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This paper presents a study of a high-performance vector database implementation in Go, addressing the growing need for efficient similarity search systems in machine learning and artificial intelligence applications. The research contributes a novel architecture that combines multiple indexing strategies including linear search, Locality-Sensitive Hashing (LSH), and Inverted File (IVF) indexing within a unified framework. Our implementation demonstrates superior performance characteristics compared to existing solutions, achieving sub-millisecond query times for datasets containing up to 100,000 high-dimensional vectors. The system architecture incorporates advanced concurrency patterns, memory management optimisations, and a RESTful API design that ensures scalability and maintainability. Extensive empirical evaluation across different workloads and vector dimensions validates the effectiveness of our approach, with particular emphasis on real-world machine learning scenarios involving embedding similarity search. The research provides both theoretical analysis of the implemented algorithms and practical guidelines for deployment in production environments.