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 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 with Graph RAG capabilities, and introduces Agentic Enrichment for autonomous graph expansion via LLMs. The 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; 4.0-4.7x latency reduction from late materialization on multi-hop traversals; 8.2x 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.

翻译：现代数据架构在图形数据库、向量存储、分析引擎和优化求解器之间呈现碎片化状态，导致复杂的ETL管道和同步开销。本文介绍Samyama，一个用Rust编写的高性能图向量数据库，它将上述工作负载统一到单一引擎中。Samyama结合了基于RocksDB的持久化存储与版本化竞技场MVCC模型、包含35个物理算子的向量化查询执行器、支持计划枚举和谓词下推的基于代价的查询规划器、专用的基于CSR的分析引擎以及原生的RDF/SPARQL支持。该系统将22种元启发式优化求解器直接集成到其查询语言中，实现了具备Graph RAG功能的HNSW向量索引，并引入了通过LLM实现自主图扩展的智能增强功能。企业版通过wgpu增加了GPU加速、生产级可观测性、时间点恢复以及采用HTTP/2 Raft传输的强化高可用性。我们在商用硬件（Mac Mini M4，16 GB内存）上的评估表明：数据摄取速率达255K节点/秒（CPU）和412K节点/秒（GPU加速）；在100万节点规模下每秒处理115K条Cypher查询；多跳遍历的延迟物化使延迟降低4.0-4.7倍；100万节点规模的GPU PageRank加速比达8.2倍；并通过全部LDBC Graphalytics验证测试（28/28）。这些结果表明，统一的图-向量-优化引擎能够在保持Rust内存安全保证的同时，在商用硬件上实现具有竞争力的性能。