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wifi-densepose/vendor/ruvector/crates/rvlite/docs/01_SPECIFICATION.md

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Phase 1: Specification

S - Specification Phase

Duration: Weeks 1-2 Goal: Define complete requirements, constraints, and success criteria

1. Product Vision

1.1 Mission Statement

RvLite is a standalone, WASM-first vector database that brings the full power of ruvector-postgres to any environment - browser, Node.js, edge workers, mobile apps - without requiring PostgreSQL installation.

1.2 Target Users

  1. Frontend Developers - Building AI-powered web apps with in-browser vector search
  2. Edge Computing - Serverless/edge environments (Cloudflare Workers, Deno Deploy)
  3. Mobile Developers - React Native, Capacitor apps with local vector storage
  4. Data Scientists - Rapid prototyping without infrastructure setup
  5. Embedded Systems - IoT, embedded devices with limited resources

1.3 Use Cases

UC-1: In-Browser Semantic Search

// User browses documentation site
// All searches happen locally, no backend needed
const db = await RvLite.create();
await db.loadDocuments(docs);
const results = await db.searchSimilar(queryEmbedding);

UC-2: Edge AI Search

// Cloudflare Worker handles product search
// Vector DB runs at the edge, globally distributed
export default {
  async fetch(request) {
    const db = await RvLite.create();
    return searchProducts(db, query);
  }
}

UC-3: Knowledge Graph Exploration

// Interactive graph visualization in browser
// SPARQL + Cypher queries run client-side
const db = await RvLite.create();
await db.cypher('MATCH (a)-[r]->(b) RETURN a, r, b');
await db.sparql('SELECT ?s ?p ?o WHERE { ?s ?p ?o }');

UC-4: Self-Learning Agent

// AI agent learns from user interactions
// ReasoningBank stores patterns locally
const db = await RvLite.create();
await db.learning.recordTrajectory(state, action, reward);
const nextAction = await db.learning.predictBest(state);

2. Functional Requirements

2.1 Core Database Features

FR-1: Vector Operations

  • FR-1.1 Support vector types: vector(n), halfvec(n), binaryvec(n), sparsevec(n)
  • FR-1.2 Distance metrics: L2, cosine, inner product, L1, Hamming
  • FR-1.3 Vector operations: add, subtract, scale, normalize
  • FR-1.4 SIMD-optimized computations using WASM SIMD

FR-2: Indexing

  • FR-2.1 HNSW index for approximate nearest neighbor search
  • FR-2.2 Configurable parameters: M (connections), ef_construction, ef_search
  • FR-2.3 Dynamic index updates (insert/delete)
  • FR-2.4 B-Tree index for scalar columns
  • FR-2.5 Triple store indexes (SPO, POS, OSP) for RDF data

FR-3: Query Languages

FR-3.1 SQL Support

-- Table creation
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(384)
);

-- Index creation
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- Vector search
SELECT *, embedding <=> $1 AS distance
FROM documents
ORDER BY distance
LIMIT 10;

-- Hybrid search
SELECT *
FROM documents
WHERE content ILIKE '%query%'
ORDER BY embedding <=> $1
LIMIT 10;

FR-3.2 SPARQL 1.1 Support

# SELECT queries
SELECT ?subject ?label
WHERE {
  ?subject rdfs:label ?label .
  FILTER(lang(?label) = "en")
}

# CONSTRUCT queries
CONSTRUCT { ?s foaf:knows ?o }
WHERE { ?s :similar_to ?o }

# INSERT/DELETE updates
INSERT DATA {
  <http://example.org/person1> foaf:name "Alice" .
}

# Property paths
SELECT ?person ?friend
WHERE {
  ?person foaf:knows+ ?friend .
}

FR-3.3 Cypher Support

// Pattern matching
MATCH (a:Person)-[:KNOWS]->(b:Person)
WHERE a.age > 30
RETURN a.name, b.name

