vrr/ruvector
2
0
Fork 0
mirror of https://github.com/ruvnet/RuVector.git synced 2026-05-24 05:43:58 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 19
ruvector/examples
History
rUv 326fc77bda
feat: DrAgnes + Common Crawl WET + Gemini grounding agents (#282) 
* docs: DrAgnes project overview and system architecture research

Establishes the DrAgnes AI-powered dermatology intelligence platform
research initiative with comprehensive system architecture covering
DermLite integration, CNN classification pipeline, brain collective
learning, offline-first PWA design, and 25-year evolution roadmap.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: DrAgnes HIPAA compliance strategy and data sources research

Comprehensive HIPAA/FDA compliance framework covering PHI handling,
PII stripping pipeline, differential privacy, witness chain auditing,
BAA requirements, and risk analysis. Data sources document catalogs
18 training datasets, medical literature sources, and real-world data
streams including HAM10000, ISIC Archive, and Fitzpatrick17k.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: DrAgnes DermLite integration and 25-year future vision research

DermLite integration covers HUD/DL5/DL4/DL200 device capabilities,
image capture via MediaStream API, ABCDE criteria automation, 7-point
checklist, Menzies method, and pattern analysis modules. Future vision
spans AR-guided biopsy (2028), continuous monitoring wearables (2040),
genomic fusion (2035), BCI clinical gestalt (2045), and global
elimination of late-stage melanoma detection by 2050.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: DrAgnes competitive analysis and deployment plan research

Competitive analysis covers SkinVision, MoleMap, MetaOptima, Canfield,
Google Health, 3Derm, and MelaFind with feature matrix comparison.
Deployment plan details Google Cloud architecture with Cloud Run
services, Firestore/GCS data storage, Pub/Sub events, multi-region
strategy, security configuration, cost projections ($3.89/practice at
1000-practice scale), and disaster recovery procedures.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: ADR-117 DrAgnes dermatology intelligence platform

Proposes DrAgnes as an AI-powered dermatology platform built on
RuVector's CNN, brain, and WASM infrastructure. Covers architecture,
data model, API design, HIPAA/FDA compliance strategy, 4-phase
implementation plan (2026-2051), cost model showing $3.89/practice
at scale, and acceptance criteria targeting >95% melanoma sensitivity
with offline-first WASM inference in <200ms.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): deployment config — Dockerfile, Cloud Run, PWA manifest, service worker

Add production deployment infrastructure for DrAgnes:
- Multi-stage Dockerfile with Node 20 Alpine and non-root user
- Cloud Run knative service YAML (1-10 instances, 2 vCPU, 2 GiB)
- GCP deploy script with rollback support and secrets integration
- PWA manifest with SVG icons (192x192, 512x512)
- Service worker with offline WASM caching and background sync
- TypeScript configuration module with CNN, privacy, and brain settings

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(dragnes): user-facing documentation and clinical guide

Add comprehensive DrAgnes documentation covering:
- Getting started and PWA installation
- DermLite device integration instructions
- HAM10000 classification taxonomy and result interpretation
- ABCDE dermoscopy scoring methodology
- Privacy architecture (DP, k-anonymity, witness hashing)
- Offline mode and background sync behavior
- Troubleshooting guide
- Clinical disclaimer and regulatory status

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): brain integration — pi.ruv.io client, offline queue, witness chains, API routes

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): CNN classification pipeline with ABCDE scoring and privacy layer

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(dragnes): resolve build errors by externalizing @ruvector/cnn

Mark @ruvector/cnn as external in Rollup/SSR config so the dynamic
import in the classifier does not break the production build.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): app integration, health endpoint, build validation

- Add DrAgnes nav link to sidebar NavMenu
- Create /api/dragnes/health endpoint with config status
- Add config module exporting DRAGNES_CONFIG
- Update DrAgnes page with loading state & error boundaries
- All 37 tests pass, production build succeeds

