ruvnet/ruvector

Features

• Vector Databases - Provides a specialized database optimized for storing and querying high-dimensional vector embeddings.
• Local AI Inference - Executes compressed machine learning models on local hardware and in browsers via WebAssembly.
• Local LLM Execution - Executes large language models on local hardware with specialized processor acceleration.
• Result Reranking - Applies a graph neural network layer over the vector index to rerank and improve search results.
• Packaged Deployments - Packages models, data, and a microkernel into a single binary for instant deployment.
• GPU-Accelerated Vector Indexing - Leverages hardware acceleration to perform similarity searches with sub-millisecond latency.
• GNN Reranking Layers - Refines nearest neighbor search rankings by applying a graph neural network structural attention layer over the vector index.
• Graph Relationship Queries - Provides complex traversals and relationship lookups using standard graph query syntax.
• Hybrid Vector-Graph Databases - Combines vector search with graph neural network structures to support both semantic and relational queries.
• Vector Stores - Implements a memory-efficient vector store in Rust for local inference and nearest neighbor search.
• GNN-Based Indexes - Uses a graph neural network index to refine search rankings through structural attention.
• Cognitive Container Formats - Implements a unique cognitive container format that bundles models and data into a single bootable binary.
• Cognitive Container Formats - Bundles models, data, and a bootable microkernel into a single binary for instant deployment.
• Catastrophic Forgetting Protections - Employs weight consolidation to prevent catastrophic forgetting when integrating new learning patterns.
• Weight Adapters - Uses a lightweight adapter mechanism for near-instantaneous weight adaptation for specific requests.
• Weight Consolidation Methods - Prevents catastrophic forgetting by protecting critical existing information when integrating new patterns into model weights.
• Consensus Log Replication - Ensures strong consistency and high availability for metadata through Raft-based consensus log replication.
• WebAssembly Inference Runtimes - Ships a minimal WebAssembly runtime for executing vector search and model inference in browsers.
• Raft Consensus Implementations - Utilizes Raft consensus to manage replicated logs and ensure high availability of metadata.
• Quantized Memory Optimization - Lowers memory footprints using quantization and weight transformation to run large models on limited hardware.
• WebAssembly Inference Runtimes - Runs full vector search and model inference directly in the browser using a minimal WebAssembly footprint.
• GPU-Accelerated Quantum Simulators - Provides a hardware-accelerated environment for executing state-vector quantum circuit simulations.
• Quantum Circuit Execution - Runs state-vector simulations using hardware acceleration to execute complex quantum algorithms and search patterns.
• Quantum Simulators - Provides a hardware-accelerated environment for executing state-vector simulations of quantum circuits.
• Mutation Witness Chaining - Generates cryptographic witness chains to provide structural trust and verification for every data mutation.
• Mutation Witness Chaining - Generates cryptographic proofs and witness chains to ensure structural trust for every data change.
• Database Systems - Self-learning vector database for local LLM hosting.
• Databases & Data - Self-learning vector database for local LLM scaling.

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