Samyama: A Unified Graph-Vector Database with In-Database Optimization, Agentic Enrichment, and Hardware Acceleration

Abstract: Modern data architectures are fragmented across graph databases, vector stores, analytics engines, and optimization solvers, resulting in complex ETL pipelines and synchronization overhead. We present \textbf{Samyama}, a high-performance graph-vector database written in Rust that unifies these workloads into a single engine. Samyama combines a RocksDB-backed persistent store with a versioned-arena MVCC model, a vectorized query executor with 35 physical operators, a cost-based query planner with plan enumeration and predicate pushdown, a dedicated CSR-based analytics engine, and native RDF/SPARQL support. The system integrates 22 metaheuristic optimization solvers directly into its query language, implements HNSW vector indexing~\citep{malkov2020hnsw} with Graph RAG capabilities, and introduces ``Agentic Enrichment'' for autonomous graph expansion via LLMs. The \textbf{Enterprise Edition} adds GPU acceleration via wgpu, production-grade observability, point-in-time recovery, and hardened high availability with HTTP/2 Raft transport. Our evaluation on commodity hardware (Mac Mini M4, 16\,GB RAM) demonstrates: ingestion at 255K nodes/s (CPU) and 412K nodes/s (GPU-accelerated); 115K Cypher queries/sec at 1M nodes; 4.0--4.7$\times$ latency reduction from late materialization on multi-hop traversals; 8.2$\times$ GPU PageRank speedup at 1M nodes; and 100\% LDBC Graphalytics validation (28/28 tests). These results demonstrate that a unified graph-vector-optimization engine can achieve competitive performance on commodity hardware while maintaining Rust's memory safety guarantees.