Vector search engines, such as Pinecone, offer a significant shift from traditional text-based search systems by operating on numerical vector representations, making them particularly adept at similarity searches in high-dimensional data spaces. These engines are applied across various domains, including natural language processing for semantic search and translation tasks, image and video searches by converting visual content into numerical vectors, fraud detection by identifying anomalies in transaction data, and recommendation systems for personalized suggestions. Pinecone is a managed vector search platform that allows users to efficiently index and search through high-dimensional vectors, reducing the operational complexities involved in deploying and scaling such infrastructures. By integrating Pinecone with modern streaming data platforms like Redpanda, which offers a simplified alternative to Apache Kafka, data engineers can enhance their data processing pipelines with advanced search capabilities. A practical demonstration of this integration is shown in a fraud detection use case, where transactional data is streamed to Redpanda, indexed in Pinecone, and similarity comparisons are made to identify fraudulent transactions. This seamless interaction between Redpanda's efficient data streaming and Pinecone's vector search capabilities exemplifies a robust solution for real-time anomaly detection in financial ecosystems.