langchain-neo4j

import os from langchain_neo4j import Neo4jGraph, GraphCypherQAChain from langchain_openai import ChatOpenAI # Set environment variables for Neo4j connection NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687') NEO4J_USERNAME = os.environ.get('NEO4J_USERNAME', 'neo4j') NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD', 'password') OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '') if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY environment variable not set.") # Initialize Neo4jGraph connection graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USERNAME, password=NEO4J_PASSWORD) # Optional: Insert some sample movie data if the database is empty graph.query( """ MERGE (m:Movie {title:'The Matrix'}) WITH m UNWIND ['Keanu Reeves', 'Laurence Fishburne', 'Carrie-Anne Moss'] AS actor MERGE (a:Actor {name:actor}) MERGE (a)-[:ACTED_IN]->(m) """ ) # Initialize LLM llm = ChatOpenAI(temperature=0, api_key=OPENAI_API_KEY) # Create GraphCypherQAChain # IMPORTANT: allow_dangerous_requests=True is used for demonstration. # In production, ensure narrowly-scoped credentials and careful prompt engineering. chain = GraphCypherQAChain.from_llm( llm=llm, graph=graph, verbose=True, allow_dangerous_requests=True ) # Invoke the chain with a natural language query result = chain.invoke({"query": "Who acted in The Matrix?"}) print(result['result'])