Add node neighborhood tool

memgraph-toolbox/README.md

@@ -19,6 +19,8 @@ Below is a list of tools included in the toolbox, along with their descriptions:
 7. `CypherTool` - Executes arbitrary [Cypher queries](https://memgraph.com/docs/querying) on a Memgraph database.
 8. `ShowConstraintInfoTool` - Shows [constraint](https://memgraph.com/docs/fundamentals/constraints) information from a Memgraph database.
 9. `ShowConfigTool` - Shows [configuration](https://memgraph.com/docs/database-management/configuration) information from a Memgraph database.
+10. `NodeVectorSearchTool` - Searches the most similar nodes using the Memgraph's [vector search](https://memgraph.com/docs/querying/vector-search).
+11. `NodeNeighborhoodTool` - Searches for the data attached to a given node using Memgraph's [deep-path traversals](https://memgraph.com/docs/advanced-algorithms/deep-path-traversal).
 
 ## Usage
 

memgraph-toolbox/src/memgraph_toolbox/memgraph_toolbox.py

@@ -7,6 +7,7 @@
 from .tools.constraint import ShowConstraintInfoTool
 from .tools.cypher import CypherTool
 from .tools.index import ShowIndexInfoTool
+from .tools.node_neighborhood import NodeNeighborhoodTool
 from .tools.node_vector_search import NodeVectorSearchTool
 from .tools.page_rank import PageRankTool
 from .tools.schema import ShowSchemaInfoTool
@@ -37,6 +38,7 @@ def __init__(self, db: Memgraph):
             self.add_tool(ShowConstraintInfoTool(db))
             self.add_tool(CypherTool(db))
             self.add_tool(ShowIndexInfoTool(db))
+            self.add_tool(NodeNeighborhoodTool(db))
             self.add_tool(NodeVectorSearchTool(db))
             self.add_tool(PageRankTool(db))
             self.add_tool(ShowSchemaInfoTool(db))

memgraph-toolbox/src/memgraph_toolbox/tests/test_toolbox.py

@@ -51,7 +51,7 @@ def test_memgraph_toolbox():
     tools = toolkit.get_all_tools()
 
     # Check if we have all 9 tools
-    assert len(tools) == 10
+    assert len(tools) == 11
 
     # Check for specific tool names
     tool_names = [tool.name for tool in tools]
@@ -66,6 +66,7 @@ def test_memgraph_toolbox():
         "show_schema_info",
         "show_storage_info",
         "show_triggers",
+        "node_neighborhood",
     ]
 
     for expected_tool in expected_tools:

memgraph-toolbox/src/memgraph_toolbox/tests/test_tools.py

@@ -6,6 +6,7 @@
 from ..tools.constraint import ShowConstraintInfoTool
 from ..tools.cypher import CypherTool
 from ..tools.index import ShowIndexInfoTool
+from ..tools.node_neighborhood import NodeNeighborhoodTool
 from ..tools.node_vector_search import NodeVectorSearchTool
 from ..tools.page_rank import PageRankTool
 from ..tools.schema import ShowSchemaInfoTool
@@ -282,3 +283,31 @@ def test_node_vector_search_tool():
         'MATCH (n:Person) WHERE "embedding" IN keys(n) DETACH DELETE n'
     )
     memgraph_client.query("DROP VECTOR INDEX my_index")
+
+
+def test_node_neighborhood_tool():
+    """Test the NodeNeighborhood tool."""
+    url = "bolt://localhost:7687"
+    user = ""
+    password = ""
+    memgraph_client = Memgraph(url=url, username=user, password=password)
+
+    label = "TestNodeNeighborhoodToolLabel"
+    memgraph_client.query(f"MATCH (n:{label}) DETACH DELETE n;")
+    memgraph_client.query(
+        f"CREATE (p1:{label} {{id: 1}})-[:KNOWS]->(p2:{label} {{id: 2}}), (p2)-[:KNOWS]->(p3:{label} {{id: 3}});"
+    )
+    memgraph_client.query(
+        f"CREATE (p4:{label} {{id: 4}})-[:KNOWS]->(p5:{label} {{id: 5}});"
+    )
+    ids = memgraph_client.query(
+        f"MATCH (p1:{label} {{id:1}}) RETURN id(p1) AS node_id;"
+    )
+    assert len(ids) == 1
+    node_id = ids[0]["node_id"]
+
+    node_neighborhood_tool = NodeNeighborhoodTool(db=memgraph_client)
+    result = node_neighborhood_tool.call({"node_id": node_id, "max_distance": 2})
+    assert isinstance(result, list)
+    assert len(result) == 2
+    memgraph_client.query(f"MATCH (n:{label}) DETACH DELETE n;")

memgraph-toolbox/src/memgraph_toolbox/tools/__init__.py

@@ -0,0 +1 @@
+

memgraph-toolbox/src/memgraph_toolbox/tools/node_neighborhood.py

@@ -0,0 +1,59 @@
+from typing import Any, Dict, List
+
+from ..api.memgraph import Memgraph
+from ..api.tool import BaseTool
+
+
+class NodeNeighborhoodTool(BaseTool):
+    """
+    Tool for finding nodes within a specified neighborhood distance in Memgraph.
+    """
+
+    def __init__(self, db: Memgraph):
+        super().__init__(
+            name="node_neighborhood",
+            description=(
+                "Finds nodes within a specified distance from a given node. "
+                "This tool explores the graph neighborhood around a starting node, "
+                "returning all nodes and relationships found within the specified radius."
+            ),
+            input_schema={
+                "type": "object",
+                "properties": {
+                    "node_id": {
+                        "type": "string",
+                        "description": "The ID of the starting node to find neighborhood around",
+                    },
+                    "max_distance": {
+                        "type": "integer",
+                        "description": "Maximum distance (hops) to search from the starting node. Default is 1.",
+                        "default": 1,
+                    },
+                    "limit": {
+                        "type": "integer",
+                        "description": "Maximum number of nodes to return. Default is 100.",
+                        "default": 100,
+                    },
+                },
+                "required": ["node_id"],
+            },
+        )
+        self.db = db
+
+    def call(self, arguments: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Execute the neighborhood search and return the results."""
+        node_id = arguments["node_id"]
+        max_distance = arguments.get("max_distance", 1)
+        limit = arguments.get("limit", 100)
+
+        query = f"""MATCH (n)-[r*..{max_distance}]-(m) WHERE id(n) = {node_id} RETURN DISTINCT m LIMIT {limit};"""
+        try:
+            results = self.db.query(query, {})
+            processed_results = []
+            for record in results:
+                node_data = record["m"];
+                properties = {k: v for k, v in node_data.items()}
+                processed_results.append(properties)
+            return processed_results
+        except Exception as e:
+            return [{"error": f"Failed to find neighborhood: {str(e)}"}]