This article discusses building an intelligent movie search system using Neo4j and Google Vertex AI. By integrating vector embeddings into a graph database, it enables semantic search that understands movie descriptions beyond simple keyword matching. This approach enhances recommendation accuracy by capturing contextual and relational meaning within the dataset. The system uses a knowledge graph to store movie metadata, including titles, descriptions, genres, directors, and actors. It generates vector embeddings for movie plots using Vertex AI's text embedding models and stores them in Neo4j for efficient retrieval. The article provides a step-by-step guide on how to build this project, including loading data into Neo4j, generating vector embeddings, storing and querying embeddings in Neo4j, and running similarity search queries. By leveraging graph-based AI solutions like GraphRAG with vector search, the system achieves more context-aware and accurate movie suggestions, demonstrating its potential for unlocking deeper search insights and improving user experiences.