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): benchmarks, dataset metadata, federated learning, deployment runbook

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(dragnes): use @vite-ignore for optional @ruvector/cnn import

Prevents Vite dev server from failing on the optional WASM dependency
by using /* @vite-ignore */ comment and variable-based import path.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(dragnes): reduce false positives with Bayesian-calibrated classifier

Apply HAM10000 class priors as Bayesian log-priors to demo classifier,
learned from pi.ruv.io brain specialist agent patterns:
- nv (66.95%) gets strong prior, reducing over-classification of rare types
- mel requires multiple simultaneous features (dark + blue + multicolor +
  high variance) to overcome its 11.11% prior
- Added color variance analysis as asymmetry proxy
- Added dermoscopic color count for multi-color detection
- Platt-calibrated feature weights from brain melanoma specialist

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(dragnes): require ≥2 concurrent evidence signals for melanoma

A uniformly dark spot was triggering melanoma at 74.5%. Now requires
at least 2 of: [dark >15%, blue-gray >3%, ≥3 colors, high variance]
to overcome the melanoma prior. Proven on 6 synthetic test cases:
0 false positives, 1/1 true melanoma detected at 91.3%.

Co-Authored-By: claude-flow <ruv@ruv.net>

* data(dragnes): HAM10000 metadata and analysis script

Add comprehensive analysis of the HAM10000 skin lesion dataset based on
published statistics from Tschandl et al. 2018. Generates class distribution,
demographic, localization, diagnostic method, and clinical risk pattern
analysis. Outputs both markdown report and JSON stats for the knowledge module.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): HAM10000 clinical knowledge module with demographic adjustment

Add ham10000-knowledge.ts encoding verified HAM10000 statistics as structured
data for Bayesian demographic adjustment. Includes per-class age/sex/location
risk multipliers, clinical decision thresholds (biopsy at P(mal)>30%, urgent
referral at P(mel)>50%), and adjustForDemographics() function implementing
posterior probability correction based on patient demographics.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): integrate HAM10000 knowledge into classifier

Add classifyWithDemographics() method to DermClassifier that applies Bayesian
demographic adjustment after CNN classification. Returns both raw and adjusted
probabilities for transparency, plus clinical recommendations (biopsy, urgent
referral, monitor, or reassurance) based on HAM10000 evidence thresholds.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(dragnes): wire HAM10000 demographics into UI

- Add patient age/sex inputs in Capture tab
- Toggle for HAM10000 Bayesian adjustment
- Pass body location from DermCapture to classifyWithDemographics()
- Clinical recommendation banner in Results tab with color-coded
  risk levels (urgent_referral/biopsy/monitor/reassurance)
- Shows melanoma + malignant probabilities and reasoning

Co-Authored-By: claude-flow <ruv@ruv.net>

* refactor(dragnes): move to standalone examples/dragnes/ app

Extract DrAgnes dermatology intelligence platform from ui/ruvocal/ into
a self-contained SvelteKit application under examples/dragnes/. Includes
all library modules, components, API routes, tests, deployment config,
PWA assets, and research documentation. Updated paths for standalone
routing (no /dragnes prefix), fixed static asset references, and
adjusted test imports.

Co-Authored-By: claude-flow <ruv@ruv.net>

* revert: restore ui/ruvocal to main state -- remove DrAgnes commingling

Remove all DrAgnes-related files, components, routes, and config from
ui/ruvocal/ so it matches the main branch exactly. DrAgnes now lives
as a standalone app in examples/dragnes/.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(ruvocal): fix icon 404 and FoundationBackground crash

- Manifest icon paths: /chat/chatui/ → /chatui/ (matches static dir)
- FoundationBackground: guard against undefined particles in connections

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(ruvocal): MCP SSE auto-reconnect on stale session (404/connection errors)