// Graph creation
CREATE (a:Person {name: 'Alice', embedding: $emb})
CREATE (b:Person {name: 'Bob'})
CREATE (a)-[:KNOWS]->(b)

// Vector-enhanced queries
MATCH (p:Person)
WHERE vector.cosine(p.embedding, $query) > 0.8
RETURN p.name, p.embedding
ORDER BY vector.cosine(p.embedding, $query) DESC

FR-4: Graph Operations

  • FR-4.1 Graph traversal (BFS, DFS)
  • FR-4.2 Shortest path algorithms (Dijkstra, A*)
  • FR-4.3 Community detection
  • FR-4.4 PageRank and centrality metrics
  • FR-4.5 Vector-enhanced graph search

FR-5: Graph Neural Networks (GNN)

  • FR-5.1 GCN (Graph Convolutional Networks)
  • FR-5.2 GraphSage
  • FR-5.3 GAT (Graph Attention Networks)
  • FR-5.4 GIN (Graph Isomorphism Networks)
  • FR-5.5 Node/edge embeddings
  • FR-5.6 Graph classification

FR-6: Self-Learning (ReasoningBank)

  • FR-6.1 Trajectory recording (state, action, reward)
  • FR-6.2 Pattern recognition
  • FR-6.3 Memory distillation
  • FR-6.4 Strategy optimization
  • FR-6.5 Verdict judgment
  • FR-6.6 Adaptive learning rates

FR-7: Hyperbolic Embeddings

  • FR-7.1 Poincaré disk model
  • FR-7.2 Lorentz/hyperboloid model
  • FR-7.3 Hyperbolic distance metrics
  • FR-7.4 Exponential/logarithmic maps
  • FR-7.5 Hyperbolic neural networks

FR-8: Storage & Persistence

FR-8.1 In-Memory Storage

  • Primary storage: DashMap (concurrent hash maps)
  • Fast access: O(1) lookup for primary keys
  • Thread-safe concurrent access

FR-8.2 Persistence Backends

// Browser: IndexedDB
await db.save(); // Saves to IndexedDB
const db = await RvLite.load(); // Loads from IndexedDB

// Browser: OPFS (Origin Private File System)
await db.saveToOPFS();
await db.loadFromOPFS();

// Node.js/Deno/Bun: File system
await db.saveToFile('database.rvlite');
await RvLite.loadFromFile('database.rvlite');

FR-8.3 Serialization Formats

  • Binary: rkyv (zero-copy deserialization)
  • JSON: For debugging and exports
  • Apache Arrow: For data exchange

FR-9: Transactions (ACID)

  • FR-9.1 Atomic operations (all-or-nothing)
  • FR-9.2 Consistency (integrity constraints)
  • FR-9.3 Isolation (snapshot isolation)
  • FR-9.4 Durability (write-ahead logging)

FR-10: Quantization

  • FR-10.1 Binary quantization (1-bit)
  • FR-10.2 Scalar quantization (8-bit)
  • FR-10.3 Product quantization (configurable)
  • FR-10.4 Automatic quantization selection

3. Non-Functional Requirements

3.1 Performance

Metric Target Measurement
WASM bundle size < 6MB gzipped du -h rvlite_bg.wasm
Initial load time < 1s Performance API
Query latency (1k vectors) < 20ms Benchmark suite
Insert throughput > 10k/s Benchmark suite
Memory usage (100k vectors) < 200MB Chrome DevTools
HNSW search recall@10 > 95% ANN benchmarks

3.2 Scalability

Dimension Limit Rationale
Max table size 10M rows Memory constraints
Max vector dimensions 4096 WASM memory limits
Max tables 1000 Reasonable use case
Max indexes per table 10 Performance trade-off
Max concurrent queries 100 WASM thread pool

3.3 Compatibility

Browser Support

  • Chrome/Edge 91+ (WASM SIMD)
  • Firefox 89+ (WASM SIMD)
  • Safari 16.4+ (WASM SIMD)

