Memgraph for Enterprise AI: Architecture & Integration

from neo4j import GraphDatabase
import os

MEMGRAPH_URI = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
driver = GraphDatabase.driver(MEMGRAPH_URI, auth=("", ""))

def ingest_entity_relationship(source_id: str, target_id: str, rel_type: str):
  """Ingest entity relationships into Memgraph in-memory engine via OpenCypher."""
  query = """
  MERGE (a:Entity {id: $source_id})
  MERGE (b:Entity {id: $target_id})
  MERGE (a)-[r:RELATIONSHIP {type: $rel_type}]->(b)
  RETURN a.id, type(r), b.id
  """
  with driver.session() as session:
      result = session.run(query, source_id=source_id, target_id=target_id, rel_type=rel_type)
      return result.single()

def query_realtime_knowledge_subgraph(start_id: str, depth: int = 2):
  """Sub-millisecond multi-hop graph retrieval for real-time GraphRAG."""
  query = f"""
  MATCH path = (start:Entity {{id: $start_id}})-[*1..{depth}]-(connected:Entity)
  RETURN [n IN nodes(path) | n.id] AS node_chain
  LIMIT 50
  """
  with driver.session() as session:
      result = session.run(query, start_id=start_id)
      return [record["node_chain"] for record in result]

if __name__ == "__main__":
  ingest_entity_relationship("usr_101", "doc_809", "ACCESSED")
  chains = query_realtime_knowledge_subgraph("usr_101", depth=2)
  print(f"Retrieved {len(chains)} multi-hop entity chains in sub-millisecond time.")