This blog post details how to build a GraphRAG agent using the Neo4j graph database and Milvus vector database. This agent combines the power of graph databases and vector search to provide accurate and relevant answers to user queries. In this example, we will use LangGraph, Llama 3.1 8B with Ollama and GPT-4o.
Traditional Retrieval Augmented Generation (RAG ) systems rely solely on vector databases to retrieve relevant documents. Our approach goes further by incorporating Neo4j to capture ...