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3bd64a65cb
User pivot: "the radar is attached to usb" — meaning the radar feeds
the host directly, not the ESP32. The parser I already wrote and
on-device-tested in iter 113-114 was the right code in the wrong
crate. Lift it into a standalone shared crate so both callers consume
one tested state machine.
New crates/ruvector-mmwave/
Cargo.toml standalone, no_std-compatible (default features)
with optional `std` feature for host-side helpers.
src/lib.rs MR60BHA2 frame state machine (moved from
examples/esp32-mmwave-sensor/src/parser.rs).
no_std attribute added; 10 unit tests preserved.
Cargo.lock path-dep crate generates its own lock.
examples/esp32-mmwave-sensor (firmware unchanged behaviorally)
Cargo.toml + path dep on ruvector-mmwave (default features).
src/main.rs dropped `mod parser`, added `use ruvector_mmwave
as parser` alias so the rest of the file reads
identically.
src/selftest.rs imports moved from `crate::parser` to
`ruvector_mmwave`. Same 8 fixtures.
src/parser.rs deleted (moved to crates/ruvector-mmwave/src/lib.rs).
Verified the lift didn't break the firmware: cross-compiled clean,
flashed at 460800 baud, captured /dev/ttyACM0 — `selftest=PASS(8)`
still appears on every status line, exactly as before.
New crates/ruvector-hailo-cluster/src/bin/mmwave-bridge.rs
Host-side daemon. Three modes:
--device <path> read a specific tty (e.g. /dev/ttyUSB0)
--auto scan /dev/ttyUSB* + /dev/ttyACM* for the radar
by probing for an MR60BHA2 SOF + valid checksum
(1.5s budget per candidate)
--simulator synthesise frames at a configurable rate; no
hardware required — useful for demoing the
full pipeline today and for iter-116 soak tests
Shared options:
--baud <N> --rate <Hz> --quiet --help --version
Output: JSONL on stdout, one event per line:
{"t_ms":150,"kind":"heart_rate","bpm":72}
{"t_ms":300,"kind":"distance","cm":160}
Decoded checksum errors / resyncs are intentionally NOT printed —
iter 116 will surface them as counter increments alongside cluster
RPC stats so a noisy cable doesn't pollute the event stream.
Live evidence (--simulator @ 10 Hz, 2-second window):
20 events emitted; cycle correctness verified through breathing
(12→13→14 bpm random walk), heart-rate (60-99), distance (random
cm), presence (alternates true/false on the 8-tick cycle).
Validation:
- crates/ruvector-mmwave: cargo test → 10/10 pass
- examples/esp32-mmwave-sensor: cargo +esp build --release → clean
+ on-device flash + selftest=PASS(8) live captured
- crates/ruvector-hailo-cluster: cargo test --features tls → 132 pass
unchanged; clippy --all-targets -D warnings clean for both default
and tls feature configs
- ruvector-mmwave-bridge --simulator → 20 JSONL events in 2s
Iter 116 (next, gated on direction): wire --workers / --workers-file-sig
flags + the GrpcTransport::with_tls path so each decoded vital posts as
an embed RPC into the cluster's §1b-gated path. The bin is structured
so adding network sink is a 50-100 LOC delta, no architectural change.
Co-Authored-By: claude-flow <ruv@ruv.net>
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| .. | ||
| a2a-swarm | ||
| agentic-jujutsu | ||
| apify | ||
| app-clip | ||
| benchmarks | ||
| boundary-discovery | ||
| brain-boundary-discovery | ||
| climate-consciousness | ||
| cmb-boundary-discovery | ||
| cmb-consciousness | ||
| data | ||
| decompiler-dashboard | ||
| delta-behavior | ||
| dna | ||
| docs | ||
| dragnes | ||
| earthquake-boundary-discovery | ||
| ecosystem-consciousness | ||
| edge | ||
| edge-full/pkg | ||
| edge-net | ||
| esp32-mmwave-sensor | ||
| exo-ai-2025 | ||
| frb-boundary-discovery | ||
| gene-consciousness | ||
| google-cloud | ||
| graph | ||
| gw-consciousness | ||
| health-boundary-discovery | ||
| infrastructure-boundary-discovery | ||
| market-boundary-discovery | ||
| meta-cognition-spiking-neural-network | ||
| mincut | ||
| music-boundary-discovery | ||
| neural-trader | ||
| nodejs | ||
| onnx-embeddings | ||
| onnx-embeddings-wasm | ||
| OSpipe | ||
| pandemic-boundary-discovery | ||
| prime-radiant | ||
| pwa-loader | ||
| quantum-consciousness | ||
| real-eeg-analysis | ||
| real-eeg-multi-seizure | ||
| refrag-pipeline | ||
| robotics | ||
| rust | ||
| ruvLLM | ||
| rvf | ||
| rvf-desktop | ||
| rvf-kernel-optimized | ||
| scipix | ||
| seizure-clinical-report | ||
| seizure-therapeutic-sim | ||
| seti-boundary-discovery | ||
| seti-exotic-signals | ||
| spiking-network | ||
| subpolynomial-time | ||
| temporal-attractor-discovery | ||
| train-discoveries | ||
| ultra-low-latency-sim | ||
| vectorvroom@4c2527b452 | ||
| verified-applications | ||
| vibecast-7sense | ||
| void-boundary-discovery | ||
| vwm-viewer | ||
| wasm/ios | ||
| wasm-react | ||
| wasm-vanilla | ||
| weather-boundary-discovery | ||
| bounded_instance_demo.rs | ||
| README.md | ||
| security_hardened.rvf | ||
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