RuVector-WASM: Standalone Vector Database Architecture

🎯 Vision

A complete, self-contained vector database with graph and semantic capabilities that runs anywhere - browser, Node.js, Deno, Bun, edge workers - without PostgreSQL.

Think DuckDB-WASM but for vector/graph/semantic workloads.

✨ What You Get

Complete Feature Set

✅ Vector Operations: All types (f32, f16, binary, sparse), SIMD-optimized distances
✅ Graph Database: Cypher queries, graph traversal, GNN layers
✅ Semantic Search: HNSW indexing, quantization, hybrid search
✅ RDF/SPARQL: W3C-compliant SPARQL 1.1, triple store
✅ SQL Interface: Standard SQL for vector queries
✅ Self-Learning: ReasoningBank, attention mechanisms, adaptive patterns
✅ Hyperbolic Embeddings: Poincaré, Lorentz spaces
✅ AI Routing: Tiny Dancer intelligent routing

Zero Dependencies

❌ No PostgreSQL installation
❌ No Docker required
❌ No native compilation
✅ Pure WASM (~5-10MB bundle)
✅ npm install @ruvector/wasm and go!

Universal Runtime

# Browser
<script type="module" src="https://cdn.jsdelivr.net/npm/@ruvector/wasm"></script>

# Node.js
npm install @ruvector/wasm

# Deno
import { RuVector } from "https://esm.sh/@ruvector/wasm"

# Bun
bun add @ruvector/wasm

# Cloudflare Workers (edge)
import { RuVector } from "@ruvector/wasm"

🏗️ Architecture

High-Level Design

┌─────────────────────────────────────────────────────────────┐
│  Query Layer (TypeScript/JavaScript API)                    │
│  - SQL parser & executor                                     │
│  - Cypher parser & executor                                  │
│  - SPARQL parser & executor                                  │
│  - REST-like API (insert, query, delete)                     │
└──────────────────────┬──────────────────────────────────────┘
                       │ wasm-bindgen
┌──────────────────────▼──────────────────────────────────────┐
│  RuVector Core (Rust → WASM)                                │
│  ┌────────────────────────────────────────────────────────┐ │
│  │ Storage Engine                                         │ │
│  │ - In-memory tables (DashMap)                          │ │
│  │ - IndexedDB persistence (browser)                     │ │
│  │ - OPFS storage (browser)                              │ │
│  │ - File system (Node.js/Deno/Bun)                      │ │
│  └────────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────────┐ │
│  │ Index Layer                                            │ │
│  │ - HNSW vector index                                    │ │
│  │ - B-Tree for scalar columns                            │ │
│  │ - Triple store (SPO, POS, OSP indexes)                │ │
│  │ - Graph adjacency lists                                │ │
│  └────────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────────┐ │
│  │ Execution Engine                                       │ │
│  │ - SQL query planner & executor                         │ │
│  │ - Cypher graph pattern matching                        │ │
│  │ - SPARQL triple pattern matching                       │ │
│  │ - Vector similarity search                             │ │
│  │ - GNN computation (GCN, GraphSage, GAT)               │ │
│  │ - ReasoningBank self-learning                          │ │
│  └────────────────────────────────────────────────────────┘ │
│  ┌────────────────────────────────────────────────────────┐ │
│  │ SIMD & Optimization                                    │ │
│  │ - WASM SIMD (128-bit)                                  │ │
│  │ - Quantization (binary, scalar, product)               │ │
│  │ - Parallel query execution (rayon → WASM threads)      │ │
│  └────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

Component Breakdown

1. Storage Layer (`ruvector-wasm/src/storage/`)

In-Memory Store (primary):

pub struct Database {
    tables: DashMap<String, Table>,
    triple_store: Arc<TripleStore>,
    graph: Arc<GraphStore>,
    indexes: DashMap<String, Index>,
}

pub struct Table {
    schema: Schema,
    rows: RwLock<Vec<Row>>,
    vector_columns: HashMap<String, VectorColumn>,
}

Persistence Adapters:

trait PersistenceBackend {
    async fn save(&self, db: &Database) -> Result<()>;
    async fn load(&self) -> Result<Database>;
}

