)
+    all_one_hop_nodes_data = [all_one_hop_nodes_data_dict.get(e) for e in all_one_hop_nodes]
 
     # Add null check for node data
     all_one_hop_text_units_lookup = {
@@ -1512,29 +1517,34 @@ async def _get_edge_data(
     if not len(results):
         return "", "", ""
 
-    edge_datas, edge_degree = await asyncio.gather(
-        asyncio.gather(
-            *[knowledge_graph_inst.get_edge(r["src_id"], r["tgt_id"]) for r in results]
-        ),
-        asyncio.gather(
-            *[
-                knowledge_graph_inst.edge_degree(r["src_id"], r["tgt_id"])
-                for r in results
-            ]
-        ),
+    # Prepare edge pairs in two forms:
+    # For the batch edge properties function, use dicts.
+    edge_pairs_dicts = [{"src": r["src_id"], "tgt": r["tgt_id"]} for r in results]
+    # For edge degrees, use tuples.
+    edge_pairs_tuples = [(r["src_id"], r["tgt_id"]) for r in results]
+
+    # Call the batched functions concurrently.
+    edge_data_dict, edge_degrees_dict = await asyncio.gather(
+        knowledge_graph_inst.get_edges_batch(edge_pairs_dicts),
+        knowledge_graph_inst.get_edges_degree_batch(edge_pairs_tuples)
     )
 
-    edge_datas = [
-        {
-            "src_id": k["src_id"],
-            "tgt_id": k["tgt_id"],
-            "rank": d,
-            "created_at": k.get("__created_at__", None),
-            **v,
-        }
-        for k, v, d in zip(results, edge_datas, edge_degree)
-        if v is not None
-    ]
+    # Reconstruct edge_datas list in the same order as results.
+    edge_datas = []
+    for k in results:
+        pair = (k["src_id"], k["tgt_id"])
+        edge_props = edge_data_dict.get(pair)
+        if edge_props is not None:
+            # Use edge degree from the batch as rank.
+            combined = {
+                "src_id": k["src_id"],
+                "tgt_id": k["tgt_id"],
+                "rank": edge_degrees_dict.get(pair, k.get("rank", 0)),
+                "created_at": k.get("__created_at__", None),
+                **edge_props,
+            }
+            edge_datas.append(combined)
+    
     edge_datas = sorted(
         edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True
     )
@@ -1640,24 +1650,23 @@ async def _find_most_related_entities_from_relationships(
             entity_names.append(e["tgt_id"])
             seen.add(e["tgt_id"])
 
-    node_datas, node_degrees = await asyncio.gather(
-        asyncio.gather(
-            *[
-                knowledge_graph_inst.get_node(entity_name)
-                for entity_name in entity_names
-            ]
-        ),
-        asyncio.gather(
-            *[
-                knowledge_graph_inst.node_degree(entity_name)
-                for entity_name in entity_names
-            ]
-        ),
+    # Batch approach: Retrieve nodes and their degrees concurrently with one query each.
+    nodes_dict, degrees_dict = await asyncio.gather(
+        knowledge_graph_inst.get_nodes_batch(entity_names),
+        knowledge_graph_inst.get_node_degrees_batch(entity_names)
     )
-    node_datas = [
-        {**n, "entity_name": k, "rank": d}
-        for k, n, d in zip(entity_names, node_datas, node_degrees)
-    ]
+
+    # Rebuild the list in the same order as entity_names
+    node_datas = []
+    for entity_name in entity_names:
+        node = nodes_dict.get(entity_name)
+        degree = degrees_dict.get(entity_name, 0)
+        if node is None:
+            logger.warning(f"Node '{entity_name}' not found in batch retrieval.")
+            continue
+        # Combine the node data with the entity name and computed degree (as rank)
+        combined = {**node, "entity_name": entity_name, "rank": degree}
+        node_datas.append(combined)
 
     len_node_datas = len(node_datas)
     node_datas = truncate_list_by_token_size(