tauri-plugin-velesdb

Crates.iotauri-plugin-velesdb
lib.rstauri-plugin-velesdb
version1.1.1
created_at2025-12-22 09:41:42.931079+00
updated_at2026-01-13 16:24:26.892993+00
descriptionTauri plugin for VelesDB - Vector search in desktop apps
homepage
repositoryhttps://github.com/cyberlife-coder/VelesDB
max_upload_size
id1999437
size231,937
Wiscale (cyberlife-coder)

documentation

README

tauri-plugin-velesdb

Crates.io License

A Tauri plugin for VelesDB - Vector search in desktop applications.

Features

  • 🚀 Fast Vector Search - Microsecond latency similarity search
  • 📝 Text Search - BM25 full-text search across payloads
  • 🔀 Hybrid Search - Combined vector + text with RRF fusion
  • 🔄 Multi-Query Fusion - MQG support with RRF/Weighted strategies ⭐ NEW
  • 🗃️ Collection Management - Create, list, and delete collections
  • 📊 VelesQL - SQL-like query language
  • 🔒 Local-First - All data stays on the user's device

Installation

Rust (Cargo.toml)

[dependencies]
tauri-plugin-velesdb = "0.1"

JavaScript (package.json)

{
  "dependencies": {
    "@wiscale/tauri-plugin-velesdb": "^0.6.0"
  }
}
npm install @wiscale/tauri-plugin-velesdb
# pnpm add @wiscale/tauri-plugin-velesdb
# yarn add @wiscale/tauri-plugin-velesdb

Usage

Rust - Plugin Registration

fn main() {
    tauri::Builder::default()
        .plugin(tauri_plugin_velesdb::init("./velesdb_data"))
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
}

JavaScript - Frontend API

import { invoke } from '@tauri-apps/api/core';

// Create a collection
await invoke('plugin:velesdb|create_collection', {
  request: {
    name: 'documents',
    dimension: 768,
    metric: 'cosine',  // cosine, euclidean, dot, hamming, jaccard
    storageMode: 'full'  // full, sq8, binary
  }
});

// List collections
const collections = await invoke('plugin:velesdb|list_collections');
console.log(collections);
// [{ name: 'documents', dimension: 768, metric: 'cosine', count: 0 }]

// Insert vectors
await invoke('plugin:velesdb|upsert', {
  request: {
    collection: 'documents',
    points: [
      {
        id: 1,
        vector: [0.1, 0.2, 0.3, /* ... 768 dims */],
        payload: { title: 'Introduction to AI', category: 'tech' }
      },
      {
        id: 2,
        vector: [0.4, 0.5, 0.6, /* ... */],
        payload: { title: 'Machine Learning Guide', category: 'tech' }
      }
    ]
  }
});

// Vector similarity search
const results = await invoke('plugin:velesdb|search', {
  request: {
    collection: 'documents',
    vector: [0.15, 0.25, 0.35, /* ... */],
    topK: 5
  }
});
console.log(results);
// { results: [{ id: 1, score: 0.98, payload: {...} }], timingMs: 0.5 }

// Text search (BM25)
const textResults = await invoke('plugin:velesdb|text_search', {
  request: {
    collection: 'documents',
    query: 'machine learning guide',
    topK: 10
  }
});

// Hybrid search (vector + text)
const hybridResults = await invoke('plugin:velesdb|hybrid_search', {
  request: {
    collection: 'documents',
    vector: [0.1, 0.2, /* ... */],
    query: 'AI introduction',
    topK: 10,
    vectorWeight: 0.7  // 0.0-1.0, higher = more vector influence
  }
});

// Multi-query fusion search (MQG) ⭐ NEW
const mqResults = await invoke('plugin:velesdb|multi_query_search', {
  request: {
    collection: 'documents',
    vectors: [
      [0.1, 0.2, /* ... query 1 */],
      [0.3, 0.4, /* ... query 2 */],
      [0.5, 0.6, /* ... query 3 */]
    ],
    topK: 10,
    fusion: 'rrf',  // 'rrf', 'average', 'maximum', 'weighted'
    fusionParams: { k: 60 }  // RRF parameter
  }
});

// Weighted fusion (like SearchXP scoring)
const weightedResults = await invoke('plugin:velesdb|multi_query_search', {
  request: {
    collection: 'documents',
    vectors: [[...], [...], [...]],
    topK: 10,
    fusion: 'weighted',
    fusionParams: {
      avgWeight: 0.6,
      maxWeight: 0.3,
      hitWeight: 0.1
    }
  }
});

// VelesQL query
const queryResults = await invoke('plugin:velesdb|query', {
  request: {
    query: "SELECT * FROM documents WHERE content MATCH 'rust' LIMIT 10",
    params: {}
  }
});

// Delete collection
await invoke('plugin:velesdb|delete_collection', { name: 'documents' });

API Reference

Commands

Command Description
create_collection Create a new vector collection
delete_collection Delete a collection
list_collections List all collections
get_collection Get info about a collection
upsert Insert or update vectors
get_points Retrieve points by IDs
delete_points Delete points by IDs
search Vector similarity search
batch_search Batch vector search (multiple queries)
multi_query_search Multi-query fusion search ⭐ NEW
text_search BM25 full-text search
hybrid_search Combined vector + text search
query Execute VelesQL query

Storage Modes

Mode Compression Best For
full 1x Maximum accuracy
sq8 4x Good accuracy/memory balance
binary 32x Edge/IoT, massive scale

Distance Metrics

Metric Best For
cosine Text embeddings (default)
euclidean Spatial/geographic data
dot Pre-normalized vectors
hamming Binary vectors
jaccard Set similarity

Permissions

Add to your capabilities/default.json:

{
  "permissions": [
    "velesdb:default"
  ]
}

Or for granular control:

{
  "permissions": [
    "velesdb:allow-create-collection",
    "velesdb:allow-search",
    "velesdb:allow-upsert"
  ]
}

Example App

See the examples/basic-app directory for a complete Tauri app using this plugin.

Performance

Operation Latency
Vector search (10k vectors) < 1ms
Text search (BM25) < 5ms
Hybrid search < 10ms
Insert (batch 100) < 10ms

License

Elastic License 2.0 (ELv2)

See LICENSE for details.

Commit count: 487

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