// Browser: IndexedDB
struct IndexedDBBackend;

// Browser: OPFS (Origin Private File System)
struct OPFSBackend;

// Node/Deno/Bun: File system
struct FileSystemBackend;

2. Query Engines (`ruvector-wasm/src/query/`)

SQL Engine:

// Reuse existing PostgreSQL SQL functions, but with custom executor
pub struct SqlExecutor {
    parser: SqlParser,
    planner: QueryPlanner,
    executor: Executor,
}

// Example: Vector similarity in SQL
// SELECT * FROM docs ORDER BY embedding <=> $1 LIMIT 10

SPARQL Engine (already exists!):

// From ruvector-postgres/src/graph/sparql/
pub use crate::graph::sparql::{
    parse_sparql,
    execute_sparql,
    TripleStore,
};

// Example:
// SELECT ?subject ?label WHERE {
//   ?subject rdfs:label ?label .
//   FILTER vector:similar(?subject, ?query_vector, 0.8)
// }

Cypher Engine (already exists!):

// From ruvector-postgres/src/graph/cypher/
pub use crate::graph::cypher::{
    parse_cypher,
    execute_cypher,
};

// Example:
// MATCH (a:Person)-[:KNOWS]->(b:Person)
// WHERE vector.cosine(a.embedding, $query) > 0.8
// RETURN a, b

3. WASM Bindings (`ruvector-wasm/src/lib.rs`)

use wasm_bindgen::prelude::*;

#[wasm_bindgen]
pub struct RuVector {
    db: Arc<Database>,
}

#[wasm_bindgen]
impl RuVector {
    /// Create a new in-memory database
    #[wasm_bindgen(constructor)]
    pub fn new() -> Result<RuVector, JsValue> {
        Ok(RuVector {
            db: Arc::new(Database::new()),
        })
    }

    /// Execute SQL query
    #[wasm_bindgen(js_name = executeSql)]
    pub async fn execute_sql(&self, query: &str) -> Result<JsValue, JsValue> {
        let results = self.db.execute_sql(query).await?;
        Ok(serde_wasm_bindgen::to_value(&results)?)
    }

    /// Execute SPARQL query
    #[wasm_bindgen(js_name = executeSparql)]
    pub async fn execute_sparql(&self, query: &str) -> Result<JsValue, JsValue> {
        let results = self.db.execute_sparql(query).await?;
        Ok(serde_wasm_bindgen::to_value(&results)?)
    }

    /// Execute Cypher query
    #[wasm_bindgen(js_name = executeCypher)]
    pub async fn execute_cypher(&self, query: &str) -> Result<JsValue, JsValue> {
        let results = self.db.execute_cypher(query).await?;
        Ok(serde_wasm_bindgen::to_value(&results)?)
    }

    /// Insert vectors
    #[wasm_bindgen(js_name = insertVectors)]
    pub async fn insert_vectors(
        &self,
        table: &str,
        vectors: Vec<f32>,
        metadata: JsValue,
    ) -> Result<(), JsValue> {
        // Implementation
        Ok(())
    }

    /// Vector similarity search
    #[wasm_bindgen(js_name = searchSimilar)]
    pub async fn search_similar(
        &self,
        table: &str,
        query_vector: Vec<f32>,
        limit: usize,
    ) -> Result<JsValue, JsValue> {
        // Implementation
        Ok(serde_wasm_bindgen::to_value(&results)?)
    }

    /// Save database to storage
    #[wasm_bindgen(js_name = save)]
    pub async fn save(&self) -> Result<(), JsValue> {
        self.db.persist().await?;
        Ok(())
    }

    /// Load database from storage
    #[wasm_bindgen(js_name = load)]
    pub async fn load() -> Result<RuVector, JsValue> {
        let db = Database::load().await?;
        Ok(RuVector { db: Arc::new(db) })
    }
}

4. TypeScript API (`npm/packages/wasm/src/index.ts`)

export class RuVector {
  private db: WasmRuVector;

  static async create(options?: DatabaseOptions): Promise<RuVector> {
    await init(); // Load WASM
    const db = new WasmRuVector();
    return new RuVector(db);
  }

