llama-index-vector-stores-neo4jvector

import os from llama_index.vector_stores.neo4jvector import Neo4jVectorStore from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings from llama_index.core.embeddings import resolve_embed_model # Set environment variables for Neo4j and OpenAI # Ensure you have a running Neo4j instance (e.g., Docker, AuraDB) # NEO4J_URI = "bolt://localhost:7687" # NEO4J_USERNAME = "neo4j" # NEO4J_PASSWORD = "password" # OPENAI_API_KEY = "sk-..." # Configure LlamaIndex settings (LLM and Embedding Model) Settings.embed_model = resolve_embed_model("openai") # Or any other embedding model # Neo4j connection details neo4j_url = os.environ.get("NEO4J_URI", "bolt://localhost:7687") neo4j_username = os.environ.get("NEO4J_USERNAME", "neo4j") neo4j_password = os.environ.get("NEO4J_PASSWORD", "password") embedding_dimension = 1536 # OpenAI's default embedding dimension # Initialize Neo4jVectorStore try: neo4j_vector_store = Neo4jVectorStore( username=neo4j_username, password=neo4j_password, url=neo4j_url, embedding_dimension=embedding_dimension, index_name="vector", node_label="Chunk", embedding_node_property="embedding", text_node_property="text", ) except ValueError as e: print(f"Error connecting to Neo4j: {e}. Please ensure Neo4j is running and credentials are correct.") exit() # Create a dummy document for demonstration (in a 'data' directory) # You might need to create a 'data' directory and a 'test.txt' file # e.g., echo "This is a test document about LlamaIndex and Neo4j integration." > data/test.txt # Load documents # Ensure 'data' directory exists and contains documents try: documents = SimpleDirectoryReader("data").load_data() except Exception as e: print(f"Error loading documents: {e}. Make sure a 'data' directory exists and contains files.") documents = [] if not documents: print("No documents loaded. Creating a dummy document.") from llama_index.core.schema import Document documents = [Document(text="LlamaIndex integrates with Neo4j to provide a powerful vector store for RAG applications.")] # Create a VectorStoreIndex index = VectorStoreIndex.from_documents(documents, vector_store=neo4j_vector_store) # Query the index query_engine = index.as_query_engine() response = query_engine.query("What is LlamaIndex?") print(response) # Example of retrieving documents directly (without LLM synthesis) retriever = index.as_retriever(similarity_top_k=2) nodes = retriever.retrieve("How does LlamaIndex work with Neo4j?") for node in nodes: print(f"Retrieved Node: {node.text[:100]}...") # Clean up (optional, depends on your use case) # neo4j_vector_store._driver.close()