neo4j-graphrag

import os from neo4j import GraphDatabase from neo4j_graphrag.graph_rag_pipeline import GraphRAGPipeline from neo4j_graphrag.llm import OpenAILLM # --- Configuration (Set environment variables or replace placeholders) --- 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") NEO4J_DATABASE = os.environ.get("NEO4J_DATABASE", "neo4j") OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "") # Required for OpenAILLM if not all([NEO4J_URI, NEO4J_USERNAME, NEO4J_PASSWORD, OPENAI_API_KEY]): print("Warning: Please set NEO4J_URI, NEO4J_USERNAME, NEO4J_PASSWORD, and OPENAI_API_KEY environment variables for a runnable example.") print("Using default values for Neo4j, which may not connect without a running instance.") print("OpenAI API key is missing. The LLM initialization might fail or proceed without API calls.") try: # 1. Initialize Neo4j Driver driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD)) # Verify connection to ensure Neo4j is reachable and credentials are correct driver.verify_connectivity() print("Neo4j connection verified successfully.") # 2. Initialize your chosen LLM (e.g., OpenAI) llm = OpenAILLM(api_key=OPENAI_API_KEY) # 3. Initialize the GraphRAG pipeline # This pipeline is designed to work with a knowledge graph. For best results, # ensure your Neo4j database has relevant data, possibly built using SimpleKGPipeline. pipeline = GraphRAGPipeline( llm=llm, graph_driver=driver, database=NEO4J_DATABASE ) # 4. Ask a question to the pipeline question = "Who is the CEO of Neo4j?" print(f"\nQuestion: {question}") response = pipeline.query(question) print(f"Answer: {response.answer}") except Exception as e: print(f"An error occurred during quickstart: {e}") finally: if 'driver' in locals(): driver.close() print("Neo4j driver closed.")