  // SQL interface
  async sql<T = any>(query: string, params?: any[]): Promise<T[]> {
    return await this.db.executeSql(query);
  }

  // SPARQL interface
  async sparql(query: string): Promise<SparqlResults> {
    return await this.db.executeSparql(query);
  }

  // Cypher interface
  async cypher(query: string): Promise<CypherResults> {
    return await this.db.executeCypher(query);
  }

  // Convenience methods
  async insertVectors(table: string, data: VectorData[]): Promise<void> {
    // Batch insert
  }

  async searchSimilar(
    table: string,
    queryVector: Float32Array,
    options?: SearchOptions
  ): Promise<SearchResult[]> {
    return await this.db.searchSimilar(table, queryVector, options.limit);
  }

  // Persistence
  async save(): Promise<void> {
    await this.db.save();
  }

  static async load(): Promise<RuVector> {
    await init();
    const db = await WasmRuVector.load();
    return new RuVector(db);
  }
}

// Export types
export type { VectorData, SearchResult, SearchOptions, SparqlResults, CypherResults };

📦 Project Structure

ruvector/
├── crates/
│   ├── ruvector-core/          # Shared core (NO PostgreSQL deps)
│   │   ├── types/               # Vector types
│   │   ├── distance/            # Distance metrics
│   │   ├── index/               # HNSW, IVF
│   │   ├── quantization/        # Binary, scalar, product
│   │   └── simd/                # WASM SIMD
│   │
│   ├── ruvector-wasm/          # NEW: Standalone WASM database
│   │   ├── Cargo.toml           # wasm-bindgen, wasm-pack
│   │   ├── src/
│   │   │   ├── lib.rs           # WASM bindings
│   │   │   ├── storage/         # In-memory + persistence
│   │   │   ├── query/           # SQL/SPARQL/Cypher engines
│   │   │   ├── graph/           # Graph operations (from postgres)
│   │   │   ├── learning/        # ReasoningBank (from postgres)
│   │   │   ├── gnn/             # GNN layers (from postgres)
│   │   │   └── hyperbolic/      # Hyperbolic spaces (from postgres)
│   │   └── tests/
│   │
│   └── ruvector-postgres/      # Existing PostgreSQL extension
│       └── ... (unchanged)
│
├── npm/
│   └── packages/
│       ├── wasm/                # NEW: @ruvector/wasm
│       │   ├── package.json
│       │   ├── src/
│       │   │   ├── index.ts     # TypeScript API
│       │   │   ├── types.ts     # Type definitions
│       │   │   └── workers/     # Web Worker support
│       │   ├── dist/
│       │   │   ├── ruvector_bg.wasm
│       │   │   ├── index.js
│       │   │   └── index.d.ts
│       │   └── examples/
│       │       ├── browser.html
│       │       ├── node.js
│       │       ├── deno.ts
│       │       └── cloudflare-worker.js
│       │
│       └── ... (existing packages)
│
└── docs/
    ├── RUVECTOR_WASM_QUICKSTART.md
    ├── SQL_API.md
    ├── SPARQL_API.md
    ├── CYPHER_API.md
    └── DEPLOYMENT_GUIDE.md

🚀 Implementation Plan

Phase 1: Core Extraction (Week 1-2)

Extract from ruvector-postgres to create standalone ruvector-core:

# crates/ruvector-core/Cargo.toml
[package]
name = "ruvector-core"
version = "0.1.0"

[lib]
crate-type = ["lib"]

[features]
default = ["std"]
std = []  # Standard library
wasm = ["wasm-bindgen"]  # WASM support

[dependencies]
# NO PostgreSQL dependencies!
half = { version = "2.4", default-features = false }
simsimd = { version = "5.9", default-features = false }
serde = { version = "1.0", default-features = false, features = ["alloc"] }
dashmap = "6.0"

[target.'cfg(target_arch = "wasm32")'.dependencies]
wasm-bindgen = { version = "0.2", optional = true }