Runtime Support

  • Node.js 18+
  • Deno 1.30+
  • Bun 1.0+
  • Cloudflare Workers
  • Vercel Edge Functions
  • Netlify Edge Functions

Platform Support

  • x86-64 (Intel/AMD)
  • ARM64 (Apple Silicon, AWS Graviton)
  • WebAssembly (universal)

3.4 Security

  • SEC-1 No arbitrary code execution
  • SEC-2 Memory-safe (Rust guarantees)
  • SEC-3 No SQL injection (prepared statements)
  • SEC-4 Sandboxed WASM execution
  • SEC-5 CORS-compliant (browser)
  • SEC-6 No sensitive data in errors

3.5 Usability

  • US-1 Zero-config installation: npm install @rvlite/wasm
  • US-2 TypeScript-first API with full type definitions
  • US-3 Comprehensive documentation with examples
  • US-4 Error messages with helpful suggestions
  • US-5 Debug logging (optional, configurable)

3.6 Maintainability

  • MAIN-1 Test coverage > 90%
  • MAIN-2 CI/CD pipeline (GitHub Actions)
  • MAIN-3 Semantic versioning (semver)
  • MAIN-4 Automated releases
  • MAIN-5 Deprecation warnings (6-month notice)

4. Constraints

4.1 Technical Constraints

WASM Limitations

  • Single-threaded by default (multi-threading experimental)
  • Limited to 4GB memory (32-bit address space)
  • No direct file system access (browser)
  • No native threads (use Web Workers)

Rust/WASM Constraints

  • No std::fs in wasm32-unknown-unknown
  • No native threading (use wasm-bindgen-futures)
  • Must use no_std or WASM-compatible crates
  • Size overhead from Rust std library

4.2 Performance Constraints

  • WASM is ~2-3x slower than native code
  • SIMD limited to 128-bit (vs 512-bit AVX-512)
  • Garbage collection overhead (JS interop)
  • Copy overhead for large data transfers

4.3 Resource Constraints

Development Team

  • 1 developer (8 weeks)
  • Community contributions (optional)

Timeline

  • 8 weeks total
  • 2 weeks per major phase
  • Beta release by Week 8

Budget

  • Open source (no monetary budget)
  • CI/CD: GitHub Actions (free tier)
  • Hosting: npm registry (free)

5. Success Criteria

5.1 Functional Completeness

  • All vector operations working
  • SQL queries execute correctly
  • SPARQL queries pass W3C test suite
  • Cypher queries compatible with Neo4j syntax
  • GNN layers produce correct outputs
  • ReasoningBank learns from trajectories
  • Hyperbolic operations validated

5.2 Performance Benchmarks

  • Bundle size < 6MB gzipped
  • Load time < 1s (browser)
  • Query latency < 20ms (1k vectors)
  • HNSW recall@10 > 95%
  • Memory usage < 200MB (100k vectors)

5.3 Quality Metrics

  • Test coverage > 90%
  • Zero clippy warnings
  • All examples working
  • Documentation complete
  • API stable (no breaking changes)

5.4 Adoption Metrics (Post-Release)

  • 100+ npm downloads/week
  • 10+ GitHub stars
  • 3+ community contributions
  • Featured in blog posts/articles

6. Out of Scope (v1.0)

Not Included in Initial Release

  • Multi-user access - Single-user database only
  • Distributed queries - No sharding or replication
  • Advanced SQL - No JOINs, subqueries, CTEs (future)
  • Full-text search - Basic LIKE only (no Elasticsearch-level)
  • Geospatial - No PostGIS-like features
  • Time series - No specialized time-series optimizations
  • Streaming queries - No live query updates
  • Custom UDFs - No user-defined functions in v1.0

Future Considerations (v2.0+)

  • Multi-threading support (WASM threads)
  • Advanced SQL features (JOINs, CTEs)
  • Streaming/reactive queries
  • Plugin system for extensions
  • Custom vector distance metrics
  • GPU acceleration (WebGPU)

