Frequently Asked Questions

const sv = new SolVec({ network: 'devnet' });
const collection = sv.collection('my-data');
await collection.upsert([...vectors]);
// Merkle root posted to Solana automatically ✓

const proof = await collection.verify();
console.log(proof.solanaExplorerUrl);
// https://explorer.solana.com/address/8iLpy...75Fm7?cluster=devnet

// Automatic encryption in SDK
await collection.upsert([{ id: 'v1', values: [...] }]);
// Vector encrypted with your wallet key before leaving your machine ✓

await collection.upsert([{
  id: 'mem_001',
  values: [...],              // Encrypted ✓
  metadata: {                 // Encrypted ✓
    text: 'Sensitive data',
    user_id: 'user_123'
  }
}]);

const proof = await collection.verify();

console.log(proof.merkleRoot);      // 32-byte hash of all vector IDs
console.log(proof.onChainRoot);     // Root stored on Solana
console.log(proof.match);           // true if they match
console.log(proof.timestamp);       // When root was posted
console.log(proof.solanaExplorerUrl); // View on blockchain

await collection.upsert([...1000_vectors]);
// Single Solana transaction for 1000 vectors ✓

# Before (Pinecone)
from pinecone import Pinecone
pc = Pinecone(api_key="YOUR_KEY")
index = pc.Index("my-index")

# After (VecLabs)
from solvec import SolVec
sv = SolVec(wallet="~/.config/solana/id.json")
index = sv.collection("my-index")

# Everything else stays identical
index.upsert(vectors=[...])
index.query(vector=[...], top_k=10)

from langchain.vectorstores import VectorStore
from solvec import SolVec

class SolVecStore(VectorStore):
    def __init__(self):
        self.sv = SolVec(wallet="~/.config/solana/id.json")
        self.collection = self.sv.collection("langchain-memory")
    
    def add_texts(self, texts):
        # Implement using collection.upsert()
        pass
    
    def similarity_search(self, query, k=4):
        # Implement using collection.query()
        pass

import OpenAI from 'openai';
import { SolVec } from 'solvec';

const openai = new OpenAI();
const sv = new SolVec({ network: 'devnet' });
const collection = sv.collection('openai-embeddings', { dimensions: 1536 });

// Generate embeddings
const response = await openai.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'Your text here'
});

// Store in VecLabs
await collection.upsert([{
  id: 'doc_001',
  values: response.data[0].embedding,
  metadata: { text: 'Your text here' }
}]);

const vectors = await collection.exportAll();
fs.writeFileSync('backup.json', JSON.stringify(vectors));

Storage cost = (num_vectors × dims × 4 bytes) × $0.000039/MB/epoch
Write cost = num_writes × $0.00025
Query cost = $0 (runs on your infrastructure)

solana airdrop 2 --url devnet

const collection = sv.collection('my-collection', {
  dimensions: 1536,
  create: true  // Auto-create if not exists
});

const stats = await collection.stats();
console.log(stats.indexSize);  // Number of vectors
console.log(stats.dimensions); // Vector dimensions
console.log(stats.metric);     // Distance metric