mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-23 21:25:02 +00:00
* chore: Add proptest regression data from test run Records edge cases found during property testing that cause integer overflow failures. These will help reproduce and fix the boundary condition bugs in distance calculations. * fix: Resolve property test failures with overflow handling - Fix ScalarQuantized::distance() i16 overflow: use i32 for diff*diff (255*255=65025 overflows i16 max of 32767) - Fix ScalarQuantized::quantize() division by zero when all values equal (handle scale=0 case by defaulting to 1.0) - Bound vector_strategy() to -1000..1000 range to prevent overflow in distance calculations with extreme float values All 177 tests now pass in ruvector-core. * fix(cli): Resolve short option conflicts in clap argument definitions - Change --dimensions from -d to -D to avoid conflict with global --debug - Change --db from -d to -b across all subcommands (Insert, Search, Info, Benchmark, Export, Import) to avoid conflict with global --debug Fixes clap panic in debug builds: "Short option names must be unique" Note: 4 CLI integration tests still fail due to pre-existing issue where VectorDB doesn't persist its configuration to disk. When reopening a database, dimensions are read from config defaults (384) instead of from the stored database metadata. This is an architectural issue requiring VectorDB changes to implement proper metadata persistence. * feat(core): Add database configuration persistence and fix CLI test - Add CONFIG_TABLE to storage.rs for persisting DbOptions - Implement save_config() and load_config() methods in VectorStorage - Modify VectorDB::new() to load stored config for existing databases - Fix dimension mismatch by recreating storage with correct dimensions - Fix test_error_handling CLI test to use /dev/null/db.db path This ensures database settings (dimensions, distance metric, HNSW config, quantization) are preserved across restarts. Previously opening an existing database would use default settings instead of stored configuration. * fix(ruvLLM): Guard against edge cases in HNSW and softmax - memory.rs: Fix random_level() to handle r=0 (ln(0) = -inf) - memory.rs: Fix ml calculation when hnsw_m=1 (ln(1) = 0 → div by zero) - router.rs: Add division-by-zero guard in softmax for larger arrays These edge cases could cause undefined behavior or NaN propagation. --------- Co-authored-by: Claude <noreply@anthropic.com> |
||
|---|---|---|
| .. | ||
| agentic-jujutsu | ||
| docs | ||
| exo-ai-2025 | ||
| google-cloud | ||
| graph | ||
| meta-cognition-spiking-neural-network | ||
| nodejs | ||
| onnx-embeddings | ||
| refrag-pipeline | ||
| rust | ||
| ruvLLM | ||
| scipix | ||
| spiking-network | ||
| ultra-low-latency-sim | ||
| wasm-react | ||
| wasm-vanilla | ||
| README.md | ||
RuVector Examples
Comprehensive examples demonstrating RuVector's capabilities across multiple platforms and use cases.
Directory Structure
examples/
├── rust/ # Rust SDK examples
├── nodejs/ # Node.js SDK examples
├── graph/ # Graph database features
├── wasm-react/ # React + WebAssembly integration
├── wasm-vanilla/ # Vanilla JS + WebAssembly
├── agentic-jujutsu/ # AI agent version control
├── exo-ai-2025/ # Advanced cognitive substrate
├── refrag-pipeline/ # Document processing pipeline
└── docs/ # Additional documentation
Quick Start by Platform
Rust
cd rust
cargo run --example basic_usage
cargo run --example advanced_features
cargo run --example agenticdb_demo
Node.js
cd nodejs
npm install
node basic_usage.js
node semantic_search.js
WebAssembly (React)
cd wasm-react
npm install
npm run dev
WebAssembly (Vanilla)
cd wasm-vanilla
# Open index.html in browser
Example Categories
| Category | Directory | Description |
|---|---|---|
| Core API | rust/basic_usage.rs |
Vector DB fundamentals |
| Batch Ops | rust/batch_operations.rs |
High-throughput ingestion |
| RAG Pipeline | rust/rag_pipeline.rs |
Retrieval-Augmented Generation |
| Advanced | rust/advanced_features.rs |
Hypergraphs, neural hashing |
| AgenticDB | rust/agenticdb_demo.rs |
AI agent memory system |
| GNN | rust/gnn_example.rs |
Graph Neural Networks |
| Graph | graph/ |
Cypher queries, clustering |
| Node.js | nodejs/ |
JavaScript integration |
| WASM React | wasm-react/ |
Modern React apps |
| WASM Vanilla | wasm-vanilla/ |
Browser without framework |
| Agentic Jujutsu | agentic-jujutsu/ |
Multi-agent version control |
| EXO-AI 2025 | exo-ai-2025/ |
Cognitive substrate research |
| Refrag | refrag-pipeline/ |
Document fragmentation |
Feature Highlights
Vector Database Core
- High-performance similarity search
- Multiple distance metrics (Cosine, Euclidean, Dot Product)
- Metadata filtering
- Batch operations
Advanced Features
- Hypergraph Index: Multi-entity relationships
- Temporal Hypergraph: Time-aware relationships
- Causal Memory: Cause-effect chains
- Learned Index: ML-optimized indexing
- Neural Hash: Locality-sensitive hashing
- Topological Analysis: Persistent homology
AgenticDB
- Reflexion episodes (self-critique)
- Skill library (consolidated patterns)
- Causal memory (hypergraph relationships)
- Learning sessions (RL training data)
- Vector embeddings (core storage)
EXO-AI Cognitive Substrate
- exo-core: IIT consciousness, thermodynamics
- exo-temporal: Causal memory coordination
- exo-hypergraph: Topological structures
- exo-manifold: Continuous deformation
- exo-exotic: 10 cutting-edge experiments
- exo-wasm: Browser deployment
- exo-federation: Distributed consensus
- exo-node: Native bindings
- exo-backend-classical: Classical compute
Running Benchmarks
# Rust benchmarks
cargo bench --example advanced_features
# Refrag pipeline benchmarks
cd refrag-pipeline
cargo bench
# EXO-AI benchmarks
cd exo-ai-2025
cargo bench
Related Documentation
License
MIT OR Apache-2.0