Move these modules (already written!):

types/ → Vector types (no changes needed)
distance/ → Distance metrics (remove pgrx deps)
index/hnsw.rs → HNSW index (remove pgrx deps)
quantization/ → Quantization (no changes)
graph/ → SPARQL, Cypher (remove pgrx, add standalone storage)
learning/ → ReasoningBank (remove pgrx deps)
gnn/ → GNN layers (remove pgrx deps)
hyperbolic/ → Hyperbolic spaces (no changes)

Phase 2: Storage Engine (Week 3)

Create in-memory database:

// ruvector-wasm/src/storage/database.rs
use dashmap::DashMap;
use parking_lot::RwLock;

pub struct Database {
    tables: DashMap<String, Table>,
    triple_store: Arc<TripleStore>,
    graph: Arc<GraphStore>,
}

pub struct Table {
    schema: Schema,
    rows: RwLock<Vec<Row>>,
    vector_index: Option<HnswIndex>,
}

impl Database {
    pub fn create_table(&self, name: &str, schema: Schema) {
        self.tables.insert(name.to_string(), Table::new(schema));
    }

    pub fn insert(&self, table: &str, row: Row) -> Result<()> {
        let table = self.tables.get(table)?;
        table.rows.write().push(row);

        // Update vector index if present
        if let Some(idx) = &table.vector_index {
            idx.insert(row.vector)?;
        }
        Ok(())
    }

    pub fn search_similar(
        &self,
        table: &str,
        query: &[f32],
        k: usize,
    ) -> Result<Vec<Row>> {
        let table = self.tables.get(table)?;
        let idx = table.vector_index.as_ref()?;
        let ids = idx.search(query, k)?;

        let rows = table.rows.read();
        Ok(ids.iter().map(|&id| rows[id].clone()).collect())
    }
}

Add persistence backends:

// ruvector-wasm/src/storage/persist.rs

#[cfg(target_arch = "wasm32")]
mod browser {
    use wasm_bindgen::prelude::*;
    use web_sys::{IdbDatabase, IdbObjectStore};

    pub async fn save_to_indexeddb(db: &Database) -> Result<()> {
        let idb = open_indexeddb("ruvector").await?;
        // Serialize database to IndexedDB
        Ok(())
    }
}

#[cfg(not(target_arch = "wasm32"))]
mod node {
    use std::fs;
    use bincode;

    pub async fn save_to_file(db: &Database, path: &str) -> Result<()> {
        let bytes = bincode::serialize(db)?;
        fs::write(path, bytes)?;
        Ok(())
    }
}

Phase 3: Query Engines (Week 4-5)

SQL Executor:

// ruvector-wasm/src/query/sql.rs

pub struct SqlExecutor {
    db: Arc<Database>,
}

impl SqlExecutor {
    pub async fn execute(&self, query: &str) -> Result<QueryResult> {
        let ast = parse_sql(query)?;

        match ast {
            Statement::Select(select) => self.execute_select(select).await,
            Statement::Insert(insert) => self.execute_insert(insert).await,
            Statement::CreateTable(create) => self.execute_create(create).await,
            _ => todo!(),
        }
    }

    async fn execute_select(&self, select: Select) -> Result<QueryResult> {
        // Vector similarity: ORDER BY embedding <=> $1
        if let Some(order) = &select.order_by {
            if order.op == VectorDistance {
                return self.vector_search(select).await;
            }
        }

        // Standard SELECT
        self.scan_table(select).await
    }
}

SPARQL Executor (already exists, just adapt!):

// Already in ruvector-postgres/src/graph/sparql/executor.rs
// Just remove pgrx dependencies and use our Database instead

pub async fn execute_sparql(
    db: &Database,
    query: &str,
) -> Result<SparqlResults> {
    let ast = parse_sparql(query)?;

    match ast.query_form {
        QueryForm::Select => execute_select(db, ast).await,
        QueryForm::Construct => execute_construct(db, ast).await,
        // ... already implemented!
    }
}

Cypher Executor (already exists!):

