How does Samyama handle billions of edges efficiently?

Samyama is optimized for scale, capable of querying 1 billion edges on a single machine at low cost. It achieves this through its Rust-based architecture, parallelized graph algorithms (using Rayon), and a cost-based graph-native query planner.

What are the key use cases for Samyama?

Samyama is ideal for building large-scale knowledge graphs, powering Graph RAG applications, advanced biomedical research, fraud detection, supply chain optimization, social network analysis, and enterprise security operations.

Samyama is a high-performance graph-vector database built in Rust, designed to manage and query large-scale graph-vector datasets. It combines the power of graph structures with vector embeddings for advanced analytics.

Samyama

Name: Samyama
Author: samyama-ai

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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.

主な機能

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

ユースケース

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