- Widen isConnectionClosedError to catch 404, fetch failed, ECONNRESET
- Add transport readyState check in clientPool for dead connections
- Retry logic now triggers reconnection on stale SSE sessions

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: update gitignore for nested .env files and Cargo.lock

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: update links in README for self-learning, self-optimizing, embeddings, verified training, search, storage, PostgreSQL, graph, AI runtime, ML framework, coherence, domain models, hardware, kernel, coordination, packaging, routing, observability, safety, crypto, and lineage sections

* docs: ADR-115 cost-effective strategy + ADR-118 tiered crawl budget

Add Section 15 to ADR-115 with cost-effective implementation strategy:
- Three-phase budget model ($11-28/mo -> $73-108 -> $158-308)
- CostGuardrails Rust struct with per-phase presets
- Sparsifier-aware graph management (partition on sparse edges)
- Partition timeout fix via caching + background recompute
- Cloud Scheduler YAML for crawl jobs
- Anti-patterns and cost monitoring

Create ADR-118 as standalone cost strategy ADR with:
- Detailed per-phase cost breakdowns
- Guardrail enforcement points
- Partition caching strategy with request flow
- Acceptance criteria tied to cost targets

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add pi.ruv.io brain guidance and project structure to CLAUDE.md

- When/how to use brain MCP tools during development
- Brain REST API fallback when MCP SSE is stale
- Google Cloud secrets and deployment reference
- Project directory structure quick reference
- Key rules: no PHI/secrets in brain, category taxonomy, stale session fix

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: Common Crawl Phase 1 benchmark — pipeline validation results

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(brain): make InjectRequest.source optional for batch inject

The batch endpoint falls back to BatchInjectRequest.source when items
don't have their own source field, but serde deserialization failed
before the handler could apply this logic (422). Adding #[serde(default)]
lets items omit source when using batch inject.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: Common Crawl Phase 1 deployment script — medical domain scheduler jobs

Deploy CDX-targeted crawl for PubMed + dermatology domains via Cloud Scheduler.
Uses static Bearer auth (brain server API key) instead of OIDC since Cloud Run
allows unauthenticated access and brain's auth rejects long JWT tokens.

Jobs: brain-crawl-medical (daily 2AM, 100 pages), brain-crawl-derm (daily 3AM,
50 pages), brain-partition-cache (hourly graph rebuild).

Tested: 10 new memories injected from first run (1568->1578). CDX falls back to
Wayback API from Cloud Run. ADR-118 Phase 1 implementation.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: ADR-119 historical crawl evolutionary comparison

Implement temporal knowledge evolution tracking across quarterly
Common Crawl snapshots (2020-2026). Includes:
- ADR-119 with architecture, cost model, acceptance criteria
- Historical crawl import script (14 quarterly snapshots, 5 domains)
- Evolutionary analysis module (drift detection, concept birth, similarity)
- Initial analysis report on existing brain content (71 memories)

Cost: ~$7-15 one-time for full 2020-2026 import.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: update ADR-115/118/119 with Phase 1 implementation results

- ADR-115: Status → Phase 1 Implemented, actual import numbers (1,588 memories,
  372K edges, 28.7x sparsifier), CDX vs direct inject pipeline status
- ADR-118: Status → Phase 1 Active, scheduler jobs documented, CDX HTML
  extractor issue + direct inject workaround, actual vs projected cost
- ADR-119: 30+ temporal articles imported (2020-2026), search verification
  confirmed, acceptance criteria progress tracked

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: WET processing pipeline for full medical + CS corpus import (ADR-120)

Bypasses broken CDX HTML extractor by processing pre-extracted text
from Common Crawl WET files. Filters by 30 medical + CS domains,
chunks content, and batch injects into pi.ruv.io brain.

Includes: processor, filter/injector, Cloud Run Job config,
orchestrator for multi-segment processing.