// Already in ruvector-postgres/src/graph/cypher/executor.rs
pub async fn execute_cypher(
    db: &Database,
    query: &str,
) -> Result<CypherResults> {
    // Already implemented, just adapt to our Database
}

Phase 4: WASM Compilation (Week 6)

Configure for WASM:

# ruvector-wasm/Cargo.toml
[package]
name = "ruvector-wasm"
version = "0.1.0"

[lib]
crate-type = ["cdylib"]  # For WASM

[dependencies]
ruvector-core = { path = "../ruvector-core", features = ["wasm"] }
wasm-bindgen = "0.2"
wasm-bindgen-futures = "0.4"
serde-wasm-bindgen = "0.6"
js-sys = "0.3"
web-sys = { version = "0.3", features = ["Window", "IdbDatabase", "IdbObjectStore"] }

[profile.release]
opt-level = "z"     # Optimize for size
lto = true          # Link-time optimization
codegen-units = 1   # Single codegen unit

Build script:

#!/bin/bash
# scripts/build-wasm.sh

echo "Building ruvector-wasm..."

# Install wasm-pack
cargo install wasm-pack

# Build for web
wasm-pack build crates/ruvector-wasm \
  --target web \
  --out-dir ../../npm/packages/wasm/dist \
  --release

# Build for Node.js
wasm-pack build crates/ruvector-wasm \
  --target nodejs \
  --out-dir ../../npm/packages/wasm/dist-node \
  --release

# Size report
echo "WASM size:"
du -h npm/packages/wasm/dist/ruvector_bg.wasm

Phase 5: NPM Package (Week 7)

TypeScript wrapper:

// npm/packages/wasm/src/index.ts
import init, {
  RuVector as WasmRuVector,
  InitOutput
} from '../dist/ruvector_wasm';

let wasmInit: Promise<InitOutput> | null = null;

async function ensureInit(): Promise<void> {
  if (!wasmInit) {
    wasmInit = init();
  }
  await wasmInit;
}

export class RuVector {
  private db: WasmRuVector;

  private constructor(db: WasmRuVector) {
    this.db = db;
  }

  static async create(options?: DatabaseOptions): Promise<RuVector> {
    await ensureInit();
    const db = new WasmRuVector();
    return new RuVector(db);
  }

  async sql<T = any>(query: string): Promise<T[]> {
    const result = await this.db.executeSql(query);
    return JSON.parse(result);
  }

  async sparql(query: string): Promise<SparqlResults> {
    const result = await this.db.executeSparql(query);
    return JSON.parse(result);
  }

  async cypher(query: string): Promise<any> {
    const result = await this.db.executeCypher(query);
    return JSON.parse(result);
  }

  async close(): Promise<void> {
    // Cleanup
  }
}

export * from './types';

Package configuration:

{
  "name": "@ruvector/wasm",
  "version": "0.1.0",
  "description": "Standalone vector database with SQL/SPARQL/Cypher - runs anywhere",
  "type": "module",
  "main": "./dist/index.js",
  "types": "./dist/index.d.ts",
  "exports": {
    ".": {
      "types": "./dist/index.d.ts",
      "import": "./dist/index.js",
      "require": "./dist/index.cjs"
    },
    "./node": {
      "types": "./dist-node/index.d.ts",
      "import": "./dist-node/index.js"
    }
  },
  "files": ["dist", "dist-node"],
  "keywords": [
    "vector-database",
    "wasm",
    "graph-database",
    "sparql",
    "cypher",
    "sql",
    "semantic-search",
    "embeddings"
  ],
  "engines": {
    "node": ">=18.0.0"
  }
}

Phase 6: Examples & Documentation (Week 8)

Browser example:

<!-- examples/browser.html -->
<!DOCTYPE html>
<html>
<head>
  <title>RuVector WASM Demo</title>
</head>
<body>
  <h1>Standalone Vector Database in Your Browser!</h1>
  <div id="results"></div>

  <script type="module">
    import { RuVector } from 'https://cdn.jsdelivr.net/npm/@ruvector/wasm/+esm';

    const db = await RuVector.create();

