QuantumVault Secure Key Management v1.2.0
Post-quantum cryptographic key orchestration engine with hardware token fallback.
How to implement local vector search and RAG embeddings using HyperSQLite. High-performance, zero-cloud C/SQLite extension for desktop and edge AI. Eliminate external database clusters and network latency by executing sub-millisecond similarity queries directly in local SQLite databases.
Deploying dedicated vector databases like Pinecone or Milvus adds significant monthly recurring costs ($50–$300/mo) and 60ms–250ms of network latency per query. For desktop applications, edge microservices, and privacy-sensitive enterprise software, in-process SQLite vector calculations execute in under 2 milliseconds while keeping all data on-device.
SELECT id, title, content,
vec_cosine(embedding, ?) AS similarity_score
FROM documents
WHERE user_id = ? AND status = 'active'
ORDER BY similarity_score DESC
LIMIT 5;
Post-quantum cryptographic key orchestration engine with hardware token fallback.
Distributed microservice tracing agent with eBPF hooks and sub-millisecond anomaly detection.
No. HyperSQLite is an embedded C extension that links directly into standard SQLite. It runs in-process inside your application runtime (Node.js, Python, Rust, Go, or Electron) without external daemons.
HyperSQLite supports Cosine Similarity, Euclidean Distance (L2), and Inner Product (Dot Product). Distance calculations leverage CPU SIMD instructions (AVX-512, AVX2, and ARM NEON).
Cloud vector databases introduce network latency and monthly server costs. HyperSQLite keeps embeddings alongside relational data in a single local .sqlite file, achieving 5x–10x lower latency for edge devices.
Yes. You can combine SQL WHERE clauses, full-text search (FTS5), and vector similarity ranking in a single atomic SQL query without managing two separate database engines.