Memgraph has introduced vector search as a new feature, enhancing data retrieval capabilities by addressing the limitations of traditional search methods that rely on exact keyword matches and struggle with unstructured data. Vector search allows for contextual querying and semantic filtering, making it particularly useful in AI-driven applications, semantic search, and large-scale datasets. It employs vector embeddings, which are high-dimensional mathematical representations of data, enabling machines to understand and process unstructured data like text, images, and audio. This approach supports use cases involving large language models and knowledge graphs, allowing for more meaningful and contextual data connections. However, vector search requires significant computational resources and careful data modeling to ensure accuracy and relevance. To optimize results, hybrid methods combine vector search with traditional techniques, enhancing precision by initially filtering with vectors and refining with exact matches. Memgraph's vector search is designed to support applications such as recommendation systems, fraud detection, and AI-powered tools by integrating seamlessly with GraphRAG-based solutions.