micro-hnsw-wasm

WASM bindings for ruvector-nervous-system, enabling browser and edge deployment of spiking neural networks with BTSP and EWC for vector similarity tasks. Supports neuromorphic learning in web environments for AI vector applications.

ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.

Self-optimizing on-device vector database with HNSW, graph RAG, and WASM deployment for low-latency edge AI ops across browsers/IoT/mobile. Supports real-time self-learning retrieval; lighter and offline vs cloud Qdrant.

Rust core for high-performance on-device HNSW vector search with SIMD and compression, achieving low-latency multi-threaded queries for edge AI RAG. Up to 3,597 QPS; optimized for real-time vs cloud alternatives.

High-throughput vector database for RAG and LLM memory, utilizing HNSW/flat indexes with 256x quantization for memory efficiency and 65k QPS performance. Includes MCP server for AI agents, auto-embedding, and pluggable storage like BadgerDB. Fits real-time recommendations and analytics; lighter open-source option vs Milvus, adds MCP unlike standard Qdrant.

Unified WASM bindings for 18+ attention mechanisms including neural, DAG, and Mamba SSM, optimized for vector search and processing.