Target: full corpus in 6 weeks at ~$200 total cost.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: Cloud Run Job deployment for full 6-year Common Crawl import

- Expanded domain list to 60+ medical + CS domains with categorized tagging
- Cloud Run Job config: 10 parallel tasks, 100 segments per crawl
- Multi-crawl orchestrator for 14 quarterly snapshots (2020-2026)
- Enhanced generateTags with domain-specific labels for oncology, dermatology,
  ML conferences, research labs, and academic institutions
- Target: 375K-500K medical/CS pages over 5 months

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: correct Cloud Run Job deploy to use env-vars-file and --source build

- Use --env-vars-file (YAML) to avoid comma-splitting in domain list
- Use --source deploy to auto-build container from Dockerfile
- Use correct GCS bucket (ruvector-brain-us-central1)
- Use --tasks flag instead of --task-count

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: bake WET paths into container image to avoid GCS auth at runtime

- Embed paths.txt directly into Docker image during build
- Remove GCS bucket dependency from entrypoint
- Add diagnostic logging for brain URL and crawl index per task

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: update ADR-120 with deployment results and expanded domain list

- Status → Phase 1 Deployed
- 8 local segments: 109 pages injected from 170K scanned
- Cloud Run Job executing (50 segments, 10 parallel)
- 4 issues fixed (paths corruption, task index, comma splitting, gsutil)
- Domain list expanded 30 → 60+
- Brain: 1,768 memories, 565K edges, 39.8x sparsifier

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: WET processor OOM — process records inline, increase memory to 2Gi

Node.js heap exhausted at 512MB buffering 21K WARC records.
Fix: process each record immediately instead of accumulating in
pendingRecords array. Also cap per-record content length and
increase Cloud Run Job memory from 1Gi to 2Gi with --max-old-space-size=1536.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: add 30 physics domains + keyword detection to WET crawler

Add CERN, INSPIRE-HEP, ADS, NASA, LIGO, Fermilab, SLAC, NIST,
Materials Project, Quanta Magazine, quantum journals, IOP, APS,
and national labs. Physics keyword detection for dark matter,
quantum, Higgs, gravitational waves, black holes, condensed matter,
fusion energy, neutrinos, and string theory.

Total domains: 90+ (medical + CS + physics).

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: expand WET crawler to 130+ domains across all knowledge areas

Added: GitHub, Stack Overflow/Exchange, patent databases (USPTO, EPO),
preprint servers (bioRxiv, medRxiv, chemRxiv, SSRN), Wikipedia,
government (NSF, DARPA, DOE, EPA), science news, academic publishers
(JSTOR, Cambridge, Sage, Taylor & Francis), data repositories
(Kaggle, Zenodo, Figshare), and ML explainer blogs.

Total: 130+ domains covering medical, CS, physics, code, patents,
preprints, regulatory, news, and open data.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(brain): update Gemini model to gemini-2.5-flash with env override

Old model ID gemini-2.5-flash-preview-05-20 was returning 404.
Updated default to gemini-2.5-flash (stable release).
Added GEMINI_MODEL env var override for future flexibility.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(brain): integrate Google Search Grounding into Gemini optimizer (ADR-121)

Add google_search tool to Gemini API calls so the optimizer verifies
generated propositions against live web sources. Grounding metadata
(source URLs, support scores, search queries) logged for auditability.

- google_search tool added to request body
- Grounding metadata parsed and logged
- Configurable via GEMINI_GROUNDING env var (default: true)
- Model updated to gemini-2.5-flash (stable)
- ADR-121 documents integration

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(brain): deploy-all.sh preserves env vars, includes all features

CRITICAL FIX: Changed --set-env-vars to --update-env-vars so deploys
don't wipe FIRESTORE_URL, GEMINI_API_KEY, and feature flags.

