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Samyama

samyama-aibysamyama-ai
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187
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Gestión de Bases de Datos
Herramientas para Desarrolladores
Ciencia de Datos y ML

Manages and queries large-scale graph-vector datasets with high performance, supporting OpenCypher, vector search, and advanced graph algorithms.

Samyama is a high-performance graph-vector database engineered in Rust, designed to handle massive datasets with ease, demonstrated by its ability to query 1 billion edges for just $2.50. It supports OpenCypher queries, Redis protocol, and integrates vector search (HNSW) and 14 graph algorithms into a single, efficient binary without a JVM or GC pauses. Samyama excels in scalability and concurrency, providing rapid query responses across complex, interconnected data, making it ideal for advanced analytics, knowledge graphs, and AI applications like Graph RAG and AI agents.

Características Principales

01Natural Language Query (NLQ) translation to Cypher and auto-generated AI agents
0214 rayon-parallelized graph algorithms (e.g., PageRank, BFS, Dijkstra)
03HNSW vector indexing for semantic search and Graph RAG
04High-performance graph-vector database handling billions of edges on a single machine
0544 GitHub stars
06OpenCypher query language (90% compatible) and Redis protocol support

Casos de Uso

01Building and querying large-scale knowledge graphs in biomedical research and other domains
02High-performance fraud detection and financial transaction analysis
03Enabling semantic search and Graph RAG for AI applications