7. Dependencies & Licenses

Rust Crates (MIT/Apache-2.0)

[dependencies]
wasm-bindgen = "0.2"
serde = { version = "1.0", features = ["derive"] }
serde-wasm-bindgen = "0.6"
js-sys = "0.3"
web-sys = { version = "0.3", features = ["Window", "IdbDatabase"] }
dashmap = "6.0"
parking_lot = "0.12"
simsimd = "5.9"
half = "2.4"
rkyv = "0.8"
once_cell = "1.19"
thiserror = "1.0"

[dev-dependencies]
wasm-bindgen-test = "0.3"
criterion = "0.5"

License

MIT License (permissive, compatible with ruvector-postgres)

8. Risk Analysis

High Risk

Risk Impact Probability Mitigation
WASM size > 10MB High Medium Aggressive tree-shaking, feature gating
Performance < 50% of native High Medium WASM SIMD, optimized algorithms
Browser compatibility issues High Low Polyfills, fallbacks

Medium Risk

Risk Impact Probability Mitigation
IndexedDB quota limits Medium Medium OPFS fallback, compression
Memory leaks in WASM Medium Low Careful lifetime management
Breaking API changes Medium Medium Semver, deprecation warnings

Low Risk

Risk Impact Probability Mitigation
Dependency vulnerabilities Low Low Dependabot, security audits
Documentation outdated Low Medium CI checks, automated validation

9. Validation & Acceptance

9.1 Validation Methods

Unit Tests

#[cfg(test)]
mod tests {
    #[test]
    fn test_vector_cosine_distance() {
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![0.0, 1.0, 0.0];
        let dist = cosine_distance(&a, &b);
        assert!((dist - 1.0).abs() < 0.001);
    }
}

Integration Tests

import { RvLite } from '@rvlite/wasm';

describe('Vector Search', () => {
  it('should find similar vectors', async () => {
    const db = await RvLite.create();
    await db.sql('CREATE TABLE docs (id INT, vec VECTOR(3))');
    await db.sql('INSERT INTO docs VALUES (1, $1)', [[1, 0, 0]]);
    const results = await db.sql('SELECT * FROM docs ORDER BY vec <=> $1', [[1, 0, 0]]);
    expect(results[0].id).toBe(1);
  });
});

Benchmark Tests

use criterion::{black_box, criterion_group, criterion_main, Criterion};

fn bench_hnsw_search(c: &mut Criterion) {
    let index = build_hnsw_index(1000);
    let query = random_vector(384);

    c.bench_function("hnsw_search_1k", |b| {
        b.iter(|| index.search(black_box(&query), 10))
    });
}

9.2 Acceptance Criteria

Must Have

  • All functional requirements implemented
  • Performance benchmarks met
  • Test coverage > 90%
  • Documentation complete
  • Examples working in browser, Node.js, Deno

Should Have

  • TypeScript types accurate
  • Error messages helpful
  • Debug logging available
  • Migration guide from ruvector-postgres

Could Have

  • Interactive playground
  • Video tutorials
  • Community forum

10. Glossary

Term Definition
WASM WebAssembly - binary instruction format for stack-based virtual machine
HNSW Hierarchical Navigable Small World - graph-based ANN algorithm
ANN Approximate Nearest Neighbor - fast similarity search
SIMD Single Instruction Multiple Data - parallel computation
GNN Graph Neural Network - neural networks for graph data
SPARQL SPARQL Protocol and RDF Query Language - RDF query language
Cypher Neo4j's graph query language
ReasoningBank Self-learning framework for AI agents
RDF Resource Description Framework - semantic web standard
Triple Store Database for storing RDF triples (subject-predicate-object)
OPFS Origin Private File System - browser file storage API
IndexedDB Browser-based NoSQL database

Next: 02_API_SPECIFICATION.md - Complete API design

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