VecStore

The SQLite of vector search. Embed semantic search directly in your app—no server required.

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Why VecStore?

Feature	VecStore	Pinecone/Weaviate	FAISS
Runs in browser	Yes	No	No
No server needed	Yes	No	Yes
Hybrid search	Yes	Yes	No
Metadata filtering	Yes	Yes	No
Python + Rust + JS	Yes	Partial	Python only

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Quick Start

Rust

[dependencies]
vecstore = "0.0.1"

use vecstore::VecStore;

let mut store = VecStore::open("vectors.db")?;

// Insert vectors with metadata
store.upsert("doc1", vec![0.1, 0.2, 0.3], json!({"title": "Hello"}))?;

// Semantic search
let results = store.query(&vec![0.1, 0.2, 0.3], 10)?;

// Filtered search
let results = store.query_with_filter(&query_vec, 10, "category = 'tech'")?;

Python

pip install vecstore-rs

import vecstore

store = vecstore.VecStore("vectors.db")
store.upsert("doc1", [0.1, 0.2, 0.3], {"title": "Hello"})
results = store.query([0.1, 0.2, 0.3], k=10)

JavaScript (Browser)

npm install vecstore-wasm

import init, { WasmVecStore } from 'vecstore-wasm';

await init();
const store = new WasmVecStore(384);

store.upsert("doc1", new Float32Array([...]), { title: "Hello" });
const results = store.query(queryVector, 10);

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Features

Core

• HNSW Index - Sub-millisecond search on 100K+ vectors
• 9 Distance Metrics - Cosine, Euclidean, Dot Product, Manhattan, Hamming, Jaccard, and more
• Metadata Filtering - SQL-like expressions: category = 'tech' AND score > 0.5
• Hybrid Search - Combine vector similarity with BM25 keyword matching
• Snapshots - Point-in-time backups and restore

Browser-First

• WASM Support - Full vector search in the browser, no backend
• Offline-Capable - Works without network connection
• Privacy-First - Data never leaves the user's device
• Sub-ms Latency - 0.2ms search on 100K vectors

Production

• Batch Operations - Parallel ingestion for large datasets
• Soft Delete + TTL - Flexible data lifecycle management
• Write-Ahead Log - Crash recovery with wal_enabled: true
• gRPC + HTTP Server - Optional server mode for multi-client access

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Use Cases

1. Local RAG - Semantic search for LLM context retrieval
2. Browser Search - Privacy-first document search (legal, medical, financial)
3. Offline Apps - Mobile/desktop apps with embedded search
4. Prototyping - Test semantic search ideas without infrastructure
5. Edge Computing - IoT devices with local vector search

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Performance

Dataset	Search Latency	Memory
10K vectors (384d)	0.3ms	~20MB
100K vectors (384d)	0.2ms	~180MB
1M vectors (128d)	0.2ms	~200MB

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Documentation

• WASM Guide - Browser integration with React, Vue, Next.js
• Python API - Full Python documentation
• Architecture - System design overview
• Security Policy - Vulnerability reporting

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Roadmap

Shipping Now

• HNSW with 9 distance metrics
• Metadata filtering
• Hybrid search (vector + BM25)
• Python bindings
• WASM/browser support
• Snapshots

Coming Soon

• LangChain/LlamaIndex integration
• Product Quantization (8-32x memory reduction)
• GPU acceleration

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Alpha Notice

VecStore is in active development (0.0.x). APIs and file formats may change. Not recommended for production workloads with data you can't regenerate.

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Contributing

We welcome contributions! See CONTRIBUTING.md.

High-impact areas:

1. LangChain/LlamaIndex Python wrappers
2. Browser demo applications
3. Performance benchmarks

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License

Apache 2.0 - see LICENSE.