    // Create table
    await db.sql(`
      CREATE TABLE documents (
        id INTEGER PRIMARY KEY,
        content TEXT,
        embedding VECTOR(384)
      )
    `);

    // Create vector index
    await db.sql(`
      CREATE INDEX ON documents USING hnsw (embedding)
    `);

    // Insert documents
    const embedding = new Float32Array(384).map(() => Math.random());
    await db.sql(`
      INSERT INTO documents (id, content, embedding)
      VALUES (1, 'Hello world', $1)
    `, [embedding]);

    // Vector search
    const results = await db.sql(`
      SELECT content, embedding <=> $1 AS distance
      FROM documents
      ORDER BY distance
      LIMIT 5
    `, [embedding]);

    console.log('Search results:', results);

    // SPARQL query
    const sparqlResults = await db.sparql(`
      SELECT ?subject ?label WHERE {
        ?subject rdfs:label ?label .
      }
    `);

    // Cypher query
    const cypherResults = await db.cypher(`
      MATCH (a:Person)-[:KNOWS]->(b:Person)
      RETURN a.name, b.name
    `);

    // Save to IndexedDB
    await db.save();
  </script>
</body>
</html>

📊 Size Budget

Component	Size	Cumulative
Core WASM runtime	~500KB	500KB
Vector operations + SIMD	~300KB	800KB
HNSW index	~400KB	1.2MB
SQL parser & executor	~600KB	1.8MB
SPARQL engine	~800KB	2.6MB
Cypher engine	~600KB	3.2MB
Graph operations	~400KB	3.6MB
GNN layers	~800KB	4.4MB
ReasoningBank learning	~600KB	5.0MB
Hyperbolic spaces	~300KB	5.3MB
Total (gzipped)		~5-6MB

Comparison:

DuckDB-WASM: ~6-8MB
SQLite-WASM: ~1MB (no vector/graph features)
PGlite: ~3MB (minimal Postgres, no vector by default)
RuVector-WASM: ~5-6MB (FULL features!)

🎯 Success Metrics

Metric	Target
Bundle size (gzipped)	< 6MB
Load time (browser)	< 1s
Query latency (1k vectors)	< 20ms
Memory usage (100k vectors)	< 200MB
Browser support	Chrome 91+, Firefox 89+, Safari 16.4+
Node.js support	18+

🚀 Usage Examples

Browser - Semantic Search

import { RuVector } from '@ruvector/wasm';

// Create database
const db = await RuVector.create();

// Setup schema
await db.sql(`
  CREATE TABLE articles (
    id SERIAL PRIMARY KEY,
    title TEXT,
    content TEXT,
    embedding VECTOR(768)
  )
`);

// Insert with embeddings
await db.insertVectors('articles', [
  {
    title: 'AI in 2025',
    content: '...',
    embedding: await getEmbedding('AI in 2025')
  },
  // ... more articles
]);

// Semantic search
const results = await db.searchSimilar(
  'articles',
  queryEmbedding,
  { limit: 10, threshold: 0.7 }
);

Node.js - Knowledge Graph

import { RuVector } from '@ruvector/wasm/node';

const db = await RuVector.create();

// Cypher: Create knowledge graph
await db.cypher(`
  CREATE (a:Person {name: 'Alice', embedding: $a_emb})
  CREATE (b:Person {name: 'Bob', embedding: $b_emb})
  CREATE (a)-[:KNOWS]->(b)
`, { a_emb: aliceEmbedding, b_emb: bobEmbedding });

// Cypher: Find similar people
await db.cypher(`
  MATCH (p:Person)
  WHERE vector.cosine(p.embedding, $query) > 0.8
  RETURN p.name
`);

Cloudflare Workers - Edge Search

import { RuVector } from '@ruvector/wasm';

export default {
  async fetch(request: Request, env: Env) {
    const db = await RuVector.create();

    // Load from Durable Object or KV
    // await db.load();

    const { query } = await request.json();
    const embedding = await generateEmbedding(query);

    const results = await db.sql(`
      SELECT * FROM products
      ORDER BY embedding <=> $1
      LIMIT 10
    `, [embedding]);

    return Response.json(results);
  }
};