Now includes:
- FIRESTORE_URL auto-constructed from PROJECT_ID
- GEMINI_API_KEY fetched from Google Secrets Manager
- All 22 feature flags (GWT, SONA, Hopfield, HDC, DentateGyrus,
  midstream, sparsifier, DP, grounding, etc.)
- Session affinity for SSE MCP connections

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: update ADR-121 with deployment verification and optimization gaps

- Verified: Gemini 2.5 Flash + grounding working
- Brain: 1,808 memories, 611K edges, 42.4x sparsifier
- Documented 5 optimization opportunities:
  1. Graph rebuild timeout (>90s for 611K edges)
  2. In-memory state loss on deploy
  3. SONA needs trajectory injection path
  4. Scheduler jobs need first auto-fire
  5. WET daily needs segment rotation

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: design rvagent autonomous Gemini grounding agents (ADR-122)

Four-phase system for autonomous knowledge verification and enrichment
of the pi.ruv.io brain using Gemini 2.5 Flash with Google Search
grounding. Addresses the gap where all 11 propositions are is_type_of
and the Horn clause engine has no relational data to chain.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: ADR-122 Rev 2 — candidate graph, truth maintenance, provenance

Applied 6 priority revisions from architecture review:
1. Reworked cost model with 3 scenarios (base/expected/worst)
2. Added candidate vs canonical graph separation with promotion gates
3. Narrowed predicate set to causes/treats/depends_on/part_of/measured_by
4. Replaced regex-only PHI with allowlist-based serialization
5. Added truth maintenance state machine (7 proposition states)
6. Added provenance schema for every grounded mutation

Status: Approved with Revisions

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: implement 4 Gemini grounding agents + Cloud Run deploy (ADR-122)

Phase 1 (Fact Verifier): verified 2 memories with grounding sources
Phase 2 (Relation Generator): found 1 'contradicts' relation
Phase 3 (Cross-Domain Explorer): framework working, needs JSON parse fix
Phase 4 (Research Director): framework working, needs drift data

Scripts: gemini-agents.js, deploy-gemini-agents.sh
Cloud Run Job + 4 scheduler entries deploying.
Brain grew: 1,809 → 1,812 (+3 from initial run)

Co-Authored-By: claude-flow <ruv@ruv.net>

* perf(brain): upgrade to 4 CPU / 4 GiB / 20 instances + rate limit WET injector

- Cloud Run: 2 CPU → 4 CPU, 2 GiB → 4 GiB, max 10 → 20 instances
- WET injector: 1s delay between batch injects to prevent brain saturation
- Deploy script updated to match new resource allocation

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: ADR-122 Rev 2 — candidate graph, truth maintenance, provenance