Deno - SPARQL RDF Store

import { RuVector } from "https://esm.sh/@ruvector/wasm";

const db = await RuVector.create();

// SPARQL: Query RDF data
const results = await db.sparql(`
  PREFIX foaf: <http://xmlns.com/foaf/0.1/>
  PREFIX vec: <http://ruvector.dev/vector/>

  SELECT ?person ?name ?similarity WHERE {
    ?person foaf:name ?name .
    BIND(vec:cosine(?person, ?query) AS ?similarity)
    FILTER(?similarity > 0.8)
  }
  ORDER BY DESC(?similarity)
  LIMIT 10
`);

📅 8-Week Timeline

Week	Milestone
1-2	Extract `ruvector-core`, remove PostgreSQL deps
3	Build storage engine (in-memory + persistence)
4-5	Adapt query engines (SQL, SPARQL, Cypher)
6	WASM compilation, optimization, testing
7	NPM package, TypeScript API, CI/CD
8	Documentation, examples, beta release

✅ Advantages Over PGlite Extension

Feature	PGlite Extension	RuVector-WASM
PostgreSQL dependency	✅ Included (3MB)	❌ None
Full ruvector features	❌ Limited (size)	✅ All features
SPARQL/Cypher	❌ Hard to add	✅ Built-in
GNN/Learning	❌ Too large	✅ Included
Build complexity	❌ Emscripten + PGlite fork	✅ wasm-pack only
Maintenance	❌ Track PGlite updates	✅ Independent
Size	~3MB + extension	~5-6MB total

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RuVector-WASM: Standalone Vector Database Architecture

🎯 Vision

✨ What You Get

Complete Feature Set

Zero Dependencies

Universal Runtime

🏗️ Architecture

High-Level Design

Component Breakdown

1. Storage Layer (`ruvector-wasm/src/storage/`)

2. Query Engines (`ruvector-wasm/src/query/`)

3. WASM Bindings (`ruvector-wasm/src/lib.rs`)

4. TypeScript API (`npm/packages/wasm/src/index.ts`)

📦 Project Structure

🚀 Implementation Plan

Phase 1: Core Extraction (Week 1-2)

Phase 2: Storage Engine (Week 3)

Phase 3: Query Engines (Week 4-5)

Phase 4: WASM Compilation (Week 6)

Phase 5: NPM Package (Week 7)

Phase 6: Examples & Documentation (Week 8)

📊 Size Budget

🎯 Success Metrics

🚀 Usage Examples

Browser - Semantic Search

Node.js - Knowledge Graph

Cloudflare Workers - Edge Search

Deno - SPARQL RDF Store

📅 8-Week Timeline

✅ Advantages Over PGlite Extension

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RuVector-WASM: Standalone Vector Database Architecture

🎯 Vision

✨ What You Get

Complete Feature Set

Zero Dependencies

Universal Runtime

🏗️ Architecture

High-Level Design

Component Breakdown

1. Storage Layer (ruvector-wasm/src/storage/)

2. Query Engines (ruvector-wasm/src/query/)

3. WASM Bindings (ruvector-wasm/src/lib.rs)

4. TypeScript API (npm/packages/wasm/src/index.ts)

📦 Project Structure

🚀 Implementation Plan

Phase 1: Core Extraction (Week 1-2)

Phase 2: Storage Engine (Week 3)

Phase 3: Query Engines (Week 4-5)

Phase 4: WASM Compilation (Week 6)

Phase 5: NPM Package (Week 7)

Phase 6: Examples & Documentation (Week 8)

📊 Size Budget

🎯 Success Metrics

🚀 Usage Examples

Browser - Semantic Search

Node.js - Knowledge Graph

Cloudflare Workers - Edge Search

Deno - SPARQL RDF Store

📅 8-Week Timeline

✅ Advantages Over PGlite Extension

1. Storage Layer (`ruvector-wasm/src/storage/`)

2. Query Engines (`ruvector-wasm/src/query/`)

3. WASM Bindings (`ruvector-wasm/src/lib.rs`)

4. TypeScript API (`npm/packages/wasm/src/index.ts`)