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-23 10:12:50 -04:00
..
agentic-jujutsu feat: Add comprehensive agentic-jujutsu integration examples and tests 2025-11-22 03:12:31 +00:00
apify Add WebAssembly binary and TypeScript definitions for rvlite 2025-12-25 19:50:53 +00:00
app-clip feat(app-clip): add Swift App Clip skeleton for RVQS QR seed decoding 2026-02-15 18:34:13 +00:00
benchmarks fix: apply cargo fmt across workspace and fix CI issues 2026-02-21 20:56:38 +00:00
data feat: 10 exotic frontier discovery datasets — 233 entries across 10 domains 2026-03-16 23:21:01 -04:00
delta-behavior feat(delta-behavior): Complete Δ-behavior implementation with WASM 2026-01-28 04:18:34 +00:00
dna Merge remote-tracking branch 'origin/main' into claude/exo-ai-capability-review-LjcVx 2026-02-27 16:27:34 +00:00
docs docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
dragnes feat: DrAgnes + Common Crawl WET + Gemini grounding agents (#282) 2026-03-23 10:12:50 -04:00
edge fix(security): Address critical security and performance issues in ZK proofs 2026-01-01 19:52:44 +00:00
edge-full/pkg feat(edge-full): add unified WASM package with all modules 2025-12-31 20:59:29 +00:00
edge-net fix: ruvector-postgres v0.3.1 — audit bug fixes, 46 SQL functions, Docker publish (#227) 2026-03-03 12:53:10 -05:00
exo-ai-2025 chore: publish EXO-AI crates v0.1.1 with bug fixes and READMEs 2026-02-27 16:26:40 +00:00
google-cloud style: apply cargo fmt formatting 2026-03-12 20:57:18 -04:00
graph docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
meta-cognition-spiking-neural-network feat(gnn-v2): Comprehensive GNN v2 implementation with cognitive substrate (#43) 2025-12-02 11:26:10 -05:00
mincut fix(ci): Fix formatting and workflow permission issues 2025-12-26 22:11:57 +00:00
neural-trader docs(neural-trader): comprehensive README with features, benchmarks, use cases 2025-12-31 18:03:56 +00:00
nodejs docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
onnx-embeddings fix: resolve 5 P0 critical issues + 2 pre-existing compile errors 2026-03-06 14:03:42 +00:00
onnx-embeddings-wasm docs(onnx-wasm): comprehensive README update for v0.1.2 2025-12-31 05:10:36 +00:00
OSpipe fix: migrate attention/dag/tiny-dancer to workspace versioning and fix all dep version specs 2026-02-23 13:29:46 +00:00
prime-radiant fix: publish-readiness for 6 solver crates + npm package 2026-02-20 19:02:50 +00:00
pwa-loader feat(pwa-loader): add in-browser RVF seed decoder PWA 2026-02-15 18:38:10 +00:00
refrag-pipeline fix(ci): Fix formatting and workflow permission issues 2025-12-26 22:11:57 +00:00
robotics style: apply cargo fmt formatting 2026-03-12 20:57:18 -04:00
rust feat(postgres): Add HNSW index and embedding functions support (#62) 2025-12-09 11:14:52 -05:00
ruvLLM fix(security): harden intelligence providers — type-safe enums, input validation, file size limits 2026-02-21 18:29:33 +00:00
rvf feat: deep discovery analyses + brain MCP training integration 2026-03-16 23:14:43 -04:00
rvf-desktop feat(rvf): add Causal Atlas dashboard, solver fixes, and desktop app 2026-02-20 18:01:09 +00:00
rvf-kernel-optimized fix: format all files, add EXO crate READMEs, convert path deps to version deps 2026-02-27 16:21:14 +00:00
scipix fix: resolve compilation errors across workspace 2026-03-16 23:15:25 -04:00
spiking-network feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 2025-12-01 22:30:15 -05:00
subpolynomial-time fix(ci): Fix formatting and workflow permission issues 2025-12-26 22:11:57 +00:00
train-discoveries feat: 691 discoveries, 50 cross-domain correlations via per-node PPR 2026-03-16 23:17:00 -04:00
ultra-low-latency-sim feat(examples): Add ultra-low-latency meta-simulation engine (#53) 2025-12-04 18:00:21 -05:00
verified-applications fix: format all files, add EXO crate READMEs, convert path deps to version deps 2026-02-27 16:21:14 +00:00
vibecast-7sense docs(mincut): Add ADR/DDC for Anytime-Valid Coherence Gate (#115) 2026-01-17 14:36:52 -05:00
vwm-viewer feat: add package.json for rvdna example with WASM bindings and build scripts 2026-02-12 15:32:55 +00:00
wasm/ios feat(training): RuvLTRA v2.4 Ecosystem Edition - 100% routing accuracy (#123) 2026-01-20 20:08:30 -05:00
wasm-react docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
wasm-vanilla docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
bounded_instance_demo.rs feat(mincut): Add subpolynomial-time dynamic minimum cut system (#74) 2025-12-23 07:53:32 -05:00
README.md docs: Organize examples/ with comprehensive READMEs 2025-11-29 14:05:04 +00:00
security_hardened.rvf feat(security): Security Hardened RVF v3.0 — 30 capabilities verified 2026-02-21 17:39:58 +00:00

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